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500+ Business Research Topics

Business research is an essential component of any successful organization, as it allows companies to make informed decisions based on data-driven insights. Whether it’s market research to identify new opportunities, or analyzing internal processes to improve efficiency, there are a vast array of business research topics that companies can explore. With the constantly evolving business landscape, it’s critical for organizations to stay up-to-date with the latest research trends and best practices to remain competitive. In this post, we’ll explore some of the most compelling business research topics that are currently being studied, providing insights and actionable recommendations for businesses of all sizes.

Business Research Topics

Business Research Topics are as follows:

• The impact of social media on consumer behavior
• Strategies for enhancing customer satisfaction in the service industry
• The effectiveness of mobile marketing campaigns
• Exploring the factors influencing employee turnover
• The role of leadership in organizational culture
• Investigating the relationship between corporate social responsibility and financial performance
• Assessing the impact of employee engagement on organizational performance
• The challenges and opportunities of global supply chain management
• Analyzing the effectiveness of e-commerce platforms
• Investigating the impact of organizational culture on employee motivation
• The role of corporate governance in ensuring ethical business practices
• Examining the impact of digital marketing on brand equity
• Strategies for managing diversity and inclusion in the workplace
• Exploring the effects of employee empowerment on job satisfaction
• The role of innovation in business growth
• Analyzing the impact of mergers and acquisitions on company performance
• Investigating the impact of workplace design on employee productivity
• The challenges and opportunities of international business expansion
• Strategies for managing talent in the knowledge economy
• The role of artificial intelligence in transforming business operations
• Examining the impact of customer loyalty programs on retention and revenue
• Investigating the relationship between corporate social responsibility and brand reputation
• The role of emotional intelligence in effective leadership
• The impact of digital transformation on small and medium-sized enterprises
• Analyzing the effectiveness of green marketing strategies
• The role of entrepreneurship in economic development
• Investigating the impact of employee training and development on organizational performance
• The challenges and opportunities of omnichannel retailing
• Examining the impact of organizational change on employee morale and productivity
• The role of corporate social responsibility in attracting and retaining millennial talent
• Analyzing the impact of employee motivation on organizational culture
• Investigating the impact of workplace diversity on team performance
• The challenges and opportunities of blockchain technology in business operations
• Strategies for managing cross-functional teams
• The role of big data analytics in business decision-making
• Examining the impact of corporate social responsibility on customer loyalty
• Investigating the relationship between corporate social responsibility and employee engagement
• The impact of social media marketing on customer engagement and brand loyalty.
• The effectiveness of AI in improving customer service and satisfaction.
• The role of entrepreneurship in economic development and job creation.
• The impact of the gig economy on the labor market.
• The effects of corporate social responsibility on company profitability.
• The role of data analytics in predicting consumer behavior and market trends.
• The effects of globalization on the competitiveness of small businesses.
• The impact of e-commerce on traditional brick-and-mortar retail.
• The role of emotional intelligence in leadership and team management.
• The effects of workplace diversity on employee productivity and satisfaction.
• The role of corporate culture in employee retention and satisfaction.
• The impact of employee training and development on company performance.
• The effectiveness of performance-based pay structures on employee motivation.
• The impact of sustainability practices on company reputation and profitability.
• The effects of artificial intelligence on job displacement and the future of work.
• The role of innovation in the growth and success of small businesses.
• The impact of government regulations on business operations and profitability.
• The effects of organizational structure on company performance and efficiency.
• The role of emotional labor in service industries.
• The impact of employee empowerment on job satisfaction and retention.
• The effects of workplace flexibility on employee productivity and well-being.
• The role of emotional intelligence in negotiation and conflict resolution.
• The impact of branding on consumer behavior and purchase decisions.
• The effects of customer experience on brand loyalty and advocacy.
• The role of storytelling in marketing and advertising.
• The impact of consumer psychology on pricing strategies and sales.
• The effects of influencer marketing on consumer behavior and brand loyalty.
• The role of trust in online transactions and e-commerce.
• The impact of product design on consumer perception and purchasing decisions.
• The effects of customer satisfaction on company profitability and growth.
• The role of social entrepreneurship in addressing societal problems and creating value.
• The impact of corporate governance on company performance and stakeholder relations.
• The effects of workplace harassment on employee well-being and company culture.
• The role of strategic planning in the success of small businesses.
• The impact of technology on supply chain management and logistics.
• The effects of customer segmentation on marketing strategies and sales.
• The role of corporate philanthropy in building brand reputation and loyalty.
• The impact of intellectual property protection on innovation and creativity.
• The effects of trade policies on international business operations and profitability.
• The role of strategic partnerships in business growth and expansion.
• The impact of digital transformation on organizational structure and operations.
• The effects of leadership styles on employee motivation and performance.
• The role of corporate social activism in shaping public opinion and brand reputation.
• The impact of mergers and acquisitions on company performance and stakeholder value.
• The effects of workplace automation on job displacement and re-skilling.
• The role of cross-cultural communication in international business operations.
• The impact of workplace stress on employee health and productivity.
• The effects of customer reviews and ratings on online sales and reputation.
• The role of competitive intelligence in market research and strategy development.
• The impact of brand identity on consumer trust and loyalty.
• The impact of organizational structure on innovation and creativity
• Analyzing the effectiveness of virtual teams in global organizations
• The role of corporate social responsibility in crisis management
• The challenges and opportunities of online marketplaces
• Strategies for managing cultural diversity in multinational corporations
• The impact of employer branding on employee retention
• Investigating the impact of corporate social responsibility on investor behavior
• The role of technology in enhancing customer experience
• Analyzing the impact of social responsibility initiatives on customer satisfaction
• Investigating the impact of supply chain disruptions on business performance
• The role of business ethics in organizational decision-making
• The challenges and opportunities of artificial intelligence in customer service
• Strategies for managing employee burnout and stress in the workplace.
• Impact of social media on consumer behavior and its implications for businesses.
• The impact of corporate social responsibility on company performance.
• An analysis of the relationship between employee satisfaction and customer loyalty.
• The effect of advertising on consumer behavior.
• A study on the effectiveness of social media marketing in building brand image.
• The impact of technological innovations on business strategy and operations.
• The relationship between leadership style and employee motivation.
• A study of the effects of corporate culture on employee engagement.
• An analysis of the factors influencing consumer buying behavior.
• The effectiveness of training and development programs in enhancing employee performance.
• The impact of global economic factors on business decision-making.
• The role of organizational communication in achieving business goals.
• The relationship between customer satisfaction and business success.
• A study of the challenges and opportunities in international business.
• The effectiveness of supply chain management in improving business performance.
• An analysis of the factors influencing customer loyalty in the hospitality industry.
• The impact of employee turnover on organizational performance.
• A study of the impact of corporate governance on company performance.
• The role of innovation in business growth and success.
• An analysis of the relationship between marketing and sales performance.
• The effect of organizational structure on employee behavior.
• A study of the impact of cultural differences on business negotiations.
• The effectiveness of pricing strategies in increasing sales revenue.
• The impact of customer service on customer loyalty.
• A study of the role of human resource management in business success.
• The impact of e-commerce on traditional brick-and-mortar businesses.
• An analysis of the relationship between employee empowerment and job satisfaction.
• The effectiveness of customer relationship management in building brand loyalty.
• The role of business ethics in corporate decision-making.
• A study of the impact of digital marketing on consumer behavior.
• The effect of organizational culture on employee turnover.
• An analysis of the factors influencing employee engagement in the workplace.
• The impact of social media on business communication and marketing.
• A study of the relationship between customer service and customer loyalty in the airline industry.
• The role of diversity and inclusion in business success.
• The effectiveness of performance management systems in improving employee performance.
• The impact of corporate social responsibility on employee engagement.
• A study of the factors influencing business expansion into new markets.
• The role of brand identity in customer loyalty and retention.
• The effectiveness of change management strategies in organizational change.
• The impact of organizational structure on organizational performance.
• A study of the impact of technology on the future of work.
• The relationship between innovation and competitive advantage in the marketplace.
• The effect of employee training on organizational performance.
• An analysis of the impact of online reviews on consumer behavior.
• The role of leadership in shaping organizational culture.
• The effectiveness of talent management strategies in retaining top talent.
• The impact of globalization on small and medium-sized enterprises.
• A study of the relationship between corporate social responsibility and brand reputation.
• The effectiveness of employee retention strategies in reducing turnover rates.
• The role of emotional intelligence in leadership and employee engagement.
• The impact of digital marketing on customer behavior
• The role of organizational culture in employee engagement and retention
• The effects of employee training and development on organizational performance
• The relationship between corporate social responsibility and financial performance
• The impact of globalization on business strategy
• The importance of supply chain management in achieving competitive advantage
• The role of innovation in business growth and sustainability
• The impact of e-commerce on traditional retail businesses
• The role of leadership in managing change in organizations
• The effects of workplace diversity on organizational performance
• The impact of social media on brand image and reputation
• The relationship between employee motivation and productivity
• The role of organizational structure in promoting innovation
• The effects of customer service on customer loyalty
• The impact of globalization on small businesses
• The role of corporate governance in preventing unethical behavior
• The effects of technology on job design and work organization
• The relationship between employee satisfaction and turnover
• The impact of mergers and acquisitions on organizational culture
• The effects of employee benefits on job satisfaction
• The impact of cultural differences on international business negotiations
• The role of strategic planning in organizational success
• The effects of organizational change on employee stress and burnout
• The impact of business ethics on customer trust and loyalty
• The role of human resource management in achieving competitive advantage
• The effects of outsourcing on organizational performance
• The impact of diversity and inclusion on team performance
• The role of corporate social responsibility in brand differentiation
• The effects of leadership style on organizational culture
• The Impact of Digital Marketing on Brand Equity: A Study of E-commerce Businesses
• Investigating the Relationship between Employee Engagement and Organizational Performance
• Analyzing the Effects of Corporate Social Responsibility on Customer Loyalty and Firm Performance
• An Empirical Study of the Factors Affecting Entrepreneurial Success in the Technology Sector
• The Influence of Organizational Culture on Employee Motivation and Job Satisfaction: A Case Study of a Service Industry
• Investigating the Impact of Organizational Change on Employee Resistance: A Comparative Study of Two Organizations
• An Exploration of the Impact of Artificial Intelligence on Supply Chain Management
• Examining the Relationship between Leadership Styles and Employee Creativity in Innovative Organizations
• Investigating the Effectiveness of Performance Appraisal Systems in Improving Employee Performance
• Analyzing the Role of Emotional Intelligence in Leadership Effectiveness: A Study of Senior Managers
• The Impact of Transformational Leadership on Employee Motivation and Job Satisfaction in the Healthcare Sector
• Evaluating the Effectiveness of Talent Management Strategies in Enhancing Organizational Performance
• A Study of the Effects of Customer Relationship Management on Customer Retention and Loyalty
• Investigating the Impact of Corporate Governance on Firm Performance: Evidence from Emerging Markets
• The Relationship between Intellectual Capital and Firm Performance: A Case Study of Technology Firms
• Analyzing the Effectiveness of Diversity Management in Improving Organizational Performance
• The Impact of Internationalization on the Performance of Small and Medium-sized Enterprises: A Comparative Study of Developed and Developing Countries
• Examining the Relationship between Corporate Social Responsibility and Financial Performance: A Study of Listed Firms
• Investigating the Influence of Entrepreneurial Orientation on Firm Performance in Emerging Markets
• Analyzing the Impact of E-commerce on Traditional Retail Business Models: A Study of Brick-and-Mortar Stores
• The Effect of Corporate Reputation on Customer Loyalty and Firm Performance: A Study of the Banking Sector
• Investigating the Factors Affecting Consumer Adoption of Mobile Payment Systems
• The Role of Corporate Social Responsibility in Attracting and Retaining Millennial Employees
• Analyzing the Impact of Social Media Marketing on Brand Awareness and Consumer Purchase Intentions
• A Study of the Effects of Employee Training and Development on Job Performance
• Investigating the Relationship between Corporate Culture and Employee Turnover: A Study of Multinational Companies
• The Impact of Business Process Reengineering on Organizational Performance: A Study of Service Industries
• An Empirical Study of the Factors Affecting Internationalization Strategies of Small and Medium-sized Enterprises
• The Effect of Strategic Human Resource Management on Firm Performance: A Study of Manufacturing Firms
• Investigating the Influence of Leadership on Organizational Culture: A Comparative Study of Two Organizations
• The Impact of Technology Adoption on Organizational Productivity: A Study of the Healthcare Sector
• Analyzing the Effects of Brand Personality on Consumer Purchase Intentions: A Study of Luxury Brands
• The Relationship between Corporate Social Responsibility and Customer Perceptions of Product Quality: A Study of the Food and Beverage Industry
• Investigating the Effectiveness of Performance Management Systems in Improving Employee Performance: A Study of a Public Sector Organization
• The Impact of Business Ethics on Firm Performance: A Study of the Banking Industry
• Examining the Relationship between Employee Engagement and Customer Satisfaction in the Service Industry
• Investigating the Influence of Entrepreneurial Networking on Firm Performance: A Study of Small and Medium-sized Enterprises
• The Effect of Corporate Social Responsibility on Employee Retention: A Study of High-tech Firms
• The impact of workplace communication on employee engagement
• The role of customer feedback in improving service quality
• The effects of employee empowerment on job satisfaction
• The impact of innovation on customer satisfaction
• The role of knowledge management in organizational learning
• The effects of product innovation on market share
• The impact of business location on customer behavior
• The role of financial management in business success
• The effects of corporate social responsibility on employee engagement
• The impact of cultural intelligence on cross-cultural communication
• The role of social media in crisis management
• The effects of corporate branding on customer loyalty
• The impact of globalization on consumer behavior
• The role of emotional intelligence in leadership effectiveness
• The effects of employee involvement in decision-making on job satisfaction
• The impact of business strategy on market share
• The role of corporate culture in promoting ethical behavior
• The effects of corporate social responsibility on investor behavior
• The impact of sustainability on brand image and reputation
• The role of corporate social responsibility in reducing carbon emissions.
• The effectiveness of loyalty programs on customer retention
• The benefits of remote work for employee productivity
• The impact of environmental sustainability on consumer purchasing decisions
• The role of brand identity in consumer loyalty
• The relationship between employee satisfaction and customer satisfaction
• The impact of e-commerce on traditional brick-and-mortar stores
• The effectiveness of online advertising on consumer behavior
• The impact of leadership styles on employee motivation
• The role of corporate social responsibility in brand perception
• The impact of workplace diversity on organizational performance
• The effectiveness of gamification in employee training programs
• The impact of pricing strategies on consumer behavior
• The effectiveness of mobile marketing on consumer engagement
• The impact of emotional intelligence on leadership effectiveness
• The role of customer service in consumer loyalty
• The impact of technology on supply chain management
• The effectiveness of employee training programs on job performance
• The impact of culture on consumer behavior
• The effectiveness of performance appraisal systems on employee motivation
• The impact of social responsibility on organizational performance
• The role of innovation in business success
• The impact of ethical leadership on organizational culture
• The effectiveness of cross-functional teams in project management
• The impact of government regulations on business operations
• The role of strategic planning in business growth
• The impact of emotional intelligence on team dynamics
• The effectiveness of supply chain management on customer satisfaction
• The impact of workplace culture on employee satisfaction
• The role of employee engagement in organizational success
• The impact of globalization on organizational culture
• The effectiveness of virtual teams in project management
• The impact of employee turnover on organizational performance
• The role of corporate social responsibility in talent acquisition
• The impact of technology on employee training and development
• The effectiveness of knowledge management on organizational learning
• The impact of organizational structure on employee motivation
• The role of innovation in organizational change
• The impact of cultural intelligence on global business operations
• The effectiveness of marketing strategies on brand perception
• The impact of change management on organizational culture
• The role of leadership in organizational transformation
• The impact of employee empowerment on job satisfaction
• The effectiveness of project management methodologies on project success
• The impact of workplace communication on team performance
• The role of emotional intelligence in conflict resolution
• The impact of employee motivation on job performance
• The effectiveness of diversity and inclusion initiatives on organizational performance.
• The impact of social media on consumer behavior and buying decisions
• The role of diversity and inclusion in corporate culture and its effects on employee retention and productivity
• The effectiveness of remote work policies on job satisfaction and work-life balance
• The impact of customer experience on brand loyalty and revenue growth
• The effects of environmental sustainability practices on corporate reputation and financial performance
• The role of corporate social responsibility in consumer purchasing decisions
• The effectiveness of leadership styles on team performance and productivity
• The effects of employee motivation on job performance and turnover
• The impact of technology on supply chain management and logistics efficiency
• The role of emotional intelligence in effective leadership and team dynamics
• The impact of artificial intelligence and automation on job displacement and workforce trends
• The effects of brand image on consumer perception and purchasing decisions
• The role of corporate culture in promoting innovation and creativity
• The impact of e-commerce on traditional brick-and-mortar retail businesses
• The effects of corporate governance on financial reporting and transparency
• The effectiveness of performance-based compensation on employee motivation and productivity
• The impact of online reviews and ratings on consumer trust and brand reputation
• The effects of workplace diversity on innovation and creativity
• The impact of mobile technology on marketing strategies and consumer behavior
• The role of emotional intelligence in customer service and satisfaction
• The effects of corporate reputation on financial performance and stakeholder trust
• The impact of artificial intelligence on customer service and support
• The role of organizational culture in promoting ethical behavior and decision-making
• The effects of corporate social responsibility on employee engagement and satisfaction
• The impact of employee turnover on organizational performance and profitability
• The role of customer satisfaction in promoting brand loyalty and advocacy
• The effects of workplace flexibility on employee morale and productivity
• The impact of employee wellness programs on absenteeism and healthcare costs
• The role of data analytics in business decision-making and strategy formulation
• The effects of brand personality on consumer behavior and perception
• The impact of social media marketing on brand awareness and customer engagement
• The role of organizational justice in promoting employee satisfaction and retention
• The effects of corporate branding on employee motivation and loyalty
• The impact of online advertising on consumer behavior and purchasing decisions
• The role of corporate entrepreneurship in promoting innovation and growth
• The effects of cultural intelligence on cross-cultural communication and business success
• The impact of workplace diversity on customer satisfaction and loyalty
• The role of ethical leadership in promoting employee trust and commitment
• The effects of job stress on employee health and well-being
• The impact of supply chain disruptions on business operations and financial performance
• The role of organizational learning in promoting continuous improvement and innovation
• The effects of employee engagement on customer satisfaction and loyalty
• The impact of brand extensions on brand equity and consumer behavior
• The role of strategic alliances in promoting business growth and competitiveness
• The effects of corporate transparency on stakeholder trust and loyalty
• The impact of digital transformation on business models and competitiveness
• The role of business ethics in promoting corporate social responsibility and sustainability
• The effects of employee empowerment on job satisfaction and organizational performance.
• The role of corporate governance in mitigating unethical behavior in multinational corporations.
• The effects of cultural diversity on team performance in multinational corporations.
• The impact of corporate social responsibility on consumer loyalty and brand reputation.
• The relationship between organizational culture and employee engagement in service industries.
• The impact of globalization on the competitiveness of small and medium enterprises (SMEs).
• The effectiveness of performance-based pay systems on employee motivation and productivity.
• The relationship between innovation and corporate performance in the pharmaceutical industry.
• The impact of digital marketing on the traditional marketing mix.
• The role of emotional intelligence in leadership effectiveness in cross-cultural teams.
• The relationship between corporate social responsibility and financial performance in the banking sector.
• The impact of diversity management on employee satisfaction and retention in multinational corporations.
• The relationship between leadership style and organizational culture in family-owned businesses.
• The impact of e-commerce on supply chain management.
• The effectiveness of training and development programs on employee performance in the retail sector.
• The impact of global economic trends on strategic decision-making in multinational corporations.
• The relationship between ethical leadership and employee job satisfaction in the healthcare industry.
• The impact of employee empowerment on organizational performance in the manufacturing sector.
• The relationship between corporate social responsibility and employee well-being in the hospitality industry.
• The impact of artificial intelligence on customer service in the banking industry.
• The relationship between emotional intelligence and employee creativity in the technology industry.
• The impact of big data analytics on customer relationship management in the telecommunications industry.
• The relationship between organizational culture and innovation in the automotive industry.
• The impact of internationalization on the performance of SMEs in emerging markets.
• The effectiveness of performance appraisal systems on employee motivation and retention in the public sector.
• The relationship between diversity management and innovation in the pharmaceutical industry.
• The impact of social entrepreneurship on economic development in developing countries.
• The relationship between transformational leadership and organizational change in the energy sector.
• The impact of online customer reviews on brand reputation in the hospitality industry.
• The effectiveness of leadership development programs on employee engagement in the finance industry.
• The relationship between corporate social responsibility and employee turnover in the retail sector.
• The impact of artificial intelligence on the recruitment and selection process in the technology industry.
• The relationship between organizational culture and employee creativity in the fashion industry.
• The impact of digital transformation on business models in the insurance industry.
• The relationship between employee engagement and customer satisfaction in the service industry.
• The impact of mergers and acquisitions on organizational culture and employee morale.
• The effectiveness of knowledge management systems on organizational performance in the consulting industry.
• The impact of social media marketing on brand loyalty in the food and beverage industry.
• The relationship between emotional intelligence and customer satisfaction in the airline industry.
• The impact of blockchain technology on supply chain management in the logistics industry.
• The relationship between corporate social responsibility and employee engagement in the technology industry.
• The impact of digitalization on talent management practices in the hospitality industry.
• The effectiveness of reward and recognition programs on employee motivation in the manufacturing industry.
• The impact of industry 4.0 on organizational structure and culture in the aerospace industry.
• The relationship between leadership style and team performance in the construction industry.
• The impact of artificial intelligence on financial forecasting and decision-making in the banking sector.
• The relationship between corporate social responsibility and customer loyalty in the automotive industry.
• The impact of virtual teams on employee communication and collaboration in the pharmaceutical industry.
• The impact of remote work on employee productivity and job satisfaction
• The effects of social media marketing on customer engagement and brand loyalty
• The role of artificial intelligence in streamlining supply chain management
• The effectiveness of employee training and development programs on organizational performance
• The impact of diversity and inclusion initiatives on organizational culture and employee satisfaction
• The role of corporate social responsibility in enhancing brand reputation and customer loyalty
• The effects of e-commerce on small businesses and local economies
• The impact of big data analytics on marketing strategies and customer insights
• The effects of employee empowerment on organizational innovation and performance
• The impact of globalization on the hospitality industry
• The effects of corporate governance on organizational performance and financial outcomes
• The role of customer satisfaction in driving business growth and profitability
• The impact of artificial intelligence on financial forecasting and risk management
• The effects of corporate culture on employee engagement and retention
• The role of green marketing in promoting environmental sustainability and brand reputation
• The impact of digital transformation on the retail industry
• The effects of employee motivation on job performance and organizational productivity
• The role of customer experience in enhancing brand loyalty and advocacy
• The impact of international trade agreements on global business practices
• The effects of artificial intelligence on customer service and support
• The role of organizational communication in facilitating teamwork and collaboration
• The impact of corporate social responsibility on employee motivation and retention
• The effects of global economic instability on business decision-making
• The role of leadership styles in organizational change management
• The impact of social media influencers on consumer behavior and purchasing decisions
• The effects of employee well-being on organizational productivity and profitability
• The role of innovation in driving business growth and competitive advantage
• The impact of digital marketing on consumer behavior and brand perception
• The role of strategic planning in organizational success and sustainability
• The impact of e-commerce on consumer privacy and data security
• The effects of corporate reputation on customer acquisition and retention
• The role of diversity and inclusion in organizational creativity and innovation
• The impact of artificial intelligence on customer relationship management
• The effects of customer feedback on product development and innovation
• The role of employee job satisfaction in reducing turnover and absenteeism
• The impact of global competition on business strategy and innovation
• The effects of corporate branding on customer loyalty and advocacy
• The role of digital transformation in enhancing organizational agility and responsiveness
• The effects of employee empowerment on customer satisfaction and loyalty
• The role of entrepreneurial leadership in driving business innovation and growth
• The impact of digital disruption on traditional business models
• The effects of organizational culture on innovation and creativity
• The role of marketing research in developing effective marketing strategies
• The impact of social media on customer relationship management
• The effects of employee engagement on organizational innovation and competitiveness
• The role of strategic partnerships in promoting business growth and expansion
• The impact of global trends on business innovation and entrepreneurship

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Business analytics research

Business analytics research requires a rigorous approach to model formulation and estimation as well as the skills to analyse the outputs of these models. Our Business Analytics scholars regularly publish in leading international journals. Particular fields of interest include:

• big data analytics 
• applied econometrics
• electricity markets
• financial econometrics and quantitative risk forecasting
• Bayesian methods
• forecasting, sensitivity analysis
• micro-econometrics, multivariate statistical methods
• panel data methods and models
• scheduling problems
• statistical machine learning
• stochastic non-life insurance and actuarial problems
• supply chains
• testing and modelling structural change
• time series and forecasting.

We welcome approaches from potential PhD students with an interest in any of these areas.

Meet our academics and research students.

Head of Discipline

Associate Professor  Dmytro Matsypura

Deputy Head of Discipline

Professor Artem Prokhorov (Research & Recruitment)

Associate Professor Anastasios Panagiotelis (Education)

Professor  Junbin Gao

Professor  Richard Gerlach

Professor  Daniel Oron

Professor Peter Radchenko

Professor  Bala Rajaratnam

Associate Professor  Boris Choy

Associate Professor Erick Li

Associate Professor  Jie Yin

Associate Professor  Minh Ngoc Tran

Associate Professor  Andrey Vasnev

Senior Lecturers

Dr Wilson Chen

Dr  Bern Conlon

Dr  Nam Ho-Nguyen

Dr  Pablo Montero-Manso

Dr  Stephen Tierney

Dr  Chao Wang

Dr Qin Fang

Dr  Simon Loria

Dr Bradley Rava

Dr  Marcel Scharth

Dr Firouzeh Taghikhah

Dr Alison Wong

Adjunct Senior Lecturer

Dr  Steven Sommer

Adjunct Lecturer

Research associates, postdoctoral research associate.

Dr  Tomas Ignacio Lagos

Honorary and emeritus staff

Emeritus professor.

Professor Eddie Anderson

Professor Robert Bartels

Honorary Professors

Professor Robert Kohn

Professor Ganna Pogrebna

Professor Michael Smith

Honorary Associates

John Goodhew

Hoda Davarzani

John Watkins

David Grafton

Yakov Zinder

Higher degree by research students

View our current  higher degree by research students . 

Research groups

Time series and forecasting research group, productivity, efficiency and measurement analytics (pema), research seminars.

The Discipline of Business Analytics holds a regular seminar series. Seminars are usually held on Fridays at 11am in Room 5070, Abercrombie Building (H70) . The seminar organiser is Bradley Rava .

Please email  [email protected]  if you wish to be included in the BA seminar series mailing list.

Below is an outline of our recent and upcoming activity. 

2018 seminars

Finding critical links for closeness centrality.

• Date: 10 Aug 2018 at 11am
• Venue: Rm 3010, Abercrombie Building (H70)
• Speaker: Professor Oleg Prokopyev, Department of Industrial Engineering, University of Pittsburg

Risk management with POE, VaR, CVaR and bPOE: Applications in finance

• Venue: Rm 4150, Abercrombie Building (H70)
• Speaker: Professor Stan Uryasev, Department of Industrial and Systems Engineering, University of Florida

My experience as EIC of OMEGA

• Date: 9 Aug 2018 at 11am
• Venue: Rm 2240, Abercrombie Building (H70)
• Speaker: Prof Benjamin Lev, LeBow College of Business, Drexel University

Heterogeneous component MEM models for forecasting trading volumes

• Date: 27 Jul 2018 at 11am
• Venue: Rm 3190, Abercrombie Building (H70)
• Speaker: Professor Giuseppe Storti, Department of Economics and Statistics, University of Salerno UNISA

Realised stochastic volatility models with generalised asymmetry and periodic long memory

• Date: 1 Jun 2018 at 11am
• Venue: Rm 2290, Abercrombie Building (H70)
• Speaker: Professor Manabu Asai, Faculty of Economics, Soka University

Improving hand hygiene process compliance through process monitoring in healthcare

• Date: 24 May 2018 at 11am
• Venue: Rm 1080, Abercrombie Building (H70)
• Speaker: Associate Professor Chung-Li Tseng, Operations Management, UNSW Business School

Exact IP-based approaches for the longest induced path problem

• Date: 18 May 2018 at 11am
• Speaker: Dr Dmytro Matsypura, Discipline of Business Analytics, The University of Sydney

Bayesian deep net GLM and GLMM

• Date: 11 May 2018 at 11am
• Speaker: Mr Nghia Nguyen, Discipline of Business Analytics, The University of Sydney

Computational intelligence-based predictive snalytics: Applications with multi-output support vector regression

• Date: 13 Apr 2018 at 11am
• Speaker: Professor Yukun Bao, School of Management, Huazhong University of Science and Technology (HUST)

Entrywise functions preserving positivity: Connections between analysis, algebra, combinatorics and statistics

• Date: 5 Apr 2018 at 3.30pm
• Venue: Rm 3120, Abercrombie Building (H70)
• Speaker: Associate Professor Apoorva Khare, Department of Mathematics, Indian Institute of Science

Large-scale multivariate modelling of financial asset returns and portfolio optimisation

• Date: 23 Feb 2018 at 11am
• Speaker: Professor Marc Paolella, Department of Banking and Finance, University of Zurich

Statistical inference on the Canadian middle class

• Date: 16 Feb 2018 at 11am
• Speaker: Professor Russell Davidson, Department of Economics, McGill University

2017 seminars

Heterogeneous structural breaks in panel data models.

• Date: 1 Sep 2017 at 11am
• Venue: Rm 1050, Abercrombie Building (H70)
• Speaker: Dr Wendun Wang, Erasmus School of Economics, Erasmus University

Externalities, optimisation and regulation in queues

• Date: 25 Aug 2017 at 11am
• Speaker: Dr Nadja Klein, Melbourne Business School, University of Melbourne

A partial identification subnetwork approach to discrete games in large networks: An application to quantifying peer effects

• Date: 11 Aug 2017 at 11am
• Speaker: Professor Tong Li, Department of Economics, Vanderbilt University

An introduction to knowledge management and some common entry points

• Date: 4 Aug 2017 at 11am
• Venue: Rm 2090, Abercrombie Building (H70)
• Speaker: Prof Eric Tsui, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University

Two applications of serial inventory systems

• Date: 21 Jul 2017 at 11:00am
• Venue: Rm 5070, Abercrombie Building (H70)
• Speaker: Associate Professor Ying Rong, Operations Management, Shanghai Jiao Tong University

Methods of matrix factorisation

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Business Analytics for Business and Economic Sectors: A Review and Bibliometrics Analysis from 2012 to 2022

• First Online: 12 July 2023

Cite this chapter

• Fatihah Mohd   ORCID: orcid.org/0000-0002-4420-4908 3 ,
• Nurul Izyan Mat Daud   ORCID: orcid.org/0000-0001-9649-5156 3 ,
• Noor Raihani Zainol   ORCID: orcid.org/0000-0002-6091-2509 5 ,
• Nur Ain Ayunni Sabri   ORCID: orcid.org/0000-0002-2154-0423 3 ,
• Nik Madeeha Binti Nik Mohd Munir   ORCID: orcid.org/0000-0002-8114-2435 3 &
• Azila Jaini   ORCID: orcid.org/0000-0001-5689-1621 4  

Part of the book series: Contributions to Management Science ((MANAGEMENT SC.))

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Business Analytics (BA) generally refers to the application of models to analyze the data that is implemented by an organization in supporting decision-making. Currently, the use of business analytics has become a necessity to improve organizational performance and increase added business value. This situation has also attracted researchers to contribute studies in business analytics, mainly looking at current trends. This paper describes business analytics based on a study of the relevant literature, followed by a discussion of the current state of business analytics research and potential future paths. Based on the 1541 reviews and articles gathered from the Web of Science (WoS) between 2012 and 2022, we specifically carried out a bibliometric analysis of the influential studies of BA in terms of various aspects, such as research areas, journals, countries or regions, authors, most cited publications, and author keywords. The findings of the study report that the major research areas related to business analytics were “Management” (926, 60.09%), “Business” (759, 49.25%), and “Information Science and Library Science” (159, 10.32%) with TP and TPR%. The most productive journal was the Journal of Business Research, with a TP of 63. The USA, UK, and China were the top three contributing countries. Furthermore, “big data,” “big data analytics,” “business analytics,” “business intelligence,” and “analytics” were the most popular author keywords in the current ten years since 2012, apart from the author keywords of BA. When combined with the most cited articles in recent years, the topics on business intelligence by Chen in 2012 maintain in ten years as the hottest articles with the highest value of total citations, 2395.

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Fatihah Mohd, Nurul Izyan Mat Daud, Nur Ain Ayunni Sabri & Nik Madeeha Binti Nik Mohd Munir

Faculty of Business and Management, Universiti Teknologi MARA, Cawangan Johor, Kampus Segamat, Segamat, Malaysia

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Mohd, F., Daud, N.I.M., Zainol, N.R., Sabri, N.A.A., Munir, N.M.B.N.M., Jaini, A. (2023). Business Analytics for Business and Economic Sectors: A Review and Bibliometrics Analysis from 2012 to 2022. In: Mansour, N., Bujosa Vadell, L.M. (eds) Finance, Accounting and Law in the Digital Age. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-031-27296-7_30

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There are plenty of definitions proposed for business analytics–some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and methodologies. The common denominator of all of these definitions is that business analytics is the encapsulation of all mechanisms that help convert data into actionable insight for better and faster decision-making. Although the name is new, its purpose has been around for several decades, characterised under different labels. Largely driven by the need in the business world, business analytics has become one of the most active research areas in academics and in industry/practice. The Journal of Business Analytics is created to establish a dedicated home for analytics researchers to publish their research outcomes. Covering all facets of business analytics (descriptive/diagnostic, predictive, and prescriptive), the journal is destined to become the pinnacle for rigorous and relevant analytics research manuscripts. Herein we provide an overview of research challenges and opportunities for business analytics to lay the groundwork for this new journal.

• Business analytics
• descriptive analytics
• machine learning
• network science
• predictive analytics
• prescriptive analytics

ASJC Scopus subject areas

• Information Systems
• Industrial and Manufacturing Engineering

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• 10.1080/2573234X.2018.1507324

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• Industry Engineering & Materials Science 100%
• Outcomes Research Business & Economics 40%
• Encapsulation Engineering & Materials Science 32%
• Diagnostics Business & Economics 30%
• Decision Making Business & Economics 28%
• Labels Engineering & Materials Science 24%
• Decision making Engineering & Materials Science 20%
• Methodology Business & Economics 16%

T1 - Research challenges and opportunities in business analytics

AU - Delen, Dursun

AU - Ram, Sudha

N1 - Publisher Copyright: © 2018 Operational Research Society.

PY - 2018/1/2

Y1 - 2018/1/2

N2 - There are plenty of definitions proposed for business analytics–some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and methodologies. The common denominator of all of these definitions is that business analytics is the encapsulation of all mechanisms that help convert data into actionable insight for better and faster decision-making. Although the name is new, its purpose has been around for several decades, characterised under different labels. Largely driven by the need in the business world, business analytics has become one of the most active research areas in academics and in industry/practice. The Journal of Business Analytics is created to establish a dedicated home for analytics researchers to publish their research outcomes. Covering all facets of business analytics (descriptive/diagnostic, predictive, and prescriptive), the journal is destined to become the pinnacle for rigorous and relevant analytics research manuscripts. Herein we provide an overview of research challenges and opportunities for business analytics to lay the groundwork for this new journal.

AB - There are plenty of definitions proposed for business analytics–some of them focus on the scope/coverage/problem, some on the nature of the data, and some concentrate on the enabling methods and methodologies. The common denominator of all of these definitions is that business analytics is the encapsulation of all mechanisms that help convert data into actionable insight for better and faster decision-making. Although the name is new, its purpose has been around for several decades, characterised under different labels. Largely driven by the need in the business world, business analytics has become one of the most active research areas in academics and in industry/practice. The Journal of Business Analytics is created to establish a dedicated home for analytics researchers to publish their research outcomes. Covering all facets of business analytics (descriptive/diagnostic, predictive, and prescriptive), the journal is destined to become the pinnacle for rigorous and relevant analytics research manuscripts. Herein we provide an overview of research challenges and opportunities for business analytics to lay the groundwork for this new journal.

KW - Business analytics

KW - descriptive analytics

KW - machine learning

KW - network science

KW - predictive analytics

KW - prescriptive analytics

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Business analytics refers to the statistical methods and computing technologies for processing, mining and visualizing data to uncover patterns, relationships and insights that enable better business decision-making.

Business analytics involves companies that use data created by their operations or publicly available data to solve business problems, monitor their business fundamentals, identify new growth opportunities, and better serve their customers.

Business analytics uses data exploration, data visualization, integrated dashboards, and more to provide users with access to actionable data and business insights.

This IBM ebook uncovers the value of integrating a business analytics solution that turns insights into action.

Read the guide for data leaders

Business intelligence (BI) enables better business decisions that are based on a foundation of business data. Business analytics (BA) is a subset of business intelligence, with business analytics providing the analysis, while the umbrella business intelligence infrastructure includes the tools for the identification and storage of the data that will be used for decision-making. Business intelligence collects, manages and uses both the raw input data and also the resulting knowledge and actionable insights generated by business analytics. The ongoing purpose of business analytics is to develop new knowledge and insights to increase a company’s total business intelligence.

Business analytics can be used to answer questions about what happened in the past, make predictions and forecast business results. 1 An organization can gain a more complete picture of its business, enabling it to understand user behavior more effectively.

Data scientists and advanced data analysts use business analytics to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis which uses previous sales data to estimate customer lifetime value, and cluster analysis for analyzing and segmenting high-usage and low-usage users in a particular area.

Business analytics solutions provide benefits for all departments, including finance , human resources , supply chain , marketing , sales  or information technology , plus all industries, including healthcare , financial services and consumer goods .

Business analytics uses analytics—the action of deriving insights from data—to drive increases in business performance. 4 types of valuable analytics are often used:

As the name implies, this type of analytics describes the data it contains. An example would be a pie chart that breaks down the demographics of a company’s customers.

Diagnostic analytics helps pinpoint the root cause of an event. It can help answer questions such as: What are the series of events that influenced the business outcomes?  Where do the true correlation and causality lie within a given historical time frame? What are the drivers behind the findings? For example, manufacturers can analyze a failed component on an assembly line and determine the reason behind its failure.

Predictive analytics mines existing data, identifies patterns and helps companies predict what might happen in the future based on that data. It uses predictive models that make hypotheses about future behaviors or outcomes. For example, an organization could make predictions about the change in coat sales if the upcoming winter season is projected to have warmer temperatures. Predictive modeling 2 also helps organizations avoid issues before they occur, such as knowing when a vehicle or tool will break and intervening before it occurs, or knowing when changing demographics or psychographics will positively or negatively impact their product lines. 

These analytics help organizations make decisions about the future based on existing information and resources. Every business can use prescriptive analytics by reviewing their existing data to make a guess about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.

Business analytics practices involve several tools that help companies make sense of the data they are collecting and use it to turn that data into insights. Here are some of the most common tools, disciplines and approaches:

• Data management: Data management is the practice of ingesting, processing, securing and storing an organization’s data. It is then used for strategic decision-making to improve business outcomes. The data management discipline has become an increasing priority as expanding data stores has created significant challenges, such as data silos, security risks and general bottlenecks to decision-making.
• Data mining or KDD : Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets and is a significant component of big data analytics. The growing importance of big data makes data mining a critical component of any modern business by assisting companies in transforming their raw data into useful knowledge.
• Data warehousing : A data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources, including apps, Internet of Things (IoT) devices, social media and spreadsheets into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI) and machine learning (ML). A data warehouse system enables an organization to run powerful analytics on large amounts of data (petabytes and petabytes) in ways that a standard database cannot.
• Data visualization : The representation of data by using graphics such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easier to understand, being especially helpful for nontechnical staff to understand analytics concepts, and helping show patterns in multiple data points. Data visualization can also help with idea generation, idea illustration or visual discovery.
• Forecasting : This tool takes historical data and current market conditions and then makes predictions as to how much revenue an organization can expect to bring in over the next few months or years. Forecasts are adjusted as new information becomes available. When companies embrace data and analytics with well-established planning and forecasting best practices, they enhance strategic decision-making and can be rewarded with more accurate plans and more timely forecasts.
• Machine learning algorithms : A machine learning algorithm is a set of rules or processes used by an AI system to conduct tasks, most often to discover new data insights and patterns, or to predict output values from a given set of input variables. Machine learning algorithms enable machine learning to learn, delivering the power to analyze data, identify trends and predict issues before they occur.
• Reporting : Business analytics runs on the fuel of data to help organizations make informed decisions. Enterprise-grade reporting software can extract information from various applications used by an enterprise, analyze the data and generate reports.
• Statistical analysis : Statistical analysis enables an organization to extract actionable insights from its data. Advanced statistical analysis procedures help ensure high accuracy and quality decision-making. The analytics lifecycle includes data preparation and management to analysis and reporting.
• Text analysis : Identifies textual patterns and trends within unstructured data by using machine learning, statistics and linguistics. By transforming the data into a more structured format through text mining and text analysis , more quantitative insights can be found.

Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up frequently and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors who do not.

Faster and better-informed decisions: Having a flexible and expansive view of all the data an organization possesses can eliminate uncertainty, prompt an organization to take action faster, and improve business processes. If an organization’s data suggests that sales of a particular product line are declining precipitously, it might decide to discontinue that line. If climate risk impacts the harvesting of a raw material another organization depends on, it might need to source a new material from somewhere else. It’s especially helpful when considering pricing strategies.

How a company prices its goods or services is based on thousands of data points, many of which do not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to access real-time data to make smarter short- and long-term pricing data is critical. For organizations that want to incorporate dynamic pricing, business analytics enables them to use thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.

Single-window view of information: Increased collaboration between departments and line-of-business users means that everyone has the same data and is talking from the same playbook. Having that single pane of glass shows more unseen patterns, enabling different departments to understand the company’s holistic approach and increase an organization’s ability to respond to changes in the marketplace.

Enhanced customer service: By knowing what customers want, when and how they want it, organizations encourage happier customers and build greater loyalty. In addition to an improved customer experience , by being able to make smarter decisions on resource allocation or manufacturing, organizations are likely able to offer those goods or services at a more affordable price.

Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent hands-on analytical and communication skills.

Here are some of the employees that they need to take advantage of the full potential of robust business analytics strategies:

Data scientists: These people are responsible for managing the algorithms and models that power the business analytics programs. Organizational data scientists  either use open source libraries, such as the natural language toolkit (NTLK) for algorithms or build their own to analyze data. They excel at problem-solving and usually need to know several programming languages, such as Python, which helps access out-of-the-box machine learning algorithms and structured query language (SQL) , which helps extract data from databases to feed into a model.

In recent years, an increasing number of schools offer Master of Science or Bachelor’s degrees in data science where students engage in degree program coursework that teaches them computer science, statistical modeling and other mathematical applications.

Data engineers: They create and maintain information systems that collect data from different places that are cleaned and sorted, and placed into a master database. They are often responsible for helping to ensure that data can be easily collected and accessed by stakeholders to provide organizations with a unified view of their data operations.

Data analysts: They play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they might collect and analyze the data sets and build the data visualizations, or they might take the work created by other data scientists and focus on building strong storytelling for the key takeaways.

To maximize the benefits of an organization’s business analytics, it needs to clean and connect its data, create data visualizations and provide insights on where the business is today while helping predict what will happen tomorrow. This usually involves these steps:

First, organizations must identify all the data they have on hand and what external data they want to incorporate to understand what opportunities for business analytics they have.

Unfortunately, much of a company's data remains uncleaned, rendering it useless for accurate analysis until addressed.

Here are some reasons why an organization’s data might need cleaning:

• Incorrect data fields: Due to manual entry or incorrect data transfers, an organization might have bad data mixed in with accurate data. If it has any bad data in the system, this has the potential to render the entire set meaningless.
• Outdated data values: Certain data sets, including customer information, might need editing due to customers leaving, product lines being discontinued or other historical data that is no longer relevant.
• Missing data: Companies might have changed how they collect data or the data they collect, which means historic entries might be missing data that is crucial to future business analysis. Companies in this situation might need to invest in either manual data entry or identify ways to use algorithms  or machine learning  to predict what the correct data should be.
• Data silos: If an organization’s existing data is in multiple spreadsheets or other types of databases, it might need to merge the data so it’s all in one place. While the foundation of any business analytics approach is first-party data (data the company has collected from stakeholders and owns), they might want to append third-party data (data they’ve purchased or gleaned from other organizations) to match their data with external insights.

Companies can now query and quickly parse gigabytes or terabytes of data rapidly with more cloud computing . Data scientists can analyze data more effectively by using machine learning, algorithms, artificial intelligence (AI ) and other technologies. Doing so can produce actionable insights based on an organization’s key performance indicators (KPIs) .

Business analytics programs can now quickly take huge amounts of that analyzed data to create dashboards, visualizations and panels where the data can be stored, viewed, sorted, manipulated and sent to stakeholders.

Data visualization best practices include understanding which visual best fits the data an organization is using and the key points it hopes to make, keeping the visual as clean and simple as possible, and providing the right explanations and content to help ensure that the audience understands what they’re viewing.

Ongoing data management is conducted in tandem with what was mentioned earlier. An organization that embraces business analytics must create a comprehensive strategy for maintaining its cleaned data, especially as it incorporates new data sources.

Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision-making.

• Financial and operational planning: Business analytics provides valuable insights to help organizations align their financial planning and operations more seamlessly. It does this by setting rules for supply chain management , integrating data across functions, and improving supply chain analytics and demand forecasting.
• Planning analytics: An integrated business planning approach that combines spreadsheets and database technologies to make effective business decisions about topics such as demand and lead generation, optimization of operating costs, and technology requirements based on solid metrics. Many organizations have historically used tools including Microsoft Excel for business planning, but some are transitioning to tools such as IBM Planning Analytics .
• Integrated sales and marketing planning: Most organizations have historical data about their lead generation, sales conversions and customer retention success rates. Organizations looking to create more accurate revenue plans and forecasts and gain deeper visibility into their marketing and sales data are using business analytics to allocate resources based on performance or changing demand to meet business objectives.
• Integrated workforce performance planning: As organizations undergo digital transformation and otherwise react to changing landscapes, they might need to ensure they have the right workforce with the right analytical skills. This is especially true in a world where employees are more likely to leave a company for a new job. Workforce performance planning helps organizations understand their workforce requirements, identify and address skill gaps, and better recruit and retain talent to meet the organization's needs today and in the future.

The flexibility of spreadsheets. Control of a database. The power of integrated business planning. Now available as a Service on AWS.

AI-powered automation and insights in Cognos Analytics enable everyone in your organization to unlock the full potential of your data. 

Detects application and business risks affecting the customer experience, enabling users to correlate application service level objectives with underlying infrastructure resourcing.

Learn more about business analytics by reading these blogs and articles. 

IBM Planning Analytics has helped support organizations across not only the office of finance but all departments in their organization.

A growing number of forward-looking companies are successfully navigating complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, and more.

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.

Scale AI workloads for all your data, anywhere, with IBM watsonx.data, a fit-for-purpose data store built on an open data lakehouse architecture.

1 Business intelligence versus business analytics  (link resides outside ibm.com), Harvard Business School. 2  How predictive analytics can boost product development  (link resides outside ibm.com), McKinsey, August 16, 2018.

Research Topics & Ideas: Business

50+ Management Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a business/management-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of  research ideas and topic thought-starters for management-related research degrees (MBAs/DBAs, etc.). These research topics span management strategy, HR, finance, operations, international business and leadership.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the management domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: Business Research Topics

• Business /management strategy
• Human resources (HR) and industrial psychology
• Finance and accounting
• Operations management
• International business
• Actual business dissertations & theses

Strategy-Related Research Topics

• An analysis of the impact of digital transformation on business strategy in consulting firms
• The role of innovation in transportation practices for creating a competitive advantage within the agricultural sector
• Exploring the effect of globalisation on strategic decision-making practices for multinational Fashion brands.
• An evaluation of corporate social responsibility in shaping business strategy, a case study of power utilities in Nigeria
• Analysing the relationship between corporate culture and business strategy in the new digital era, exploring the role of remote working.
• Assessing the impact of sustainability practices on business strategy and performance in the motor vehicle manufacturing industry
• An analysis of the effect of social media on strategic partnerships and alliances development in the insurance industry
• Exploring the role of data-driven decision-making in business strategy developments following supply-chain disruptions in the agricultural sector
• Developing a conceptual framework for assessing the influence of market orientation on business strategy and performance in the video game publishing industry
• A review of strategic cost management best practices in the healthcare sector of Indonesia
• Identification of key strategic considerations required for the effective implementation of Industry 4.0 to develop a circular economy
• Reviewing how Globalisation has affected business model innovation strategies in the education sector
• A comparison of merger and acquisition strategies’ effects on novel product development in the Pharmaceutical industry
• An analysis of market strategy performance during recessions, a retrospective review of the luxury goods market in the US
• Comparing the performance of digital stakeholder engagement strategies and their contribution towards meeting SDGs in the mining sector

Topics & Ideas: Human Resources (HR)

• Exploring the impact of digital employee engagement practices on organizational performance in SMEs
• The role of diversity and inclusion in the workplace
• An evaluation of remote employee training and development programs efficacy in the e-commerce sector
• Comparing the effect of flexible work arrangements on employee satisfaction and productivity across generational divides
• Assessing the relationship between gender-focused employee empowerment programs and job satisfaction in the UAE
• A review of the impact of technology and digitisation on human resource management practices in the construction industry
• An analysis of the role of human resource management in talent acquisition and retention in response to globalisation and crisis, a case study of the South African power utility
• The influence of leadership style on remote working employee motivation and performance in the education sector.
• A comparison of performance appraisal systems for managing employee performance in the luxury retail fashion industry
• An examination of the relationship between work-life balance and job satisfaction in blue-collar workplaces, A systematic review
• Exploring HR personnel’s experiences managing digital workplace bullying in multinational corporations
• Assessing the success of HR team integration following merger and acquisition on employee engagement and performance
• Exploring HR green practices and their effects on retention of millennial talent in the fintech industry
• Assessing the impact of human resources analytics in successfully navigating digital transformation within the healthcare sector
• Exploring the role of HR staff in the development and maintenance of ethical business practices in fintech SMEs
• An analysis of employee perceptions of current HRM practices in a fully remote IT workspace

Topics & Ideas: Finance & Accounting

• An analysis of the effect of employee financial literacy on decision-making in manufacturing start-ups in Ghana
• Assessing the impact of corporate green innovation on financial performance in listed companies in Estonia
• Assessing the effect of corporate governance on financial performance in the mining industry in Papua New Guinea
• An evaluation of financial risk management practices in the construction industry of Saudi Arabia
• Exploring the role of leadership financial literacy in the transition from start-up to scale-up in the retail e-commerce industry.
• A review of influential macroeconomic factors on the adoption of cryptocurrencies as legal tender
• An examination of the use of financial derivatives in risk management
• Exploring the impact of the cryptocurrency disruption on stock trading practices in the EU
• An analysis of the relationship between corporate social responsibility and financial performance in academic publishing houses
• A comparison of financial ratios performance in evaluating E-commerce startups in South Korea.
• An evaluation of the role of government policies in facilitating manufacturing companies’ successful transitioning from start-up to scale-ups in Denmark
• Assessing the financial value associated with industry 4.0 transitions in the Indian pharmaceutical industry
• Exploring the role of effective e-leadership on financial performance in the Nigerian fintech industry
• A review of digital disruptions in CRM practices and their associated financial impact on listed companies during the Covid-19 pandemic
• Exploring the importance of Sharia-based business practices on SME financial performance in multicultural countries

Ideas: Operations Management

• An assessment of the impact of blockchain technology on operations management practices in the transport industry of Estonia
• An evaluation of supply chain disruption management strategies and their impact on business performance in Lithuania
• Exploring the role of lean manufacturing in the automotive industry of Malaysia and its effects on improving operational efficiency
• A critical review of optimal operations management strategies in luxury goods manufacturing for ensuring supply chain resilience
• Exploring the role of globalization on Supply chain diversification, a pre/post analysis of the COVID-19 pandemic
• An analysis of the relationship between quality management and customer satisfaction in subscription-based business models
• Assessing the cost of sustainable sourcing practices on operations management and supply chain resilience in the Cocao industry.
• An examination of the adoption of behavioural predictive analytics in operations management practices, a case study of the
• Italian automotive industry
• Exploring the effect of operational complexity on business performance following digital transformation
• An evaluation of barriers to the implementation of agile methods in project management within governmental institutions
• Assessing how the relationship between operational processes and business strategy change as companies transition from start-ups to scale-ups
• Exploring the relationship between operational management and innovative business models, lessons from the fintech industry
• A review of best practices for operations management facilitating the transition towards a circular economy in the fast food industry
• Exploring the viability of lean manufacturing practices in Vietnam’s plastics industry
• Assessing engagement in cybersecurity considerations associated with operations management practices in industry 4.0 manufacturing

Topics & Ideas: International Business

• The impact of cultural differences in communication on international business relationships
• An evaluation of the role of government import and export policies in shaping international business practices
• The effect of global shipping conditions on international business strategies
• An analysis of the challenges of managing multinational corporations: branch management
• The influence of social media marketing on international business operations
• The role of international trade agreements on business activities in developing countries
• An examination of the impact of currency fluctuations on international business and cost competitiveness
• The relationship between international business and sustainable development: perspectives and benefits
• An evaluation of the challenges and opportunities of doing business in emerging markets such as the renewable energy industry
• An analysis of the role of internationalisation via strategic alliances in international business
• The impact of cross-cultural management on international business performance
• The effect of political instability on international business operations: A case study of Russia
• An analysis of the role of intellectual property rights in an international technology company’s business strategies
• The relationship between corporate social responsibility and international business strategy: a comparative study of different industries
• The impact of technology on international business in the fashion industry

Topics & Ideas: Leadership

• A comparative study of the impact of different leadership styles on organizational performance
• An evaluation of transformational leadership in today’s non-profit organizations
• The role of emotional intelligence in effective leadership and productivity
• An analysis of the relationship between leadership style and employee motivation
• The influence of diversity and inclusion on leadership practices in South Africa
• The impact of Artificial Intelligence technology on leadership in the digital age
• An examination of the challenges of leadership in a rapidly changing business environment: examples from the finance industry
• The relationship between leadership and corporate culture and job satisfaction
• An evaluation of the role of transformational leadership in strategic decision-making
• The use of leadership development programs in enhancing leadership effectiveness in multinational organisations
• The impact of ethical leadership on organizational trust and reputation: an empirical study
• An analysis of the relationship between various leadership styles and employee well-being in healthcare organizations
• The role of leadership in promoting good work-life balance and job satisfaction in the age of remote work
• The influence of leadership on knowledge sharing and innovation in the technology industry
• An investigation of the impact of cultural intelligence on cross-cultural leadership effectiveness in global organizations

Business/Management Dissertation & Theses

While the ideas we’ve presented above are a decent starting point for finding a business-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various management-related degree programs (e.g., MBAs, DBAs, etc.) to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• Sustaining Microbreweries Beyond 5 Years (Yanez, 2022)
• Perceived Stakeholder and Stockholder Views: A Comparison Among Accounting Students, Non-Accounting Business Students And Non-Business Students (Shajan, 2020)
• Attitudes Toward Corporate Social Responsibility and the New Ecological Paradigm among Business Students in Southern California (Barullas, 2020)
• Entrepreneurial opportunity alertness in small business: a narrative research study exploring established small business founders’ experience with opportunity alertness in an evolving economic landscape in the Southeastern United States (Hughes, 2019)
• Work-Integrated Learning in Closing Skills Gap in Public Procurement: A Qualitative Phenomenological Study (Culver, 2021)
• Analyzing the Drivers and Barriers to Green Business Practices for Small and Medium Enterprises in Ohio (Purwandani, 2020)
• The Role of Executive Business Travel in a Virtual World (Gale, 2022)
• Outsourcing Security and International Corporate Responsibility: A Critical Analysis of Private Military Companies (PMCs) and Human Rights Violations (Hawkins, 2022)
• Lean-excellence business management for small and medium-sized manufacturing companies in Kurdistan region of Iraq (Mohammad, 2021)
• Science Data Sharing: Applying a Disruptive Technology Platform Business Model (Edwards, 2022)
• Impact of Hurricanes on Small Construction Business and Their Recovery (Sahu, 2022)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Topic Ideation

If you’d like hands-on help to speed up your topic ideation process and ensure that you develop a rock-solid research topic, check our our Topic Kickstarter service below.

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Great help. thanks

Hi, Your work is very educative, it has widened my knowledge. Thank you so much.

Thank you so much for helping me understand how to craft a research topic. I’m pursuing a PGDE. Thank you

Effect of Leadership, computerized accounting systems, risk management and monitoring on the quality of financial Reports among listed banks

May you assist on a possible PhD topic on analyzing economic behaviours within environmental, climate and energy domains, from a gender perspective. I seek to further investigate if/to which extent policies in these domains can be deemed economically unfair from a gender perspective, and whether the effectiveness of the policies can be increased while striving for inequalities not being perpetuated.

healthy work environment and employee diversity, technological innovations and their role in management practices, cultural difference affecting advertising, honesty as a company policy, an analysis of the relationships between quality management and customer satisfaction in subscription based business model,business corruption cases. That I was selected from the above topics.

Research topic accounting

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Top 10 Analytics And Business Intelligence Trends For 2024

The Top Trends In Business Intelligence

Become data-driven in 2024.

Over the past decade, business intelligence has been revolutionized. Data exploded and became big. And just like that, we all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly, advanced analytics wasn’t just for analysts.

2023 was a particularly major year for the business intelligence industry. The trends we presented last year will continue to play out through 2024. However, the BI landscape is evolving, and the future of business intelligence is playing now, with emerging trends to watch. In 2024, BI tools and strategies will become increasingly customized. Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics, but what is the best BI solution for their specific needs?

Businesses are no longer wondering if visualizations improve analyses but what the best way to tell each data story is, especially with the help of modern BI dashboard software. 2024 will be the year of data security and discovery: clean and secure data combined with a simple and powerful presentation. It will also be a year of collaborative BI and AI, where multiple industries will dive into analytics to benefit from the power of data, such as the case of construction business intelligence , which we will discuss in detail later in the post.

We are excited to see what this new year will bring. Read on to see our top 10 business intelligence trends for 2024!

1) Construction Analytics

As mentioned in the introduction, construction is one of the many industries that has jumped into the BI train and benefited from advanced analytics. The construction case is interesting because this massive industry has always been recognized as reluctant to change, probably because it is one of the oldest industries in the world, with processes that have been tested repeatedly with positive results, at least until now. However, as the world becomes more digitally driven, the construction industry must keep up with changes. In 2024, we can expect an even bigger adoption of digital construction technologies that will drive the industry forward.

Construction analytics can solve many challenges for companies in the building sector, including disparate data sources, outdated reporting, and decentralized teams and systems, among many others. All these issues lead to costly errors that make projects longer and more expensive. By integrating data management practices and technologies into their workflows, construction companies can benefit from real-time data to make projects more time and cost-efficient from preconstruction planning all the way to the completion and handover stage.

This is possible with the support of professional BI software that offers a centralized location to aggregate data from multiple sources and visualize it in interactive construction reports that anyone can access and understand. Plus, many processes that were once done manually, like a quantity takeoff or a bill of quantities , can now be done automatically, saving teams hours of work and significantly reducing the risks of manual errors.

That being said, the benefits are not purely operational. From a teamwork perspective, implementing analytics can also be a big driver of productivity. It is no secret that collaboration and communication in construction projects have always been a challenge. With project stakeholders working from different locations, it is common for communication barriers to bring bigger issues. The right construction analytics software can easily solve this issue by offering 24/7 access to real-time data in an online environment. This means anyone, regardless of device or location, can access and share the data with others to collaborate and communicate, ensuring everyone is working from a single source of truth.

That said, adopting new software and practices is not easy. Leaders in the building industry will need to implement well-thought-out construction change management strategies to ensure the process goes smoothly. Involving all employees in the process can boost engagement and make it more effective.

2024 will be an exciting year for the building sector. As the importance of construction project management grows, industry leaders need to invest in technology that will boost teamwork and modernize processes that can no longer be carried out analogically. We look forward to seeing it happen!

2) Artificial Intelligence

We will start analyzing what is new in business intelligence with AI. Gartner is covering this trend extensively in its latest Strategic Technology Trends report, combining AI with engineering and hyper-automation and concentrating on the level of security in which AI risks developing vulnerable points of attack.

Artificial intelligence (AI) is the science that aims to make machines execute what is usually done by complex human intelligence. Often seen as the highest foe-friend of the human race in movies ( Skynet in Terminator, The Machines of Matrix, or the Master Control Program of Tron), AI is not yet on the verge of destroying us, despite the legitimate warnings of some reputed scientists and tech entrepreneurs.

While we work on programs to avoid such inconvenience, AI and machine learning are revolutionizing the way we interact with our analytics and data management, while increments in security measures must be taken into account. The fact is that it is and will affect our lives, whether we like it or not.

It is expected that AI will evolve into a more responsible and scalable technology in the coming year as organizations will require a lot more from AI-based systems. According to Gartner’s Data and Analytics research for 2021, with COVID-19 completely changing the business landscape, historical data will no longer be the main driver of AI-based technologies. In change, these solutions will need to work with smaller datasets and more adaptive machine learning while also being compliant with new privacy regulations. This concept is known as ethical AI, and it aims to ensure that organizations use AI systems in a way that will not break the law. To this day, many organizations have faced legal issues for illegally collecting user data. The Facebook and Cambridge Analytica scandal is a perfect example of that.

In that sense, implementing systems and models to ensure the correct use of AI-related technologies will become even more important in the coming years. In fact, the US government recently released a blueprint for the “AI Bill of Rights ,” presenting 5 principles that should guide the design, use, and deployment of automated systems “to protect the American public in the age of artificial intelligence.”

In response to this increasing need for AI accountability, Gartner presents  AI TRiSM as one of the concepts that will help organizations ensure “AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection.” This cross-functional framework must be implemented from the earliest stages of system design and involve people from compliance, legal, IT, and analytics for a successful approach. By 2026, businesses that apply this framework to their AI models are expected to be 50% more successful in adoption, business goals, and user acceptance.

It can’t be denied that AI is still a topic of concern even today. The number of AI-based applications has become so big that many IT professionals don’t even know how to use or interpret them. This leaves the doors open for breaches and financial losses that can significantly impact companies and customers alike. As a response, terms such as explainable AI (XAI) will be at the center of the conversation during 2024. XAI is an emerging field that aims to apply specific processes and methods to allow humans to understand the results and outputs created by machine learning and AI algorithms. The end goal of this field is to ensure trust and transparency with these systems to give humans control over them.

AI-based business analytics

When it comes to analytics, businesses are evolving from static, passive reports of things that have already happened to proactive analytics with dashboards that help them see what is happening at every second and give alerts when something is not how it should be. Solutions such as an AI algorithm based on the most advanced neural networks provide high accuracy in anomaly detection as it learns from historical trends and patterns. That way, any unexpected event will be immediately registered, and the system will notify the user.

Another feature that AI has on offer in BI solutions is the upscaled insights capability. It basically fully analyzes your dataset automatically without needing effort on your end. You simply choose the data source you want to analyze and the column/variable (for instance, revenue) that the algorithm should focus on. Then, calculations will be run and come back to you with growth/trends/forecast, value driver, key segments correlations, anomalies, and what-if analysis. That is an incredible time gain as what is usually handled by a data scientist will be performed by a tool, providing business users with access to high-quality insights and a better understanding of their information, even without a strong IT background.

Time gain is also present in the form of AI assistants. Tools have started developing AI features that enable users to communicate with the software in plain language—the user types a question or request, and the AI generates the best possible answer. If you are interested in this, then keep reading because we will dive into it in more detail later in the post with the natural language processing trend.

The demand for real-time online data analysis software is increasing, and the arrival of the IoT (Internet of Things) also brings countless amounts of data, promoting statistical analysis and management at the top of the priority list. However, businesses today want to go further, and Adaptive AI might be the answer. As stated by Gartner, Adaptive AI systems “support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise.” These systems are so interesting for companies today because they can learn from behavioral patterns and adjust to real-world changes, making it easier to make fast and improved decisions.

In that same realm, Generative AI is another technology that has revolutionized the industry in 2023 and will continue to do so in 2024. It basically enables AI systems to generate text, images, audio, and other types of content based on human-generated input. A famous example of Generative AI is ChatGPT. In 2023, the tool revolutionized the industry with its ability to generate well-written texts based on a short input. However, as with many AI-related innovations, ChatGPT was quickly scrutinized because it could generate biases, copyright infringement, fake news, and more if not used ethically.

From a business perspective, using technologies like Adaptive and Generative AI has facilitated several processes, including data collection, cleaning, and analysis, which can be automated and tailored to the company’s needs. Risk management is another area in which these technologies thrive. Businesses can use Generative AI to predict any kind of fraud or attack, as well as generate risk simulations and test strategies in an imaginary scenario.

Overall, we cannot deny the value of AI and its continued development over the years. That said, regulators and decision-makers must ensure ethical and secure measures are imposed when implementing these systems. It all comes back to security; we will discuss it in more detail in our next trend.

3) Data Security

As you saw with our extensive AI trend, data and information security have been on everyone’s lips in 2023, and they will continue to buzz the world in 2024. The implementation of privacy regulations such as the GDPR ( General Data Protection Regulation ) in the EU, the CCPA ( California Consumer Privacy Act ) in the USA, and the LGPD ( General Personal Data Protection Law ) in Brazil have set building blocks for data security and management of customers personal information.

Moreover, the overturn by the European Court of Justice of the legal framework called Data Privacy Shield hasn’t made software companies’ lives much easier. The Shield was a legal framework that enabled them to transfer data from the EU to the USA, but with recent legal developments causing the invalidation of the process, companies that have their headquarters in the US don’t have the right to transfer any of the EU data subjects.

Actually, a similar situation happened in 2015 when the EU and the USA had no legally valid agreements on this matter for a while. Many US-based (software) providers argue that they use European servers, and there is no data transfer to the US at all. However, from a legal perspective, even this solution is questionable, as, in theory, the US judiciary could force US-based businesses to reveal even data from EU-based servers. In essence, the information that is located in the EU needs to stay in the EU. In practice, that means that EU-based businesses that use in the current situation, US-based software vendors that store any kind of data for them are taking hazards as they operate in a legal grey area.

Taking all this into account, businesses have been forced to invest in security to stay compliant with the new regulations and also to protect themselves from cybercrime. In fact, global spending on cybersecurity products is expected to reach $1.75 trillion in the next 5 years. This is not a surprise to the experts as, during 2020 and the beginning of COVID-19, companies of all sizes were forced to mutate from physical to digital, and, to accelerate the transformation, they relied on online services, leaving a gap for cybercriminals to attack. According to the 2023 KPMG CEO Outlook Pulse survey , cybersecurity is among the top 10 “risks to growth” topics for CEOs in the coming years. Even more concerning is that 27% of the surveyed CEOs admit to being unprepared for a potential attack, which increased compared to 24% in the previous year.

This might change now that company boards recognize cybersecurity as an overall business risk more than an IT-related issue. According to Gartner’s Cybersecurity Predictions for 2023-2024, by 2026, 70% of boards will include one member with cybersecurity expertise.

Amongst the measures organizations are taking in the coming years, we will see an increase in adopting the Zero Trust framework. Zero Trust doesn’t describe a specific technology but an approach in which businesses remove the “implicit trust” from all computing infrastructure by verifying every stage of digital interaction from devices to users, regardless of location. This means every user who wants to interact with the company’s systems needs to be validated and verified. According to Gartner, by 2026, 10% of large enterprises will have a “comprehensible, mature and measurable” Zero Trust program in place, compared to the less than 1% that have one today. However, almost half of them might fail as a Zero Trust approach requires full organizational involvement and connection to business goals to succeed.

The concern in cybersecurity also presents a challenge for SaaS BI tools as they need to ensure they offer a secure product that clients will trust with their sensitive data. Like any other cloud BI solution, online business intelligence software is also subjected to security risks. Some of them include processing data quickly to provide real-time insights that might be subjected to regulatory compliance, vulnerabilities when moving data from user’s systems to the BI tool’s cloud, or when the tool provides access to data from multiple devices that may be unsafe and exposed to attacks. To prevent any of this from happening, BI software needs to have a clear focus on security.

Cybersecurity mesh architecture is one of the latest trends in business intelligence to help SaaS BI solutions stay safe. Cybersecurity mesh is a composable and scalable security control that protects digital assets that reside in applications, in the cloud, IoT, and others. It seeks to establish a defined security perimeter around a person or a specific point with a more modular approach, enabling users to securely access data from their smartphones. One of Gartner’s cybersecurity predictions for 2021-2022 stated that by the end of 2024, organizations adopting cybersecurity mesh architecture will reduce the financial impact of security incidents by around 90% . Since data breaches have been regularly in the news, buzzing industries, and average users, the demand for security products and services is understandable.

With these security threats increasing, businesses must adopt an organizational approach to protect their data. That is why data governance will remain one of the hottest topics related to security in 2024. This concept refers to a set of processes, policies, and roles that ensure appropriate valuation, creation, consumption, and control of business data at a strategic, tactical, and operational level. It establishes roles and responsibilities regarding who can manipulate the data, in which situation, and with what tools and methods to ensure a secure and efficient data management process.

In the past years, due to tighter regulations, such as GDPR, organizations were obligated to ensure a secure environment for sensitive data, enhancing the need for stronger governance processes. As we mentioned earlier, companies of all sizes are exposed to attacks and breaches, leaving massive amounts of sensitive information from customers, suppliers, employees, and more exposed to misuse. In that sense, implementing a well-crafted governance plan will help organizations comply with government regulations while setting the perfect environment to use quality data and achieve their goals.

In today’s highly competitive business environment, where data collection keeps growing every second, data governance becomes a mandatory practice. A well-implemented governance framework not only assists organizations in staying compliant but also in minimizing risks, reducing costs, improving communication from an internal and external point of view, and achieving strategic goals, among other things.

3) Data Discovery/Visualization

Data discovery using visuals has opened the analytical doors to a wider audience and is expected to keep growing in the coming years. As stated by a survey conducted by the Business Application Research Center, data discovery was already listed in the top 6 business intelligence trends by the importance hierarchy for 2023 and is expected to keep growing in 2024. BI practitioners steadily show that the empowerment of business users is a strong and consistent trend.

Essentially, data discovery is the process of collecting data from various internal and external sources and using advanced visual analytics tools to consolidate all the information. This allows businesses to engage every relevant stakeholder with the information by empowering them to intuitively analyze and manipulate it and extract actionable insights. To achieve this, businesses of all sizes turn to modern solutions such as business intelligence tools that offer data integration, interactive visualizations, a user-friendly interface, and the flexibility to work with big amounts of data efficiently and intuitively.

An essential element to consider is that data discovery tools depend upon a process, and the generated findings will bring business value. It requires understanding the relationship between data through data preparation, visual analysis, and guided advanced analytics. “The high demand for data discovery solutions reflects a huge shift in the BI world towards increased data usage and the extraction of insights,” the Research Center emphasizes. Using online data visualization tools to perform those actions is an invaluable resource for producing relevant insights and creating a sustainable decision-making process. That being said, business users require software that is:

• Easy to use
• Agile and flexible
• Reduces time to insight
• Allows easy handling of a high volume and variety of data

Discovering trends in business operations that you didn’t even know existed or enabling immediate actions when a business anomaly occurs have become invaluable tools in effectively managing businesses of all sizes.

Data visualization has evolved into a state-of-the-art solution to present and interact with numerous graphics on a single screen, whether it’s focused on developing sales charts or comprehensive interactive reports. The point is that data discovery is a process that enables decision-makers to reveal insights, and by using visualizations, teams have the chance to spot trends and major outliers within minutes.

In 2024, the dashboard will continue to be a major visual communication tool that will enhance collaboration between teams by being the analytical hub of a project. But more than just a visualization tool, KPI dashboards will take their interactivity features to the next level with technologies such as AI-based alarms and real-time data. Since humans process visual information better, the data discovery trend will be one of the most important BI trends in 2024.

4) D&A Sustainability

Moving on with our list of the new trends in business intelligence, we have data and analytics (D&A) sustainability. The topic is one of the most important ones we will discuss in this post, as climate change remains a global concern for the next years.

In recent years, businesses have started exploring sustainability, mostly as a marketing tactic to brand themselves as “conscious.” As the topic becomes increasingly important, with new regulations forcing organizations to report on their ESG initiatives , decision-makers have realized that sustainability also represents a big way to reduce operating costs and increase overall profitability and efficiency. That is where D&A sustainability comes into the picture.

Now that businesses of all sizes and across industries have realized the hidden potential of sustainability, we will start to see many using data and analytics to boost their strategies and maximize their efforts. By tracking important metrics like energy consumption, gas emissions, labor rights, supply chain performance, and others, organizations can extract valuable insights to guide their sustainability journey.

In 2024 and beyond, we can expect organizations to use D&A sustainability to anticipate changes in demand and adjust their resource purchases and usage to be more financially intelligent. However, we will also see other factors coming into play besides just purely resource-related data. Production levels, sales volume, employee headcounts, and even weather data will help paint a more accurate picture to facilitate real-time decision-making.

We can also expect to see different tools emerge to help track sustainability data from a past, present, and future perspective, providing a big competitive advantage for companies that manage to adopt it correctly. That being said, ensuring all employees and relevant stakeholders are involved in the process is also necessary. Implementing training instances to engage employees with the process is a good way to start.

Linking ESG initiatives to business outcomes is not an easy task. As of today, sustainability analytics is valuable for three main reasons: the first one is to stay compliant with the law, the second one is to track the performance of ESG goals, and the third one is to uncover new opportunities to keep integrating sustainability into operations. Organizational leaders must take charge to ensure all these aspects are covered and supported with the best tools and technologies.

At RIB Software, we are committed to making the construction industry greener by providing our clients with the best solutions to track their carbon footprint with the help of our professional carbon estimating software , RIB CostX.

It is no secret that sustainability has transitioned from a buzzword to a mandatory practice in the business world. It is a growing trend that we will see everywhere in 2024 and many more years to come.

5) Data Sharing

Data and analytics have become a business’s most valuable competitive asset. Making informed decisions based on accurate insights can skyrocket success to a whole new level. That being said, analyzing data and extracting insights is not enough. Especially considering how accessible it has become to extract and manage valuable business data. To really extract the maximum potential out of your analytical journey, it is necessary to ensure full organizational adoption through powerful data sharing practices, which leads us to our next trend.

Gartner already identified data sharing as one of the top 10 data and analytics trends for 2023. Stating that businesses that implement efficient data sharing processes with internal and external stakeholders will outperform their competitors on most business value metrics.

While the importance of data sharing might seem obvious to some, it presents a challenge for most organizations as, for decades, it was the norm to say, “don’t share data unless….” The issue is that in today’s context, where most businesses are undergoing digital transformations, not sharing data can be detrimental, as everyone across the company needs to be united to connect analytics to general business goals. In that sense, Gartner advises organizations to switch their mindset to “must share data unless..”. Doing so will enable more robust data and analytics strategies, empowering stakeholders to make agile and informed decisions.

Changing the mindset might not be easy, and organizations that don’t take it seriously might fail in the process. Gartner suggests establishing trust-based mechanisms to ensure decision-makers trust the data they collect and use to inform their strategies. This way, they will feel confident in using it, sharing it, and re-sharing it with those who might need it. This can be easily done by tracking data quality metrics and implementing catalogs to compile all the information related to the trustworthiness of the data.

When discussing data sharing, the term “self-service BI” quickly pops up because those solutions do not require an IT team to access, interpret, and understand all the data. These online BI tools make sharing easier by generating automated reports that can be scheduled at specific times and to specific people. For instance, they enable you to set up business intelligence alerts and share public or embedded dashboards with a flexible level of interactivity. All these possibilities are accessible on all devices, which enhances the decision-making and problem-solving processes critical for today’s ever-changing environment. This is especially necessary now that the pandemic has forced businesses to shift to a home office dynamic in which collaboration needs to be supported by the right tools more than ever.

Collaborative information, information enhancement, and collaborative decision-making are the key focus of new BI solutions. However, data sharing does not only occur around the exchange or updates of some documents. It has to track the progress of meetings, calls, e-mail exchanges, and ideas collection. More recent insights predict that collaborative business intelligence will become more connected to greater systems and larger sets of users. The team’s performance will be affected, and the decision-making process will thrive in this new concept.

In fact, it is expected that, in 2024, data sharing will move further from just sharing insights and will start from earlier stages. Starting from data exploration and spreading across the entire analytical workflow for a more efficient decision-making process that includes every stakeholder, regardless of location. This last point is especially important when considering the growing security concerns many businesses face today. Implementing a collaborative BI approach enables every stakeholder and data user to be accountable for the decisions he or she makes, ensuring a more secure workflow.

In response to all these changes, data analytics and BI providers are prioritizing collaboration for 2024, introducing multiple capabilities that connect users at every stage of their work and with a level of interactivity that breaks the barriers between data and analytics and the different business functions. A recent survey shows that 75% of executives say their business functions are competing rather than collaborating. This presents a major challenge, especially for companies still undergoing a digital transformation due to the pandemic. By implementing a collaborative approach supported by the right tools and processes, developers and average business users are expected to work together under the same analytics umbrella, enabling more united communication and a productive work environment. Let’s see how it will be developed in the business intelligence trends topics of 2024.

6) Data Literacy

As data becomes the foundation of strategic decisions for businesses of all sizes, understanding and using this data as a collaborative tool that everyone in the organization can use becomes critical for success. That said, data literacy will be one of the relevant data analytics trends to look out for in 2024.

Data literacy is defined as the ability to understand, read, write, and communicate data in a specific context. This means understanding the techniques and methods used to analyze the data as well as the tools and technologies implemented. According to Gartner , poor data literacy is listed as the second-biggest roadblock to the success of the CDO’s office, and it adds that by 2024, data literacy will become essential in driving business value.

Even with the rise of self-service tools that are accessible to everyone, data literacy continues to be the foundation of a successful data-driven culture. Business leaders are responsible for providing the needed training and tools to the entire organization to empower everyone to work with data and analytics. To achieve a successful data literacy process, a careful assessment of the skills of employees and managers needs to be made in order to identify weak spots and gaps. Gartner recommends starting by identifying fluent data users that can serve as “mediators” for non-skilled groups as well as identifying communication barriers where data is failing its purpose. With all this knowledge in hand, the creation of targeted training instances will become an easier task.

In the long run, with the proper training and the right tools, users from all levels of knowledge will be able to perform advanced analysis and use data as their main language. With technologies such as predictive analytics becoming accessible for regular users, data science will no longer need to be performed by experts- shifting these professionals to focus on other advanced tasks such as Machine Learning or MLOps. In fact, according to Gartner, it is expected that by 2025, the shortage of data scientists will no longer be an obstacle to businesses adopting advanced technological processes. That said, data literacy will be one of the most important business intelligence market trends in the coming year.

7) Natural Language Processing (NLP)

Natural Language Processing (NLP) is one of the recent trends in business intelligence that is revolutionizing how companies approach their analytical processes. Considered amongst the most powerful branches of AI, NLP enables computers and machines to understand, learn from, and interpret human language in a spoken or written form, and it can be divided into two subsets: natural language understanding (NLU) and natural language generation (NLG). NLU focuses on understanding the meaning behind text and speech, while NLG focuses on text generation based on specific data input.

The growth of this trend has been such in the past years that its $3 billion worldwide market revenue from 2017 is expected to be almost 14 times larger by 2025, reaching $43 billion, according to research by Statista. This is not surprising as language-processing applications are already present in our daily lives in the shape of car navigation systems, smart voice assistants like Siri or Alexa, autocomplete text features on our phones, and translation apps, just to name a few.

Considering all of that, it is not surprising that businesses have begun to adopt this technology to manage the large amounts of unstructured text data they gather from different sources such as emails, social media, or surveys. As a response, multiple BI software providers offer their users language insight features. There are two major use cases for which language processing is becoming increasingly popular in the BI industry. Let’s look at them in more detail below:

BI data assistant: Similar to the chatbots we see on multiple websites today, a data assistant is integrated into BI software to answer any analytical questions that a user might have. All you need to do is write a question in human language, and the assistant will provide you with the answer. As the technology matured in the past years, AI-based assistants went from simply showing search results for users to analyze to be able to filter and organize the data to generate analytical insights as an answer. This development has also helped democratize data as non-technical users can simply type a question, and the software will automatically show them an answer without needing complicated calculations or analysis.

Sentiment analysis : Also known as opinion mining, it is the process of analyzing text data to identify the emotional tone behind it. Businesses often use it to analyze comments on social media, emails, blog posts, webchats, and more and define if the tone of what is being said is negative, positive, or neutral. Through this, organizations can extract useful insights regarding product development and brand positioning, as well as understand pain points to improve the customer experience on different touch points.

NPL is one of the business intelligence emerging trends we will see developing in multiple areas over the coming years. BI software that exploits this capability with a self-service approach will gain a competitive advantage by allowing users to conduct efficient analysis without the need for any calculations. We will definitely be watching how this technology develops in 2024.

8) Predictive & Prescriptive Analytics Tools

Business analytics of tomorrow is focused on the future and tries to answer the question: what will happen? How can we make it happen? Accordingly, predictive, and prescriptive analytics are by far the most discussed business analytics trends among BI professionals, especially since big data is becoming the main focus of analytics processes being leveraged by big enterprises and small and medium-sized businesses.

Predictive analytics is the practice of extracting information from existing data sets to forecast future probabilities. It’s an extension of data mining that refers only to past data. Predictive analytics includes estimated future data and, therefore, always includes the possibility of errors from its definition, although those errors steadily decrease as software that manages large volumes of data today becomes smarter and more efficient. Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessments. Applied to business, predictive analytics is used to analyze current data and historical facts to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities. In contrast, bankers use it to generate a credit score – the number generated by a predictive model that incorporates all the data relevant to a person’s creditworthiness. There are plenty of big data examples used in real life, shaping our world, be it in the buying experience or managing customers’ data.

Predictive analytics must also become accessible for everyone, and in 2024, we will witness even more relevance that will cater to that notion. Self-service analytical possibilities are becoming a criterion for BI vendors and companies alike; both can profit from it and bring more value to their businesses. The predictive models, in practice, use mathematical models, in other words, forecast engines, to predict future happenings. Users simply select past data points, and the software automatically calculates predictions based on historical and current data, as shown in the example:

Among different predictive analytics methods, two are quite popular among data scientists: artificial neural networks (ANN) and autoregressive integrated moving averages (ARIMA).

In artificial neural networks, data is processed in a similar way as to biological neurons. Technology duplicates biology: information flows into the mathematical neuron, is processed by it, and the results flow out. This single process becomes a mathematical formula that is repeated multiple times. As in the human brain, the power of neural networks lies in their capability to connect sets of neurons together in layers and create a multidimensional network. The input to the second layer is from the output of the first layer, and the situation repeats itself with every layer. This procedure allows for capturing associations or discovering regularities within a set of patterns with a considerable volume, number of variables, or diversity of the data.

ARIMA is a model used for time series analysis that applies data from the past to model the existing data and make predictions about the future. The analysis includes inspection of the autocorrelations – comparing how the current data values depend on past values – especially choosing how many steps into the past should be considered when making predictions. Each part of ARIMA takes care of different sides of model creation – the autoregressive part (AR) tries to estimate the current value by considering the previous one. Any difference between predicted data and real value is used by the moving average (MA) part. We can check if these values are normal, random, and stationary – with constant variation. Any deviations in these points can bring insight into the data series behavior, predict new anomalies, or help to discover underlying patterns not visible by the bare eye. ARIMA techniques are complex, and concluding the results may not be as straightforward as for more basic statistical analysis approaches. However, once the basic principles are grasped, the ARIMA provides a powerful predictive analysis tool.

Prescriptive analytics goes a step further into the future. It examines data or content to determine what decisions should be made and which steps are taken to achieve an intended goal. It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning. Prescriptive analytics tries to see what the effect of future decisions will be to adjust the decisions before they are actually made. This greatly improves decision-making, as future outcomes are considered in the prediction. Prescriptive analytics can help you optimize scheduling, production, inventory, and supply chain design to deliver what your customers want in the most optimized way, and these are some of the emerging trends in business intelligence 2024 that we will hear more about.

10) Embedded Analytics

When data analytics occurs within a user’s natural workflow, embedded analytics is the name of the game. Businesses have recognized the potential of embedding various BI components, such as dashboards or reports, into their own application, thus improving their decision-making processes and increasing productivity. Formerly strangled by spreadsheets, companies have realized how utilizing embedded dashboards enables them to provide higher value within their own applications. In fact, according to Allied Market research, the embedded analytics market is projected to reach $77.52 BN by 2026, with a CAGR of 13.6% from 2017 to 2023 , and this is one of the business analytics topics we will hear even more in 2024.

Whether you need to create a sales report or send multiple dashboards to clients, embedded analytics is becoming a standard in business operations. In 2024, we will see even more companies adopting it. Departments and company owners seek professional solutions to present their data without building their own software. By simply white labeling the chosen application, organizations can achieve a polished presentation and reporting they can offer consumers.

More than just embedding a dashboard or BI features in an application, embedding analytics allows for collaboration by keeping every single stakeholder involved. By allowing clients and employees to manipulate the data in a well-known environment, you facilitate the extraction of insights from every area of your business. This makes it one of the fastest-growing business intelligence trends on this list.

Business Wire recently published a report called “Global Embedded Analytics Market (2021 to 2026) – Growth, Trends, COVID-19 Impact, and Forecasts,” in which they mention that “organizations are deploying embedded analytics solutions to realize significant gains in revenue growth, marketplace expansion, and competitive advantage.” They also add that embedding analytics will grow significantly in the healthcare industry in the coming years. Considering the massive amounts of data that hospitals collect, which got even bigger with COVID-19 and telemedicine interactions, healthcare businesses “switch from paying for service volume toward service value.” By using powerful healthcare analytics software that can be embedded, hospital managers can extract valuable insights that will help them optimize processes from a clinical, operational, and financial point of view.

This is one of the trends in business analytics that can be implemented immediately since many vendors already offer this opportunity and ensure that the application works seamlessly and without much complexity.

In this article, we’ve summed up what the near future of business intelligence looks like for us. Here are the top 10 analytics and business intelligence trends we will talk about in 2024:

• Artificial Intelligence
• Data Security
• Data Discovery/Visualization
• D&A Sustainability
• Data Sharing
• Continuous Intelligence
• Data Literacy
• Natural Language Processing
• Predictive And Prescriptive Analytics Tools
• Embedded Analytics

Being data-driven is no longer an ideal; it is an expectation in the modern business world. 2024 will be an exciting year of looking past all the hype and moving towards extracting the maximum value from state-of-the-art online business intelligence software.

At RIB Software, we are committed to offering construction companies the best solutions to meet their needs. Our BI software, RIB BI+ , is a state-of-the-art platform with innovative features to take your analytical journey to the next level. If you are ready to boost your performance and increase the ROI of your construction projects with data-driven insights, get a demo today!

We hope you enjoyed this overview, and stay tuned for more business intelligence industry trends!

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4 Examples of Business Analytics in Action

• 15 Jan 2019

Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.

“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond , who teaches the online course Business Analytics . “It’s the way the world is going.”

Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.

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What Is Business Analytics?

Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions.

There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. Diagnostic analytics can be used to identify the root cause of a problem. In the case of prescriptive analytics , testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.

Related : 4 Types of Data Analytics to Improve Decision-Making

Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.

Check out the video below to learn more about business analytics, and subscribe to our YouTube channel for more explainer content!

Business Analytics vs. Data Science

It’s important to highlight the difference between business analytics and data science . While both processes use big data to solve business problems they’re separate fields.

The main goal of business analytics is to extract meaningful insights from data to guide organizational decisions, while data science is focused on turning raw data into meaningful conclusions through using algorithms and statistical models. Business analysts participate in tasks such as budgeting, forecasting, and product development, while data scientists focus on data wrangling , programming, and statistical modeling.

While they consist of different functions and processes, business analytics and data science are both vital to today’s organizations. Here are four examples of how organizations are using business analytics to their benefit.

Business Analytics Examples

According to a recent survey by McKinsey , an increasing share of organizations report using analytics to generate growth. Here’s a look at how four companies are aligning with that trend and applying data insights to their decision-making processes.

1. Improving Productivity and Collaboration at Microsoft

At technology giant Microsoft , collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by increasing the number of employees per building and reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft , which found that people are more likely to collaborate when they’re more closely located to one another.

In an article for the Harvard Business Review , the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members and an estimated savings of $520,000 per year in employee time.

The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.

For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.

2. Enhancing Customer Support at Uber

Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.

COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.

For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing —a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.

Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.

At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.

With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand , with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, which typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s past recipe preferences, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has successfully measured the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.

By employing predictive analytics to better understand customers, Blue Apron has improved its user experience, identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings.

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

Consumers are crucial to the success of multinational food and beverage company PepsiCo. The company supplies retailers in more than 200 countries worldwide , serving a billion customers every day. To ensure the right quantities and types of products are available to consumers in certain locations, PepsiCo uses big data and predictive analytics.

PepsiCo created a cloud-based data and analytics platform called Pep Worx to make more informed decisions regarding product merchandising. With Pep Worx, the company identifies shoppers in the United States who are likely to be highly interested in a specific PepsiCo brand or product.

For example, Pep Worx enabled PepsiCo to distinguish 24 million households from its dataset of 110 million US households that would be most likely to be interested in Quaker Overnight Oats. The company then identified specific retailers that these households might shop at and targeted their unique audiences. Ultimately, these customers drove 80 percent of the product’s sales growth in its first 12 months after launch.

PepsiCo’s analysis of consumer data is a prime example of how data-driven decision-making can help today’s organizations maximize profits.

Developing a Data Mindset

As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” Hammond says . “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Do you want to leverage the power of data within your organization? Explore Business Analytics —one of our online business essentials courses —to learn how to use data analysis to solve business problems.

This post was updated on March 24, 2023. It was originally published on January 15, 2019.

About the Author

Business Analytics Dissertation: what to expect and how to make the most of it

• Tuesday, June 18, 2019

Juan Felipe Alvarez

• minute read

My academic year has been filled with numerous activities. Ever since fresher’s week started in September, there have been things going on (both academic and non-academic) and I can assure you that you will have a very intense and gratifying year as an MSc student.

Now that it's June and I have already finished with classes and exams, it is time to focus on the dissertation. I am writing this blog in order to let you know what to expect and my top tips for enjoying the experience.

Let’s start with the basics: what is a dissertation and what are the business school’s expectations? The objective of the dissertation is to provide students with an opportunity to produce an original piece of work and specialize in a particular topic of interest. The typical word count would be between 15,000 and 18,000 words, depending on the topic and needs to be developed between June and September.

The MSc in Business Analytics has a very pragmatical approach to dissertations; they do this, so you can try and solve a real-world problem with real-world data, which I think is exciting and will teach you applied problem-solving skills. You can either propose your own topic or choose to participate in one of the several ongoing research projects that the University has. If you have a very strong interest in one field and have the means to obtain the data, you can propose your own topic before February. However, most students decide to take part in the topics that are proposed by the University, like I did.

By the end of January, we received a list containing more than 60 different projects that were very diverse in terms of research field and industry. Many of them were in collaboration with companies that had proprietary datasets and were interested in analysing them thoroughly, while others dealt with publicly available datasets. In regards of the type of analysis there was plenty to choose from: text mining and natural language processing, machine learning modelling, simulation, operations research, forecasting, optimisation and decision analysis, just to mention a few. One of the ones that really caught my eye was to build a machine learning model to predict the best strategy for goalkeepers during penalty kicks.

We had to select 7 topics then rank them and the programme director would try to assign you the topic that was highest in your priority. My suggestion that will help you make an informed choice is to read very well the project description and understand what it is about. Don’t choose the dissertation topic only because the company has a well-known name. For me, it is more important to choose a type of analysis that you really like and feel comfortable doing. If you already know the supervisor, you could drop him an email asking for more information about the project and about the required skills.

You will be assigned a topic and a supervisor by the beginning of March and if you selected a topic with a company, some meetings might start taking place during the following months. It is very important to talk early to your supervisor and my tip would be to take the initiative and contact them with plenty of time. Your supervisor will help you to structure the research question, build the appropriate methodology and assess the quality of your work. Make sure you establish clear communication from the beginning in order to make your life easier. Another tip I could give you is to be ordered with your meetings and keep a minute for each one, so you can keep track of the tasks you are supposed to be doing.

A little bit about my topic: In case you are interested, I will be working with Europe’s Largest digital healthcare providers and I will be creating a model to optimise their medicine buying strategy. I have already met the company’s representatives and I hope to be able to frame my research question in the following weeks.

In order to close, I would like to add that the dissertation might look a little bit intimidating at the beginning. I just wanted to let you know that as soon as you begin to divide the whole deliverable into smaller sub-parts, you will feel that you are making quick progress. I hope that this article would be useful to level your expectations about this topic and I wish you the best when the time comes for you to start with your dissertation.

I'm Juan Felipe Alvarez, an MSc Business Analytics student from the class of 2019. Follow my blogs for an insight into life as a student at Alliance MBS.

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Business analytics approach to artificial intelligence

Melva inés gómez-caicedo.

1 Economic Sciences, The Liberators University, Bogotá, Colombia

Mercedes Gaitán-Angulo

2 Business School, Konrad Lorenz University Foundation, Bogotá, Colombia

Jorge Bacca-Acosta

Carlos yesid briñez torres.

3 Faculty of Mathematics and Engineering, Pilot University of Colombia, Bogotá, Colombia

Jenny Cubillos Díaz

4 Economics and Management, University Corporation of Meta, Villavicencio, Colombia

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Artificial Intelligence has become an essential element for strengthening the business fabric. The advances obtained in recent years as a result of the incorporation of technology for the improvement of productive activities and the positioning of companies in the markets are remarkable. Hence, the purpose of this paper is to analyze the origin, evolution and development of business analytics (BA) and its relationship with Artificial Intelligence (AI); from the conceptualization, evolution and identification of the main characteristics and research areas of AI and BA, as well as research conducted and published in journals indexed in Scopus between 2002 and 2022. The aim is to define the incidence of BA in business activities and analyze scientific activity and advances of BA to define new research horizons in this field. For this purpose, a bibliometric and documentary analysis is applied, allowing to highlight the findings that provide recognition and comparison of the results. This will facilitate the understanding of the current dynamics, its importance for organizations, and its impact in the face of the new challenges generated by the requirements of world trade.

Introduction

The term business analytics is relatively new. Initially, it was presented as a concept related to economics and the way of managing resources. Business analytics can be defined as the process of collecting data, processing this data and gain insights from the data (Gupta, 2021 ). Business analytics is also defined as the science of discovering insights from data to support timely decision-making (Delen and Ram, 2018 ).

In the 1950s when time and motion studies began to be used in production processes, and the 1960s when computers were used for automatic data processing (Delen and Ram, 2018 ), analytics was used for the use of mathematical models to provide a solution to the problem. Analytics is used for the use of mathematical models to provide solutions to problems identified in organizations (Sharma, 2016 ).

In this way, Business analytics uses statistical and mathematical models to respond to problems or needs in organizations. Simon ( 2017 ) states that analytics is the process of using raw data to obtain clues and improve the understanding of a topic or phenomenon.

Frequently in the literature, it is also clarified that Business analytics cannot be considered as report generation, because the latter is limited to the visualization of the behavior of one or several variables, while the former gives answers to questions of interest to an organization around its business (Simon, 2017 ).

Camm et al. ( 2020 ) consider that business analytics is “the scientific process of transforming data into clues for making better decisions” (p. 5).

For Muñoz-Hernandez et al. ( 2016 ) business analytics allows the efficient use of all the information available to organizations, to obtain a competitive advantage. Other authors such as Evans and Lindner ( 2012 ) consider that it is fundamental for decision making.

Thus, Zumstein et al. ( 2022 ) in their research highlight the increase in the level of maturity, benefits, challenges and development of companies and economies from the use of artificial intelligence.

Likewise, authors such as Shi et al. ( 2022 ), indicate that a large amount of data and the limits of digital products drive organizations to use business analytics (BA) to increase customer engagement. Within their main findings, they indicate that BA culture does not directly improve performance on its own but must be integrated with existing organizational strengths. In addition, the development of the literature on this subject shows that BA techniques moderate the relationship between customers and innovation since it allows companies to be better informed when data is available online.

This has led several companies to choose to have in their structure an area dedicated exclusively to business analytics and the relationship between research and data analysis processes, which contribute to the improvement of productive conditions (Acito and Khatri, 2014 ).

Silva et al. ( 2021 ) indicate that the term “Industry 4.0” has emerged to characterize various adoptions of Information and Communication Technologies (ICT) in production processes. Hence, business analytics (BA) is considered as a technological advancement that facilitates decision making (Namvar et al., 2021 ). Thus, business analytics facilitates data analysis and relates it to the use of emerging technologies, which enable the transformation of decision-making dynamics in organizations (Seufert and Schiefer, 2005 ; Ward et al., 2014 ).

Resource management starts from the premise that technology and data analytics are at the service of operational efficiency, which allows organizations to understand their information and use this analysis to identify problems and decisions.

During the last decades, the dynamics of markets and companies have generated conditions that strengthen productive activity, facilitating the development of processes that tend to strengthen productive activity. Hence, several elements have been identified that, when used efficiently, promote growth and competitiveness, such as resource management based on the premise that technology and data analytics are at the service of operational efficiency, which allows organizations to understand their own information and use this analysis to identify problems and decisions.

In that regard, business analytics uses statistical analysis, predictive modeling, data mining and other techniques to employ information and develop a competitive advantage in its favor (Evans and Lindner, 2012 ; Medina, 2012 ).

The link between business analytics and artificial intelligence can be seen from different perspectives. One the one hand, artificial intelligence is considered to be one of the three pillars of business analytics together with visualization and statistical modeling (Raghupathi and Raghupathi, 2021 ). In particular, the machine learning subset of artificial intelligence is the most common component of this pillar of business analytics. Machine learning provides the techniques and methods to gain insights from business data. On the other hand, artificial intelligence is also considered to be the evolution of traditional analytics and the era of artificial intelligence is called analytics 4.0 in the context of business analytics (Davenport, 2018 ). Moreover, other authors suggest that business analytics is often supported by artificial intelligence to transform data into information (Schmitt, 2022 ).

Hence, the objective of this research is to analyze the evolution and development of artificial intelligence and its relationship with Business Analytics, based on its conceptualization, evolution, identification of its main characteristics, research areas and the recognition of publications indexed in Scopus between 2002 and 2022. In this sense, in the first part of this document a systematic and historical review of business analytics is made, in the second part the main publications associated with this concept from 2001 to 2018 are presented, the most cited authors, the countries that are most interested in the subject, and finally, how research networks have been created from its relationship with Artificial Intelligence.

Historical analysis of business analytics

To understand the origin of business analytics it is important to understand how statistics and mathematics have been used throughout history as a support for the development of competitive intelligence in organizations.

During the industrial revolution, statistics began to be used in standardization and manufacturing processes as a control tool. Additionally, the focus of organizations begins to be the minimization of waste and therefore the optimization of production costs, becoming a trend and consolidating as the quality movement (Quality Movement) (Sharma, 2016 ). From this movement emerged several years later practices such as Six Sigma and Toyota's just-in-time manufacturing methodology.

Years later, when the United States participated in World War I, quality and standardization become fundamental aspects of production processes because ammunition had to be compatible with weapons from different manufacturers and countries. At this time, organizations begin to invest in Total Quality Management training and statistical measurement processes (van Kemenade and Hardjono, 2018 ), allowing the emergence of various techniques such as control charts, histograms, Pareto and scatter diagrams (Sadeghi Moghadam et al., 2021 ).

Toward the decade of the 1920s, the quality control method is known as Statistical Process Control also emerged as a mechanism to control production processes seeking their best standardization and with the minimum possible waste (Zan et al., 2019 ).

Likewise, historical data began to be used for climate prediction. In 1950 the first numerical weather prediction was developed, performed on the ENIAC computer by a group of meteorologists and mathematicians. In 1956 engineer Bill Fair and mathematician Earl Isaac found Fair Isaac Corporation (FICO) as a company to use data intelligently for the development of competitive intelligence (Sharma, 2016 ). Two years later they launch their credit risk and scoring system for investments in the United States.

In 1958, in the IBM Journal of Research and Development, an article written by Luhn is published where one of the initial references to the term Business Intelligence is made. This article proposes the construction of an intelligent system that uses data processing mechanisms to perform auto-summarization and auto-coding of documents to provide different information profiles according to the organization's lines of action (Luhn, 1958 ).

During the 1960s most companies began to use centralized systems for inventory control and in the 1970s guidelines were developed to facilitate materials planning. It should be noted that, during this period, data collection was done annually, and the available data came from manual processes through interviews and questionnaires from which mathematical models could be built to solve optimization problems with constraints. In this way, those problems that could not be solved with linear and non-linear models were addressed through simulations (Delen and Ram, 2018 ).

In the 1970s, Rule-Based Enterprise Systems also emerged with the promise that the knowledge of an expert in a specific domain could be represented as a set of rules that could be processed by a machine and could be used to solve queries as an expert would (Simões et al., 2020 ).

Subsequently, in the 1980s, Enterprise Resource Planning (ERP) or Enterprise Resource Planning (ERP) systems emerge and become the first data collection and storage systems for organizations to provide support in areas such as planning, sales, manufacturing, distribution and costs (Sharma, 2016 ).

Thus, the emergence of relational database systems enabled the capture, storage and organization of data to avoid duplication. At this time, the amount of data being stored was larger and one of the major challenges was to maintain data integrity and consistency.

This is how the concept of enterprise data warehouses or Enterprise Data Warehouse (EDW) emerged as unified data storage systems for organizations. These systems were also upgraded so that they could respond to various changes in data effectively to display information in real time l which gave rise to real-time data warehouse systems (Delen and Ram, 2018 ).

In this regard, EDWs facilitated the collection of data from different sources which was subsequently used to extract knowledge and information of interest to organizations and this gave rise to the term Business Intelligence (BI) in the first decade of the 2000s, initially focusing on the analysis of data collected by organizations to know the progress of the organization.

The 1990s also saw the emergence of executive information systems, i.e., decision support systems, which displayed information using graphs and charts to facilitate decision making (Delen and Ram, 2018 ).

Sharma ( 2016 ) states that between the years 1990 and 2000 organizations began to see the need to use the data obtained to be able to generate predictive analytics, through descriptive, inferential, differential and associative statistical techniques. Hence, the amount of data produced by users or consumers through devices and interaction with social networks and other digital media led to the emergence of a new term: big data, which refers to techniques and procedures to analyze large amounts of unstructured data and the emergence of methods such as “Deep learning.”

It is important to note that the term business analytics has been associated with other terms such as Business Intelligence and Supply Chain Management that have been in the research spotlight for some years. However, the studies derived from each of the terms differ in the analysis and results obtained. For example, Supply Chain Management was one of the central topics in business research during the decade from 2000 to 2005. However, this term was transformed into Business Intelligence, because conducting research focused solely on the Supply Chain left aside other types of information.

Business Intelligence was born as a response to the lack of information from organizations for the analysis of existing dynamics. In addition, the decrease in the costs of data storage services has increased the volume of data that organizations keep and this has allowed the growth of Business Intelligence as a fundamental area for organizations. It has been estimated that the storage volume to be reached by 2020 will be 40 zettabytes (1,021 bytes or 1 sextillion bytes) (Sharma, 2016 ).

Today, some researchers are still being conducted that ensure that the predictive value of Business Intelligence interferes with the natural dynamics of information coming from organizations. Therefore, business analytics generates new information from company data without creating new data, but rather by analyzing existing data.

Based on this premise, business analytics studies indicate that there is a close relationship between the use of data analysis and the performance of an organization in terms of revenue, competitiveness, profitability and shareholder return. This means that entities with better performance are those in which the use of data analysis is an extra component compared to their competitors and this gives them a greater probability of strengthening their competitiveness (Davenport and Harris, 2007 ; Evans and Lindner, 2012 ).

The results suggest a statistically significant relationship between organizations' competencies have analytics on performance and the effect of business process-oriented information systems. The results provide a better understanding of the areas where the impact of business analytics may be the strongest (Trkman et al., 2010 ).

Related works

Previous works that have attempted to synthesize research in the area of business intelligence can be found in the literature. For example, Gimenez et al. ( 2015 ) conducted a systematic review of 22 articles on the applications of business analytics in the supply chain. The authors report the main challenges and trends in the area. Similarly, Mishra et al. ( 2018 ) conducted a bibliometric analysis on the topics of Big Data and Supply Chain Management between 2006 and 2016, analyzing 280 publications in the 20 most important journals in the area.

However, these two references only allow us to appreciate the research is done in business analytics (BA) in its relationship with Supply Chain. Recently, Yin and Fernandez ( 2020 ) conducted a systematic literature review (40 articles in the area of BA) to present a common definition, its applications, research methods and its relationship with Business Intelligence (BI). The authors include a bibliometric review focused on the evolution of publications between 2000 and 2018, as well as the journals where the topic is most published and the most relevant authors. However, the bibliometric review is focused only on articles that have received a certain number of citations, which means that the results do not fully reflect the overall BI landscape.

Sahoo ( 2021 ) conducted a bibliometric review of 89 articles focusing on the terms Big Data and BA as they relate to the topic of “manufacturing.” In this article, the authors identify several areas of future work and research challenges. However, although the reported results are valuable to the scientific community, the bibliometric review is focused solely on the area of manufacturing.

Similarly, Silva et al. ( 2021 ) conducted a systematic literature review of 169 articles to identify the relationship between business analytics and Industry 4.0. From this literature review, the authors conclude that there are still many open questions surrounding its application.

The research was conducted by Dahish ( 2021 ), who conducted a literature review of 57 articles on Business Intelligence and social networks; and authors such as Purnomo et al. ( 2021 ) who reviewed Scopus databases on the same subject in the period between 1975 and 2020 stand out. The research was focused on identifying relevant topics in the area.

Ting-Peng and Yu-Hsi ( 2018 ) conducted a bibliometric literature review with a broader focus on articles indexed in Web of Science on the topics of Big Data and Business Intelligence during the years 1990 and 2017. One of the main findings emerged much earlier than the concept of Big Data, publications have grown faster and are associated with algorithmic and computational topics, while the area of Business Intelligence is more associated with management, data analytics and predictive analytics topics.

Zumstein and Kotewski ( 2020 ) indicate in their research that digital commerce is growing in most countries, it is a medium used by new and established online retailers to promote their different products, digital and customer services are considered essential to increase business success. Within the results of this study we found, that digital analytics is important to study and monitor digital business. By analyzing different success factors, this technique contributes to online stores having customer, service and data orientation generating high conversion rates and revenues. Finally, successful omnichannel marketers use various digital marketing channels, such as search engine optimization and advertising, marketing.

Chiang et al. ( 2018 ) highlight in their research the importance of the correct accumulation of data for analysis, decision making and strategic planning in organizations, based on the design and application of different analytical techniques since it is concluded that data analysis without generating value offers no contribution to organizations, regardless of whether the data is big or small.

Methodology

Bibliometrics emerged in the field of library and information science, allowing statistical and quantitative analysis of scholarly outputs, including descriptive statistics, networks on keywords, texts, citations, authors, institutions and their connections. This methodology allows establishing the frequency, connectedness, centrality, author and text group, publication trends, knowledge base, citation pattern, author network, reader usage, impact and importance of a topic or article (Huang et al., 2017 ).

For there to be conceptual clarity, scientific research must be associated with an exhaustive review of the area of knowledge to be worked on to be clear about the possible areas for the development of the research and its difficulty. For this, there is the bibliometric review, a quantitative analysis technique that uses mathematical and statistical methods to know the main characteristics of the topic under consideration (Ejdys, 2016 ; Sarkar and Searcy, 2016 ).

This article will carry out a historical review of artificial intelligence that will provide the basis for developing a descriptive bibliometric analysis that allows us to synthesize and understand the evolution of business analytics in certain fields of knowledge. For this analysis, publications published between 2002 and 2022 will be reviewed, in the Scopus bibliographic database, in the indexed academic literature that addresses specific topics directly related to the subject.

The first stage shows the route used to carry out the bibliometric study was:

(TITLE-ABS-KEY (“business analytics”) OR TITLE-ABS-KEY (“business analytics”) OR TITLE-ABS-KEY (“analyse des affaires”) OR TITLE-ABS-KEY (“análise de negócios”) OR TITLE-ABS-KEY (“analisi aziendale”)) AND (EXCLUDE (PREFNAMEAUID, “Undefined#Undefined”)) AND (LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “bk”)) AND (EXCLUDE (PUBYEAR, 2023))).

The search filters consisted of the terms “business analytics,” which could be located in the title, abstract and keywords; the search was conducted in English, Spanish, French, Italian, Portuguese and French; it was limited to conference papers, articles, book chapters, reviews and books and was limited to the period from 2002 to 2022. The search yielded 1,605 documents.

The purpose of the second stage was to analyze the information using the Bibliometrix package of R. This was done to visually organize the information downloaded from Scopus and to obtain schemas that would feed the research carried out.

Finally, in stage 3, the analysis of the descriptive results was carried out, where the information obtained was condensed, relating the evolution of artificial intelligence together with business analytics and the results obtained.

Figure 1 shows how the articles included in this bibliometric review were selected.

PRISMA. Source: Own elaboration using Prisma Statement 2020. http://www.prisma-statement.org/ . Source: Page et al., 2021 .

Descriptive results

From the results of the search, it can be noted that business analytics has been a topic of interest to researchers since 2002. It is possible to make this inference since the information searches do not yield results from before that year.

In addition, it was possible to identify that the number of research projects is increasing and has reached 210 publications in 2021 alone (see Figure 2 ; Table 1 ).

Annual scientific production. Source: Own elaboration using Bibliometrix.

Production per year.

Source: Own elaboration using Bibliometrix.

Moreover, not only the annual output indicates the evolution of the term, other factors such as the average number of citations of articles per year indicate the value of the academic output and its usefulness in another research (see Figure 3 ).

Average number of article citations per year. Source: Own elaboration using Bibliometrix.

Among the findings of the search, it is possible to point out that the articles produced in 2002 are those that have been most cited by other researchers. Likewise, it can be observed that in 2004 there was a non-significant number of citations, while from 2005 onwards they increase, with two peaks of higher citations between 2010 and 2014. This tendency may be due to the changes and topics published in those years (see Figure 6 ).

Graph of three fields: Authors, subjects, and journal names. Source: Own elaboration using Bibliometrix.

It was also possible to identify the authors who have published the most papers on the subject: Shanks with more than 20 papers associated with business analytics, followed by Duan with ~10 papers and Cao, Marjanovic, and Sharma with eight papers each (see Figure 4 ; Table 2 ).

Most relevant authors. Source: Own elaboration using Bibliometrix.

Production by authors.

In addition, the results allow us to analyze the number of publications made by the authors according to the year of publication (see Figure 5 ), which shows that there are authors such as Shanks G., De Oliveira MPV, Na Na, who maintain their production levels between 2010 and 2022, with some insignificant variations per year. The graph shows the number of publications, with the larger the circle indicating the author and the year, the higher the author's level of production. For example, Bekmamedova had a high volume of publications in 2012, but in 2013 it decreased and in the following years there were no publications. Also, authors such as Sharda R., Daily S. Doster B. Ryan J. and Lewis C. started research on the subject in 2013 and have maintained the publication trend until 2022.

Production of the main authors by year. Source: Own elaboration using Bibliometrix.

One of the reasons why authors such as Shanks have remained significant authors in business analytics is due to the consistent level of publications per year (see Figure 5 ). It is also important to note that the relationship that exists between authors, the topics to be covered and the journals that publish these topics are the central dynamic that ensures the success of business analytics as a major Research Topic (see Figure 6 ).

For example, Shanks, the author with the most publications (see Figure 4 ), has researched topics related to business analytics and predictive analytics, publishing in journals such as Resource-based View. On the other hand, Duan and Cao, the second and third authors respectively, have worked on business analytics and Big Data and have published in journals such as Communications of the association for information systems and Communications in computer and information science.

Regarding the dynamics of the publication sources, the results showed similar indicators to the period in which the topic had the highest number of publications. For example, in 2010, publications began to increase ( Figure 2 ) and the main publication sources also began to increase the level of publications on business analytics. The source with the highest growth is AMCIS 2017—Americas conference on information systems: A tradition of innovation. The second fastest growing source is ACM International Conference Proceeding Series (see Figure 7 ). However, most of the sources show a similar development and between 2010 and 2012 the growth was much more significant.

Growth of publication sources. Source: Own elaboration using Bibliometrix.

The relationship between sources and countries of the publication provides clues about the dynamics of researchers. For example, the United States is the country with the highest number of publications with ~2,400 publications. This contrasts significantly with the second country, Australia, which has ~400 publications (see Figure 8 ; Table 3 ). The difference is significant and exposes the importance of the United States for business analytics.

Countries of publication. Source: Own elaboration using Bibliometrix.

Most cited countries.

It is also significant to note that Brazil is the only Latin American country to appear in the top 20 ranking for business analytics publications.

However, most of the publications produced by the countries are international collaborations. Figure 9 shows how the dynamics of co-authored publications are generated. In green are the publications of Multiple Country Publications and in orange are the publications of the Single Country Publications type. It is possible to point out that collaborative publications have a greater impact and significantly position the country in the publication rankings.

Correspondence between author and country. Source: Own elaboration using Bibliometrix.

It is also important to note that partnerships between countries are vital to accounting for the research development of business analytics. The results show that the United States is the country with the highest number of collaborations. Within its relationships are countries such as France, Spain, Italy, Germany, China, Brazil, India, Portugal, Norway, Sweden and Finland, which are the countries in dark blue (see Figure 10 ). The strongest connection between countries is between the United States and Australia with a combined frequency of 21 citations. Countries such as New Zealand, South Africa, Egypt, Saudi Arabia, Nigeria, Colombia, Chile and Canada are countries with a medium number of publications on business analytics and mostly have partnerships with the United States and Australia. Finally, the countries in gray are those with no publications in this area.

Map of collaboration between countries. Source: Own elaboration using Bibliometrix.

Within these country associations, there are common themes that help to relate and understand what the topics of interest are according to the country, the keywords and the journal of publication. For example, the United States has worked on business analytics related to Big Data, business value, predictive analytics, business intelligence, data mining, social media, predictive analytics, among others, with the journal Information Technology having the largest number of publications (see Figure 11 ).

Three-Fields Plot Keyword Chart. Source: Own elaboration using Bibliometrix.

On the other hand, Australia remains the second country in the ranking of publications and works on topics such as big data, social media, knowledge management, decision making, support systems, among others similar to those researched in the United States, hence the strong relationship that exists between the countries concerning publishing partnerships.

Thus, according to the topics that are most related to business analytics, it can be observed that data mining, decision marketing, information systems, Big Data and competitive intelligence are the topics that are most related and with which business analytics has been most researched (see Figure 12 ).

Keywords. Source: Own elaboration using Bibliometrix.

The search results indicate that there are co-citation networks between authors that determine the alliances that exist for business analytics research. For example, in Figure 13 it can be seen that there are three co-citation networks (indicated in blue, red and green) which are distinguished from each other by the connections established.

Co-citation networks. Source: Own elaboration using Bibliometrix.

One of the largest networks is identified with red color, it is composed of authors such as Davenport, Watson, Shanks, Lavalle, Simon, Yin, among others, which are shown related to the citation lines between them. The second significant network is identified with blue and is composed of authors such as Wang, Zhan, Yang, Wu, Han, Lee, Kim among others, it is an extensive network, however, the composition of the figure in terms of size and position it can be established that it has less impact than the red network. Finally, there is the green network, which is smaller, but is immersed between the red and blue networks, and is composed by authors such as Chen, Cohen, Anderson, Manykil, Bose, among others.

Business Intelligence is a tool that allows organizations to take advantage of all the information of competitive advantages in the market and decision making, as by combining its analysis with emerging technologies, it allows information to be obtained and projects possible situations that may arise in the development of the productive activity. The results of this bibliometric analysis show that there is an increasing interest in the field because the number of publications is growing every year. This result confirms the findings of previous studies that reported an exponential growth in the number of publications in this field (Yin and Fernandez, 2020 ). However, business analytics is still an emerging field (Raghupathi and Raghupathi, 2021 ) and further research is needed to uncover its affordances and benefits for a timely deciation to improve the conditions in which resources are used. Hence, its relationship with business analytics facilitates the generation-making support in companies.

With the results obtained throughout this research, it was possible to establish the influence of AI and BA on productive development. It should be noted that the trend of growth in the number of research and publications to be generated will increase and contribute significantly to the improvement of the business and competitive fabric of economies.

The development of AI and BA research in the United States stands out as reported in previous studies in the field of business analytics (Yin and Fernandez, 2020 ), followed by Australia, Germany, India, the United Kingdom, and Canada. However, unlike previous studies in the field, in this paper we identified that India is another country that is publishing research in the field of business analytics and is currently in the second position in the most productive countries in this field.

The topics that commonly appear connected with the term business analytics are: data mining, decision marketing, information systems, big data and competitive intelligence. This result shows that other field such as big data and artificial intelligence are relevant for the implementation of business analytics approaches in companies around the world. In that regard, the support of different fields is important to overcome some challenges that business analytics face today such as the need to collect data from multiple sources and process them effectively and in real-time so that the results can be used for making decisions and void the lag between data collection and data analysis (Raghupathi and Raghupathi, 2021 ).

Another topic that appeared connected with business analytics was descriptive analytics, which is one of the three types of analytics often reported in the literature. The other two types of analytics (predictive and prescriptive analytics) did not appeared frequently in this bibliometric analysis. A possible interpretation of this result might be that research on descriptive analytics has captured the attention of researchers in the first era of business analytics. However, further research is needed to investigate the use of predictive and prescriptive analytics for decision-making processes at companies. Moreover, recent research suggest a new type of business analytics: discovery analytics. This later type of analytics might be considered the next step in business analytics and is focused on supporting the discovery of new markets, products and strategies (Raghupathi and Raghupathi, 2021 ).

The results also show that there are three co-citation networks on this topic, one of the largest networks is composed of authors such as Davenport, Watson, Shanks, Lavalle, Simon, Yin, the second of authors such as Wang, Zhan, Yang, Wu, Han, Lee, Kim among others, and the third of authors such as Chen, Cohen, Anderson, Manykil, Bose, among others.

Conclusions

This paper presents an overview of the research landscape in business analytics. We found that business analytics is an emerging field that has attracted the attention of many researchers around the world and the number of publications is increasing year by year. To further develop this field and increase the impact of this field in the industry, there is a need of a synergy between scholars and companies to identify the best practices in business analytics that are effective for companies.

Future research directions in the field of business analytics include the investigation of the impact of predictive, prescriptive and discovery analytics through case studies to uncover the affordances and benefits of these types of analytics for the timely decision-making of companies around the world. Moreover, it is important to continue working in this line of knowledge as we have seen the benefits of this topic, we can continue to deepen in topics such as digital analytics, due to the importance for companies to study and improve their campaigns, user experience, search engine marketing and the achievement of digital business objectives.

Data availability statement

Author contributions.

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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• PS Preethika Sainam is an Assistant Professor of Global Marketing at Thunderbird School of Global Management, Arizona State University.
• SA Seigyoung Auh is Professor of Global Marketing at Thunderbird School of Global Management, Arizona State University, and Research Faculty at the Center for Services Leadership at the WP Carey School of Business, Arizona State University.
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Top 20 Business Analytics Project in 2024 [With Source Code]

Home Blog Business Management Top 20 Business Analytics Project in 2024 [With Source Code]

As a beginner in business management, one of the most crucial skills is gathering and analyzing data to make informed decisions. Business analytics uses data and statistical methods to extract insights and make data-driven decisions. The good news is that there are countless business analytics project ideas that you can start working on to improve your skills and help your business thrive. This blog will explore the top 10 business analytics projects you can do online as a beginner or an experienced professional. So, let’s dive in and discover how you can use business analytics projects to gain a competitive advantage in today’s fast-paced business world.

Why are Business Analytics Projects Important?

Business analytics is an amalgamation of business management and data analytics. High-value projects aimed at business development add value to the profile or resume of candidates who opt for a business analytics career. Business analytics projects are important because they enable data-driven decision-making, helping businesses uncover valuable insights from their data. These projects optimize operations, identify growth opportunities, and enhance overall efficiency, leading to improved profitability and competitiveness. Moreover, they provide a foundation for predictive and prescriptive analytics, enabling organizations to proactively address challenges and capitalize on emerging trends.

List of Business Analytics Projects [Based on Levels]

Here is a list of business analytics projects based on levels of experience:

Business Analytics Project Ideas : 

• Sales Data Analysis
• Customer Review Sentiment Analysis
• Market Basket Analysis
• Price Optimization
• Stock Market Data Analysis
• Customer Segmentation
• Fraud Detection
• Equity Research
• Social Media Reputation Monitoring
• Real-Time Pollution Analysis

Business Analytics Project Ideas for Beginners: 

• Employee Attrition and Performance
• Prediction of Sales in Tourism for the Next Five Years
• Prediction of the Success of an Upcoming Movie
• Prediction of the Fate of a Loan Application

Business Analytics Projects for Intermediates: 

• Creating Product Bundles
• Life Expectancy Analysis
• Building a BI app

Business Analytics Projects Topics for MBA Students

• Predicting Customer Churn Rate
• Prediction of Selling Prices for Different Products
• Store Sales Prediction

Top 10 Business Analytics Project Ideas

Here are the top 10 projects in business analytics, each offering unique insights and opportunities for data-driven decision-making in various industries. 

1. Sales Data Analysis 

It involves the analysis of data on every aspect of a company’s sales. It determines the total number of sales, average monthly sales, demographics of customers, and patterns of selling periods. It allows the company to make informed decisions to prioritize the production of specific products and scale them. To analyze the sales data, students can use different tools and languages. Students can use SQL to extract data from the database. Excel or Google Sheets can clean and analyze data for charts and graphs. For advanced visualizations and dashboards, Tableau or Power BI can be used. Python or R is good for advanced data analysis and statistical modeling, like looking for trends or making predictions.

• Sales Analysis Source Code  

2. Customer Review Sentiment Analysis

It is the process of determining the emotional state of customers after they purchase or use the products. It allows the company to realize the possible reasons for customer complaints and measures to improve the features and quality. Students can use Python or R for data analysis. Tools like TextBlob and NLTK for sentiment analysis.

• Reviews Sentiment Source Code

3. Market Basket Analysis 

It involves the analysis of the correlation between the ales of different products when combined. It helps improve the business by identifying the best combinations and increasing the preferences of customers for the products. For this project, students can analyze data using the Apriori algorithm. They can use either Python or R programming languages.

• Market Basket Analysis Source Code

4. Price Optimization 

It involves investigating historical prices, crucial price factors, the markets where the company operates (and their economic contexts), the profiles of potential clients, etc. Programming Languages like Python or R are suitable for this project. Regression analysis and demand forecasting models are used to analyze the data.

• Tensor House Source Code

5. Stock Market Data Analysis

The project involves determining the frequency of rise and fall in price, the general trend of average monthly closing prices over the year, and trading volumes. Candidates can select a specific dataset and explore the company’s stock performance history. To analyze the data for this project, Python and R is used. Tools like Pandas and Numpy are used for manipulating the data.

• Stock Market and Analysis Source Code

6. Customer Segmentation

It refers to categorizing a company’s clients into different groups based on their purchasing behavior, financial level, interests, needs, and loyalty to the business. It helps optimize marketing campaigns and maximize the profits from each client. The K-means and Hierarchical clustering algorithms are generally used for this project.

• Customer Segmentation Source Code

7. Fraud Detection

Credit card fraud, identity theft, and cyber-attack are common fraudster challenges faced across various industries. Projects on fraud detection involve choosing a dataset and running statistical analyses to identify fraudulent operations. Machine learning algorithms such as decision trees and logistic regression are used for fraud detection.

• Fraud Detection Source Code  

8. Equity Research

Equity is the value of the returns received by a company’s shareholders after liquidating all the assets and clearance debts incurred by the company. Equity research plays a crucial role in the successful run of both shareholders and companies. Students can use Excel and Python to analyze the financial datasets for this project. Tools such as ratio analysis and financial statement analysis are in equity research.

• Equity Research Source Code

9. Social Media Reputation Monitoring

It is the process of gauging the presence and influence of a brand on customers through social media. Using analytical tools and techniques, the project audits, monitors, and interprets social media users’ opinions about the products. It helps revise social media marketing strategies to promote the business. Social media monitoring tools such as Hootsuite and Sprout Social are used to analyze the data.

• Social media reputation monitoring

10. Real-Time Pollution Analysis

It is a typical data visualization project, allowing the candidates to learn univariate and multivariate data analysis. The methodology can be reproducible to business aspects. Students can use either Python or R to build the project. Matplotlib or Plotly are used for creating visualizations.

• Air Pollution Tracker Source Code

Business Analytics Projects for Beginners

Graduates from several fields, including engineering, with an inclination for business, choose management as their career path. Business Management for beginners , augmented with business analytics projects, provide potential platforms to lay a strong foundation to build their career. The following are the most-edifying sample business analytics projects for students.

1. Employee Attrition and Performance

These projects are ideal for acquiring the qualitative analysis skills of employee attrition to find answers for the event’s who, when, and why. They also predict quantitative aspects of human resource dynamics for the organization’s next 5 to 10 years. The balance between attrition and retention is the secret to optimal human resources and talent utilization. To do this, students can use Excel to clean the data. SQL is used for data extraction. Python or R for data analysis.

• Employee Attrition Performance Source Code  

2. Prediction of Sales in Tourism for the Next Five Years

This project helps business analysts to improve their skills in applying data mining to determine patterns and correlations among tourism packages and their preferences. It has two approaches: qualitative and quantitative. Both approaches help beginners to hone their analytical and judgmental skills. To predict sales, statistical analysis tools like R or Python are used. Excel and SQL are used for cleaning and extracting data, respectively.

3. Prediction of the Success of an Upcoming Movie 

Business management professionals have a good scope in the film industry as numerous films enter the screen. These projects involve forecasting success based on the analysis of variables, including genre, language, directors, actors, actresses, budget, locations, etc. The prediction depends on the model devised based on the data of predetermined variables associated with previously released movies against their success. Like the other projects, students can use Python or R to predict the success of the upcoming movie.

4. Prediction of the Fate of a Loan Application 

These projects expose beginners to several machine-learning tools and techniques, and datasets. They also introduce the candidates to various parameters and help them gain the ability to recognize variables under eccentric circumstances. The top 3 machine-learning solution approaches for loan prediction are as follows.

• Support vector machine 
• Random forest

Pandas are the most straightforward and powerful Python libraries for beginners used for the prediction of the fate of loan applications.

Business Analytics Project Ideas for MBA Students

ECBA certificate training is among the best options to improve the profile of business analytics aspirants. A merit of this program is the opportunities for business analytics projects for MBA students. Three top business analytics project ideas are as follows.

1. Predicting Customer Churn Rate

It involves predicting the decline of customer rates. It has scope for stakeholders to identify setbacks in the business. It helps learn several statistical tools, such as SHAP (Shapley Additive exPlanations), RandomSearch, and GridSearch, for univariate and multivariate analysis on a retrieved dataset.

• Customer Churn Analysis Source Code

2. Prediction of Selling Prices for Different Products

It refers to the determination of the price of a product that attracts customers with an optimal profit margin. Further, it also helps companies to determine the offers to improve business. These projects help acquire skills to employ machine learning algorithms like Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks that use different metrics to test each of their performances.

3. Store Sales Prediction

These projects involve working with numeric and categorical feature variables and performing univariate & bivariate analysis to find the redundancy in variables associated with the store chain of a company. They help the candidates learn machine learning models such as the ARIMA time series model. 

• Store Item Demand Forecasting Source Code

Business Analytics Project Topics for Intermediate

Business analytics project ideas for experienced professionals should involve a complex combination of statistical parameters and real-world scenarios to enhance their skills significantly. Following are the business analytics project examples suitable for the intermediate levels.

1. Creating Product Bundles

It is a method that combines different products from the same company and sells them as a single unit. Under these projects, candidates learn market basket analysis and time series clustering methods to identify product bundles using sales data.

Here is the Product Bundle Source Code  

2. Life Expectancy Analysis

These projects aim to determine the monetary value of the potential consumer of the products and services of a company. Traditionally, government organizations utilize life expectancy analysis to determine the correlation between life expectancy and a nation’s GDP.

• Life Expectancy Analysis Source Code  

3. Building a BI app 

Business intelligence apps or tools play a critical role in finding urgent solutions to issues that are high for the business. Low to no-code custom apps for decision-making and long-term strategies are invaluable for an organization.

Here is the Business Intelligence Analysis Source Code  

Key Tools for Business Analytics Project

Here is a list of top tools that are required business analytics projects: 

• Data Visualization Tools (e.g., Microsoft Power BI, Looker) 
• ETL/ELT Tools
• Data Warehousing (e.g., Amazon Redshift, Google BigQuery, etc.)
• Data Analysis and Manipulation Tools
• Data Mining and Machine Learning Tools (e.g., Scikit-learn, RapidMiner)
• Data Quality Management Tools
• Data Integration Tools
• BI Suites 
• Data Catalog Tools (e.g., Collibra)

Are Business Analytics Projects Difficult to Complete?

Business analytic projects face several challenges that hamper their successful implementation. Technological advancement expands the options for tools and techniques. Still, they create a grey zone wherein the new tools emerge with overlapping functionalities interfering with decision-making. Other reasons for the failure of business analytics projects are: 

• Lack of well-defined and explicit goals 
• Poor data integration 
• Lack of conversion of insights and outcomes into actions.
• Poor adaptations to the ongoing development

Final Thoughts

Business analytics is blooming parallel to technological advancements, and every business is leveraging analytical tools and techniques to optimize its actions. Whether experienced or fresher, diverse business analyst projects for resume help you upgrade your profile. KnowledgeHut Business Management for beginners is highly recommendable for a firm foundation before undertaking business analytics projects, as it provides top-quality augmentation to your aptitude for the discipline.

Frequently Asked Questions (FAQs)

The common challenges faced in business analytics projects are: 

• Changing requirements or business needs 
• Conflicts with stakeholders 
• Poorly documented processes 
• Unrealistic timelines. 

Predictive analytics is a branch of analytics that predicts future outcomes using models based on historical data. Businesses use customer data and transaction information to predict the performance of the products and make strategies to optimize profits. 

Popular business analytics tools are SAS business analytics, Sisense, Microstrategy, KNIMETIBCO Spotfire, Tableau big data analytics, Power BI, and Excel.

Mansoor Mohammed

Mansoor Mohammed is a dynamic and energetic Enterprise Agile Coach, P3M & PMO Consultant, Trainer, Mentor, and Practitioner with over 20 years of experience in Strategy Execution and Business Agility. With a background in Avionics, Financial Services, Banking, Telecommunications, Retail, and Digital, Mansoor has led global infrastructure and software development teams, launched innovative products, and enabled Organizational Change Management. As a results-driven leader, he excels in collaborating, adapting, and driving partnerships with stakeholders at all levels. With expertise in Change Management, Transformation, Lean, Agile, and Organizational Design, Mansoor is passionate about aligning strategic goals and delivering creative solutions for successful business outcomes. Connect with him to explore change, Agile Governance, implementation delivery, and the future of work.

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Website Analytics: A Beginner’s Guide

Updated: Jun 14, 2024, 6:09am

Table of Contents

What is website analytics, why is website analytics important, types of website analytics, how to use web analytics, collect data, process data, report data, optimize your website, best practices for website analytics, bottom line, frequently asked questions (faqs).

As a small business owner, you know the importance of your website. It’s where current and prospective customers go to learn more about your offerings and, hopefully, make a purchase, schedule a consultation or perform your preferred action. With website analytics, you can receive the information you need to make the most out of your site and ensure it sets you up for success. Here’s what you need to know about website analytics for your startup or small business.

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Website analytics refers to collecting, reporting and analyzing data that comes from users who interact with your website. It’s a process that will allow you to home in on how your site is performing and what you can do to improve it. Without website analytics, it can be difficult to optimize your web pages and increase the conversions that come from them.

Depending on the data you track, website analytics can inform you of how visitors land on your site, which pages they visit most and what actions they typically take. You’ll be able to pinpoint trends and gain a great deal of valuable information that can lead to smart, data-driven decisions that fuel growth and improve your bottom line.

No matter your industry, website analytics is essential. Several of the many benefits of website analytics include:

• Understand user behavior : Website analytics can tell you everything you need to know about what users do once they arrive at your website. It takes the guesswork out of trying to determine their preferences and how you can optimize your site to meet them.
• Improve search engine rankings : Search engine optimization (SEO) can allow you to reach your audience organically, without investing in paid ads. Through website analytics, you’ll find it easier to enhance your SEO strategy.
• Refine your marketing strategy : A focus on website analytics is vital if you’d like to perfect the way you market to your customers. You’ll know what they find most engaging and be able to use this information in various marketing materials.
• Increase conversions : If one of your primary goals is to earn more money via online conversions, website analytics can come in handy. It can provide you with the data and insights you need to improve user experience (UX) and, in turn, convert as many users as possible.
• Stand out from your competitors : Using website analytics may convince users to choose you over the competition. You’ll know how to improve your site in a way that resonates with your audience and helps them believe your business will provide them with the offerings and services they’re looking for.

There are several website analytics you can use to make informed decisions for your business, including the following options.

Pageviews refer to the total number of times users view a page on your website. Every time a page on your site is loaded, a pageview occurs. Keep in mind that because a page has a lot of views, it’s not necessarily popular or effective. It may mean that a small group of users have visited it often. Many pageviews might also indicate that a page confused your users and caused them to return to it several times.

Unique Pageviews

Unique pageviews explain how many times a page was viewed by separate users. It’s crucial because it can give you an idea of how users are engaging with your site and whether they’re clicking through to different pages. Also, since it eliminates repeat views from the same users, unique pageviews can help show you the true reach of your site.

A session occurs any time a user goes to your website and interacts with it. Sessions are sorted by different user interactions, such as pageviews, clicks on call-to-action buttons, downloads and more. Each analytics tool has its own time frame on sessions but, typically, sessions are over after users have been inactive for 30 minutes or when another source brings a user to your site.

New Visitors

Also known as unique visitors or new users, new visitors refer to those who come to your site for the first time. In general, well-optimized, conversion-oriented websites have more new visitors than poor-quality sites. Usually, new visitors are tracked by a unique identifier, such as a tracking code you can install on your website. They can help you understand how your marketing efforts are working.

Returning Visitors

Returning users or returning visitors is the number of users on your website who have been to your site before. Often, returning visitors are likely to convert so it’s important to pay attention to them in addition to new visitors. Factors like your industry, years in business and the types of incentives you offer on your site will determine your returning visitor ratio.

Traffic Sources

Traffic sources tell you where your users are coming from and typically are tracked with a tracking code. Some examples of common traffic sources include organic search, paid search, social media, email marketing and website referrals. By learning about your traffic sources, you’ll be in a better position to optimize your site content and marketing initiatives.

Demographics

While demographics are often overlooked, they’re very important as they can explain who your visitors are. Depending on the analytics tool, you can uncover the age, gender, geographic locations and interests of your users. This information is key if you’d like to ensure your website is designed and optimized properly for your particular visitors.

Bounce Rate

Bounce rate is the percentage of users that leave your site after they view a page. Ideally, users would spend some time on your site to read your content and, hopefully, convert. If you notice a high bounce rate of 70% or more, you may have to make some changes to your pages. There might also be an issue with your loading time or external links.

Now that you understand the most popular website analytics, let’s go over how to use them effectively.

First and foremost, you’ll need to gather website data. If possible, invest in a quality web analytics tool to do so, such as Google Analytics, HubSpot, Crazy Egg and Ahrefs. Once you set it up, the tool should do all the work for you and gather all of the data you tell it to automatically.

A lot of data in one place can be overwhelming. That’s why it’s a good idea to process it through graphs and charts. You can choose points that are most valuable or interesting to you and your team. Fortunately, many analytics tools will make it easier to process data or do it on your behalf.

While data analytics tools usually collect and process data, it’s up to you to report your insights. Review the graphs and charts and compare them against your key performance indicators (KPIs), which may be monthly sales growth, click-through percentage, cost per new hire, customer retention rate or anything else that’s important. This is where you should make conclusions, uncover patterns and decide on the best next steps.

Once you collect, process and report data, it’s time to put it to good use. Take what you learned from your insights and improve your site. You may perform an A/B test to find out what works best, change your design, improve your content or alter your marketing strategy. The way you optimize your site depends on what you discover as well as your budget and resources.

If you make the smart decision to leverage the power of website analytics, be sure to keep the following best practices in mind.

Use Web Analytics Tools

Thanks to technology, collecting and processing analytics doesn’t have to be a hassle. Do your research and find a web analytics tool (or a few of them) to do the heavy lifting for you. The right platforms will depend on your budget, requirements and preferences. Several of the most popular tools you might want to explore include Google Analytics, Crazy Egg, Ahrefs, Hotjar and HubSpot.

Collect Analytics as Soon as Possible

Don’t put off collecting website analytics. The more you have, the more insights you can make and the more opportunities you’ll find to improve your site. Ideally, you’d set up your analytics tool before you launch your site. If your site is already live or has been for quite some time, set up analytics as soon as you can or hire a digital marketing pro to help you do so.

Choose the Right Analytics

Think about what analytics are most valuable to your business. Ask yourself about your site goals and use that information to zero in on what you’d like to focus on. Note that while a few analytics may not provide you with information, tracking everything can be overwhelming. Narrow down your options to the analytics that will help you meet (or even exceed) your goals.

Combine Data With Insights

Raw data and numbers are meaningless unless you can turn them into valuable insights. If you find that your site traffic has gone up over the past month, dig deep to determine why and how this may affect your future traffic and next steps. Graphs and charts can help you explain your insights in an easy-to-understand format.

Involve Stakeholders

Once you have insights, don’t keep them to yourself. Share them with managers, employers and others who can use them to make smart decisions and improvements. Ask your stakeholders what they think about the insights and whether they have any ideas on how to improve UX or any other issues they may reveal.

There’s no denying that your website is a powerful marketing tool. However, you must invest in website analytics to uncover how it’s performing. Website analytics may be the secret to understanding your audience, converting more online visitors and growing your business. While it takes time and effort, you’ll likely find the rewards analytics can bring to be well worth it.

Is Google Analytics good for beginners?

Google Analytics is a popular website analytics tool you can learn without any particular skills, knowledge or coding experience. Fortunately, Google offers free courses to help you learn the tool.

What are the main types of web analytics?

The main types of web analytics are pageviews, unique pageviews, sessions, new visitors, returning visitors, traffic sources and bounce rate. You should choose to focus on the analytics that are most important to your unique business and goals.

What is the difference between data and web analytics?

Web analytics helps you understand a website. Meanwhile, data analytics is a broader term that may involve data from websites, social media channels and other sources.

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Anna Baluch is a freelance writer from Cleveland, Ohio. She enjoys writing about a variety of health and personal finance topics. When she's away from her laptop, she can be found working out, trying new restaurants, and spending time with her family. Connect to her on LinkedIn.