job analysis case study

The Missing Link of Job Analysis: A Case Study

• First Online: 23 April 2021

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• Prerna Mathur 7 &
• Shikha Kapoor 7  

Part of the book series: Asset Analytics ((ASAN))

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Any organization, in any industry, is able to perform efficiently when the objectives of the organization and the resulting objectives of the roles in the organization are unambiguous, structured, and well communicated and understood. In the event of lack of such clarity, the organization often faces various complex inter-related problems, such as wasted employee expertise, unrealistic performance standards, lack of human resource planning, incorrect talent hiring, talent gaps, low employee motivation, and so on. This case study, therefore, tries to elaborate upon the important link of job analysis that serves as a strong foundation in an organization to prevent many human-resource-related problems.

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Mathur, P., Kapoor, S. (2021). The Missing Link of Job Analysis: A Case Study. In: Kapur, P.K., Singh, G., Panwar, S. (eds) Advances in Interdisciplinary Research in Engineering and Business Management. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-16-0037-1_11

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A 7-step guide to job analysis complete with benefits and real-world applications.

If you want your employees to hit their performance targets, they need to know what you expect of them.

But all too often, employees aren't crystal clear on what they're doing or why they're doing it. Worse, their managers aren't sure either. 

Everyone can gain clarity when companies commit to the job analysis . This process defines each role's specific tasks and responsibilities in your organization. 

This guide examines the steps involved in job analysis and its benefits and application s in the workplace.

🔬 What is a job analysis ?

A job analysis is a systematic process used to identify and determine in detail the particular job duties and requirements and the relative importance of these duties for a given job. The analysis typically includes assessing the tasks that comprise the job, the skills, knowledge, abilities, and responsibilities required to perform those tasks, and the conditions under which the work will be performed.

By studying the tasks, responsibilities, skills, knowledge, and abilities required for a particular job role, we can:

• Determine the most suitable candidates for a job position.
• Design relevant training programs.
• Evaluate employee performance .
• Make decisions related to compensation and benefits.

Job analysis vs. job description vs. job specification

You may hear the terms job analysis, job description, and job specification used interchangeably to describe the process of defining a job role. 

While they are related, it's essential to understand the differences between these terms.

• Job analysis focuses on gathering data about a specific job role to understand its intricacies and requirements. From here, you can create accurate job descriptions and specifications.
• Job descriptions are written summaries of the information gathered through job analysis. A meaningful role description includes a detailed overview of the job, its key responsibilities, requirements, and reporting relationships.
• Job specifications guide the recruitment and selection process by providing candidate evaluation criteria. Understanding the minimum qualifications and skills required for a particular job position ensures the individuals hired for the job possess the necessary qualifications and attributes to perform effectively.

🧩 5 Key components of job analysis

A thorough job analysis will include the following areas: 

Job families

Job families refer to broad groups of jobs, typically within the same department or function, that have similar characteristics and require similar skills and knowledge. Assigning jobs to families enables organizations to create career paths for their employees and develop training programs for multiple job roles.

Job responsibilities

This component of job analysis involves identifying the specific tasks and duties that make up a particular job role. This can include day-to-day responsibilities and periodic or occasional tasks and projects. 

Job qualifications

A thorough job analysis will identify the skills, knowledge, and abilities that an employee needs to perform the role. This will include technical or soft skills and certifications or educational requirements.

Working conditions

The physical work environment can significantly impact a role, so it must be considered part of your analysis. This can include physical exertion, exposure to specific hazards or tools, and the stress or emotional demands involved in the work.

Performance standards

Your job analysis should include a definition of the performance standards for a particular job role. This involves creating the core competencies required for success in the role and setting clear goals or metrics to measure employee performance over time.

➡️ For further reading, check out these functional competency examples and technical competency examples . If this is a new area for you, you'll enjoy our guide to designing a competency model .

🕵️‍♀️ Why is job analysis important ? 6 Applications of job analysis in your organization

Job analysis can be an intensive task, so naturally, you'll want to understand its value to your organization.

Here are 6 ways your business will benefit: 

Recruitment and selection 

By conducting a job analysis before recruiting, you'll better understand the role and its requirements. This will allow you to create more effective job postings, screen resumes more accurately, and make better hiring decisions. 

Jarir Mallah , Human Resources Manager at Ling App , recommends setting case studies or assignments during hiring to ensure excellent candidate-to-role matches:

"All applications at Ling include what we refer to as a case study. It's a professional assignment related to the skills required for the position. A CV can only tell you on paper how suitable a candidate is, but a case study will reveal the reality. Our case studies have saved countless hours and resources in the hiring and job analysis process."

Similarly, Phil McParlane , Founder and CEO of recruitment platform 4dayweek.io , explains why the company's job analysis methodology allows them to match the right candidates with the right companies. Phil shares a recent collaboration with a tech company seeking software developers: 

" We initiated an in-depth partnership, meticulously understanding their specific needs through intensive discussions and surveys. This process unveiled the technical skills, qualifications, and essential soft traits required. With this data in hand, we harnessed advanced analytics to navigate our extensive talent pool. Leveraging our platform's intelligent algorithms, we identified candidates that perfectly aligned with the job description, and the outcome was remarkable."

Performance appraisal and management 

An in-depth job analysis delivers criteria for the role and your expectations for employees who work in it. These criteria serve as a foundation for performance evaluations, helping you to make objective decisions about an individual's contribution and potential.

• Example 1 : If a customer service representative must respond to 50 tickets per day, use this as a benchmark when evaluating their performance.
• Example 2 : If a business development manager must generate $100,000 in new sales each quarter, measure their progress towards reaching that goal during performance reviews.
• Example 3 : If a software developer must write clean, efficient code with minimal bugs, assess their technical skills, and consider areas for improvement during evaluations.

➡️ Learn more about performance appraisals and performance management , including which is the best fit for your organization. 

Training and development

Your analysis benchmarks the skills and competencies required for every job in your org chart. 

Use the results of your analysis to cross-check against your employees' existing skills. If you discover a skills gap , launch relevant upskilling or reskilling initiatives as required.

Example : Imagine you're developing a new training program for your sales team. You can refer to the job analysis to identify the skills and competencies vital for success in their role. This ensures that your training is tailored to their needs, making it more effective and likely to improve performance.

➡️ Learn more about how your organization can commit to career growth and the various employee development methods available. 

Career planning

HR and organizational leaders rely on hard data to create effective career progression frameworks . Armed with this data, they can dig into the specifics of each job, chart potential career paths , identify common progression routes, and map out the skills and experience required to reach each milestone. Organizations that share this information transparently with their employees will help them: 

• Visualize potential career trajectories.
• Plan their development goals accordingly.
• Remain loyal to the organization as they can imagine a future there. 

➡️ Learn more about how to equip your people with effective career development tools and watch them soar in your internal roles.  

Compensation and benefits

Job analysis connects the dots between individual roles and their related compensation to ensure all employees receive fair and competitive salaries and benefits packages. Your analysis lets you categorize roles into grades or bands that guide the pay scale. This helps to justify any pay discrepancies between roles without making it feel arbitrary or unfair. 

Chief People Officer Robert Kaskel explains how Checkr combines analytics with compensation decisions: 

" We pull competitive salary data from similar companies and geographic labor costs, then regularly review compensation to ensure it stays competitive. We also get our employees' feedback to ensure that the expectations and duties they face aren't exceeding or below market standards. So, every candidate enjoys full salary transparency before they accept the offer, and regular job analytics reviews help us keep up throughout every employee's lifetime with us."

➡️ Learn how and why you should conduct compensation reviews in your organization. 

Role redesign and resource realignment 

As your company evolves due to market changes or the advancement of disruptive technologies, it's only natural that your strategic goals change. People leaders can partner with managers to redefine and realign job roles to better support those goals. Sometimes, you may hire new talent or conduct training to bridge any gaps in your organizational structure. 

We spoke to Chief Executive Officer Michał Kierul on this topic, who shared how INTechHouse conducted a detailed job analysis before kicking off a large-scale, embedded-systems project. 

"We evaluated the skills, experiences, and competencies required for each role within the project. The result was eye-opening. We discovered that while we had ample software-development expertise; we were lacking in specialized firmware-programming skills. This realization led us to make strategic hires and also to invest in training for our existing team. I believe this preemptive action was crucial. It not only filled a skills gap but also boosted team morale, as they felt more prepared and capable. The project was a success, completed on time and within budget. To me, this experience reinforced the importance of job analysis as a tool for aligning resources effectively."

🔍 6 Job analysis methods

Job analysis involves large volumes of data you need to collect, analyze, and interpret to understand your job roles. 

Let's explore 6 standard techniques used by HR professionals: 

Start with supervisors or current employees to capture detailed information about their roles. An interview will deliver in-depth insights into their duties, responsibilities, and the competencies required for success.

For example, you might ask questions like: 

• What are the main tasks that make up the role?
• What skills are essential to complete those tasks effectively?
• Are there any physical or environmental demands associated with the role?
• How does this role fit into the larger organizational structure?

Position analysis questionnaires

Similar to conducting interviews, people leaders can also distribute questionnaires to employees or their managers to understand the specifics of each job. These can be paper-based or online; in either case, you'll analyze the responses to capture key information about the role. 

Direct observations

Sometimes, observing employees as they do their jobs is helpful to fully understand how they carry out their tasks and the attributes required for success. This shadowing method is beneficial when roles involve manual labor or hands-on work. 

Task inventories

Task inventories are lists of the numerous activities that make up a job. This method requires experts to review the list and rank each task based on its relevance to the role. 

Work log or diary

When two days in a role are always different, it's worth keeping a work log to understand the activities an employee completes in the role and the skills and competencies required to achieve them. To complete an inventory, employees record: 

• their daily activities;
• the time spent on each;
• the urgency of each activity.

Research similar industry positions

Compare and contrast job descriptions, duties, and responsibilities to understand how you stack up against your competitors and whether there are any areas for improvement. 

🪜What are the steps of a job analysis process ?

Follow the below steps to complete your job analysis successfully: 

1. Define the goals of the process 

Why do you want to conduct a job analysis? Have a clear understanding of what you hope to achieve through the analysis by selecting SMART goals, which should be specific, measurable, achievable, relevant, and time-bound. 

Example : By ensuring that roles are adequately aligned with employee expectations and resources, you might require your job analysis to improve employee retention figures by X% this year. 

2. Decide on data collection methods 

Choose the method that's best suited to your organization and goals. Consider conducting a mix of interviews, questionnaires, direct observations, or task inventories depending on the type of roles involved and the number of separate functions in your organization. 

3. Gather data 

Information forms the core of your analysis, so you might include the following data sets: 

• Details about the position itself (including its job family, responsibilities, and industry standards) 
• Competencies required for the role, such as technical and soft skills
• Key tasks and activities 
• Qualifications and education necessary for the role
• Expected performance standards for the role 
• Salary bands and promotion criteria 

4. Process and analyze data 

Once you have the data, analyze it to understand how each role fits into the larger picture of your organization.

These insights will also identify gaps you must address through training or hiring initiatives.

5. Identify the outcomes needed for each position

Define the outcomes needed for each job role to ensure they align with your organization's goals. Next, communicate the results to relevant stakeholders and put them into action by redefining roles, expectations, or talent needs. 

6. Assess the final results

Circle back to the SMART goals you created and assess your progress towards them by choosing relevant metrics. For example, if your goal is to improve employee retention, track your employee turnover rate before and after the job analysis process. 

7. Document your process and findings

Keep a record of your process and the data to use as a reference point for future job analyses or make any necessary adjustments to roles and responsibilities.  

📝 3 Job analysis examples

Wondering what a completed job analysis looks like? 

Here are three examples of different roles and varying levels of seniority: 

Accounting Clerk 

Job title : Accounting clerk 

Classification : Full-time or part-time employee

Department : Accounts 

Location : Minneapolis 

Summary of position : The Accounting Clerk plays a crucial role in supporting the day-to-day financial operations within the Accounts department. This position involves various tasks related to financial record-keeping, data entry, and assisting with financial reporting.

Job duties:

• Process and record financial transactions in the company's accounting software.
• Prepare and verify invoices, receipts, and other financial documents.
• Reconcile discrepancies in financial reports and escalate issues as needed.
• Assist in the preparation of financial statements and reports.
• Maintain accurate and up-to-date financial records.
• Collaborate with other departments to obtain necessary financial information.

Reporting structure: The Accounting Clerk reports directly to the Accounting Manager. The Accounting Manager provides guidance, assigns tasks, and oversees the overall performance and professional development of the Accounting Clerk.

Employee skills:

• Proficient in Microsoft Excel and other accounting software. 
• Attention to detail and accuracy in data entry.
• Basic understanding of accounting principles.
• Strong organizational and multitasking abilities.
• Effective communication skills for collaboration with team members.

Employee education: A minimum of an Associate's degree in Accounting or a related field is required. 

Environment: The Accounting Clerk works in a high-volume office environment. This role involves prolonged periods of sitting and working on a computer. It requires focusing on detailed tasks and collaborating with team members.

Certification and licenses : A diploma in bookkeeping or a related field is a plus but not mandatory.

Key performance indicators (KPIs):

• Accuracy of financial records.
• Timeliness in completing assigned tasks.
• Ability to work collaboratively within the team.

Growth opportunities : Successful performance as an Accounting Clerk could lead to advancement within the Accounts department or related areas, such as becoming a Senior Accounting Clerk or Accountant.

Marketing Manager 

Job title : Marketing Manager

Classification : Full-time employee

Department : Marketing

Location : Seattle 

Summary of position : The Marketing Manager heads up the marketing team and is responsible for planning, implementing, and managing marketing strategies to enhance the company's brand presence and achieve business objectives.

Job duties :

• Develop and execute comprehensive marketing plans to support the overall business goals.
• Oversee the creation of marketing collateral, advertising, and promotional materials.
• Conduct market research to identify trends, opportunities, and competitor activities.
• Manage digital marketing campaigns, including social media, email, and online advertising.
• Collaborate with cross-functional teams to ensure alignment of marketing strategies with overall business objectives.
• Analyze and report on the performance of marketing campaigns, using data to drive decision-making.
• Budget management for marketing initiatives.

Reporting structure : The Marketing Manager reports directly to the Director of Marketing. This position involves providing regular updates on marketing activities, discussing strategy, and collaborating on larger marketing initiatives.

• Strategic thinking and planning skills.
• Strong leadership and team management abilities.
• Excellent written and verbal communication skills.
• Proficiency in digital marketing tools and platforms.
• Analytical skills for interpreting marketing data and making informed decisions.

Employee education: A Bachelor's degree in Marketing, Business, or a related field is required. A Master's degree is a plus.

Environment : The Marketing Manager will work in the Seattle head office with a scope for flexible working hours. This role may require occasional travel for industry events or meetings. 

Certification and licenses : Certifications in digital marketing (e.g., Google Analytics, HubSpot) are advantageous.

• Return on investment (ROI) for marketing campaigns.
• Brand awareness and perception.
• Lead generation and conversion rates.
• Customer engagement and retention.

Growth opportunities : Successful performance as a Marketing Manager could lead to advancement within the marketing department, such as becoming a Director of Marketing or taking on a more specialized role in areas like product marketing or market research.

Software engineer 

Job title : Software Engineer

Department : Engineering/Development

Location : Houston

Summary of position : Our Software Engineers are critical members of the engineering team, responsible for designing, developing, and maintaining high-quality software solutions to meet the company's technical requirements.

• Collaborate with cross-functional teams to understand project requirements and objectives.
• Design and implement software solutions, ensuring scalability, security, and performance.
• Write clean, efficient, and maintainable code following company coding standards.
• Conduct thorough testing of software applications to identify and address any issues.
• Participate in code reviews to ensure code quality and knowledge sharing.
• Stay up-to-date with industry trends and emerging technologies.
• Provide technical support and troubleshooting for developed applications.
• Proficiency in programming languages such as Java, Python, or JavaScript.
• Strong problem-solving and analytical skills.
• Experience with software development methodologies, such as Agile.
• Familiarity with version control systems (e.g., Git).
• Knowledge of database management systems (e.g., MySQL, MongoDB).

Reporting structure : The Software Engineer typically reports to the Engineering Manager or Director of Engineering. 

Employee education : A Bachelor's degree in Computer Science, Software Engineering, or a related field is required. 

Environment : The Software Engineer works in an office environment and may also participate in remote collaboration depending on the nature of the projects. 

Certification and licenses: Relevant certifications, such as those from Microsoft, Oracle, or others, are beneficial but not mandatory.

• On-time delivery of software projects.
• Code quality and maintainability.
• Fast resolution time for identified issues in software applications.
• Contribution to the improvement of development processes.

Growth opportunities : Successful performance as a Software Engineer could lead to advancement within the engineering team, such as becoming a Senior Software Engineer or taking on a more specialized role in areas like software architecture or project management.

➡️ Want to be inspired by more career frameworks? Check out these 14+ career progression examples . 

🤖 How can technology be used to streamline and enhance job analysis processes?

While you could approach job analysis using manual techniques, technology will optimize the process and make it much smoother in the following ways: 

• Data collection tools such as surveys and questionnaires enable efficient feedback gathering from employees . 
• Data analytics platforms deliver in-depth analysis of large datasets, identifying patterns and trends that may not be easily identifiable through manual analysis.
• AI and machine learning assist in identifying job requirements and competencies by analyzing job performance data, reducing bias and subjectivity. 
• HR automation systems store employee performance, training, or recruitment data, providing valuable insights for job analysis.

➡️ Ensure role transparency and enable performance with Zavvy 

Zavvy provides an assortment of intuitive tools essential to the job analysis process: 

• 🧭 Career pathing : Create meaningful career frameworks at speed and document the descriptions and expectations for each role. 
• 📊 Performance review : Get a comprehensive look into what excellent performance looks like in each role.
• 💬 1:1 meeting software : Conduct job analysis interviews by holding structured meetings across your organization. 
• 🤫 Anonymous feedback tool : Gain honest input from your employees by distributing questionnaires and surveys with the option for anonymous feedback.
• 🎯 Goal management software : Set,track, and manage goals at individual, team, departmental, and organizational levels.

📅 Ready to gain granular insights into your job roles? Book a Zavvy demo today.

How often should organizations conduct a job analysis? 

Due to the dynamic nature of business, marked by rapidly changing job roles and high turnover rates, organizations should commit to regular job analysis, at least annually, or following major business events such as mergers and acquisitions. 

Who should conduct a job analysis?

An effective role analysis is a collaborative effort between HR professionals, managers or supervisors, and employees. For specialist roles, it's worth including the input of subject matter experts with technical knowledge related to the job role you're analyzing. This diverse perspective ensures that the job analysis accurately reflects the needs and expectations of all stakeholders. Additionally, involving employees in the process can increase their engagement and buy-in with any proposed changes resulting from the job analysis. 

What are the benefits of job analysis? 

A thorough and frequent job analysis provides several benefits to organizations, including:

• Keeping accurate job descriptions ensures organizations stay competitive and adapt to emerging industry trends. 
• Increasing engagement by asking employees for input about their roles.  
• Identifying skills gaps and then addressing them through training or talent acquisition strategies. 
• Offering career development opportunities based on mapping clear pathways from one role to the next.
• Enhancing efficiency and productivity by aligning job roles with organizational needs.

Veronika heads the Learning Sciences division at Zavvy. She has a background in learning psychology and helps our clients get the most out of their people enablement programs.

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Why job analysis is important, with real-world examples

February 20, 2024 - 10 min read

Job analysis is more than just a routine HR process — it’s a strategic approach that can significantly contribute to effectively managing your human resources. By delving into the details of specific job roles, including the requirements, responsibilities, and qualifications, job analysis equips organizations with the knowledge to make well-informed decisions. 

This detailed guide aims to demystify job analysis, highlight its importance, and provide real-world examples of its application. But that’s not all. We also introduce how Wrike’s robust features are designed to alleviate pain points in job analysis, such as lack of clarity in job roles and difficulty identifying skill gaps. 

So, whether you’re a seasoned HR professional or a team leader looking to optimize your team’s performance, this article is an invaluable resource. Let’s explore the world of job analysis together and see how Wrike can elevate your approach to managing your most valuable asset: your people.

What is job analysis?

Job analysis is the systematic process of gathering, examining, and interpreting information about a job role within an organization. It involves collecting data on the tasks, duties, skills, knowledge, and abilities required to perform the job successfully.

The purpose of job analysis

• Job analysis aids in talent acquisition and recruitment by identifying the essential qualifications , skills, and experience required for a particular job. This information allows recruiters to develop targeted job advertisements and establish appropriate selection criteria. In short, it’s a recruiter’s best friend. Example : In the field of marketing, Job analysis might reveal that a digital marketing specialist needs to have knowledge of search engine optimization (SEO), social media marketing, and data analytics. Armed with this information, recruiters can attract potential candidates by crafting job postings that specifically highlight these required skills.
• Job analysis also contributes to the performance appraisal process . By outlining the job duties and performance expectations, it allows supervisors and managers to evaluate employees’ performance objectively. This ensures the appraisal process focuses on relevant aspects and promotes fair and unbiased assessments. Example : In a customer service role, job analysis could show that one of the key performance indicators is the ability to handle customer complaints effectively and provide satisfactory resolutions. Using this knowledge, supervisors can assess employees’ performance in this specific area and provide constructive feedback for improvement.
• Finally, job analysis plays a vital role in facilitating career development . It provides employees with a clear understanding of the skills and competencies necessary for advancement within the organization. Furthermore, it enables HR professionals to design training and development programs that address the specific needs of the job role, fostering employee growth and progression. Example : In a managerial role, job analysis reveals that effective leadership skills, strategic thinking, and the ability to manage cross-functional teams are crucial for career advancement. As such, employees can focus on developing these competencies and seek opportunities for growth within the organization.

The importance of job analysis

Think of job analysis as a deep dive into what a job role in your company involves. It’s like taking out a magnifying glass and closely looking at the tasks, duties, and skills necessary to do the job well. 

And not just that, it’s also about understanding the knowledge and abilities that someone needs to really shine in the role. Pretty important, right?

Taking the guesswork out of job analysis

When it comes to job analysis, Wrike is your ultimate partner and can significantly simplify and enhance your job analysis process. Let’s see how.

• Identify skill gaps with dashboards : By tracking performance and progress on tasks in Wrike’s dashboards, you can pinpoint areas where team members might need additional training or support. You’ll have a clear overview of each team member’s strengths and areas for improvement, enabling you to make informed decisions about professional development.
• Work cross-functionally : Wrike provides a platform where team members can discuss tasks, share ideas, and give feedback. This enhances teamwork and facilitates a better understanding of job requirements and expectations.
• Adapt processes with custom item types : Wrike’s flexibility means it can adapt to your organization’s specific needs. Whether you’re a small team or a large enterprise, Wrike can be customized to fit your unique job analysis process.

Steps involved in job analysis

Organizations typically follow specific steps to perform a comprehensive job analysis. They’re as easy as 1-2-3 — probably because there are only three steps!

Step 1: Gather information

The first step is all about gathering information. This is where you roll up your sleeves and dive into the nitty-gritty details of the job role. What tasks are involved? What skills are needed? What responsibilities does the job entail? It’s like becoming a detective for your organization.

Now, this might sound like a daunting task but don’t worry, Wrike’s got your back. With Wrike, gathering job-related information becomes a breeze. Let’s break it down.

• Wrike folders, projects, tasks, subtasks : Create an unlimited number of items — folders, projects, tasks, and subtasks — for your team and easily view which assignee is responsible for each specific initiative. You can create individual tasks for each element of the job role, making it easier to understand and manage. Plus, you can assign these tasks to team members, providing a clear picture of who does what.
• File attachments : Wrike allows you to attach files directly to tasks. This is great for including additional information or resources related to the job role. Think job descriptions, performance metrics, training materials — you name it. Everything you need is right there in one place.

Step 2: Create the job description

So, you’ve done your detective work, gathered all the information, and now better understand the job role. Great job! Now, it’s time to take all that information and craft a clear, concise, and comprehensive job description. This is like painting a picture of the role that gives potential candidates and your team a clear view of the job.

You might think, “This sounds like a lot of work.” But with Wrike, it’s a lot easier than you might think.

• Powerful workflow automation : Wrike AI does the work for you. You can use Wrike’s automation to make intelligent suggestions uniquely tailored to you and your team to help you get started with a rough draft of the job description.
• Collaboration : One of the great things about Wrike is its collaborative features. You can share the job description with your team, gather their feedback, and make revisions in real time. This collaborative approach ensures your job description is accurate, comprehensive, and approved by all relevant parties.

So, don’t let the job description development stage intimidate you. With Wrike, it’s a smooth, collaborative process that helps ensure your job descriptions are on point. Not only does this help potential candidates understand the role better, but it also helps your current team members understand their roles and responsibilities more clearly. And that’s a crucial step toward a more effective and harmonious workplace.

Step 3: Develop the job specification

Alright, we’re making great progress! We’ve gathered all the information about the job and crafted a comprehensive job description. It’s time to move on to the next step: developing the job specification. 

This is where we outline the specific qualifications, skills, and attributes a candidate needs to perform the job effectively. It’s like creating a wishlist of the perfect candidate.

Another document to create? Don’t worry — with Wrike, this process is much more straightforward and more efficient. Wrike’s task management capabilities are helpful when starting a job specification.

• You can easily organize all the information you’ve gathered about the necessary qualifications, skills, and attributes into individual tasks.
• You can share the job specification with your team, get their input, and make real-time updates.
• Once your job specification is complete, you can easily save it in Wrike. 

Now, let’s check out some real-world job analysis examples to see the steps we walked through above in action.

Real-world examples of job analysis

Job analysis finds applications in various industries and job roles. Here are a few examples of its practical implementation, as well as a few more details on how Wrike can be your guide through the job analysis maze.

Job analysis for a social media manager

Consider a growing company that wants to enhance its online presence and decides to hire a social media manager. 

Job summary: Responsible for developing and implementing a comprehensive social media strategy, managing all social media channels, creating engaging content, and analyzing the success of campaigns. The ideal candidate will stay up to date with the latest social media trends and have a customer-focused approach to engaging the online community. This role is a fantastic opportunity for a social media enthusiast ready to take a brand’s social media to the next level.

• Creating and scheduling social media posts
• Responding to audience comments
• Analyzing engagement data
• Developing campaigns
• Degree in marketing or communication
• Familiarity with various social media platforms
• Excellent communication skills
• Experience using social media scheduling and analytics tools
• Creativity in creating engaging content
• Ability to work in a team
• Handle customer complaints professionally

Job analysis for a data scientist

Let’s take the example of a tech company that wants to leverage its data to make informed business decisions and plans to hire a data scientist. 

Job summary: Responsible for collecting, analyzing, and interpreting large datasets, developing algorithms, running complex mathematical models, and generating actionable insights to influence our business strategy.

• Collecting and interpreting complex datasets
• Creating algorithms
• Running tests
• Producing reports that influence business strategies
• Degree in data science or statistics
• Proficiency in programming languages like Python or R
• Experience with machine learning techniques
• Strong analytical skills
• Problem-solving skills
• Attention to detail
• Ability to communicate complex data insights in a transparent manner

Job analysis in the manufacturing industry

Let’s consider a manufacturing company that wants to hire a quality control inspector to ensure its products meet specific quality and safety standards. 

Job summary: Responsible for inspecting and testing materials and finished products to ensure they meet the company's quality standards and identify any defects or deviations.

• Inspecting and testing materials and finished products
• Identifying and documenting defects or deviations
• Interpreting and enforcing quality standards and procedures
• Collaborating with production teams to implement quality control measures
• Degree in quality control or a related field
• Knowledge of quality control standards and testing methods
• Proficiency in using inspection equipment and tools
• Strong reporting skills
• Excellent attention to detail
• Strong problem-solving skills
• Ability to work under pressure and meet deadlines
• Strong communication skills

These job analyses provide comprehensive overviews of each role, outlining what the job entails and the skills and qualifications required. This will help create a detailed job description and find the right candidate. So, where should you start?

Enter Wrike, your trusty companion in simplifying this complex process. 

Wrike: Your strategic tool for job analysis

Wrike boosts job analysis through its flexibility and adaptability . An adaptable tool is invaluable in an industry where change is constant and new technologies are always on the horizon. With Wrike, you can easily update job descriptions and tasks as roles evolve or new technologies are introduced. It’s like having a living, breathing document that grows and changes with your industry. 

Plus, our platform’s easy-to-use interface means you can make these updates quickly, keeping your team informed and ready to tackle their roles. 

Overall, Wrike simplifies job analysis, allowing you to streamline and standardize your process and collaborate effortlessly with others in your organization. With Wrike, you’re not just analyzing jobs — you’re building a stronger, more efficient workforce.

By clearly understanding each role, encouraging open communication, and offering insightful data, Wrike helps ensure that every team member is in a role that suits their skills and abilities. This leads to higher job satisfaction, better team performance, and a more successful organization. 

Ready to get started? Start your free trial today to ensure your team members understand their roles and contribute effectively towards your project goals.

Note: This article was created with the assistance of an AI engine. It has been reviewed and revised by our team of experts to ensure accuracy and quality.

Occasionally we write blog posts where multiple people contribute. Since our idea of having a gladiator arena where contributors would fight to the death to win total authorship wasn’t approved by HR, this was the compromise.

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How to Use Wrike for Goal-Setting and Tracking

The end of the quarter (and another year) means it's that time again: time to set new goals. Not just annual business goals, but also quarterly goals, monthly goals, and especially personal new year resolutions. One part of your mission when setting goals for your company, departments, or teams should be to share those established goals with the rest of the company. It will let everyone know what direction they should be rowing. If you're already working in Wrike, you and your team can also use it to create visibility into established goals and track progress on the way to achieving them. Building Annual Plans in Wrike Annual planning is an essential part of a successful business. It provides direction for the entire organization, defines what teams will focus energy on, and synchronizes activities of different departments to maximize resources. But in order to do all that efficiently, the entire company needs visibility into the annual plans. You can build annual plans in Wrike following our suggestion, or using your own format. Our recommendation: Create a folder called "2016 Annual Plans" at the top level of your account and right-click the folder name to share it with the entire company. Inside that folder, create project folders or tasks for all of the different priorities and big initiatives of the year. You can assign responsible directors or managers to the work, so everyone knows who to talk to if they have questions about that objective. From there, you can drill down into immediate key actions by building out tasks, setting deadlines, and assigning the steps to the appropriate parties. Finally, use Custom Fields to make notes on budgeting — data in Custom Fields can be restricted to only show for certain user groups in your account, so your private information stays that way. If you don't have access to Custom Fields, just write information you'd like to share with the entire team in your task descriptions. Establishing Quarterly Plans in Wrike Since quarterly plans only cover 3 months instead of an entire year, it's easy to brainstorm new ideas right in Wrike. On the Wrike content marketing team, we create a project folder called something like "Q2 Ideas", and then individuals on our team can create a new task for every idea they have inside that folder. Inside their task descriptions, team members can add pertinent details if their idea requires a more detailed breakdown, or they've suggested a completely new undertaking and they've already thought out a majority of the work and substeps needed to make the idea a reality. This method of brainstorming and sharing also allows our team to see if someone else has had a similar idea, so we can combine efforts and dump our parallel brainstorms into one big task. After everyone has input their ideas, we have a meeting to vet those ideas for go-no go decisions. We project the "Q1 Ideas" folder onto the big screen in our meeting room, and then discuss all of the propositions as a group. We can assign task owners and schedule due dates for ideas we definitely want to proceed with; defer ideas we think are good for a future quarter; or cancel tasks that just aren't in line with our overall vision. Once we decide what ideas we're moving forward with, we reorganize those tasks into our main work folder structure, and build out complete project plans with subtasks for the big ideas. Tracking Progress Using OKRs If you're someone who loves quantifying goals and hitting numbers, consider implementing OKRs and using project tracking tools to monitor progress. "OKRs" stands for "Objectives and Key Results." It's a system for setting goals (Objectives) and the steps necessary to achieve those goals (Key Results), popularized by Google. It's something we do at Wrike. You can set up an OKR like this: O: Publish regularly on blog KR: Write at least 3 blog posts every week KR: Research 10 new post ideas at least once per week KR: Build a backlog of 10 posts to publish later when inspiration fails Notice how the Objective is broad, and the Key Results are more direct supporting steps toward reaching that goal. You can read all about OKRs in our post on using OKRs for quarterly planning. In the meantime, here are a few quick tips on OKR creation: Start every O and KR with a verb. This ensures they are actionable. Make sure every KR includes a number. This ensures it is a measurable action that you can finally mark as complete. Make sure your goals are slightly out of reach. They shouldn't be laughably hard to attain, but if you make them just a little more difficult than you typically would, it will inspire you to work harder to go the distance. We wrote an entire post detailing exactly how you can implement the OKR system at your company using Wrike. Take a look. Setting Personal Goals Using Wrike It can't always be about work, work, work! Every person should set some personal improvement goals to help maintain that work-life balance. When you decide on what yours will be, track them where you're already tracking the rest of your work — in Wrike!   You can create a private folder at the top level of your Wrike account that no one else will be able to see. Turn each of your resolutions into a task, and if you know what it will take to turn those resolutions into a reality, break down those steps into subtasks. Create recurring tasks for things you know you'll need to do every week to reach your goal, assign them due dates for the end of each week, and mark them as complete as you make progress. Are you one of those people who never knows what to set for your New Year's Resolution? Here are some examples of what Wrikers are aiming to improve in 2016: Practice a new language at least 3x week using the Duolingo app Get better at doing Read 10 business-related improvement books Volunteer at the dog shelter at least 1x per month Keep one plant alive all year Reconnect with old friends Using Wrike for Goal-Setting Are you using Wrike to set and track your goals? Tell us your story and share how it has helped keep you on track in the comments below! If you have questions about best practices for goal-setting in Wrike, ask away in the comments and we'll get back to you with our advice.

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Job Analysis

ob analysis and Job descriptions4 Job analysis and the major components4 Reasons for job analysis4 The relationship between Job analysis and Human resources5 Job descriptions and the components involved5 The importance and use of job descriptions6 Questions for analysis6 Job description8 Job context8 Job purpose8 Position environment8 Relationships8 Core responsibilities9 Outcome statements of the position9 Selection criteria Essential9 Desirable10 Accountability10 Performance indicators10 Special circumstances11 Reflection11

Reference list13 Appendix14 Answers for job analysis14 Introduction Human resource management is defined as the process that manages the employment relationship (Seward, Dein, 2005, p. 3).

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Human resource management recognizes that the people make the organization and provide the company with a competitive advantage. The aim is to assist the organisation in using people to achieve the businesses objectives (Seward, Dein, 2005, p. 3). A Job analysis is a key activity in human resources, and provides information regarding positions in the organisation.

It is an important topic as it is a vital employment tool which can assist with HR activities and potential and current employees, ‘Job analysis is the systematic study of positions to identify their observable duties and responsibilities, as well as the knowledge, skills and abilities required to perform a particular task or group of tasks’ ( Kovac,2006, p. 1).

The significance of this essay is to conduct a job analysis to further my understanding of the topic and to develop the analysis into a job description. The importance and use of job analysis is discussed and the major components which are involved.

The method used for this job analysis is a questionnaire consisting of questions targeted at a real estate sales representative. The answers to the job analysis questions are then developed into a job description for that position. The job description includes what skills, qualifications and responsibilities are included in the job, and who would be a suitable candidate.

I state my reflections of this assignment to express the challenges and rewards I received from conducting a job analysis, and whether or not I could consider doing this as a full time profession.

Hence why job analysis is an important aspect in an organization, it allows workers to provide management with information regarding the job that others may have overlooked, ‘little opportunity is provided for active involvement of job incumbents and potential workers in design of jobs. ‘ ( Ghorpade,1988, p. 4) Managing change is also important in human resource management, which often means that positions also have to adapt to the changes in the organization.

Job analysis assists the issue of change by realigning jobs with the organizations goals, ( Ghorpade,1988, p.

6). The relationship between Job analysis and Human resources Job analysis plays an important role in HRM. The main aim of Job analysis is to supply HR with the relevant job information that is needed for the increase in performance for the organization. (Ghorpade, 1988, p. 4). For instance, a product of job analysis is job design which allows HRM to redesign jobs to increase the quality of work life, safety and productivity.

Job specification provides HRM with job data regarding what skills and qualifications are vital for that position, this in turn increases the effectiveness of the organization. Overall job analysis contributes to the value of the organizations human resource management by providing them with the data needed to improve the effectiveness, ‘ job analysis contributes something of value to the human resource management… the job variables uncovered through job analysis can be used by HR in forecasting needs, and formulating action plans’ ( Ghorpade,1988,p. 5).

Job descriptions and the components involved The final product of a job analysis is the job description, this is a written document on what the position is, how it is performed, what is needed to be performed and who the candidate should be, ‘ a job description is a written statement explaining why a job exists, what the job holder does, how he or she performs it, and under what conditions’ (Stone,1998,p. 119). The formulation of descriptions involves gathering job information (job analysis), writing a draft, reviewing or approving the draft, final authorised version of description. Clark, 1992, p. 190). Various components are involved in a job description and these differ for each job.

The job identification defines how the position in located in the structure of the organisation. This includes information such as, the job title, department, reporting relationships, and job status. (Stone,1998, p. 120). Job identification can be important to an employee (current or potential) as it provides the job with meaning and value.

The job summary explains why the job exists and what purpose or objective it has for the organisation.

The duties and responsibilities is a major focal point of the job description, it specifically states what is needed to perform the job adequately. This section of the description paints a picture of what is required by the position. The accountability category is used to indicate how the performance of the job will be analysed, ‘accountability is used to indicate the basis in which performance in the job will be judged’ (Clark, 1992, p, 195). The importance and use of job descriptions

Job descriptions have many purposes and uses in an organisation.

Selection and recruitment can use job descriptions in the hiring process to be certain that the best candidate will be selected for the position. As previously mentioned the people are the driving force towards the organisational goals. If the potential workers can possess what the job description states then the driving force towards the goal will be stronger. Job descriptions can also be used to conduct performance reviews and compensation on existing employees.

It can also assist in gaining a greater understanding on what is involved with the job, ‘help managers and current employees understand what the job is and how it is to be performed’ (Stone,1998, p.

126). Regular updated job descriptions can help HRM remove unnecessary job requirements in addition to removing conflict and dissatisfaction, ‘Job descriptions regularly updated with the most recent job analysis can immensely help the HR professionals to identify and eliminate unnecessary job requirements, areas of conflict or dissatisfaction’ (Siddique,2010, ).

A regular update of job descriptions also keeps employees informed and clear with what is to be expected and how their performance is to be aligned to achieving the objectives of the organisation, this clarity then has a positive impact on their performance, ‘workers develop a better understanding of company expectations…

This role clarity, in turn, improves workers’ morale, job satisfaction and productivity.

Related posts:

• Organizational Design Article Analysis
• Value Chain Analysis
• Understanding Job Analysis
• Swot Analysis of Tesco
• Fmcg industry analysis
• Strategic Management and Pestle Analysis
• Career Development Plan Part I-Job Analysis and Selection

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47 case interview examples (from McKinsey, BCG, Bain, etc.)

One of the best ways to prepare for   case interviews  at firms like McKinsey, BCG, or Bain, is by studying case interview examples. 

There are a lot of free sample cases out there, but it's really hard to know where to start. So in this article, we have listed all the best free case examples available, in one place.

The below list of resources includes interactive case interview samples provided by consulting firms, video case interview demonstrations, case books, and materials developed by the team here at IGotAnOffer. Let's continue to the list.

• McKinsey examples
• BCG examples
• Bain examples
• Deloitte examples
• Other firms' examples
• Case books from consulting clubs
• Case interview preparation

Click here to practise 1-on-1 with MBB ex-interviewers

1. mckinsey case interview examples.

• Beautify case interview (McKinsey website)
• Diconsa case interview (McKinsey website)
• Electro-light case interview (McKinsey website)
• GlobaPharm case interview (McKinsey website)
• National Education case interview (McKinsey website)
• Talbot Trucks case interview (McKinsey website)
• Shops Corporation case interview (McKinsey website)
• Conservation Forever case interview (McKinsey website)
• McKinsey case interview guide (by IGotAnOffer)
• McKinsey live case interview extract (by IGotAnOffer) - See below

2. BCG case interview examples

• Foods Inc and GenCo case samples  (BCG website)
• Chateau Boomerang written case interview  (BCG website)
• BCG case interview guide (by IGotAnOffer)
• Written cases guide (by IGotAnOffer)
• BCG live case interview with notes (by IGotAnOffer)
• BCG mock case interview with ex-BCG associate director - Public sector case (by IGotAnOffer)
• BCG mock case interview: Revenue problem case (by IGotAnOffer) - See below

3. Bain case interview examples

• CoffeeCo practice case (Bain website)
• FashionCo practice case (Bain website)
• Associate Consultant mock interview video (Bain website)
• Consultant mock interview video (Bain website)
• Written case interview tips (Bain website)
• Bain case interview guide   (by IGotAnOffer)
• Digital transformation case with ex-Bain consultant
• Bain case mock interview with ex-Bain manager (below)

4. Deloitte case interview examples

• Engagement Strategy practice case (Deloitte website)
• Recreation Unlimited practice case (Deloitte website)
• Strategic Vision practice case (Deloitte website)
• Retail Strategy practice case  (Deloitte website)
• Finance Strategy practice case  (Deloitte website)
• Talent Management practice case (Deloitte website)
• Enterprise Resource Management practice case (Deloitte website)
• Footloose written case  (by Deloitte)
• Deloitte case interview guide (by IGotAnOffer)

5. Accenture case interview examples

• Case interview workbook (by Accenture)
• Accenture case interview guide (by IGotAnOffer)

6. OC&C case interview examples

• Leisure Club case example (by OC&C)
• Imported Spirits case example (by OC&C)

7. Oliver Wyman case interview examples

• Wumbleworld case sample (Oliver Wyman website)
• Aqualine case sample (Oliver Wyman website)
• Oliver Wyman case interview guide (by IGotAnOffer)

8. A.T. Kearney case interview examples

• Promotion planning case question (A.T. Kearney website)
• Consulting case book and examples (by A.T. Kearney)
• AT Kearney case interview guide (by IGotAnOffer)

9. Strategy& / PWC case interview examples

• Presentation overview with sample questions (by Strategy& / PWC)
• Strategy& / PWC case interview guide (by IGotAnOffer)

10. L.E.K. Consulting case interview examples

• Case interview example video walkthrough   (L.E.K. website)
• Market sizing case example video walkthrough  (L.E.K. website)

11. Roland Berger case interview examples

• Transit oriented development case webinar part 1  (Roland Berger website)
• Transit oriented development case webinar part 2   (Roland Berger website)
• 3D printed hip implants case webinar part 1   (Roland Berger website)
• 3D printed hip implants case webinar part 2   (Roland Berger website)
• Roland Berger case interview guide   (by IGotAnOffer)

12. Capital One case interview examples

• Case interview example video walkthrough  (Capital One website)
• Capital One case interview guide (by IGotAnOffer)

13. Consulting clubs case interview examples

• Berkeley case book (2006)
• Columbia case book (2006)
• Darden case book (2012)
• Darden case book (2018)
• Duke case book (2010)
• Duke case book (2014)
• ESADE case book (2011)
• Goizueta case book (2006)
• Illinois case book (2015)
• LBS case book (2006)
• MIT case book (2001)
• Notre Dame case book (2017)
• Ross case book (2010)
• Wharton case book (2010)

Practice with experts

Using case interview examples is a key part of your interview preparation, but it isn’t enough.

At some point you’ll want to practise with friends or family who can give some useful feedback. However, if you really want the best possible preparation for your case interview, you'll also want to work with ex-consultants who have experience running interviews at McKinsey, Bain, BCG, etc.

If you know anyone who fits that description, fantastic! But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can do mock case interviews 1-on-1 with ex-interviewers from MBB firms . Start scheduling sessions today!

The IGotAnOffer team

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• Alphabetical List of Companies
• Business Analysis Topics
• Google (Alphabet) HRM: HR Planning, Job Analysis & Design

Google’s (Alphabet’s) human resource management involves different strategies to address the workforce needs of its diversified business organization. This diversification imposes significant challenges to human resource managers of the company. Nonetheless, there are certain HRM approaches that are generally applied to different areas of the information technology business organization. For instance, in human resource planning, Google’s HR managers focus on the effective use of forecast information to minimize the surplus or shortage of employees, and to establish a balance between the supply and demand for qualified employees. In terms of job analysis and design approaches, Google’s (Alphabet’s) operations management approaches are also varied because of the different types of jobs in the different businesses of the company.

This article is part of a series on Google’s (Alphabet’s) Human Resource Management:

• Google (Alphabet) HRM: Recruitment, Selection, Retention
• Google (Alphabet) HRM: Training, Performance Management
• Google (Alphabet) HRM: Compensation, Career Development

Google’s (Alphabet’s) Human Resource Planning

Forecasting . Human resource managers at Google use trend analysis and scenario analysis for forecasting. Trend analysis is a quantitative technique that allows the company to predict possible HR demand based on current conditions and changes in the business. Scenario analysis is Google’s qualitative technique for forecasting HR demand. Scenario analysis involves analyzing different combinations of variables to predict HR demand for each resulting scenario. In this way, Google uses a combination of quantitative and qualitative techniques for forecasting HR demand.

Surplus & Shortage of Employees . Concerns about surplus or shortage of employees at Google are mostly in the production processes, such as the manufacture of Chromecast and the provision of the Google Fiber Internet and IPTV service. In developing and providing web-based and software products, human resource surplus and shortage are not a significant concern. For production processes, Google’s human resource management identifies possible surpluses and shortages through forecasting techniques. Thus, the company’s human resource planning includes forecasted surpluses and shortages of human resources. Such information is used for recruitment and scheduling.

Balancing Supply and Demand . Google’s human resource management faces minimal problems when it comes to balancing HR supply and demand. Even if demand for web-based/software products and online advertising services increase, Google does not need to commensurately increase its human resources in these business areas because of the digital nature of these products. Still, the company needs to address HR supply and demand in other areas, such as the production and distribution of consumer electronics, like Pixel and Chromecast. For these areas, Google uses a flexible strategy where new employees are hired based on forecasts of human resource needs.

The combination of Google’s HR management approaches for forecasting, identifying issues with surplus and shortage of employees, and balancing of human resource supply and demand effectively supports the human resource needs of the firm. Alphabet uses conventional methods and techniques together with advanced information systems to analyze human resource data to support human resource management decisions.

Job Analysis and Design at Google (Alphabet)

Organizational Design . The company’s organizational design enables flexible approaches to addressing human resource needs. The interconnections in the organizational structure or company structure of Google (Alphabet) allow human resource managers to easily identify cross-linkages among different parts of the organization and use this information for the processes of job analysis and design. Thus, Alphabet’s organizational design facilitates and optimizes human resource management activities, particularly in job analysis and design.

Methods of Job Analysis . Google uses a combination of worker-oriented job analysis methods and work-oriented job analysis methods. However, the company emphasizes the use of work-oriented job analysis methods for research and development and for product design and manufacturing. Google emphasizes the worker-oriented job analysis methods for jobs that require significant interpersonal skills, such as human resource management positions.

Job Description & Specification . Because of the large size of the organization, Google has highly varied job descriptions and specifications. The job descriptions and specifications for positions in product development, for instance, significantly differ from the job descriptions and specifications for positions in human resource management. Nonetheless, Google’s (Alphabet’s) organizational culture or work culture emphasizes certain characteristics in all employees, such as smartness and drive for excellence in all job positions throughout the organization.

• Alphabet Inc. – Form 10-K .
• Alphabet Inc. – Google Careers – Teams .
• Murillo-Ramos, L., Huertas-Valdivia, I., & García-Muiña, F. E. (2023). Exploring the cornerstones of green, sustainable and socially responsible human resource management. International Journal of Manpower, 44 (3), 524-542.
• Priyashantha, K. G. (2023). Disruptive technologies for human resource management: A conceptual framework development and research agenda. Journal of Work-Applied Management, 15 (1), 21-36.
• Skýpalová, R., Stojanová, H., Troger, H., & Caha, Z. (2023). Human resource management across generations within the context of World of Work 4.0. Emerging Science Journal, 7 (3), 843-853.
• U.S. Department of Commerce – International Trade Administration – Software and Information Technology Industry .
• Copyright by Panmore Institute - All rights reserved.
• This article may not be reproduced, distributed, or mirrored without written permission from Panmore Institute and its author/s.
• Educators, Researchers, and Students: You are permitted to quote or paraphrase parts of this article (not the entire article) for educational or research purposes, as long as the article is properly cited and referenced together with its URL/link.

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4.10 Case Study: Job Analysis at Matrix Agricultural Systems

Case study: job analysis at matrix agricultural systems.

Matrix Agricultural Systems (MAS) is a large producer of agricultural technology products. With 25 offices in Canada and a plan for an additional 5 more in the next two years, MAS is the leader in its field within the country. It manufactures innovative web-based technologies and sensors to help farmers improve their agricultural productivity. For example, it has developed sensors, that when placed strategically around fields along with image recognition technologies, it allows farmers to view their crops from anywhere in the world. The company’s most important strategic objective is to keep up with technological advances and find ways to drive the price of their product down.

In recent years, MAS has had difficulty recruiting engineers. The problem appears to have worsened because of an increase in the turnover rate. This rate is now higher than the market average for the agricultural business sector despite the highly competitive remuneration packages that MAS pays. The company has traditionally recruited engineers with a mechanical engineering background. Recently however, the shortage of engineers has become a more acute problem, especially in industrial areas where there is an abundance of technology companies and an increasing demand for engineers.  Nathalie Lebrun, the Vice President of Human Resources is very worried that this situation may lead the company to lose its competitive edge. She has a pair of very specific mandates for Marcel Tremblay, her Talent Acquisition manager:

First, the criteria for entry-level engineer positions have to be looked at. They specify (a) an undergraduate degree in mechanical or industrial engineering and (b) at least three years’ experience. Nathalie wants Marcel to devise a method for evaluating the appropriateness and value of these as job specifications. Are these criteria really necessary to perform the job? Aren’t they unnecessarily restricting the labour pool from which MAS finds its employees? Second, Nathalie wants Marcel to develop training modules for new technical employees to be included in “Matrix Agricultural Systems University”. Consequently, Marcel needs to determine what knowledge, skills, competencies and abilities are essential or critical for the job and integrate them into a training module.

• When you read this article, what do you think they need to consider for job analysis.  Make a list.
• What do you think the priority is for MAS?  Explain why you believe this would be the priority.
• Research, and provide a list of reasons for high turnover.  What strategy could MAS use to reduce turnover?

“ 4.10 Case Study: Job Analysis at Matrix Agricultural Systems ” from Human Resources Management – 3rd Edition  by Debra Patterson is licensed under a  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Human Resources for Food Processing Copyright © 2023 by Josie Olaveson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Cross-Platform App Development for a Job Board

SkillLab's application is operational in over 20 countries, facilitating the career advancement and recruitment processes for users worldwide. The company approached Techstack with a request to enhance the user experience within the SkillLab application, specifically for Ukrainian users.

The key objective was to streamline the user journey from initial sign-up to effective job searching and matching by integrating the platform with the Ukrainian State Employment Center, allowing seamless access to its vacancies for relevant users.

Digital Transformation

Back End Development, Front End Development

The Netherlands

The integration of the Skillab platform with the Ukrainian State Employment Center presented a unique set of challenges that required innovative solutions and close collaboration between the SkillLab and Techstack teams.

These challenges encompassed various aspects of the project, including communication, technical hurdles, legal complexities, data management, and security considerations.

Resolving legal issues to obtain the production token for SSO use

Obtaining the production token for Single Sign-On (SSO) use required navigating legal complexities and ensuring compliance with the regulations of both countries involved.

Implementing Diia's verification process for Diia SSO

Implementing Diia SSO required meticulous attention to detail and close collaboration with the Diia team. The user verification process itself is handled entirely by Diia, which meant that the team had to ensure a smooth and secure handover of the authentication process to the Diia system. This required a deep understanding of Diia's authentication protocols and the establishment of secure communication channels between the SkillLab platform and Diia's servers.

Decoding IIT's signature library

Integrating with IIT's signature library presented technical challenges that required the team to decode and understand the library's functionality to ensure seamless integration.

Systemizing different-format data

The product development involved working with data from various sources and in different formats, requiring the team to develop solutions for standardizing and integrating the information into the SkillLab platform.

Facilitating 3-way communication between 2 countries

The development process required coordination and communication between SkillLab, Techstack, and the relevant authorities in both the Netherlands and Ukraine, presenting some challenges.

Working with outdated third-party technical documentation

Our team had to navigate and interpret outdated technical documentation from third-party providers, which required additional effort to understand and implement the necessary integrations.

Implemented a client-driven architecture for the API integration

This architecture ensured modularity and code readability. The team designed and implemented a client-driven architecture that focused on the codebase's modularity and readability. This approach facilitated easier maintenance, debugging, and future enhancements to the integration.

Utilized a singleton implementation to expand the vacancy providers

 By employing a singleton pattern, the team was able to efficiently manage and expand the various vacancy providers integrated into the SkillLab platform, ensuring scalability and maintainability.

Maintained effective team communication throughout the project

Clear and consistent communication channels were established between the Techstack and SkillLab teams, ensuring that all stakeholders were kept informed of progress, challenges, and decisions throughout the project lifecycle.

Implemented Single Sign-On via Diia.Signature

We successfully integrated the Diia.Signature system, allowing Ukrainian users to securely access the SkillLab platform using their Diia credentials, streamlining the user registration and login process.

Incorporated location and publication date filters for enhanced job search functionality

To improve the user experience and the relevance of job search results, the team implemented location and publication date filters, enabling users to refine their job searches based on their preferences and requirements.

Technologies Used

The project utilized cross-platform technologies to ensure compatibility and seamless integration between the SkillLab platform and the Ukrainian State Employment Center. The specific technologies used were carefully selected based on their robustness, scalability, and ability to handle the complexities of the integration process.

The workflow

While building the solution for Skillab, we followed a standard agile workflow with an incremental approach to development and regular communication. The workflow included the following key stages:

Requirements gathering and analysis

The team collaborated closely with SkillLab to gain a comprehensive understanding of their requirements and objectives. This stage involved analyzing the existing systems, identifying potential integration points, and defining the project's scope. Special attention was given to the legal and security aspects of handling sensitive user data across different countries.

Architecture design and planning

Based on the gathered requirements, the team designed a robust and scalable architecture that could accommodate the integration with the Ukrainian State Employment Center. This stage included planning for the implementation of Diia's verification process for SSO and decoding IIT's signature library. The team also considered the operational obstacles of dealing with sensitive data and developed strategies to mitigate potential risks.

Development and implementation

The development phase involved the actual coding and implementation of the planned integrations and features. The team worked on decoding IIT's signature library to ensure seamless integration with the SkillLab platform. We also implemented Diia's verification process for SSO, ensuring that users could securely access the platform using their Diia credentials. Throughout this stage, the team maintained close communication with SkillLab to resolve any legal issues related to obtaining the production token for SSO use.

Testing and quality assurance

Following the iterative approach, the team conducted thorough testing and quality assurance processes to identify and resolve any issues or bugs in the implemented solutions at every dev stage. The testing process was comprehensive and meticulous, covering every aspect of the system. As part of the quality assurance process, the team maintained a detailed list of all the items that required extra attention or further refinement. This list was regularly updated and shared with our partner, SkillLab, to ensure complete transparency and collaboration.

Deployment and integration

Once the developed solutions passed the testing and quality assurance phase, they were deployed and integrated into the SkillLab platform. The team worked closely with SkillLab to ensure a seamless integration process, minimizing disruptions to the existing system. They also provided documentation and training to SkillLab's team, enabling them to manage and maintain the integrated solutions effectively.

Maintenance and support

After the successful deployment and integration, the team provided ongoing maintenance and support to ensure the stability and performance of the implemented solutions. They monitored the system for any potential issues and promptly addressed them to minimize downtime. The team also provided guidance and support to SkillLab's team in handling sensitive user data across different countries, ensuring compliance with international data protection regulations.

About the team

The project team consisted of skilled professionals from both Techstack and SkillLab, working together to ensure the successful completion of the integration. SkillLab's team included Product Owners, who provided guidance and direction on the product's objectives and requirements, and a software engineer, who worked closely with the Techstack team to ensure smooth integration with the existing SkillLab platform. The Techstack team included:

Team composition

Project manager

Software developer

Manual QA engineer

The successful integration with the Ukrainian State Employment Center through the SkillLab platform resulted in significant benefits for both the company and its users:

Successfully integrated a foreign job board with the Ukrainian Employment Center

The product demonstrated the feasibility and value of integrating international job boards with local employment centers, opening up new opportunities for cross-border collaboration and job market integration.

Reduced effort for future application expansion

The modular and scalable architecture implemented during the integration process laid the foundation for easier future expansions and enhancements to the SkillLab platform.

Simplified sign-in and job application 

One of the most significant impacts of the project was the successful integration of the Diia Single Sign-On system. This integration allowed Ukrainian users to easily and securely access the SkillLab platform using their Diia credentials, streamlining the sign-in and job application process. This successful integration showcases the potential for collaboration between private companies and government initiatives to create more accessible and secure digital services for citizens.

Facilitated an efficient user job search process

The incorporation of location and publication date filters, along with the seamless integration of the Dutch SEC Vacancy Board, significantly improved the job search experience for Ukrainian users, enabling them to find relevant job opportunities more quickly and easily.

Infrastructure cost-savings by utilizing existing resources

By leveraging the existing infrastructure and resources of the SkillLab platform, the company was able to achieve cost-savings in terms of development, deployment, and maintenance

The successful integration with the Ukrainian State Employment Center through the SkillLab platform has not only provided Ukrainian jobseekers with a convenient and efficient way to find suitable job opportunities but has also showcased the potential for international collaboration in the field of job market integration.

The product's success serves as a testament to the expertise and dedication of the teams involved and highlights the importance of clear objectives, effective communication, and a well-structured approach to project management.

Techstack is a team of incredibly nice people with a very high degree of professionalism.

Read More Cases

Published: 5 April 2024 Contributors: Tim Mucci, Cole Stryker

Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions. This process allows organizations to leverage the exponentially growing data generated from diverse sources, including internet-of-things (IoT) sensors, social media, financial transactions and smart devices to derive actionable intelligence through advanced analytic techniques.

In the early 2000s, advances in software and hardware capabilities made it possible for organizations to collect and handle large amounts of unstructured data. With this explosion of useful data, open-source communities developed big data frameworks to store and process this data. These frameworks are used for distributed storage and processing of large data sets across a network of computers. Along with additional tools and libraries, big data frameworks can be used for:

• Predictive modeling by incorporating artificial intelligence (AI) and statistical algorithms
• Statistical analysis for in-depth data exploration and to uncover hidden patterns
• What-if analysis to simulate different scenarios and explore potential outcomes
• Processing diverse data sets, including structured, semi-structured and unstructured data from various sources.

Four main data analysis methods  – descriptive, diagnostic, predictive and prescriptive  – are used to uncover insights and patterns within an organization's data. These methods facilitate a deeper understanding of market trends, customer preferences and other important business metrics.

IBM named a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions.

Structured vs unstructured data

What is data management?

The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases . This type of database helps ensure that data is well-organized and easy for a computer to understand. Traditional data analytics relies on statistical methods and tools like structured query language (SQL) for querying databases.

Big data analytics involves massive amounts of data in various formats, including structured, semi-structured and unstructured data. The complexity of this data requires more sophisticated analysis techniques. Big data analytics employs advanced techniques like machine learning and data mining to extract information from complex data sets. It often requires distributed processing systems like Hadoop to manage the sheer volume of data.

These are the four methods of data analysis at work within big data:

The "what happened" stage of data analysis. Here, the focus is on summarizing and describing past data to understand its basic characteristics.

The “why it happened” stage. By delving deep into the data, diagnostic analysis identifies the root patterns and trends observed in descriptive analytics.

The “what will happen” stage. It uses historical data, statistical modeling and machine learning to forecast trends.

Describes the “what to do” stage, which goes beyond prediction to provide recommendations for optimizing future actions based on insights derived from all previous.

The following dimensions highlight the core challenges and opportunities inherent in big data analytics.

The sheer volume of data generated today, from social media feeds, IoT devices, transaction records and more, presents a significant challenge. Traditional data storage and processing solutions are often inadequate to handle this scale efficiently. Big data technologies and cloud-based storage solutions enable organizations to store and manage these vast data sets cost-effectively, protecting valuable data from being discarded due to storage limitations.

Data is being produced at unprecedented speeds, from real-time social media updates to high-frequency stock trading records. The velocity at which data flows into organizations requires robust processing capabilities to capture, process and deliver accurate analysis in near real-time. Stream processing frameworks and in-memory data processing are designed to handle these rapid data streams and balance supply with demand.

Today's data comes in many formats, from structured to numeric data in traditional databases to unstructured text, video and images from diverse sources like social media and video surveillance. This variety demans flexible data management systems to handle and integrate disparate data types for comprehensive analysis. NoSQL databases , data lakes and schema -on-read technologies provide the necessary flexibility to accommodate the diverse nature of big data.

Data reliability and accuracy are critical, as decisions based on inaccurate or incomplete data can lead to negative outcomes. Veracity refers to the data's trustworthiness, encompassing data quality, noise and anomaly detection issues. Techniques and tools for data cleaning, validation and verification are integral to ensuring the integrity of big data, enabling organizations to make better decisions based on reliable information.

Big data analytics aims to extract actionable insights that offer tangible value. This involves turning vast data sets into meaningful information that can inform strategic decisions, uncover new opportunities and drive innovation. Advanced analytics, machine learning and AI are key to unlocking the value contained within big data, transforming raw data into strategic assets.

Data professionals, analysts, scientists and statisticians prepare and process data in a data lakehouse, which combines the performance of a data lakehouse with the flexibility of a data lake to clean data and ensure its quality. The process of turning raw data into valuable insights encompasses several key stages:

• Collect data: The first step involves gathering data, which can be a mix of structured and unstructured forms from myriad sources like cloud, mobile applications and IoT sensors. This step is where organizations adapt their data collection strategies and integrate data from varied sources into central repositories like a data lake, which can automatically assign metadata for better manageability and accessibility.
• Process data: After being collected, data must be systematically organized, extracted, transformed and then loaded into a storage system to ensure accurate analytical outcomes. Processing involves converting raw data into a format that is usable for analysis, which might involve aggregating data from different sources, converting data types or organizing data into structure formats. Given the exponential growth of available data, this stage can be challenging. Processing strategies may vary between batch processing, which handles large data volumes over extended periods and stream processing, which deals with smaller real-time data batches.
• Clean data: Regardless of size, data must be cleaned to ensure quality and relevance. Cleaning data involves formatting it correctly, removing duplicates and eliminating irrelevant entries. Clean data prevents the corruption of output and safeguard’s reliability and accuracy.
• Analyze data: Advanced analytics, such as data mining, predictive analytics, machine learning and deep learning, are employed to sift through the processed and cleaned data. These methods allow users to discover patterns, relationships and trends within the data, providing a solid foundation for informed decision-making.

Under the Analyze umbrella, there are potentially many technologies at work, including data mining, which is used to identify patterns and relationships within large data sets; predictive analytics, which forecasts future trends and opportunities; and deep learning , which mimics human learning patterns to uncover more abstract ideas.

Deep learning uses an artificial neural network with multiple layers to model complex patterns in data. Unlike traditional machine learning algorithms, deep learning learns from images, sound and text without manual help. For big data analytics, this powerful capability means the volume and complexity of data is not an issue.

Natural language processing (NLP) models allow machines to understand, interpret and generate human language. Within big data analytics, NLP extracts insights from massive unstructured text data generated across an organization and beyond.

Structured Data

Structured data refers to highly organized information that is easily searchable and typically stored in relational databases or spreadsheets. It adheres to a rigid schema, meaning each data element is clearly defined and accessible in a fixed field within a record or file. Examples of structured data include:

• Customer names and addresses in a customer relationship management (CRM) system
• Transactional data in financial records, such as sales figures and account balances
• Employee data in human resources databases, including job titles and salaries

Structured data's main advantage is its simplicity for entry, search and analysis, often using straightforward database queries like SQL. However, the rapidly expanding universe of big data means that structured data represents a relatively small portion of the total data available to organizations.

Unstructured Data

Unstructured data lacks a pre-defined data model, making it more difficult to collect, process and analyze. It comprises the majority of data generated today, and includes formats such as:

• Textual content from documents, emails and social media posts
• Multimedia content, including images, audio files and videos
• Data from IoT devices, which can include a mix of sensor data, log files and time-series data

The primary challenge with unstructured data is its complexity and lack of uniformity, requiring more sophisticated methods for indexing, searching and analyzing. NLP, machine learning and advanced analytics platforms are often employed to extract meaningful insights from unstructured data.

Semi-structured data

Semi-structured data occupies the middle ground between structured and unstructured data. While it does not reside in a relational database, it contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Examples include:

• JSON (JavaScript Object Notation) and XML (eXtensible Markup Language) files, which are commonly used for web data interchange
• Email, where the data has a standardized format (e.g., headers, subject, body) but the content within each section is unstructured
• NoSQL databases, can store and manage semi-structured data more efficiently than traditional relational databases

Semi-structured data is more flexible than structured data but easier to analyze than unstructured data, providing a balance that is particularly useful in web applications and data integration tasks.

Ensuring data quality and integrity, integrating disparate data sources, protecting data privacy and security and finding the right talent to analyze and interpret data can present challenges to organizations looking to leverage their extensive data volumes. What follows are the benefits organizations can realize once they see success with big data analytics:

Real-time intelligence

One of the standout advantages of big data analytics is the capacity to provide real-time intelligence. Organizations can analyze vast amounts of data as it is generated from myriad sources and in various formats. Real-time insight allows businesses to make quick decisions, respond to market changes instantaneously and identify and act on opportunities as they arise.

Better-informed decisions

With big data analytics, organizations can uncover previously hidden trends, patterns and correlations. A deeper understanding equips leaders and decision-makers with the information needed to strategize effectively, enhancing business decision-making in supply chain management, e-commerce, operations and overall strategic direction.  

Cost savings

Big data analytics drives cost savings by identifying business process efficiencies and optimizations. Organizations can pinpoint wasteful expenditures by analyzing large datasets, streamlining operations and enhancing productivity. Moreover, predictive analytics can forecast future trends, allowing companies to allocate resources more efficiently and avoid costly missteps.

Better customer engagement

Understanding customer needs, behaviors and sentiments is crucial for successful engagement and big data analytics provides the tools to achieve this understanding. Companies gain insights into consumer preferences and tailor their marketing strategies by analyzing customer data.

Optimized risk management strategies

Big data analytics enhances an organization's ability to manage risk by providing the tools to identify, assess and address threats in real time. Predictive analytics can foresee potential dangers before they materialize, allowing companies to devise preemptive strategies.

As organizations across industries seek to leverage data to drive decision-making, improve operational efficiencies and enhance customer experiences, the demand for skilled professionals in big data analytics has surged. Here are some prominent career paths that utilize big data analytics:

Data scientist

Data scientists analyze complex digital data to assist businesses in making decisions. Using their data science training and advanced analytics technologies, including machine learning and predictive modeling, they uncover hidden insights in data.

Data analyst

Data analysts turn data into information and information into insights. They use statistical techniques to analyze and extract meaningful trends from data sets, often to inform business strategy and decisions.

Data engineer

Data engineers prepare, process and manage big data infrastructure and tools. They also develop, maintain, test and evaluate data solutions within organizations, often working with massive datasets to assist in analytics projects.

Machine learning engineer

Machine learning engineers focus on designing and implementing machine learning applications. They develop sophisticated algorithms that learn from and make predictions on data.

Business intelligence analyst

Business intelligence (BI) analysts help businesses make data-driven decisions by analyzing data to produce actionable insights. They often use BI tools to convert data into easy-to-understand reports and visualizations for business stakeholders.

Data visualization specialist

These specialists focus on the visual representation of data. They create data visualizations that help end users understand the significance of data by placing it in a visual context.

Data architect

Data architects design, create, deploy and manage an organization's data architecture. They define how data is stored, consumed, integrated and managed by different data entities and IT systems.

IBM and Cloudera have partnered to create an industry-leading, enterprise-grade big data framework distribution plus a variety of cloud services and products — all designed to achieve faster analytics at scale.

IBM Db2 Database on IBM Cloud Pak for Data combines a proven, AI-infused, enterprise-ready data management system with an integrated data and AI platform built on the security-rich, scalable Red Hat OpenShift foundation.

IBM Big Replicate is an enterprise-class data replication software platform that keeps data consistent in a distributed environment, on-premises and in the hybrid cloud, including SQL and NoSQL databases.

A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence and machine learning.

Business intelligence gives organizations the ability to get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Cloud computing is the on-demand access of physical or virtual servers, data storage, networking capabilities, application development tools, software, AI analytic tools and more—over the internet with pay-per-use pricing. The cloud computing model offers customers flexibility and scalability compared to traditional infrastructure.

Purpose-built data-driven architecture helps support business intelligence across the organization. IBM analytics solutions allow organizations to simplify raw data access, provide end-to-end data management and empower business users with AI-driven self-service analytics to predict outcomes.

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