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15 Power BI Projects Examples and Ideas for Practice

Master Power BI Skills By Gaining Hands-on Experience on These Amazing Power BI Projects in 2022 | ProjectPro

Preparing for your next BI developer interview? Check out these Power BI projects that will blow your mind with Power BI’s interactive dashboards, exceptional graphs and charts, and many more features.

We will look at some intriguing sample Power BI projects in this blog to help you better understand the role of Power BI in Data Science and how it can help businesses thrive.

Table of Contents

What is power bi, what is power bi used for, how to do visualization in a power bi project, 15 power bi project examples , 1. customer churn analysis, 2. product sales data analysis, 3. marketing campaign insights analysis, 4. financial performance analysis, 5. healthcare sales analysis, 6. anomaly detection in credit card transactions , 7. automl cashflow optimization for insurance company , 8. global health expenditure analysis, 9. loan application analysis, 10. movie sales visualization, 11. airport authority data analysis, 12. covid-19 insights analysis , 13. construction permit data analysis, 14. global energy trade analysis, 15. life expectancy data analysis, 16. twitter analysis dashboard, 17. ott media dashboard, 18. adventureworks database analysis, retail analysis power bi report example, global covid-19 analysis power bi report example, inventory stock analysis power bi report example, cancer analytics power bi report example, customer analysis power bi report example, master data visualization with these power bi projects.

• What projects can I do with Power BI?
• How do you practice Power BI?
• Where can I get practice data for Power BI?

Microsoft Power BI is a Business Intelligence service that enables you to create visually rich and interactive dashboards and reports based on the raw business data acquired from various sources. Apart from the variety of apps, connectors, and services offered by Power BI, there are three basic elements integrated into Power BI- 

Power BI Desktop (a desktop version),

Power BI Service (an online SaaS service), and

Power BI Mobile Applications for several platforms.

Business users utilize these services to collect data and generate BI reports. These three components are all meant to assist in building, exchanging, and leveraging business insights in the most efficient way possible for any business. Some of the benefits of leveraging Power BI include-

Easy Interaction with Existing Applications: Power BI makes it far easier to implement analytics and reporting abilities since it seamlessly connects with your current business environment.

Customized Dashboards: The information dashboard can be tailored to meet the demands of a company. The application can simply include Power BI dashboards and reports to create a smooth user experience.

Advanced Analytics with R Integration: R programming language has several packages focusing on data mining and visualization. Data scientists employ R programming language for machine learning, statistical analysis, and complex data modeling . Data models created in R may be easily integrated into Power BI dashboards and turned into visualizations.

No Storage or Performance Limits: There are no memory or speed constraints when migrating an existing BI system to a robust cloud environment with Power BI integrated, ensuring that data can be fetched and analyzed efficiently.

Securely published reports: Power BI enables automatic data refresh and publishes reports securely, allowing users to access the most up-to-date data.

New Projects

There are various tools and techniques for analytics and machine learning in the fascinating and extensive realm of data science. Power BI is a high-level, all-in-one solution for data analytics in data science. Data science aids in the discovery of relevant and productive trends and insights. It involves analyzing the data and also assists us in identifying entirely new features in it. Business intelligence is sifting through data to extract meaningful organizational ideas and insights. BI enhances and strengthens the business infrastructure to get desired or projected results.

Many data sciences and analysis tasks can be automated with Power BI, eliminating the need for spreadsheets and static presentation tools. One of Power-most BI's most impressive features is its ability to create stunning visualizations. The software is packed with excellent and eye-catching visualization templates. The integration of Power BI into Data Science holds great importance for businesses. This allows for smooth and effective data visualization, which plays a vital role in an organization’s success. 

With the help of Power BI, visualization in Data Science can be taken a notch further. Businesses and Data Scientists rely heavily on Power BI-aided data visualization for various projects. Numerous visualization types and charts are available in Power BI for creating effective visualization for data science projects .

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There are two ways to generate visualization in Power BI. First, add items to Report Canvas from the right side pane. Another way you can do this is by dragging the fields from the right sidebar to the value axis under Visualization. By default, Power BI selects the table type visualization. To each axis, you can add as many fields as necessary. You can also click and drag your visualization to rearrange it on the reporting panel. You can easily toggle between various charts and visualizations from the Visualization window. Your specified fields are transformed to the new visual format as accurately as possible.

Let us now understand how one can perform data visualization in Power BI.

The first thing you need is data. Power BI can connect several data sources- Excel, Web, etc. The options from which data can be imported into the Power BI desktop are all visible once you click the Get Data icon. You can view the data in the Fields pane once it has been loaded.

After loading the sample data into the Power BI desktop, you can modify it with the help of Query Editor. Regardless of the data source, query editors are helpful for editing datasets . In the query editor, you can perform changes like renaming a dataset and removing one or more columns, among other things.

In Power BI, removing columns from the dataset or modifying the data types in the columns is relatively easy. You can remove columns by simply choosing the Remove Columns option after selecting the desired column. Likewise, the query editor makes it simple to perform a wide range of actions like removing and adding rows, transposing, pivoting, and splitting.

Once the dataset is complete and has undergone all necessary modifications, you can move on to creating the dashboard. Both bubble and shape maps are available for map visualizations in Power BI. To plot measure values, Power BI offers several combination chart types. Suppose you want to display overall sales and revenue on the same chart. The ideal option for this type of scenario is combination charts.

You can change the colors in the charts in BI dashboards. There is a color selection option when you choose any visualization.

Additionally, the application has an analytics feature that allows you to draw lines for data visualization according to your preferences. Power BI also has the option to add various shapes, texts, and images when visualizing data.

This blog lists 15 Microsoft Power BI projects for you. We have categorized these Power BI examples into beginner, intermediate, and advanced levels. You can choose any of these power bi projects for practice to upskill yourself in the Data Science domain. 

Beginner-Level Power BI Project Basic Examples

In this section, we have included some Power BI projects for students. These simple Power BI projects will enable you to understand business intelligence applications and build a successful career as a data scientist.

The customer churn analysis project is one of the easiest and most popular Power BI sample projects. Customer Churn Analysis reveals regional customers' product sales and profits. Analytical users can use it to analyze regional business growth across geographies to gain valuable insights and distribute profits among customers. They can receive extensive data by using the right visualizations and data structure. The project includes regional cash inflows and product-specific customer churn over time.

For this beginner-friendly project, use the customer segmentation dataset available on Kaggle. In the analysis overview page, you could use Combo Charts, Cards, Bar Charts, Tables, or Line Charts; for the customer segmentation page, you could employ Column Charts, Bubble Charts, Point Maps, Tables, etc.

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Businesses must keep detailed records of their sales for a variety of reasons. However, if there is too much data, it often becomes difficult to keep track of everything. Analyzing sales data allows companies to keep track of their sales and answer all critical questions regarding their performance.

This sales data analysis project entails analyzing a company's sales data and indicating profit by product, sales, and other significant factors that might influence the company's performance. You can use Microsoft's sample dataset . The data set must be connected to Power BI Desktop to remove irrelevant data before visualizing and exporting the filtered data as dashboards. The dashboard could leverage Power BI visualization types such as Pie Charts, Bar Charts, Doughnut Charts, Funnel Charts, etc.

The project idea is to showcase the efficacy of various marketing campaigns and the performance of product groups and platforms using any marketing analytics dataset. This project is an excellent approach for a marketing manager to assess the success of marketing campaigns.

Use the Marketing Analytics dataset available on Kaggle for this beginner-level project. For the data visualization dashboard, you can explore many of the Power BI visualization types, such as Bar Charts for category-wise expenditures, Column Charts for campaign success rate, Smart Narratives for displaying the key highlights of the campaign, Bubble Charts for customer-wise spending, Cards for showing individual data insights, etc.

Financial performance analysis is one of the most intriguing Power BI project ideas for beginners. This business intelligence project approach is based on optimizing financial analysis for a firm that provides accounting services to clients who seek timely delivery of critical financial reports. You can set up the analysis to quickly access reliable financial data. The project might be used to: migrate traditional financial reporting from Excel to current BI dashboards and provide customers with an effective tool to track their financial health and productivity.

Refer to the multi-company financial dataset available on Kaggle. You can leverage Power BI data visualization in this project for three different cases-

- for the summary/overview page, you can use Funnel Charts, Combo Charts (Column Charts, Line Charts, Waterfall Charts); 

-for the income statement page, you can use Cards, Funnel Charts, and Combo Charts (Line Charts and Column Charts); and

- for the balance sheet page, you can use Cards and Tables.

This business intelligence project idea enables those in the Animal Healthcare sector to efficiently track the sale of products dedicated to treating minor animal species. Businesses may instantly compare product sales in top-ranking and bottom-ranking cities. The project could generate a thorough report on therapeutic group-wise sales and the sales trends for any specified period.

You can refer to the pharma sales dataset available on Kaggle. For the data visualization on Power BI, you might use Tables for displaying the therapeutic group-wise sales, Column Charts for monthly sales trends; Bar charts for top and bottom-ranking cities; and also, you can include Treemaps, Cards, Smart Narratives, etc. You can also check out the Microsoft Power BI community for some beginner-friendly power bi project examples.

Learn more about Big Data Tools and Technologies with Innovative and Exciting Big Data Projects Examples.

Intermediate-Level Power BI Project Ideas

If you’re already well-versed with the Power BI data visualization tool and are willing to strengthen your data analytics skills further as a Data Scientist, here are some power bi projects for resume-

The first Power BI project idea we have come up with is Anomaly Detection in Credit Card Transactions. Anomaly Detection is a machine learning technique for detecting unusual things, events, or observations that differ considerably from the rest of the data and look suspicious. You can implement this machine learning method in three ways- supervised (for labeled datasets), semi-supervised (for normally trained datasets), and unsupervised (for datasets without any labels).

For this project, you can use the credit card dataset by Delaware available on their open data platform itself or the credit card fraud detection dataset from Kaggle. You must first import the dataset into the Power BI desktop, maybe via a web connector. You can then leverage Power BI to train your anomaly detector or use a pre-trained model. To label outliers in Power BI, you'll need to run a Python script in the Power Query Editor and use the get_outliers() method. Finally, Power BI Dashboard can be used to visualize it. You could also train your anomaly detection model in any IDE or Notebook and then pass it to Power BI for labeling. You can do the data visualization in the Power BI dashboard with the help of Line Charts, Bubble Charts, TreeMaps, etc.

Automated machine learning (AutoML) is the technique of automating machine learning's time-consuming, iterative processes. It enables data scientists and analysts to rapidly create machine learning models while retaining model quality. Any AutoML solution's ultimate goal is to identify the optimal model based on performance metrics.

In this business intelligence project, you can work with the medical cost personal dataset from Kaggle. The business problem involves an insurance company that wishes to improve its cash flow projections by precisely estimating patient charges through demographic and primary patient health risk variables at the time of hospitalization. The first step is accessing Power BI Desktop, loading the dataset, and replicating it. The Python script may then be executed in Power Query, where the compare_models() method can be used to train different models, compare them, and evaluate their performance data. You may also use the automl() function to find the best-performing model out of all of them. For the resulting dashboard, you can leverage Power BI dashboard features such as Bar Charts, Bubble Charts, Tables, etc.

One of the most helpful Power BI project ideas is the Global Health Expenditure Analysis. This project idea is based on implementing clustering analysis in Power BI using PyCaret. Clustering is a method for bringing data items together that have similar features. These classifications help study a dataset, detect patterns, analyze data, and data clustering help in identifying underlying data structures.

In this case, use the current health expenditure dataset from the WHO Global Health Expenditure database. From 2000 to 2018, the dataset includes health expenditure as a percentage of national GDP for over 200 nations. Also, you could use the K-Means clustering algorithm for the clustering analysis. Visualize the cluster labels in Power BI Dashboard to gain insights after you have loaded the dataset in Power BI Desktop and trained your clustering model in Power BI. For the summary page dashboard, you might choose visualization chart types such as Filled Maps, Bar Charts, etc., while for the detailed visualization, you can use Point Maps.

This project concept entails evaluating loan application data to find abstract 'topics,' which are then used to assess the impact of specific topics (loan types) on the default rate. It is based on implementing the Latent Dirichlet Allocation (LDA) topic model in Power BI. Topic modeling aims to automatically analyze a collection of documents and determine their abstract ‘topics’.

For this project, you can use the Kiva dataset on GitHub, which covers loan data for 6,818 accepted potential borrowers. The loan amount, nationality, gender, and some text data from the borrower's application are included in the dataset. Once you have loaded the dataset in Power Bi desktop, trained your model, and the topic weights are added to the original dataset in Power BI, you can visualize it in the Power BI dashboard by employing the Word Cloud feature, Pie Charts on basic maps, Bar Charts, Scatter Charts, etc.

The movie sales visualization project is one of the most exciting Power BI project ideas. This project aims to take a dataset that shows movie sales over time and turn it into an interactive visual experience. You can use the IMDb dataset for 2006 to 2016, available on both IMDb and Kaggle. The dataset includes a list of movies having an IMDb score of 6 or higher and the parameters Budget, Gross, Genre, and Scores. 

You can create a custom Radial Bar Chart and use Slicers to pick Genre, Country, and score range to be integrated directly into PowerBI. This would display parameters such as Average Score and Gross Collections for the selected set. Plotting a Histogram on the score variable using year and genre as slicers will show the frequency distribution of the IMDb scores. A KDE plot can provide a density graph and insights into the average rating for a specific genre and distribution for a given timeframe.

Advanced-Level Power BI Project Ideas

Lastly, we have listed some of the best power bi projects for professionals who need some interesting power BI projects with dataset to try their hands on. 

The Airport Authority Data Analysis project aims to provide a clear picture of all the significant airport data. The total number of flights (incoming and departing flights), the total number of flight delays (arrivals and departures), ground processing time, and the passenger feedback section can all be included in the summary page of this project’s dashboard. In case of flight changes, emergencies, or delays, this analysis could assist airport management authorities in making timely data-driven choices.

For this project idea, you can use the Airline delays and cancellation dataset available on Kaggle. The dataset includes multi-year airline data from 2009 to 2018 to provide more time-series insights. For flight analysis, you can use Power BI visualization options like Cards, Bar Charts, Flow Maps, TreeMaps, and Tornado Charts. For passenger feedback, you might prefer to use Bar Charts, Column Charts, or Cards.

Covid-19 Insights Analysis is one of the most popular Power BI project ideas among individuals. The project aims to thoroughly overview the Covid-19 pandemic's essential parameters, the latest situation, and detailed country-level evaluations. You can create a dashboard that gives valuable information regarding cases (active, deceased, or recovered), mortality, and recovery rates by nation and timeframe. Also, the dashboard might even include a management summary of the most important KPIs and a thorough analysis of individual report pages.

Refer to the Covid-19 dataset available on Kaggle for this project idea. As for the Power BI data visualization features, you can use- Bar Charts, Point Maps, Line Charts, and Column Charts for the overview page; Doughnut Charts for category-wise case analysis; and Decomposition Trees for country-wise case analysis. You might also use Heat Maps to enhance your project’s dashboard visualization.

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The project idea is to help construction firms better understand the industry by allowing them to dig deeper into the specifics and research relevant incidents. You can focus your project on details such as investment growth over time, investment concentrations in specific areas if the investment is impacted by Category, Contractor, or Individual, market status, and which categories, individuals, and permits are driving the market.

For this project idea, use the Seattle building permits dataset from Kaggle. You can perform  Power BI data visualization with the help of Regional Maps, Pie Charts, Bar Charts, Tables, Cards for the main page, Bar Charts, and Line Charts for depicting contractor competitions and category-wise growth, etc.

One of the most unique power bi projects ideas is the Global Energy Trade Analysis. This project concept includes various topics concerning global energy exchange and production. It addresses several topics, such as the expansion of wind energy, energy consumption as a different basis for comparing national economies, etc.

For this unique project idea, use the international energy statistics dataset from Kaggle. The resulting dashboard could display total energy statistics on production, exchange, and usage of primary and secondary energy, conventional and non-conventional energy sources, and new and renewable energy sources. For the dashboard, you can use Power BI visualization types such as Bar Charts, Flow Maps, Cards for the overview page, Ribbon Charts, Treemaps, Bar Charts for the energy production and exchange pages, etc.

This project will analyze life expectancy data by looking at factors such as immunizations, mortality, finances , social factors, and other health-related issues. It will make it easier for a country to identify the predicting factor contributing to a lower life expectancy value. This will also aid in recommending to a country which areas should be prioritized to effectively raise the population's life expectancy.

Use the life expectancy dataset by WHO on Kaggle for Life Expectancy Data Analysis. Power BI visualization types such as Gauge Charts, Pie Charts, Line Charts, and Point Maps can be used for the overview page, while Tornado Charts, Doughnut Charts, Treemaps can be used for depicting country-wise data, etc.

Power BI Projects Github

Here are a few unique project ideas from GitHub that will help you better understand the various applications of Power BI.

This project entails implementing an end-to-end Twitter data analysis/ETL pipeline. For this Power Bi project idea, use the Twitter data from Github . After extracting the data, you will transform it from JSON to CSV and create all the necessary derived attributes. You will then use NLP analysis to classify the tweets' content and determine their sentiment score. Once you finish the ETL process, you can move on to visualize your data using Power Bi reports. For creating the dashboard, you will use pie charts, bar graphs, line charts, tree maps, doughnut Charts, etc.

This unique Power BI project entails visualizing various information related to multiple OTT platforms such as Netflix, Hotstar, Amazon Prime, etc. You can access this project's entire OTT media platform dataset from Github. Use the Query Editor in Power BI for data cleaning and preparation. Once your data is ready for visualization, you can display the visuals using various plots, graphs, cards, etc.

Suppose you have learned all the basics of Spark. What’s next? Start exploring ProjectPro’s repository, where you will find different Spark Projects for Practice.

You can work with the AdventureWorks data available on GitHub for analysis & visualization. Perform the query editing in Microsoft SQL Server Management Studio and use Power BI for the visualization part. Also, use MS Excel and Power BI's query editor for analysis.

Power BI Report Examples

Below are a few interesting Power Bi report examples to help you understand the power of data visualization using Power BI.

The retail analysis report presents a dashboard that analyzes product sales data from various retailers across various regions. The metrics include new-store analysis and a comparison of this year's performance to last year's in terms of sales, units, gross margin, and variation.

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With interactive visualizations based on readily accessible data, the Global Covid-19 Analysis Report contributes to fully disclosing COVID-19 trends worldwide. The report includes maps showing Vaccinations, Progress to Zero, Rt, Spread Analysis, Testing, and Risk Levels.

The inventory stock analysis report contains additional details on stock inventory and represents an analysis of stock inventory for 2018. Using this dashboard, you can forecast "Availability of Stock" and "Time to Replenish Stock" using information from the fulfillment cycle and markdown variance.

The Cancer Analytics report offers a brief overview and a thorough analysis of cancer patients in the US. It enables you to evaluate the mortality rates of each type of cancer, divided according to several factors. You can use the Cancer Analytics Dashboard to make well-informed decisions about the growing number of cancer patients in America.

The Customer Analysis Report highlights product sales and profit for local customers. It can be helpful for analytical users to gain valuable information on customer profit distribution and business growth across areas. The main highlights of the Customer Analysis report dashboard are the region-specific cash inflow and the clients' product-specific turnover.

These real-time power bi projects will help beginners and professionals upskill and master the ability to integrate business intelligence into Data Science. By working on these project ideas, you will gain a deeper understanding of leveraging Power BI for a data-driven approach to various data science industries and becoming job-ready. In case you are looking for some free Power BI projects for practice with solutions, you can head to open-source platforms like GitHub or Kaggle . ProjectPro offers more than 250 end-to-end project solutions around Data Science and Big Data, including some interesting data visualizations. 

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FAQs on Power BI Projects

1. what projects can i do with power bi.

Some of the projects you can do with Power BI are

Energy Trade Analysis

Covid-19 Analysis

Customer Churn Analysis

Movie Sales Visualization

2. How do you practice Power BI?

You can practice Power Bi by working on some unique and easy real-time Power BI examples available on ProjectPro, Github, Kaggle, etc.

3. Where can I get practice data for Power BI?

You can get practice data for Power BI in the platform itself, as the Power BI service has pre-built samples available.

Sign in to the Power BI service.

Browse to the workspace where you want to install the sample, whether it is My Workspace or another.

Select ‘Get data’ in the bottom-left corner. Choose Samples from the Get Data page that appears.

About the Author

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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Thank you for well-managed and customized course. Its so nicely explained even a beginner will be hassle-free while learning it. This course is highly recommended to those who either wants to learn data analysis or are on the verge of building a portfolio(like me).

Data Analyst

My experience with Codebasics' Power BI Data Analytics course was truly valuable and rewarding. The course content was comprehensive, well-structured, and easy to follow, making it accessible for learners of all levels. What I appreciated the most: Clear explanations: The instructor provided clear and concise explanations for each topic, making it easy to grasp the concepts and apply them in real-world scenarios. Hands-on exercises: The practical exercises and examples throughout the course were extremely helpful in solidifying my understanding of Power BI. I felt confident in using the tool to analyze and visualize data effectively. Real-world applications: The course emphasized real-world applications, which was crucial for me to see how Power BI could be used in various industries and business scenarios. Engaging instructor: Dhaval's teaching style was engaging and kept me interested throughout the course. The pace was just right, allowing me to absorb the information without feeling overwhelmed. Accessible resources: The additional resources provided, such as datasets and sample projects, were valuable in practicing and honing my skills even after completing the course. Overall, I can confidently say that Codebasics' Power BI Data Analytics course has prepared me well for data analysis and visualization tasks. It's an excellent resource for anyone looking to level up their Power BI skills. I will definitely recommend it to others interested in learning Power BI. Once again, thank you for the fantastic learning experience! Keep up the excellent work, and I look forward to exploring more courses from Codebasics in the future. 🌟🌟🌟🌟🌟

Hi, This is Himanshu Singh. I have taken Power BI course and this course is offering the best way to learn Power BI, with real-time business cases. It is really helpful to clear the concept with practical use cases during the course. Thank you for the awesome content.

Sr. Market Research Analyst

Had a great learning experience, this was my 2nd course after SQL. This course covers all the basic to advanced topics. Dhaval sir really makes every topic easily understandable and all those stakeholder meetings and discussions with Hemanand sir are really helpful, it's helpful for a beginner on how should we proceed with problems and what should be our thought process.

Technical Functional Consultant

I appreciated the clarity of the content. Concepts were explained in a straightforward manner, making it easy for me to follow along.The hands-on exercises were particularly helpful. They provided practical experience and helped solidify the theoretical concepts covered in the lectures. I enjoyed the real-world examples and case studies incorporated into the course. It helped me understand how Power BI can be applied in different scenarios. The course was well-structured, with a logical progression of topics. This made it easy to build on my knowledge as I moved through the modules. Your teaching style was engaging, and your enthusiasm for the subject matter was evident. It made the learning experience enjoyable.

• We recommend you install the Power BI desktop app (It’s free) from the Microsoft App store and check if your PC meets the below requirements.
• OS: Windows 10 version 14393.0 or higher
• Architecture: x64
• Memory: 4 GB or more (Recommended)
• Processor: 1 GHz or faster (Recommended)
• Display: 1440*900 or larger display resolution (Recommended)

Course Instructors/Creators

Dhaval Patel

Data Entrepreneur (12+ Years), YouTuber, Ex - Bloomberg, NVIDIA

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching.

Hemanand Vadivel

Ex- Data Analytics Manager, 8+ Years in Europe, Microsoft Certified, Certified Supply Chain Professional

I’m a Mechanical Engineer who transitioned to a full-time Data & Analytics Manager in the UK & Germany. I have delivered 30+ analytics projects over 15+ countries and trained professionals at different levels to equip them with valuable analytics skills.

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When You Complete This Course

You receive a ‘Certificate of Completion’ signed and addressed personally by me, your guide and mentor. – Dhaval Patel

Add and share this certificate with your Resume/CV or on your LinkedIn profile.

Frequently Asked Questions

Q.1 Will the course help me in PL - 300 Microsoft exam preparation?

Yes, this Power BI course will certainly help because we cover the majority of the skills measured in the PL-300 exam in this course. However, please be informed that this course focuses on Job ready aspects and not on all aspects required to clear PL - 300 exam. In addition to this course, you might need to visit the official learning material designed by Microsoft which is available for free -> https://docs.microsoft.com/en-us/certifications/exams/pl-300?tab=tab-learning-paths

Q.2 Power BI or Tableau which one is better?

This question depends on context. If you're discussing pure visualization capabilities, Tableau has a slight edge. However, both tools offer robust data connectors, modeling, and transformation features. Power BI is cheaper and offers tighter integration with the Microsoft environment, which many companies prefer due to their existing use of Excel and other Microsoft tools. As such, there's a significant movement towards Power BI, and job opportunities are growing at a higher rate for those who can effectively learn Power BI. Moreover, Power BI has been leading Gartner’s magic quadrant in BI as the industry leader for the last few years.

Q.3 Does Power BI work in Mac OS/Ubuntu?

Power BI desktop works only in Windows OS. Please look into the system requirements section on this page. However, you can use a virtual machine to install and work with Power BI in other Operating systems.

Q.4 I’m not sure if this course is good enough for me to invest some money. What can I do?

We got you covered. Go ahead and do the lighter version of this course which is still available on YouTube for free. If you like that course and want to learn further, this course is the perfect extension.

Q.5 Can I add this course to my resume? If Yes, how?

Absolutely, we have a section in this Power BI Certificate course explaining how you can add the learnings from this course to your resume in a way that will appeal to the hiring team.

Features and Course Content

Q.1 i already know basic power bi, what benefit do i get by taking this course.

This course is taught through a true end-to-end project in a Consumer goods company involving all the steps mimicking the real business environment, so you will learn how to execute end-to-end projects Power BI projects successfully along with the business fundamentals. You will learn a lot of extra things such as Project management tools, effective communication techniques & organizational nuances.

Q.2 Will the course be upgraded when there are new features in Power BI?

Yes, the course will be upgraded periodically to ensure you're learning the latest in Power BI. When new features roll out in Power BI, we update the course material. Learners who have already enrolled in the course will have free access to these upgrades, continuing their journey to learn Power BI.

Q.3 What dataset is used in this Power BI course? Is it some toy dataset or something that mimics a real-world business problem?

The dataset we use in this course to learn Power BI is crafted from scratch to replicate real-world business scenarios, drawing on our years of industry experience. It includes over a million rows and covers multiple facets of business data such as sales, finance, targets, forecasts, products, etc., offering a realistic and comprehensive learning experience.

Q.4 What business concepts and domains are covered in this course?

We have covered the core functions such as Sales, Marketing, Finance, and Supply Chain with their fundamentals related to this course. The domain you will learn in this course is consumer goods which is projected to have more openings and high data analytics requirements at least until 2030.

Q.5 What is different in this course from thousands of other Power BI courses available online?

Most of the courses available on the internet teach you how to build x & y without any business context and do not prepare you for the real business world. This course is rather an experience in which you will learn how to use Power BI & other non-technical skills to solve a real-life business problem using analytics. Here you focus on solving a business problem and in that process learn how Power BI can be used as a tool. This is how you will do the work when you start working as a data analyst/ Business analyst/ Power BI developer in the industry. This course will prepare you for not just fetching the job but, shine in it & grow further.

Q.6 How long is the Power BI course duration?

The Power BI course consists of 20 hours and 33 minutes of on-demand video content. Through this format, we have made sure that you learn at your own pace, ensuring that you grasp all the concepts effectively.

Job Assistance and Support

Q.1 how can i contact the instructors for any doubt/support.

We've designed every lecture to facilitate your path to learning Power BI in an easy-to-understand manner. However, chapters 6-8 introduce real-time job concepts, which might be more challenging. While working on these chapters, it's possible to encounter doubts or errors. We encourage you to develop problem-solving skills by googling and trying to find the answers. If you hit a wall, we've got you covered! Join our active discord community (https://discord.com/invite/aWpq9S5qge) under the "codebasics - power - bi - data - analytics - course" channel. Here, you can discuss and clear your doubts with fellow learners and mentors. Additionally, you can enroll in a video group discussion session at the end of the course, a bonus feature. We hold these sessions as per demand and my availability. Feel free to ask any course or career-related questions in these sessions.

Q.2 Will this course guarantee me a job?

We've had great success with a lighter version of this course available for free on codebasics YouTube channel, and many participants reported securing jobs after completing it (see testimonials). This paid course, where you can learn Power BI in-depth, is at least 5x better than the YouTube course, boosting our confidence that you can land a job after completing it. However, we want to be honest and refrain from making impractical promises. Our guarantee is to prepare you for the job market by teaching the most relevant skills, knowledge, and timeless principles good enough to fetch a job.

Eligibility

Q.1 i use tableau, can i take this course.

Absolutely, even if you primarily use Tableau, you can greatly benefit from this Power BI course. It covers concepts outside of the specific tools, including business context, problem-solving, and project management tools. Additionally, it provides a valuable opportunity to learn Power BI, which can broaden your analytics skill set.

Q.2 I don’t have a laptop, can I take this Power BI course?

We recommend learning by doing, especially if you're enrolling in our Power BI course. To effectively learn Power BI, it's crucial to have a laptop or PC with at least 4 GB of RAM for hands-on experience.

Q.3 Is there any prerequisite for taking this course?

The only prerequisite is that you need to have a functional laptop with at least 4GB ram, internet connection and a thrill to learn data analysis.

Q.4 I have never done programming in my life. Can I take this course?

Absolutely! This Power BI course is the perfect starting point for anyone who has never done coding and aims to build a career in the IT/Data Analytics industry. It's equally valuable if you simply aspire to perform better in your current job or business by leveraging data.

More Courses

Python for beginner and intermediate learners.

This course uses a project-based learning approach to teach you Python using two real-life projects (1) Hospitality domain data analysis and (2) Medical data extraction. Learning Python programming through projects helps you understand real-life applications of this awesome programming language. You will also have two solid projects that you can add to your resume and you work on end-to-end implementation. Total beginners, as well as people familiar with the language, will benefit from this Python course.

SQL Beginner to Advanced For Data Professionals

Beginners to Advanced SQL course for those preparing for a data career (Data Analyst, Data Scientist, or Data Engineer). This course is carefully curated to simulate real-time organizational experience to prepare you for the current job market and at the same time provides you with an ultimate learning experience through a storytelling mode that you would see in movies.

Excel: Mother of Business Intelligence

Beginners to Advanced Excel course for those who are preparing for a data career (especially Data Analysts). This course is carefully curated by industry experts to mimic real-world scenarios to prepare you to become a market fit. You will also get practical exercises to practice and get a simulated experience of a data analyst using Excel to solve problems.

• 20hr : 33min on-demand videos

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Top 10 Power BI Project Ideas for Practice

What is power bi, why power bi, top power bi projects, power bi projects for beginners, 1. product sales data analysis, 2. marketing campaign insights analysis, 3. financial performance analysis, 4. customer churn analysis, intermediate level power bi project ideas, 5. global health expenditure analysis, 6. energy trade analysis, 7. anomaly detection in credit card transactions , advanced level power bi project ideas, 8. covid insight analysis, 9. airport performance analysis, 10. life expectancy data analysis, additional resources.

Data has become a ubiquitous part of business today, making it essential for businesses to understand how to gain value from the wealth of raw data available to them. The right information must be gleaned from the data and presented clearly for making well-informed business decisions. Because of this, business intelligence (BI) systems have gained tremendous traction that can assist an organization in discovering useful information, suggesting conclusions, and supporting decision-making. BI is a system that analyzes data, provides actionable information, and helps users to take informed business decisions. As a proven technology leader, Microsoft has propelled itself to the forefront with the introduction of its powerful analytics tool, Power BI. 

Microsoft’s Power BI toolkit transforms your data into meaningful information, making it one of the most popular business intelligence tools in the world. The increasing popularity of Power BI has led to an increased demand for Power BI professionals and business analysts.

Are you intrigued? If so, we’ve created this detailed guide for you. In this article, we will discuss some Power BI projects ideas that you can use to boost your chances of becoming a Power BI professional. But before we start exploring Power BI projects and Power BI projects for practice, let’s take a look at exactly what Power BI is and why it is so important.

Confused about your next job?

Power BI is a Business Intelligence and Data Visualization tool designed to analyze, visualize, and process enormous volumes of raw business data and transform them into actionable and interactive insights that help business managers, corporate executives, and other users to make better informed business decisions. Raw data can be stored in an Excel spreadsheet, cloud-based databases, or on-premises data warehouses. But, Power BI makes it easy to connect to your data sources, visualize it to find out what’s important, and share it with others. Using it, you can gain insight, draw conclusions, and share results across departments in the form of analytical reports or interactive dashboards.

Power BI is a BI solution by Microsoft that provides a simple yet powerful interface that even non-technical users can use to create dashboards and analysis reports. Besides offering easy drag-and-drop functionality, the tool also provides a range of interactive data visualizations for creating interactive reports and dashboards. Among all the apps, connectors, and services provided by the Power BI platform, the following key components stand out:

• Power BI Desktop (a Windows desktop application)
• Power BI service (an online SaaS (Software as a Service) service)
• Power BI mobile applications (available for different platforms such as Windows, iOS, and Android devices)

With these three elements, you can build, exchange, and leverage business insights in a way that’s efficient and impactful for any business.

Now that you know what Power BI is, let’s look at what makes it so valuable for analytics.

The following are some of the reasons why Power BI is widely considered one of the best business intelligence tools worldwide:

• Integrates seamlessly with existing systems: Power BI enhances analytics and reporting capabilities by seamlessly integrating with your existing business processes.
• Connect seamlessly with data sources: Power BI can connect to a variety of data sources, making it possible to align data sets from different sources and generate compelling reports on the basis of that data. Over 70 connectors are available for extracting data from sources like Azure Data Warehouse, , Excel, CSV, OneDrive, Google Analytics, Dropbox, SQL databases.
• No memory or speed constraints: Migrating a BI system to a robust cloud environment with Power BI integrated does not create memory or speed constraints, enabling the data to be retrieved and analyzed efficiently. Power BI uses the pivot data modeling engine, a columnar database that reduces data sizes from about 1GB to about 200MB, thereby improving performance.
• Reports published securely: Power BI automatically refreshes data and publishes reports securely, ensuring that you always have access to the latest information.
• Custom visualization: PowerBI offers a variety of predesigned data visualizations allowing you to create interactive reports or dashboards. In addition, we can add customized visualizations created by Power BI community members to enhance the reports.

Next, let’s talk about Power BI project ideas you can work on and include in your portfolio. Furthermore, these projects allow you to practice and acquire practical experience with the various tools used for data visualization.

In order to help you broaden your knowledge and enhance your skills in Power BI, we have listed 10 Power BI project ideas based on your level of expertise. Newbies can browse through the list of Power BI projects ideas for beginners, whereas intermediates and advanced users can browse through the list of Power BI projects for intermediates and advanced users. For practice, you can select any of these power bi projects for upskilling in the Data Science space and improving portfolio. 

For beginners, it’s important to improve data analytics and visualization skills, as well as how to use different techniques to make reports more appealing. Here are some Power BI project ideas for beginners:

Businesses should keep detailed records of their sales because these records can provide valuable insight into how well their business is performing, which items are proving successful, and what changes can be made. Businesses with good records are more likely to succeed. The problem arises when there is a lot of data, and keeping track of everything can become a challenge. This is where Power BI came into play. 

Using Sales Data Analysis Project, companies are able to keep track of their sales and get answers to all of their questions about how they performed. As part of this project, we’ll be visualizing Microsoft’s sample dataset to show a profit by product and sales, as well as other key factors that may affect a company’s performance. However, the data set needs to be connected to Power BI Desktop so that irrelevant data can be removed before visualization and exporting of the filtered data as dashboards. There are various Power BI visualization types you could use for the dashboard, including Pie Charts, Bar Charts, Doughnut Charts, Funnel Charts, etc. Using the same sample dataset (Microsoft’s sample dataset), more Power BI projects can be developed and advanced visualizations can be made.

Source Code: Sales Analysis

An effective marketing campaign is a great way to reach consumers, clients and leads. An analysis of your campaign data can provide you with valuable insight into your target audience, marketing channels, and budget. Then you can figure out how (or how not) to run your next campaign. This is where Power BI came into play.

We propose this project to show off the efficiency of various marketing campaigns and the features performance analysis of products and platforms can be done. ​You can use this project to assess the success of marketing campaigns and various activities carried out under his management. This beginner-level project can be done with the Marketing Analytics dataset (this project provides insight into the customer’s profile preferences and channel performance). This dataset can be used for EDA, statistical analysis, and visualizations.  A Power BI dashboard could have several types of visualization, such as Bar Charts for category-wise expenditures, Smart Narratives for highlighting key aspects of the campaign, Bubble Charts for customer-wise spending, Column Charts for campaign success rates, Cards for highlighting individual insights, etc.

Source Code: Marketing Campaign Analysis

Microsoft Power BI is used for financial analysis to gather and analyze KPIs (Key Financial Indicators), charts, and financial statements. The purpose of this BI project is to optimize financial reporting in a firm that provides accounting services to clients seeking timely delivery of critical financial reports. Through this analysis, you will be able to access reliable financial reports quickly and efficiently. Multi-company financial datasets may be utilized for this project. 

The project outlined here can also be applied to the migration of traditional financial reporting from Excel to BI dashboards, allowing customers to track their financial health and productivity more effectively. ​In this project, Power BI data visualization options can be utilized for three scenarios as given below: 

• Funnel charts, combo charts (such as column charts, waterfall charts, line charts) for the summary page or overview page; 
• Cards, Funnel Charts, and Combo Charts (such as Line Charts and Column Charts) for the income statement page; 
• Cards and Tables for the balance sheet page.

Source Code: Financial Analysis

A major challenge that modern enterprises face is customer migration (churn). Churn, or customer attrition, is the act of customers stopping purchasing from a business or interacting with it. What can we do to overcome this challenge? The idea is to know the reasons why customers churn, the factors that impact that, so that appropriate measures can be taken to retain them. This is where Power BI comes into play.

Through this Customer Churn analysis project, one can uncover what causes your customers to stop using your product or service. Business leaders, managers, or analytical users can use this Power BI project to analyze regional business growth and the profit distribution among customers. With the right visualization and data structure, they can receive extensive data. As part of the project, regional cash inflows will be considered, as well as product-specific churn over the course of time. To complete this beginner-friendly project, you will need the customer segmentation dataset .  ​In this project, Power BI data visualization options can be utilized for different scenarios as given below: 

• Combo Charts, Bar Charts, Line Chart, Cards, Tables, etc., for overview page.
• Column Charts, Bubble Charts, Point Maps, Tables, etc., for the customer segmentation page.

Source Code: Customer Churn Analysis

You may already be familiar with the Power BI tool and are interested in strengthening your skills further as a Data Scientist . Check out these Power BI project ideas for your resumes:

A global health expenditure analysis provides comparative data on health expenditure for 192 countries over the last two decades. In this project, PyCaret (Machine learning library in Python) is used for implementing clustering analysis in Power BI. Clustering involves gathering data items having similar features. These classifications aid in examining datasets, detecting patterns, and analyzing data, while data clustering assists in identifying underlying data structures.

For this project, you can use the current health expenditure dataset from the WHO (World Health Organization) Global Health Expenditure Database. You could also use the K-Means algorithm to conduct your clustering analysis. Once the dataset has been loaded in Power BI Desktop and trained your clustering model has in Power BI, you can visualize the cluster labels in Power BI Dashboard in order to gain insights. You might choose visualization chart types such as Bar Charts, Filled Maps, etc., for the summary page dashboard, while you can use Point Maps for the detailed visualization.

In this project, various aspects of global energy production and exchange are covered. Several topics are analyzed in this project, including the expansion of wind energy the use of energy consumption to compare national economies, and many more.

Make use of the international energy statistics dataset for this project idea. ​ As a result of this effort, Power BI dashboards will display total energy statistics, including production, exchange, and usage of primary/secondary energy, new/renewable energy sources, as well as conventional/non-conventional energy sources. ​In this project, Power BI data visualization options can be utilized for different scenarios as given below: 

• Bar Charts, Cards, Flow Maps, etc., for the overview/summary page; 
• Bar Charts, Ribbon Charts, Treemaps, etc., for the energy production and exchange pages, etc.

Credit cards are being used more and more these days, which has led to an increase in fraudulent transactions (unauthorized access to an individual’s accounts or payments). Credit card fraud detection is one of the most important applications of anomaly detection. This is where Power BI comes into play. Anomaly Detection in Credit Card Transactions Project is basically a method used to identify the suspicious occurrence of data or events (outliers) that may pose problems for the concerned authorities. It is a machine learning technique used to find unusual events, things, or observations that seem suspicious during credit card transactions. There are three ways you can apply this method as given below: 

• Semi-supervised (for trained datasets)
• Unsupervised (for datasets without any labels)
• Supervised (for labeled datasets) 

If you are working on this project, you can either use the card fraud detection dataset or the credit card dataset. The first step is to import the dataset into the Power BI desktop, possibly through a web connector. Power BI can then be used to train your anomaly detector or you can use a pre-trained model. Outliers in Power BI can be labeled by running a Python script in the Power Query Editor and then using the get_outliers() method. After that, the Outliers can be visualized in Power BI Dashboards.  Line charts, bubble charts, treemaps, etc., are all can be used in Power BI dashboards for data visualization.

Source Code: Credit Card Fraud Detection

Finally, we’ve listed a few Power BI project ideas for professionals who are looking for some interesting projects to work on:

Covid-19 wreaked havoc on the entire human civilization, which we all know. Due to the sudden outbreak across all countries, global leaders were forced to take drastic measures to prevent the disease from spreading. This is where Power BI comes into play.

This Covid insight analysis project seeks to provide a comprehensive overview of the pandemic’s essential parameters and a detailed account of the situation at the national level. A Power BI dashboard can be created which displays valuable information regarding active cases, deceased cases, and recovered cases, as well as mortality and recovery rates by nation and timeframe. A dashboard can even provide a detailed analysis of the individual report pages, along with a management summary of the most important KPIs. It is an excellent project and makes you aware of how things are going. Heat Maps can also be used to enhance the dashboard visualization of your project.

For this project idea, you can use the Covid-19 dataset. As given below, Power BI data visualization options can be used for different scenarios in this project: 

• Bar Charts, Point Maps, Line Charts, Column Charts, etc., for the overview page
• Doughnut Charts for category-wise case analysis 
• Decomposition Trees for country-wise case analysis

Source Code: Covid Analysis

Globally, the aviation industry saw massive growth in passenger numbers. Right now, there are over 5,000 aircraft taking passengers and cargo around the world. However, maintaining these flights requires a lot of planning and quick decision-making. This is where Power BI comes into play.

In the Airport Authority Data Analysis project, airport data will be analyzed to provide a clear picture. On the summary page of Power BI dashboards, you can see the number of flights (incoming and outgoing), the number of delays (arrivals and departures), feedback from passengers, and ground processing times. Airport management authorities could use this type of analysis to make timely decisions when flight changes, emergencies, or delays occur. The Airline delays and cancellation dataset can be used for this project idea. As given below, Power BI data visualization options can be used for different scenarios in this project: 

• Cards, Bar Charts, Tornado Charts, TreeMaps, Flow Maps, etc., for flight analysis. 
• Column Charts, Cards, Bar Charts, etc., for the passenger feedback.

A key metric for measuring population health is life expectancy. It is beneficial to analyze life expectancy data when considering the health of a population, its characteristics, and when studying human diseases and natural population fluctuations. This is where power BI comes into play.

In the Life Expectancy Data Analysis project, factors like immunizations, mortality, finances, social issues, and other health-related issues will be considered. This will allow countries to determine the predicting factor impacting a lower life expectancy. It will also be useful in helping a country determine which areas should be prioritized to increase life expectancy. For this project, you can utilize the WHO  life expectancy dataset . As given below, Power BI data visualization options can be used for different scenarios in this project: 

• Pie Charts, Gauge Charts, Line Charts, Point Maps, etc., for the overview page 
• Treemaps, Doughnut Charts, Tornado Charts, for depicting country-wise data, etc.

Source Code: Life Expectancy

Power BI is one of the most powerful business intelligence tools for analyzing and visualizing data. Throughout this article, we discussed what Power BI is, why it is important, and the top 10 Power BI project ideas you can implement to improve your data analysis and visualization skills. Power BI project examples generally aid companies in gaining actionable insights from better visualization of their data. Using Power BI, users can create interactive dashboards and share them across the internet. Power BI also allows you to collaborate with other users and stakeholders on separate projects.

These Power BI project ideas will allow beginners, intermediates, and professionals to hone their skills and learn how to truly integrate business intelligence and data science. ​As you work on these project ideas, you’ll gain a greater understanding of how Power BI can be used for a data-driven approach towards various data science industries.

Is Power BI going away?

Sol: Power BI is one of the most popular business intelligence tools in the world that converts data into meaningful information, and therefore its popularity will never dwindle. In addition, it is a Microsoft product, which has been a dominant force on the market worldwide. It will continue to be one of the most competitive BI tools among its rivals like Tableau, Qlik Sense, etc. 

Is Power BI used for project management?

Sol: Yes, it is possible to use Power BI for project management. Essentially, Power BI is a tool that delivers actionable insights for business leaders and project managers based on internal and external research data. Through Power BI, project managers can gain a clearer picture of their projects, track them more effectively, eliminate manual report generation, and devote more time to their teams.

Is Power BI a good career?

Sol: As the leading business analytics tool among all BI tools, Power BI is a key contender. The increasing popularity of Power BI has led to an increased demand for Power BI professionals. Among the highly sought-after Power BI career prospects are Power BI developers, consultants, and analysts. As such, Power BI has a bright future and is a popular career field to switch into

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Power BI implementation planning: Workspace-level workspace planning

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This article forms part of the Power BI implementation planning series of articles. This series focuses primarily on the Power BI experience within Microsoft Fabric . For an introduction to the series, see Power BI implementation planning .

This article covers Fabric workspace-level planning, with an emphasis on the Power BI experience. It's primarily targeted at:

• Fabric administrators: The administrators who are responsible for overseeing Fabric in the organization.
• Center of Excellence, IT, and BI team: The teams who are also responsible for overseeing data and BI and supporting self-service users throughout the organization.
• Content creators and owners: Self-service creators who need to create, publish, and manage content in workspaces.

To use workspaces effectively, there are many tactical decisions to be made. Whenever possible, individual workspace-level decisions should align with your tenant-level decisions .

The concept of a workspace originated in Power BI. With Fabric, the purpose of a workspace has become broader. The result is that a workspace can now contain items from one or more different Fabric experiences (also known as workloads). Even though the content scope has become broader than Power BI, most of the workspace planning activities described in these articles can be applied to Fabric workspace planning.

Workspace purpose

When planning for workspaces, it's important to consider not only the type of content it will store, but also the activities that the workspace is intended to support.

Consider the following two examples of finance-related workspaces. Although they're both dedicated to the same team, each workspace serves a different purpose:

• Financial month-end workspace: The Financial month-end workspace contains reconciliation and month-end closing reports. This workspace is considered an informal workspace to support collaborative efforts. A Power BI app isn't necessary for content viewers because the primary use of this workspace is collaboration by a small group of people who work closely together. Most team members have permission to edit content in this workspace.
• Financial reporting workspace: The Financial reporting workspace contains the finalized, presentation-level reports. This workspace contains content that's broadly distributed throughout the organization to many viewers (including executives) by using a Power BI app. The workspace is closely governed .

With these two examples in mind, consider two specific aspects of workspace purpose: intent for collaboration , and intent for viewing .

Intent for collaboration

The primary objective of a workspace in the Fabric portal is to facilitate collaboration among multiple people. There are many ways that collaboration can happen in a workspace:

• Team-based development: Multiple people can work together to build, test, and publish content. One user might work on the design of a lakehouse . Another user might work on the design of the semantic model ( previously known as a dataset ), while other users might focus on building reports.
• Testing and validations: Users might need to perform data validations for new content. Subject matter experts from the business unit might need to perform user acceptance testing (UAT), or a data quality team might need to validate the accuracy of the semantic model.
• Enhancements: Stakeholders and consumers of the content might suggest enhancements to the content as circumstances change.
• Ownership transfer: Another person or team might take over responsibility for content that was created by someone else.

One of the key areas of the Fabric adoption roadmap is content ownership and management . The type of collaboration that will occur in a workspace will differ based on the approach used for content ownership and management:

• Business-led self-service BI: Content is owned and managed by the content creators within a business unit or department. In this scenario, most collaboration in the workspace occurs among users within that business unit.
• Managed self-service BI: Data is owned and managed by a centralized team, whereas various content creators from business units take responsibility for reports and dashboards. In this scenario, it's highly likely that multiple workspaces will be needed to securely facilitate collaboration by multiple teams of people.
• Enterprise BI: Content is owned and managed by a centralized team, such as IT, enterprise BI, or the Center of Excellence (COE). In this scenario, collaboration efforts in the workspace are occurring among users in the centralized team.

Checklist - When considering your intentions for collaboration in a workspace, key decisions and actions include:

• Consider expectations for collaboration: Determine how workspace collaboration needs to occur and who's involved within a single team or across organizational boundaries.
• Consider expectations for content ownership and management: Think about how the different content ownership and management approaches (business-led self-service BI, managed self-service BI, and enterprise BI) will influence how you design and use workspaces.

When your needs can't be met by a single approach, be prepared to be flexible and use a different content ownership and management strategy for different workspaces. The strategy can be based on the scenario as well as the team members that are involved.

Intent for content viewing

The secondary objective for a workspace is to distribute content to consumers who need to view the content. For content viewers, the primary Fabric workload is Power BI.

There are several different ways to approach content distribution in the Power BI service:

• Reports can be viewed by using a Power BI app: Content stored in a non-personal workspace can be published to a Power BI app . A Power BI app is a more user-friendly experience than viewing reports directly in a workspace. For this reason, using a Power BI app is often the best choice for distributing content to consumers. Audiences for a Power BI app are very flexible. However, sometimes the goals for how you want to distribute content with an app are a factor in determining how to organize content in or across workspaces. For more information about securing Power BI apps, see Report consumer security planning .
• Reports can be viewed directly in the workspace: This approach is often appropriate for informal, collaborative workspaces. Workspace roles define who can view or edit the content contained in a workspace. For more information about workspace roles, see Content creator security planning .
• Reports can be shared: Use of per-item permissions (links or direct access) is useful when there's a need to provide read-only access to a single item within a workspace. We recommend that you use app permissions and workspace roles more frequently than sharing because they're easier to maintain. For more information, see Report consumer security planning .
• Reports can be embedded in another application and viewed there: Sometimes the intention is for consumers to view Power BI content that's embedded in another application. Embedding content is useful when it makes sense for the user to remain in the application to increase efficiency and stay within its workflow.

Another key area of the Fabric adoption roadmap is content delivery scope . The ways that a workspace will support content distribution will differ based on the content delivery scope:

• Personal BI: Content is intended for use by the creator. Since sharing content with others isn't an objective, personal BI is done within a personal workspace (described in the next topic).
• Team BI: Content is shared with a relatively small number of colleagues who work closely together. In this scenario, most workspaces are informal, collaborative workspaces.
• Departmental BI: Content is distributed to many consumers who belong to a large department or business unit. In this scenario, the workspace is primarily for collaboration efforts. In departmental BI scenarios, content is commonly viewed in a Power BI app (instead of directly viewed in the workspace).
• Enterprise BI: Content is delivered broadly across organizational boundaries to the largest number of target consumers. In this scenario, the workspace is primarily for collaboration efforts. For enterprise BI scenarios, content is commonly viewed in a Power BI app (instead of directly viewed in the workspace).

When you plan your workspaces, consider the needs of the audience when determining the workspace license mode . The type of license that's assigned to the workspace will impact the features that are available, including who can view or manage workspace content.

Checklist - When considering your expectations for how workspace content will be viewed, key decisions and actions include:

• Consider expectations for viewing content: Determine how you expect consumers to view content that's been published to the workspace. Consider whether viewing will happen directly in the workspace directly or by using a different method.
• Determine who the content will be delivered to: Consider who the target audience is. Also consider the workspace license mode, especially when you expect a significant number of content viewers.
• Evaluate needs for a Power BI app: Consider what the workspace purpose is as it relates to the content distribution requirements. When a Power BI app is required, it can influence decisions about creating a workspace.
• Consider expectations for content delivery scope: Consider how the different content delivery scopes (personal BI, team BI, departmental BI, and enterprise BI) will influence how you design and use workspaces.

Be prepared to be flexible. You can use a different content viewing strategy for workspaces based on the scenario as well as the team members that are involved. Also, don't be afraid to use different content delivery scope approaches for workspaces when it can be justified.

Appropriate use of personal workspaces

There are two types of workspaces:

• Personal workspaces: Every user has a personal workspace. A personal workspace can be used for publishing certain types of content to the Fabric portal. Its primary purpose is to support personal BI usage scenarios.
• Workspaces: The primary purpose of a workspace is to support collaboration among multiple users. Secondarily, a workspace can also be used for viewing content.

Using a personal workspace for anything other than learning personal BI , temporary content, or testing purposes can be risky because content in a personal workspace is managed and maintained by one person. Further, a personal workspace doesn't support collaboration with others.

To allow the creation of any type of Fabric item (like a lakehouse or warehouse), a workspace must be added to a Fabric capacity . That's true for both standard workspaces as well as personal workspaces. Therefore, you can govern who's able to create certain types of items within a personal workspace by way of its capacity assignment.

A personal workspace is limited in its options to share content with others. You can't publish a Power BI app from a personal workspace (and Power BI apps are an important mechanism for distributing content to the organization). Per-item permissions (links or direct access) are the only way to share personal workspace content with others. Therefore, extensive use of per-item permissions involves more effort and increases the risk of error. For more information, see Report consumer security planning .

Checklist - When considering your expectations for how personal workspaces should be used, key decisions and actions include:

• Understand current use of personal workspaces: Have conversations with your users and review the activity activity data to ensure you understand what users are doing with their personal workspaces.
• Decide how personal workspaces should be used: Decide how personal workspaces should (and should not) be used in your organization. Focus on balancing risk and ease of use with needs for content collaboration and viewing.
• Relocate personal workspace content when appropriate: For critical content, move content from personal workspaces to standard workspaces when appropriate.
• Create and publish documentation about personal workspaces: Create useful documentation or FAQs for your users about how to effectively use personal workspaces. Make the information available in your centralized portal and training materials.

For more information, see these Fabric adoption roadmap topics: centralized portal , training , and documentation .

Workspace ownership

One of the most important things to consider when planning workspaces is determining the ownership and stewardship roles and responsibilities. The goal is to have clarity on exactly who is accountable for creating, maintaining, publishing, securing, and supporting the content in each workspace.

Clarity on ownership is particularly relevant when responsibilities for creating and managing data are decentralized—or distributed—among departments and business units. This concept is also sometimes referred to as a data mesh architecture. For more information about data mesh, see What is data mesh? .

In Fabric, decentralized or distributed ownership is enabled through workspaces. Different areas of the organization can work independently, while still contributing to the same underlying data structure in OneLake . Each workspace can have its own administrator, access control, and capacity assignment (for billing, geographic data location, and performance monitoring).

An additional way to support workspace ownership in Fabric is with domains , which are described later in this article.

When the intent for collaboration involves decentralization and multiple teams beyond a single business unit, it can add complexity for managing workspaces. Often, it's helpful to create separate workspaces to clearly delineate which team is responsible for which content. Use of multiple workspaces allows you to be specific as to ownership and management responsibilities, and it can help you to set security according to the principle of least privilege . For more security considerations, see Content creator security planning .

Your decisions related to accountability and responsibility should correlate directly with your actions related to defining workspace access , which is described later in this article.

Checklist - When considering workspace ownership responsibilities, key decisions and actions include:

• Gain a full understanding of how content ownership works: Ensure that you deeply understand how content ownership and management is happening throughout the organization. Recognize that there likely won't be a one-size-fits-all approach to apply uniformly across the entire organization. Understand decentralized or distributed ownership needs.
• Define and document roles and responsibilities: Ensure that you define and document clear roles and responsibilities for people who collaborate in workspaces. Make this information available in onboarding activities, training materials, and your centralized portal.
• Create a responsibility matrix: Map out who is expected to handle each function when creating, maintaining, publishing, securing, and supporting content. Have this information ready when you start planning for workspace access roles.
• Consider co-ownership or multi-team ownership scenarios: Identify when a scenario exists where it would be helpful to separate out workspaces so that responsibilities are clear.
• Create workspace management documentation: Educate workspace administrators and members about how to manage workspace settings and access. Include responsibilities for workspace administrators, members, and contributors. Make the information available in your centralized portal and training materials.

Workspace organization

How to organize workspaces is one of the most important aspects of workspace planning.

Different business units and departments might use workspaces slightly differently depending on their collaboration requirements. When you need a new workspace, we recommend that you consider the factors described in this section.

Workspace subject and scope

The following options present some suggestions about how you can organize workspaces by subject and scope.

In some cases, you might already have some useful groups established in Microsoft Entra ID ( previously known as Azure Active Directory ). You can then use them to manage access to resources for the defined subject area and scope. However, you might need to create some new groups to suit this purpose. See the workspace access section below for considerations.

Option 1: Workspace per subject area or project

Creating a workspace for each subject area or project allows you to focus on its purpose. It allows you to take a balanced approach.

Examples: Quarterly Financials or Product Launch Analysis

The advantages of option 1 include:

• Managing user access for who is allowed to edit or view content is more straightforward since it's scoped per subject area.
• When content will be accessed by users across organizational boundaries, structuring workspaces by subject area is more flexible and easier to manage (compared to option 2 discussed next).
• Using a scope per subject area is a good compromise between workspaces that contain too many items and workspaces that contain too few items.

A disadvantage of option 1 is that depending on how narrow or wide workspaces are defined, there's still some risk that many workspaces will be created. Finding content can be challenging for users when content is spread across many workspaces.

When well-planned and managed, a workspace per subject area or project usually results in a manageable number of workspaces.

Option 2: Workspace per department or team

Creating a workspace per department or team (or business unit) is a common approach. In fact, alignment with the organizational chart is the most common way people start with workspace planning. However, it's not ideal for all scenarios.

Examples: Finance Department or Sales Team Analytics

The advantages of option 2 include:

• Getting started with planning is simple. All content needed by the people that work in that department will reside in one workspace.
• It's easy for users to know which workspace to use since all of their content is published to the workspace associated with their department or team.
• Managing security roles can be straightforward, especially when Microsoft Entra groups are assigned to workspace roles (which is a best practice).

The disadvantages of option 2 include:

• The result is often a broad-scoped workspace that contains many items. A broadly defined workspace scope can make it challenging for users to locate specific items.
• Because there's a one-to-one relationship between a workspace and a Power BI app, a broadly defined workspace can result in apps for users that contain lots of content. This issue can be mitigated by excluding certain workspace items from the app, and with good design of the app navigation experience .
• When users from other departments need to view specific workspace items, managing permissions can become more complex. There's a risk that people will assume that everything in the departmental workspace is for their eyes only. There's also a risk that the sharing of individual items will become overused in order to accomplish granular viewing permissions.
• If some content creators need permission to edit some items (but not all items), it's not possible to set those permissions in a single workspace. That's because workspace roles, which determine edit or view permissions, are defined at the workspace level.
• When you have a large number of workspace items, it often means you'll need to use strict naming conventions for items so that users are able to find what they need.
• Broad workspaces with many items might run into a technical limitation on the number of items that can be stored in a workspace.

When creating workspaces that align with your organizational chart, you often end up with fewer workspaces. However, it can result in workspaces that contain a lot of content. We don't recommend aligning workspaces per department or team when you expect to have a significant number of items and/or many users.

Option 3: Workspace for a specific report or app

Creating a workspace for each report or type of analysis isn't recommended except for specific circumstances.

Examples: Daily Sales Summary or Executive Bonuses

Advantages of option 3 include:

• The purpose of a narrowly defined workspace is clear.
• Ultra-sensitive content can, and often should, be segregated into its own workspace so that it can be managed and governed explicitly.
• Fine-grained workspace permissions are applicable to a few items. This setup is useful when, for instance, a user is permitted to edit one report but not another.

Disadvantages of option 3 include:

• If overused, creating narrowly defined workspaces results in a large number of workspaces.
• Having a large number of workspaces to work with involves more effort. While users can rely on search, finding the right content in the right workspace can be frustrating.
• When a larger number of workspaces exist, there's more work from an auditing and monitoring perspective.

Creating a workspace with a narrow scope, such as an individual report, should be done for specific reasons only. It should be the exception rather than the rule. Occasionally, separating scorecards into their own workspace is a useful technique. For example, using a separate workspace is helpful when a scorecard presents goals that span multiple subject areas. It's also helpful to set up specific permissions for managing and viewing the scorecard.

Checklist - When considering the subject area and scope of workspace content, key decisions and actions include:

• Assess how workspaces are currently set up: Review how people currently use workspaces. Identify what works well and what doesn't work well. Plan for potential changes and user education opportunities.
• Consider the best workspace scope: Identify how you want people to use workspaces based on purpose, subject area, scope, and who's responsible for managing the content.
• Identify where highly sensitive content resides: Determine when creating a specific workspace for highly sensitive content can be justified.
• Create and publish documentation about using workspaces: Create useful documentation or FAQs for your users about how to organize and use workspaces. Make this information available in training materials and your centralized portal.

Workspace item types

Separating data workspaces from reporting workspaces is a common practice for decoupling data assets from analytical assets.

• A data workspace is dedicated to storing and securing data items such as a lakehouse, warehouse, data pipeline, dataflow, or semantic model.
• A reporting workspace is focused more on the downstream analytical activities. It's dedicated to storing and securing items such as reports, dashboards, and metrics. Reporting workspaces primarily (but not necessarily exclusively) include Power BI content.

Each Fabric experience allows you to create various types of items. These items don't always fit neatly into the concept of what's considered data versus reporting (or analytical) content. One example is a Fabric notebook that can be used in many different ways, such as: loading and transforming data in a lakehouse, submitting Spark SQL queries, or analyzing and visualizing data with PySpark. When the workspace will contain mixed workloads, we recommend that you focus primarily on the workspace purpose and ownership of the content as described elsewhere in this article.

The advantages for separating data workspaces from reporting workspaces include:

• Report creators can locate and reuse trustworthy shared semantic models more easily. For more information, see the managed self-service BI usage scenario.
• Semantic model creators can locate trustworthy dataflows or lakehouse tables more easily. For more information, see the self-service data preparation usage scenario and the advanced self-service data preparation usage scenario.
• Access management can be centralized for critical organizational data. Managing access separately for the data workspace compared with reporting workspace(s) is useful when different people are responsible for data and reports. With managed self-service BI, it's common to have many report creators and fewer data creators.
• Limiting who can edit and manage semantic models minimizes the risk of unintentional changes, especially to critical data items that are reused for many purposes or by many users. Physical separation reduces the chances of inadvertent, or unapproved, changes. This extra layer of protection is helpful for certified semantic models, which are relied upon for their quality and trustworthiness.
• Co-ownership scenarios are clarified. When shared semantic models are delivered from a centralized BI or IT team, while reports are published by self-service content creators (in business units), it's a good practice to segregate the semantic models into a separate workspace. This approach avoids the ambiguity of co-ownership scenarios because ownership and responsibility per workspace is more clearly defined.
• Row-level security (RLS) is enforced. When you encourage creators to work in different workspaces, they won't have unnecessary edit permission to the original semantic model. The advantage is that RLS and/or object-level security (OLS) will be enforced for content creators (and also content viewers).

The disadvantages for separating data workspaces from reporting workspaces include:

• A workspace naming convention is required to be able to distinguish a data workspace from a reporting workspace.
• Extra user education is required to ensure that content authors and consumers know where to publish and find content.
• Sometimes it's challenging to clearly delineate the item types that should be contained within a workspace. Over time, a workspace can end up containing more types of content than was originally intended.
• Use of separate workspaces results in a larger number of workspaces that you need to manage and audit. As you plan for purpose, scope, and other considerations (such as the separation of development, test, and production content) the approach to workspace design can become more complicated.
• Extra change management processes could be required to track and prioritize requested changes to centralized data items, particularly when report creators have requirements beyond what can be handled by composite models and report-level measures.

Checklist - When considering the item types to store in a workspace, key decisions and actions include:

• Determine your objectives for data reuse: Decide how to achieve data reuse as part of a managed self-service BI strategy.
• Update the tenant setting for who can use semantic models across workspaces: Determine whether this capability can be granted to all users. If you decide to limit who can use semantic models across workspaces, consider using a group such as Fabric approved report creators .

Workspace access

Since the primary purpose of a workspace is collaboration, workspace access is mostly applicable to users who create and manage its content. It can also be relevant when the workspace is used for viewing content (a secondary purpose for workspaces, as described earlier in this article).

When starting to plan for workspace roles , it's helpful to ask yourself the following questions.

• What are the expectations for how collaboration will occur in the workspace?
• Will the workspace be used directly for viewing content by consumers?
• Who will be responsible for managing the content in the workspace?
• Who will view content that's stored in the workspace?
• Is the intention to assign individual users or groups to workspace roles?

It's a best practice to use groups for assigning workspace roles whenever practical. There are different types of groups you can assign. Security groups, mail-enabled security groups, distribution groups, and Microsoft 365 groups are all supported for workspace roles. For more information about using groups, see Tenant-level security planning .

When planning to use groups, you might consider creating one group per role per workspace. For example, to support the Quarterly Financials workspace, you could create the following groups:

• Fabric workspace admins – Quarterly Financials
• Fabric workspace members – Quarterly Financials
• Fabric workspace contributors – Quarterly Financials
• Fabric workspace viewers – Quarterly Financials
• Power BI app viewers – Quarterly Financials

Creating the groups listed above provides flexibility. However, it involves creating and managing many groups. Also, managing a large number of groups can be challenging when groups are only created and maintained by IT. This challenge can be mitigated by enabling self-service group management to certain satellite members. These members can include the Center of Excellence (COE), champions, or trusted users who have been trained in how to manage role memberships for their business unit. For more information, see Tenant-level security planning .

When data workspaces are separated from reporting workspaces, as described earlier in this article, it results in an even larger number of groups. Consider how the number of groups doubles from five to 10 when you separate data and reporting workspaces:

• Fabric data workspace admins – Quarterly Financials
• Fabric reporting workspace admins – Quarterly Financials
• Fabric data workspace members – Quarterly Financials
• Fabric reporting workspace members – Quarterly Financials
• Fabric data workspace contributors – Quarterly Financials
• Fabric reporting workspace contributors – Quarterly Financials
• Fabric data workspace viewers – Quarterly Financials
• Fabric reporting workspace viewers – Quarterly Financials

When multiple workspaces exist for development, test, and production, it results in an even larger number of groups. There's the potential for the number of groups to triple. For example, for just the data workspace admins, there would be these three groups:

• Fabric data workspace admins – Quarterly Financials [Dev]
• Fabric data workspace admins – Quarterly Financials [Test]

The previous examples are intended to convey that the use of groups that map to workspace roles can quickly become unmanageable.

There are times when fewer groups are needed, particularly in development. For example, you might not need to specify a workspace viewers group in development; that group is only needed for testing and production. Or you might be able to use the same workspace admins group for development, test, and production. For more information about development, test, and production, see Workspace lifecycle management later in this article.

The effective use of groups for workspace roles can require considerable planning. Be prepared to encounter scenarios when existing groups (that might be aligned with the organizational chart) don't meet all your needs for managing Fabric content. In this case, we recommend that you create groups specifically for this purpose. That's why the words Fabric or Power BI are included in the group name examples shown above. If you have multiple business intelligence tools, you can choose to use only BI as the prefix instead. That way, you can use the same groups across multiple tools.

Lastly, the examples show one workspace - Quarterly Financials - but often it's possible to manage a collection of workspaces with one set of groups. For example, multiple workspaces owned and managed by the finance team might be able to use the same groups.

You'll often plan security more broadly, taking into consideration semantic model Read and Build permission requirements, and row-level security (RLS) requirements. For more information about what to consider for supporting report consumers and content creators, see the security planning articles. For the purposes of this article, the focus is only on workspace roles as part of the workspace planning process.

Checklist - When considering workspace access, key decisions and actions include:

• Refer to roles and responsibilities: Use the roles and responsibilities information prepared earlier to plan for workspace roles.
• Identify who'll own and manage the content: Verify that all the items you expect to store in a single workspace align with the people who'll take responsibility for owning and managing the content. If there are mismatches, reconsider how the workspaces could be better organized.
• Identify who'll view content in the workspace: Determine whether people will view content directly from the workspace.
• Plan for the workspace roles: Determine which people are suited to the Admin , Member , Contributor , and Viewer roles for each workspace.
• Decide on group or individual role assignments: Determine whether you intend to assign individual users or groups to workspace roles. Check whether there are existing groups that you can use for workspace role assignments.
• Determine whether new groups need to be created: Consider carefully whether you need to create a new group per workspace role. Bear in mind that it can result in creating and maintaining many groups. Determine what the process is when a new workspace is created and how related groups will be created.
• Configure and test the workspace role assignments: Verify that users have the appropriate security settings they need to be productive while creating, editing and viewing content.

Workspace domain

As described earlier in this article, it's critical to have clarity on workspace ownership. One way to further support workspace ownership in Fabric is with domains . A domain is a way to logically group multiple workspaces that have similar characteristics.

For more information about planning for domains in your tenant, see Workspace domains .

Workspace settings

There are several settings you can set up for each individual workspace. These settings can significantly influence how collaboration occurs, who is allowed to access the workspace, and the level of data reusability across Fabric workloads.

Workspace license mode

Each workspace has a license mode setting. It can be set to Pro , Premium per user , Premium capacity , Embedded , Fabric capacity , or Trial .

At times this article refers to Power BI Premium or its capacity subscriptions (P SKUs). Be aware that Microsoft is currently consolidating purchase options and retiring the Power BI Premium per capacity SKUs. New and existing customers should consider purchasing Fabric capacity subscriptions (F SKUs) instead.

For more information, see Important update coming to Power BI Premium licensing and Power BI Premium FAQ .

The type of license is important for workspace planning because it determines:

• Features: Different features are supported. PPU includes more features (such as deployment pipelines ) that aren't available in Pro. Many more Fabric features (such as lakehouses ) become available for workspaces assigned to a Fabric capacity.
• Only users who have a PPU license (in addition to being assigned a workspace role) can access a PPU workspace.
• If you expect to deliver content to content viewers who have a free license, you'll need a license of F64 or higher .
• Data storage location: When you need to store data in a specific geographic region (outside of your home region), that becomes possible with a workspace assigned to a capacity (and, accordingly, the capacity is created in that region). For more information about data storage location, see Tenant setup .

Checklist - When considering the workspace license mode, key decisions and actions include:

• Consider which features are required for each workspace: Determine the feature requirements of each workspace. Consider differences in workload and which users you intend to access the workspace.
• Set the workspace license mode: Review and update each workspace license mode according to which features are needed by each workspace.

Workspace lifecycle management

When content creators collaborate to deliver analytical solutions that are important to the organization, there are various lifecycle management considerations. These processes are also known as continuous integration/continuous delivery (CI/CD) , which are one aspect of DevOps.

Several lifecycle management considerations include:

• How to ensure timely, reliable, and consistent delivery of content.
• How to communicate and coordinate activities between multiple content creators who are working on the same project.
• How to resolve conflicts when multiple content creators edit the same item in the same project.
• How to structure a straightforward and reliable deployment process.
• How to roll back deployed content to a previous stable, working version.
• How to balance fast releases of new features and bug fixes while safeguarding production content.

In Fabric, there are two main components of lifecycle management.

• Version control of content: Git integration allows content owners and creators to create versions of their work. It can be used with web-based development in a workspace , or when developing in a client tool , such as Power BI Desktop. Version control (also known as source control ) is achieved by tracking all revisions to a project by using branches associated with local and remote repositories in Azure DevOps . Changes are committed at regular intervals to branches in the remote repository. When a content creator has completed revisions that are tested and approved, their branch is merged with the latest version of the solution in the main remote repository (after resolving any merge conflicts ). Git integration can be specified for each workspace in the Fabric portal, providing the feature has been enabled in the tenant settings .
• Promoting content: Deployment pipelines are primarily focused on release management in order to maintain a stable environment for users. You can assign a workspace to a stage (development, test, or production) in a deployment pipeline. Then, you can easily and systematically promote , or deploy , your content to the next stage.

When combining the lifecycle management features, there are best practices to consider during your planning process. For example, you might choose to use Git integration for your development workspace and deployment pipelines to publish to your test and production workspaces. Those types of decisions require using the agreed-upon practice consistently. We recommend that you do a proof of concept to fully test your setup, processes, and permissions model .

Checklist - When planning for workspace lifecycle management, the key decisions and actions include:

• Determine how users need to use version control: Analyze how your self-service and advanced content creators work to determine whether file versioning with OneDrive for Business or SharePoint is appropriate. Introduce Git integration for advanced users who need more capabilities. Prepare to support both types of users.
• Determine how users need to promote content: Analyze how your self-service and advanced content creators work to determine whether deployment pipelines are a good fit for promoting content.
• Decide whether Git integration should be enabled: Consider whether Git integration with workspaces is a good fit for how your content creators work. Set the Users can synchronize workspace items with their Git repositories tenant setting to align with this decision. Review each of the Git integration tenant settings and set them according to your governance guidelines.
• Do a proof of concept: Conduct a technical proof of concept to clarify how you intend for Git workspaces and deployment pipelines to work together.
• Decide which workspaces should have Git integration: Consider how your content creators work, and which workspaces should be assigned to a development, test, or production (release) branch.
• Verify licenses: Confirm that you have a capacity license available to use Git integration. Ensure that each workspace is assigned to a Fabric capacity or Power BI Premium capacity.
• Set up Azure DevOps: Work with your administrator to set up the Azure DevOps projects, repositories, and branches that you'll need for each workspace. Assign appropriate access to each repository.
• Connect workspaces: Connect each workspace to the appropriate Azure DevOps repository.
• Consider who should deploy to production: Make decisions on how and who should be able to update production content. Ensure that these decisions align with how workspace ownership is handled in your organization.
• Educate content creators: Ensure that all your content creators understand when to use lifecycle management features and practices. Educate them on the workflow and how different workspaces impact lifecycle management processes.

Workspace integration with ADLS Gen2

It's possible to connect a workspace to an Azure Data Lake Storage Gen2 (ADLS Gen2) account. There are two reasons you might do that:

• Tenant-level storage , which is helpful when centralizing all data for Power BI dataflows into one ADLS Gen2 account is desired.
• Workspace-level storage , which is helpful when business units manage their own data lake or have certain data residency requirements.
• Complying with data retention requirements
• Storing routine backups as part of a disaster recovery strategy
• Storing backups in another region
• Migrating a data model

Setting Azure connections in the Fabric admin portal doesn't mean that all dataflows for the entire tenant are stored by default to an ADLS Gen2 account. To use an explicit storage account (instead of internal storage), each workspace must be explicitly connected. It's critical that you set the workspace Azure connections prior to creating any Power BI dataflows in the workspace.

Checklist - When considering workspace integration with ADLS Gen2, key decisions and actions include:

• Decide whether the workspace will be used in ways that require Azure Storage: Consider whether a bring-your-own-data-lake scenario would be useful for the storage of dataflows and/or whether you have requirements to use the semantic model backup and restore functionality.
• Determine which Azure Storage account will be used: Select an Azure Storage account that has the hierarchical namespace enabled (ADLS Gen2) for tenant-level (centralized) storage of dataflows data or semantic model backups. Ensure you have the Azure Storage account information readily available.
• Configure the tenant-level storage account: In the Fabric admin portal, set the tenant-level ADLS Gen2 storage account.
• Decide whether workspace administrators can connect a storage account: Have discussions to understand the needs of decentralized teams, and whether individual teams are currently maintaining their own Azure Storage accounts. Decide whether this capability should be enabled.
• Configure the admin setting for workspace-level storage: In the Fabric admin portal, enable the option that allows workspace administrators to connect their own storage account.
• Set the workspace-level Azure Storage connections: Specify the Azure Storage account for each individual workspace. You must set the storage account prior to creating any Power BI dataflows in the workspace. If you intend to use semantic model backups, ensure the workspace license mode is set to capacity or PPU.
• Update your workspace management documentation: Ensure that your workspace management documentation includes information about how to assign ADLS Gen2 storage accounts correctly. Make the information available in your centralized portal and training materials.

Workspace integration with Azure Log Analytics

Azure Log Analytics is a service within Azure Monitor . You can use Azure Log Analytics to review diagnostic data generated by the Analysis Services engine, which hosts Power BI semantic models. Workspace-level logs are useful for analyzing performance and trends, performing data refresh analysis, analyzing XMLA endpoint operations, and more. Azure Log Analytics is available only for workspaces assigned to capacity or PPU.

Although the names are similar, the data sent to Azure Log Analytics is different from the data captured by the Power BI activity log . The data sent to Azure Log Analytics is concerned with events generated by the Analysis Services engine (for example, Query begin and Query end events). Conversely, the activity log is concerned with tracking user activities (for example, View report or Edit report events).

For more information about semantic model event logs, see Data-level auditing .

For more information about how to set up Azure Log Analytics for use with Power BI, see Configuring Azure Log Analytics for Power BI . Be sure to understand the prerequisites you must have in place to make the integration work.

Checklist - When considering workspace integration with Azure Log Analytics, key decisions and actions include:

• Decide whether workspace administrators can connect to Log Analytics: Determine whether all, or some, workspace administrators will be permitted to use Azure Log Analytics for analyzing workspace-level logs. If access is to be restricted to only certain people, decide which group to use.
• Set up the tenant setting for Log Analytics connections: In the Fabric admin portal, set the tenant setting according to the decision for which workspace administrators set connections.
• Set the Log Analytics workspace for each workspace: In the workspace settings, specify the Azure Log Analytics information for each workspace. To capture workspace-level logs, ensure that the workspace license mode is set to capacity or PPU.
• Update your workspace management documentation: Ensure that your workspace management documentation includes information about how to assign a workspace to Azure Log Analytics.

Other workspace properties

There are several other workspace properties that can provide helpful information. For governed workspaces , we recommend that you set these properties.

Here are some suggestions for how to set these key settings to improve the experience for your users.

• The purpose for the workspace
• The target audience
• The type of content published to the workspace
• Whether the workspace is considered governed
• Whether the workspace includes development, test, or production data
• Who to contact should there be any questions (occasionally it's crucial to display this information as prominently as possible, in addition to the contact list that's described next)
• Workspace contacts: The workspace contact list includes the workspace administrators by default. If you have technical content owners that are different from the subject matter experts, you might find it helpful to specify other contacts. Other contacts could be groups or individuals who can answer questions about the workspace content.
• The domain or subject area
• Which business unit or team owns and manages the content
• Whether it's a data workspace (one that's dedicated to storing reusable items, such as a lakehouse, warehouse, data pipeline, dataflow, or semantic model)
• Whether it's a reporting workspace (one that's dedicated to storing analytical items, such as reports, dashboards, or metrics)
• Data model settings: Allows workspace members, administrators, and users with Build permission on the semantic model(s) to edit Power BI data models by using the web interface. This setting is used together with the Users can edit data models in the Power BI service tenant setting. This setting should align with your decisions and processes for how content is created, managed, and deployed. Also, consider your method for version control as described earlier in this article.

Checklist - When considering the other workspace properties, the key decisions and actions include:

• Specify the workspace description: Ensure that there's a helpful and thorough description included in the workspace description.
• Use a helpful image for the workspace: Set a consistent image for the workspace that'll visually help users understand its subject area, who owns and manages content in the workspace, and/or the type of content stored in the workspace.
• Identify contacts for the workspace: Verify whether the workspace administrators should be the workspace contacts, or whether specific users or groups should be specified.
• Specify data model settings: Consider which workspaces can permit web-based data model editing. Set the Users can edit data models in the Power BI service tenant setting according to your preferences for who can edit and manage content.

Other technical factors

There are other technical factors that might influence your workspace setup.

• If you integrate content with other tools and services, there could be licensing implications. For example, if you embed a Power Apps visual in a Power BI report, you'll need appropriate Power Apps licenses.
• There are per-workspace storage limits that apply to the amount of data you can store in a Pro workspace. If using capacity or PPU isn't an option, consider how to work within the storage limits during the workspace planning process.
• When you install a template app from AppSource, it will create a new workspace that will have a narrow subject and scope.

Checklist - When considering other technical factors, key decisions and actions include:

• Pay attention to technical factors: As you work through the planning process, determine whether there's a technical reason (such as per-workspace storage limits) that could influence your decision-making process.
• Reorganize workspace content: If storage limits could become a problem, create separate workspaces now and republish content to these new workspaces.

Related content

For more considerations, actions, decision-making criteria, and recommendations to help you with Power BI implementation decisions, see Power BI implementation planning .

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