You have diligently followed tutorials, learned the difference between a measure and a calculated column, and understand the core functions of Power BI. The theoretical knowledge is in place, but the path to becoming a proficient data analyst or business intelligence professional is paved with practical application. This is where project-based learning becomes not just beneficial, but essential. Working on end-to-end projects is the single most effective way to solidify your skills, build confidence, and create a tangible portfolio that showcases your capabilities to potential employers.
Companies are not just looking for candidates who can define DAX; they are seeking professionals who can take a raw dataset, understand a business problem, and transform that data into a compelling story that drives strategic decisions. A portfolio of well-executed projects is undeniable proof of this ability. It demonstrates your technical proficiency, your analytical mindset, and your understanding of how data is used in a real-world business context. This series will guide you through a curated set of projects, from beginner to intermediate levels, designed to build these exact skills.
This initial part of our series will focus on laying the groundwork. Before we dive into the first case study, it is crucial to understand why projects are so vital for your development and to ensure you have the foundational knowledge in place. We will explore the core components of a Power BI workflow, the mindset required to approach an analytical problem, and how these initial steps will prepare you for the hands-on work that will transform your skills from novice to professional.
Why Projects are Essential for Skill Development
Learning a tool like Power BI through tutorials is like learning the alphabet and grammar rules of a new language. It gives you the building blocks, but it does not make you a storyteller. Projects are where you learn to write the novel. They force you to integrate all the individual skills you have learned—data cleaning in Power Query, modeling relationships, writing DAX measures, and designing visualizations—into a single, cohesive workflow. This integration is a skill in itself and is something that can only be learned through practice.
Furthermore, projects expose you to the messy, imperfect nature of real-world data. Datasets are rarely clean and perfectly structured. You will encounter missing values, incorrect data types, and inconsistent formatting. Working through these challenges in a project environment is invaluable experience. It teaches you the critical data preparation skills that often occupy a significant portion of an analyst’s time. This hands-on problem-solving builds a level of practical competence that tutorials alone cannot provide.
Finally, completing a project provides you with a tangible asset for your professional portfolio. When you can walk into an interview and not just talk about your skills but actually show a well-designed dashboard that answers a complex business question, you immediately stand out from other candidates. It demonstrates initiative, a passion for data, and the ability to deliver a finished product. Each project you complete is a new chapter in your professional story, showcasing your growth and expertise.
The Anatomy of a Power BI Workflow
Every Power BI project, regardless of its specific domain, generally follows a consistent end-to-end workflow. Understanding these stages is fundamental to managing a project effectively. The first stage is Data Extraction and Transformation. This is where you connect to your data sources and use the Power Query Editor to clean, shape, and prepare the data for analysis. This can involve tasks like removing errors, changing data types, splitting columns, and creating new custom columns. A solid foundation here is critical for the accuracy of your entire report.
The second stage is Data Modeling. Once your data is clean, you load it into Power BI and define the relationships between your different data tables. A well-structured data model, typically a star schema, is the backbone of an efficient and powerful report. This is where you create the logical connections that allow your data to be sliced and diced in meaningful ways.
The third stage is Calculation and Analysis with DAX. This is where you use Data Analysis Expressions (DAX) to create calculated columns and, more importantly, measures. Measures are dynamic calculations that respond to user interactions in the report, allowing you to calculate key performance indicators (KPIs) like total sales, year-over-year growth, or customer churn rate. Mastery of DAX is what separates a basic user from a Power BI expert.
The final stage is Data Visualization and Storytelling. This is where you bring your analysis to life by creating charts, graphs, and tables on the report canvas. The goal is not just to present data, but to arrange it in a way that tells a clear and compelling story, guiding the user to the most important insights. This involves choosing the right visuals, using color and layout effectively, and creating an intuitive user experience.
Setting Up Your Power BI Environment
Before you begin your first project, ensure your environment is set up for success. The primary tool you will be using is Power BI Desktop, a free application for Windows that is the main authoring tool for creating reports. You can download it directly from the Microsoft website or from the Microsoft Store. It is recommended to use the Microsoft Store version as it provides seamless automatic updates, ensuring you always have the latest features and security patches.
While Power BI Desktop is the authoring tool, you should also familiarize yourself with the Power BI Service. This is the cloud-based platform where you will publish, share, and collaborate on your reports. You can sign up for a free account with a work or school email address. The service allows you to create dashboards, set up scheduled data refreshes, and share your work with stakeholders. Understanding how Desktop and the Service work together is key to understanding the full Power BI ecosystem.
For more advanced development, consider installing external tools that can enhance your Power BI workflow. Tools like DAX Studio and Tabular Editor are invaluable for writing, debugging, and optimizing complex DAX code. While not strictly necessary for beginner projects, becoming aware of them early on will be beneficial as you progress to more complex analyses. Make sure your setup is comfortable and that you are familiar with the basic interface of Power BI Desktop before diving into your first case study.
The Analyst’s Mindset: Asking the Right Questions
A successful Power BI project is not just about technical execution; it is about analytical thinking. Before you write a single line of DAX or create a single chart, you must adopt the mindset of an analyst. This begins with understanding the business problem you are trying to solve. What is the core objective of the analysis? What key questions are the stakeholders trying to answer? A beautiful dashboard that does not answer the right questions is ultimately a failure.
For each project, you should start by defining the key business questions. For a customer churn analysis, the questions might be, “What is our overall churn rate?”, “Which customer segments are churning the most?”, and “What are the primary reasons for churn?”. These questions will act as your guide, focusing your analysis and ensuring that your final report is relevant and actionable. They provide the “why” behind your work.
This inquisitive mindset should continue throughout the project. As you explore the data, be curious. Look for patterns, trends, and outliers. When you find something interesting, ask “why?”. Why did sales spike in a particular month? Why is one product performing so much better than another? The insights are often found in the second or third layer of questioning. The tools in Power BI are powerful, but it is your curiosity and critical thinking that will unlock their true potential.
Finding and Evaluating Datasets
The projects in this series will come with their own curated datasets, but as you grow your skills, you will want to find your own data to work on. The ability to find and evaluate datasets is a valuable skill for any analyst. There are many excellent sources for free, public data. Government websites, such as data.gov in the United States, are treasure troves of demographic, economic, and social data. Academic institutions and non-profit organizations also often publish data.
Online data communities are another fantastic resource. Websites that host data science competitions offer a wide range of clean and interesting datasets on various topics. These are perfect for portfolio projects as they are often well-documented and come with a clear problem statement. When you find a dataset, the first step is to evaluate its quality. Open it up and perform some initial exploratory data analysis.
Check for completeness. Are there a lot of missing values? Look at the data types. Are dates stored as dates and numbers as numbers? Assess the “tidiness” of the data. Is it in a structured, tabular format that will be easy to work with in Power BI? Finally, consider its relevance. Does the dataset contain enough interesting variables to allow for a meaningful analysis and to answer the questions you have in mind?
Business Problem
In this first project, you will step into the role of a data analyst for a fictional telecommunications company called Databel. The company is facing a significant business challenge: a high rate of customer churn. Churn, also known as customer attrition, is the rate at which customers stop doing business with a company. For a subscription-based business like a telecom provider, retaining existing customers is far more cost-effective than acquiring new ones. Therefore, understanding and reducing churn is a top priority for Databel’s leadership.
Your mission is to use Power BI to analyze the company’s customer data and uncover insights into this churn problem. This is a classic and highly relevant business use case that is applicable across many industries. You will be responsible for the entire analytical process, from preparing the data to building a comprehensive report that not only quantifies the churn rate but also explores the underlying reasons why customers are leaving. The final deliverable will be a dashboard that provides actionable insights for the company’s retention team.
This project is perfect for beginners as it touches upon all the core components of the Power BI workflow. You will practice data transformation with Power Query, create a simple but effective data model, write fundamental DAX measures, and design a multi-page report with interactive visualizations. By the end of this project, you will have a complete, portfolio-worthy case study that demonstrates your ability to solve a real-world business problem.
Step 1: Data Preparation and Exploration in Power Query
The first step in any analysis is to connect to the data and ensure it is clean and properly structured. For this project, you will be provided with a dataset containing customer information, including their demographics, the services they are subscribed to, their tenure with the company, and, most importantly, a column indicating whether they have churned. You will begin by loading this data into the Power Query Editor in Power BI Desktop.
Once the data is loaded, your first task is to perform an initial exploration. Examine each column. What is the data type? Are there any errors or missing values? The Power Query interface provides column quality profiles that give you a quick overview of the validity and completeness of your data. This initial review is crucial for identifying the data cleaning tasks you will need to perform.
Next, you will perform several transformation steps. This might include changing the data types of certain columns to ensure they are correctly interpreted by Power BI. You might replace null values with more meaningful text, such as “Not Applicable.” You could also create new custom columns to make your analysis easier. For example, you might create a “Tenure in Years” column by dividing the “Tenure in Months” column by twelve. These cleaning and shaping steps are fundamental to ensuring the accuracy and reliability of your final analysis.
Step 2: Building the Data Model
For this beginner project, the data is contained within a single flat file, which simplifies the data modeling process. A data model is the structure that defines the relationships between the different tables in your dataset. Even though we only have one main data table, it is a best practice to create a separate “Calendar” or “Date” table. This is a foundational concept in Power BI data modeling that enables powerful time intelligence calculations.
You can create a simple calendar table using a DAX function like CALENDARAUTO(), which automatically scans your data for the earliest and latest dates and creates a table with a continuous range of dates. Once this table is created, you would establish a relationship between the date column in your calendar table and the relevant date column in your main customer data table.
This simple star schema, with the main customer table as the “fact” table and the calendar table as the “dimension” table, is a robust and scalable model. It allows you to analyze churn trends over time with greater ease and performance. Understanding this basic modeling principle is a key learning outcome of this project and will serve you well in all your future Power BI work.
Step 3: Creating Essential DAX Measures and Columns
With your data cleaned and your model in place, the next step is to use Data Analysis Expressions (DAX) to create the calculations needed for your analysis. You will start by creating some basic calculated columns. For example, you might create a “Churned Customer” column that contains a “1” if the customer churned and a “0” if they did not. This can be useful for simplifying calculations later on.
The real power of DAX, however, lies in creating measures. Measures are dynamic calculations that aggregate data and respond to the context of your report. The first and most important measure you will create is the “Churn Rate.” This would be calculated by dividing the total number of churned customers by the total number of customers. You would use DAX functions like SUM or COUNTROWS to get these totals and the DIVIDE function to perform the calculation safely, handling any potential division by zero errors.
You will also create other essential measures, such as “Total Customers,” “Total Churned Customers,” and “Average Customer Tenure.” Each measure you create is a reusable piece of business logic that can be used in multiple visualizations throughout your report. Writing these foundational DAX measures is a core skill that this project is designed to develop.
Step 4: Designing the Report and Visualizations
Now it is time to bring your analysis to life on the report canvas. A good report is well-structured and easy to navigate. You will create a multi-page report, with each page dedicated to a specific aspect of the analysis. The first page could be a high-level summary or landing page that presents the most important Key Performance Indicators (KPIs) at a glance.
On this summary page, you would use “Card” visuals to display your key measures like “Total Customers” and the overall “Churn Rate.” You might also include a line chart to show the churn rate trend over time. The goal of this page is to provide a quick and clear answer to the primary business question: “What is our churn rate?”.
Subsequent pages will delve deeper into the “why.” You might have a page dedicated to customer demographics. Here, you could use bar charts to compare the churn rate across different gender groups or age brackets. Another page could focus on the services customers use, with a stacked column chart showing the churn rate for customers with different types of internet service. The key is to choose the right visualization for the data you are presenting to make the insights as clear as possible.
Step 5: Adding Interactivity with Slicers and Filters
A static report is informative, but an interactive report is empowering. Power BI’s interactivity features allow the end-user to explore the data for themselves, slicing and dicing it to uncover their own insights. The primary tool for this is the “Slicer” visual. Slicers are on-canvas filters that allow users to easily filter the data on a report page.
You will add slicers for key dimensions in your data, such as contract type, payment method, or geographic region. When a user selects an option in a slicer, all the other visuals on the page will instantly filter to show the data for that specific selection. This allows a manager to, for example, quickly see the churn rate specifically for customers on a month-to-month contract.
You will also use the “Filters” pane to apply more complex or background filters. This could involve filtering the entire report to exclude certain test accounts or to focus the analysis on a specific time period. Understanding how to use these filtering and slicing features is essential for creating a dynamic and user-friendly report that encourages exploration and discovery.
Step 6: Telling a Story with Your Data
The final and most important step is to synthesize all your analysis into a coherent and compelling story. Your dashboard should not just be a collection of charts; it should be a narrative that guides the user from the high-level problem to the specific, actionable insights. You can use text boxes and smart titles to add context and to explain what the user is seeing in each visual.
Your story should have a clear structure. Start with the main finding: the overall churn rate. Then, use your detailed analysis pages to break down the problem. Show that customers on month-to-month contracts are churning at a much higher rate than those on long-term contracts. Highlight that customers with fiber optic internet service have a higher churn rate, perhaps indicating service quality issues.
Finally, your report should conclude with a set of recommendations. Based on your analysis, what specific actions could Databel take to reduce churn? Perhaps they could offer incentives for customers to switch to annual contracts or investigate the quality of their fiber optic service in certain areas. Providing these data-driven recommendations is what transforms your report from a simple historical summary into a valuable strategic tool. This is the ultimate goal of any business intelligence project.
Business Problem
In this project, you will take on the role of a newly hired HR analyst at a fictional software company named Atlas Labs. The Human Resources department is looking to become more data-driven in its decision-making. They have collected a significant amount of data about their employees but need your expertise to turn this data into actionable insights. The primary objectives are to understand the current composition of the workforce, analyze key metrics related to employee performance, and, most importantly, investigate the growing problem of employee attrition.
Employee attrition, or turnover, is a major concern for any company, especially in the competitive tech industry where talent is scarce and expensive to replace. Your task is to build a comprehensive HR dashboard in Power BI that will serve as a single source of truth for the HR team. The report should summarize key employee metrics, identify trends in attrition, and delve into the potential factors that are contributing to employees leaving the company. The ultimate goal is to provide data-backed recommendations to improve employee retention.
This case study is an excellent next step for beginners. It reinforces the core Power BI workflow while introducing a new business domain with its own unique metrics and challenges. You will work with common HR data points like employee demographics, tenure, performance ratings, and job roles. This project will not only enhance your technical skills in Power BI but also give you valuable experience in the growing and important field of HR analytics.
Step 1: Data Preparation for HR Analysis
You will begin with a dataset containing detailed information for each employee at Atlas Labs. This will include personal information like age and gender, as well as job-related data such as their department, job role, monthly income, and years at the company. Crucially, the dataset will also contain a flag indicating whether an employee is still with the company or has attrited, along with their last performance rating. As with any project, the first step is to load this data into Power BI’s Power Query Editor.
In the Power Query Editor, your primary task is to ensure the data is clean, consistent, and ready for analysis. You will start by reviewing each column, checking for errors, and ensuring the data types are correct. For example, you will want to make sure that numerical columns like “Monthly Income” are set to a number type and not text. You may also need to perform some light transformations.
A common task in HR analytics is to create age or tenure brackets. You can use the “Conditional Column” feature in Power Query to create a new column that groups employees into logical age ranges (e.g., “20-29”, “30-39”). This will make it much easier to analyze trends across different age groups in your final report. A thorough data preparation phase is essential for the integrity of your HR analysis.
Step 2: Creating a Robust HR Data Model
For this project, you will again apply the best practice of creating a star schema, even if your initial data comes from a single file. The main employee data table will be your “fact” table. You will then create a separate “Calendar” table to handle all date-related analysis. This is particularly important in HR analytics for tracking trends in hiring and attrition over time. You can use DAX to create this calendar table and then link it to the relevant date field in your employee data.
In addition to the calendar table, you might also consider creating other “dimension” tables. For example, you could create a separate, unique list of all the departments in the company and another table for all the unique job roles. You could then link these smaller dimension tables to your main employee fact table.
While not strictly necessary for a simple dataset, this practice of separating dimensions from facts is the core principle of good data modeling. It leads to a more organized, efficient, and scalable data model. It reduces data redundancy and can improve the performance of your report. Mastering this fundamental concept of building a star schema is a key learning objective that will set you up for success with more complex, multi-table datasets in the future.
Step 3: Calculating Key HR Metrics with DAX
With a clean dataset and a solid data model, you can now focus on the heart of the analysis: creating the necessary calculations using DAX. The HR department at Atlas Labs has a specific set of Key Performance Indicators (KPIs) they want to track. Your task is to translate these business requirements into DAX measures.
You will start with some fundamental headcount metrics. You will create a measure for “Total Employees” by simply counting the rows in your employee table. You will also create a measure for “Total Attrition” to count the number of employees who have left the company. The most important KPI will be the “Attrition Rate,” which you will calculate by dividing the “Total Attrition” by the “Total Employees.”
Beyond these top-level metrics, you will create measures to analyze the workforce composition. This could include calculating the “Average Employee Age,” the “Average Monthly Income,” and the “Average Tenure” in years. You might also create measures to analyze employee performance, such as the “Average Performance Rating.” Each of these measures provides a specific piece of the puzzle, and together they will form the analytical backbone of your HR dashboard.
Step 4: Designing an Executive Summary Dashboard
The first page of your report should be an executive summary dashboard. This page is intended for senior leadership and should provide a high-level overview of the company’s human capital at a single glance. The design should be clean, clear, and focused on the most critical KPIs. You will use a combination of visuals to present this information effectively.
Start by using Card visuals to prominently display your main KPIs: “Total Employees,” “Total Attrition,” and the overall “Attrition Rate.” These cards provide immediate answers to the most important questions. You could then use a Donut chart or a Bar chart to show the breakdown of employees by department or gender, giving a quick overview of the workforce diversity.
A Line chart is an excellent choice for showing the trend of attrition over time. This can help the HR team identify if attrition is a recent problem or a long-standing one, and if there are any seasonal patterns. Finally, you might include a Table or a Matrix visual to show a summary of key metrics for each department, allowing for easy comparison. The goal of this page is to be a powerful, one-stop overview of the company’s HR health.
Step 5: A Deep Dive into Attrition Analysis
After the executive summary, you will create a dedicated page for a deep-dive analysis into employee attrition. This is where you will investigate the factors that correlate with employees leaving the company. The goal of this page is to move from “what” is happening to “why” it is happening. You will use a variety of visuals to explore the data from different angles.
You could use a series of bar charts to compare the attrition rate across different demographic groups. For example, you could compare the rate for male versus female employees, or for single versus married employees. You could also create a bar chart to show the attrition rate by job role, which might highlight that certain roles have a particularly high turnover. A scatter plot could be used to explore the relationship between monthly income and attrition.
This is also a great opportunity to use some of Power BI’s more advanced, AI-powered visuals. The “Key Influencers” visual is perfect for this use case. You can configure it to analyze what factors most influence the likelihood of an employee attriting. The visual might automatically discover that employees who work a lot of overtime or who have a low performance rating are significantly more likely to leave.
Step 6: Analyzing Employee Performance
The final analytical page of your report will focus on employee performance. Understanding performance is crucial for talent management, succession planning, and identifying areas for employee development. This page will help the HR team understand the distribution of performance across the company and its relationship with other factors.
A simple Bar chart or a Histogram can be used to show the distribution of performance ratings. This will quickly reveal if performance is normally distributed or if there is a skew towards high or low performers. You can then use a Matrix visual to break down the average performance rating by department and job role. This can help identify high-performing teams or roles that may be struggling.
It is also important to analyze the link between performance and attrition. Are low performers more likely to leave? Or is the company losing its top talent? A Clustered bar chart could be used to show the attrition rate for each performance rating category. Uncovering these relationships is vital for developing effective retention strategies. For example, if top performers are leaving, the company might need to review its compensation and career progression policies.
Step 7: Finalizing the Report and Presenting Recommendations
With all your analytical pages complete, the final step is to polish your report and formulate your recommendations. Ensure that your report has a consistent design theme, with a professional color scheme and clear, concise titles for all your visuals. Add slicers for key dimensions like department and job role to allow the HR team to interact with and explore the data.
The most important part of this final step is to synthesize your findings into a clear narrative and a set of actionable recommendations. You should add a text box to your report that summarizes the key insights. For example, “Our analysis reveals that the primary drivers of attrition at Atlas Labs are low job satisfaction and a high number of years since the last promotion. The attrition rate is highest among employees in the Sales department.”
Based on these insights, you can then provide specific recommendations. “To reduce attrition, we recommend implementing a new employee engagement survey to address job satisfaction issues and reviewing the promotion process to ensure that long-tenured employees have clear paths for career growth. We also suggest conducting exit interviews specifically with departing sales staff to understand their unique challenges.” This is how you transform your data analysis into a strategic asset for the business.
What Separates a Beginner from an Intermediate User?
You have now successfully completed two foundational projects, building a solid understanding of the core Power BI workflow. You are comfortable with Power Query, you can build a simple star schema, you can write basic DAX measures, and you can create a clean, interactive dashboard. So, what comes next? The journey from a beginner to an intermediate Power BI user is defined by a deeper understanding of the tool’s more powerful and nuanced features. It is about moving from “what” you can do to “how” you can do it more efficiently, more powerfully, and more securely.
An intermediate user thinks more like a data architect. They are not just building reports; they are designing scalable and performant analytical solutions. They have a deeper mastery of DAX, allowing them to write complex calculations and time intelligence formulas. They understand the intricacies of data modeling and can work with more complex schemas. They are also proficient in using some of Power BI’s more advanced features for scenario analysis and security.
This part of our series will serve as a bridge, introducing you to the key concepts and techniques that define this next level of proficiency. We will explore advanced data modeling, the power of time intelligence functions in DAX, how to implement row-level security to protect sensitive data, and how to create dynamic “what-if” analyses. Mastering these concepts will prepare you for the more complex and industry-specific intermediate projects that follow.
Advanced Data Modeling: The Star Schema and Beyond
As a beginner, you learned the importance of creating a simple star schema with a fact table and a few dimension tables. An intermediate user takes this a step further, understanding the nuances of relationship types and how to model more complex business scenarios. The star schema remains the gold standard because its simple structure is optimized for the way Power BI’s engine processes data, leading to faster and more efficient reports.
At the intermediate level, you will become comfortable working with multiple fact tables. For example, in a retail analysis, you might have one fact table for sales transactions and another for inventory levels. You would then have a set of shared, or “conformed,” dimension tables, such as a “Date” table, a “Product” table, and a “Store” table, that relate to both fact tables. This allows you to analyze sales and inventory together in a single report.
You will also gain a deeper understanding of relationship cardinality (one-to-one, one-to-many, many-to-many) and cross-filter direction. You will learn when it is appropriate to use bi-directional filters and, more importantly, when to avoid them to prevent ambiguity and performance issues in your model. A strong grasp of these data modeling concepts is the true foundation of any advanced Power BI development.
Mastering Time Intelligence with DAX
Time intelligence is a collection of DAX functions that make it easy to perform common date-based calculations, such as year-to-date (YTD) totals, or comparing performance to the same period in the previous year. For businesses, this type of analysis is not just useful; it is essential. An intermediate user must be proficient in using these functions.
To use time intelligence functions effectively, you must have a well-formed calendar table in your data model. This table must contain a continuous range of dates and be marked as a date table in Power BI. Once this is in place, you can unlock a whole new level of analysis. For example, you can create a “Sales YTD” measure using the TOTALYTD function or a “Sales Same Period Last Year” measure using the SAMEPERIODLASTYEAR function.
You can then combine these to create even more powerful metrics, like a “Year-over-Year Sales Growth” percentage. These functions are incredibly powerful because they are dynamic and respect the current filter context of the report. If a user filters the report to a specific month, the YTD calculation will automatically adjust to show the year-to-date total up to that month.
Implementing Row-Level Security (RLS)
In many business scenarios, you will need to restrict data access for different users. For example, a regional sales manager should only be able to see the sales data for their own region, not for the entire company. Power BI provides a powerful feature to handle this called Row-Level Security (RLS). An intermediate user should understand how to implement RLS to secure their reports.
RLS works by defining “roles” and applying filters to those roles. You would create a role, for example, named “Regional Manager.” Then, you would apply a DAX filter expression to this role that filters the data based on the user’s identity. For example, you could filter the “Sales” table where the “Region” column matches the username of the person viewing the report.
Once these roles are defined in Power BI Desktop, you publish the report to the Power BI Service. In the service, you assign your users or user groups to the appropriate roles. When a user who is part of the “Regional Manager” role opens the report, the DAX filter is automatically applied, and they will only see the data for their specific region. RLS is a critical feature for deploying reports at an enterprise scale where data security and governance are paramount.
Creating “What-If” Analysis with Parameters
Business leaders often want to explore different hypothetical scenarios. For example, “What would be the impact on our profitability if our material costs increased by 5%?” or “How many units would we need to sell to reach our revenue target?”. Power BI’s “What-if” parameter feature allows you to build this type of dynamic scenario analysis directly into your reports.
A what-if parameter creates a slicer that allows the user to input a value or to select a value from a range. Behind the scenes, Power BI generates a table with the possible values and a DAX measure that captures the user’s selection. You can then incorporate this measure into your other DAX calculations.
For example, you could create a parameter for “Production Volume.” Then, you could write a “Total Manufacturing Cost” measure that multiplies the per-unit cost by the selected production volume from the slicer. As the user moves the slicer, the total cost will be recalculated in real-time. This transforms your report from a static historical view into an interactive forecasting and planning tool, providing immense value to business decision-makers.
Using Advanced and AI-Powered Visualizations
While the standard charts and graphs are the workhorses of any report, an intermediate user should be comfortable using some of Power BI’s more advanced and AI-powered visualizations to uncover deeper insights. These visuals can often automate parts of the analytical process and reveal patterns that might be difficult to find manually.
We have already mentioned the “Key Influencers” visual, which is excellent for driver analysis. Another powerful AI visual is the “Decomposition Tree.” This visual allows you to break down a metric by multiple dimensions in a hierarchical way, helping you to understand the root causes of a particular outcome. The “Smart Narrative” visual uses natural language generation to automatically create a text summary of the key insights on your report page, which can be a great time-saver.
Beyond the built-in visuals, an intermediate user should also be aware of the custom visuals available in the AppSource marketplace. There are hundreds of specialized visuals, from Sankey diagrams and Gantt charts to advanced mapping visuals, that can be downloaded and added to your reports to meet specific business needs.
Business Problem
In this intermediate-level project, you will step into the role of a supply chain analyst for a fictional company called Tenate Industries. The company specializes in manufacturing and selling spare parts for industrial pizza ovens. Tenate Industries is at a strategic crossroads. For some of its key components, it has the capability to either manufacture them in-house or to purchase them from external suppliers. The leadership team needs a robust analytical tool to help them make these critical “make versus buy” decisions.
Your mission is to develop a comprehensive supply chain dashboard in Power BI. This tool will have two primary functions. First, it will serve as a quote analysis tool, allowing the purchasing team to compare price quotes from various suppliers for different components. Second, it will be a cost scenario analysis tool, enabling the production team to model the costs of manufacturing a component in-house at different production volumes and to compare this to the cost of buying from suppliers.
This project is designed to challenge you and to apply the intermediate concepts we have just discussed. You will work with a more complex, multi-table dataset, write more sophisticated DAX measures, implement a dynamic “what-if” parameter for scenario analysis, and secure your report using row-level security. Completing this project will be a significant step in your journey to becoming a proficient Power BI developer.
Step 1: Advanced Data Transformation and Modeling
You will be provided with several data files for this project. This will include a table of product information, a table with cost breakdowns for manufacturing each product in-house, and a table containing price quotes received from various external suppliers. Your first task is to load these tables into the Power Query Editor and perform the necessary data cleaning and transformation. You may need to unpivot data, merge tables, and handle more complex data shaping tasks than in the beginner projects.
Once your data is clean, you will move to the data modeling stage. This project requires a more sophisticated data model than the previous ones. You will have multiple fact tables (e.g., one for manufacturing costs and one for supplier quotes) and several dimension tables (e.g., for products, suppliers, and dates). Your task is to build a clean and efficient star schema, carefully defining the relationships between all these tables.
Getting the data model right is absolutely critical for this project. You will need to think carefully about the direction of your relationships and how filters will propagate through your model. A well-designed model will make your DAX calculations simpler and your report more performant. A poorly designed model will lead to incorrect results and a frustrating development experience.
Step 2: Writing Intermediate DAX for Cost Analysis
This project will require you to write more complex DAX measures to perform the necessary cost analysis. You will need to calculate the total cost of purchasing a certain number of units from a given supplier. This will involve using iterator functions like SUMX to multiply the per-unit price by the quantity for each line item in a quote.
For the in-house manufacturing cost, the calculation is more complex. The cost of manufacturing often includes both fixed costs (like machine setup) and variable costs (like raw materials per unit). Your DAX measures will need to correctly model this. You will create a measure for “Total Manufacturing Cost” that incorporates both these fixed and variable components based on a given production volume.
You will also write measures to compare these two scenarios. For example, you will create a “Cost Differential” measure that subtracts the total manufacturing cost from the total purchasing cost. A positive result would indicate that it is cheaper to manufacture in-house, while a negative result would mean it is cheaper to buy. These DAX measures will be the analytical core of your make-versus-buy decision tool.
Step 3: Implementing “What-If” Parameters for Scenario Analysis
The most powerful feature of your report will be its ability to perform dynamic scenario analysis. To achieve this, you will implement a “what-if” parameter in Power BI. You will create a parameter that allows the user to select a “Production Volume” using a slicer. This parameter will generate a new table and a measure that captures the selected volume.
You will then integrate this “Production Volume” measure into the DAX calculations you wrote in the previous step. Your “Total Manufacturing Cost” measure will now be dynamic, recalculating automatically whenever the user adjusts the production volume slicer. This is an incredibly powerful feature, as it allows a production manager to instantly see how the cost-effectiveness of manufacturing in-house changes at different scales.
This what-if analysis is what elevates your report from a simple historical summary to a forward-looking decision-making tool. It allows users to explore different possible futures and to understand the financial implications of their decisions. Mastering this technique is a key skill for any intermediate Power BI user who wants to provide strategic value to their stakeholders.
Step 4: Designing the Quote Analysis and Scenario Tool
You will design a multi-page report to present your analysis. The first page will be the “Quote Analysis Tool.” This page is designed for the purchasing team. It will contain a slicer for selecting a specific product and a matrix or table visual that shows all the supplier quotes for that product. The table will be sorted by price, allowing the team to easily identify the most cost-effective supplier.
The second, and more complex, page will be the “Make or Buy Analysis” dashboard. This page will be the heart of your project. It will feature the “Production Volume” slicer you created. It will also have card visuals that display the “Total Manufacturing Cost” and the “Best Supplier Purchase Cost” based on the selected volume. A prominent card will show the “Cost Differential,” clearly indicating which option is cheaper.
You could also include a line chart that visualizes this relationship. The x-axis would be the production volume, and the y-axis would be the total cost. You would plot two lines: one for the in-house manufacturing cost and one for the best supplier’s cost. This chart would visually show the break-even point, the production volume at which it becomes more cost-effective to manufacture in-house.
Step 5: Implementing and Testing Row-Level Security (RLS)
To add a final layer of professionalism and to practice another key intermediate skill, you will implement Row-Level Security on your report. The business requirement is that when an external supplier is given access to the report, they should only be able to see their own quotes and not the quotes from their competitors. They should also not have access to Tenate Industries’ internal manufacturing cost data.
To achieve this, you will create a new role in Power BI Desktop called “Supplier.” You will then apply a DAX filter expression to this role. The expression will filter the supplier quotes table so that it only shows rows where the supplier’s name matches the user principal name of the person viewing the report. This ensures that each supplier can only see their own data.
You will then test this role directly within Power BI Desktop using the “View as” feature. This allows you to impersonate a user in that role and to confirm that the security filters are working correctly. Implementing RLS is a critical skill for any analyst who will be sharing reports with external partners or with different departments within a large organization.
Step 6: Drawing Conclusions and Presenting the Final Deliverable
The final step is to polish your report and prepare it for delivery to the leadership team at Tenate Industries. Ensure your report has a professional design, with clear titles, helpful tooltips, and a consistent color scheme. The user experience should be intuitive, allowing managers to easily navigate between the quote analysis tool and the make-or-buy scenario planner.
You should add a summary page or use text boxes to explain how to use the tool and to highlight the key takeaways. Your report is now a powerful, secure, and interactive analytical application. It allows the purchasing team to optimize their supplier selection and enables the production team to make data-driven, strategic decisions about their manufacturing strategy based on expected volumes.
By completing this project, you have demonstrated a wide range of intermediate Power BI skills. You have worked with a complex data model, written advanced DAX, and implemented sophisticated features like what-if parameters and row-level security. This project is a substantial and impressive addition to your portfolio, showcasing your ability to tackle complex business problems and to deliver a high-value analytical solution.
From Guided Projects to Independent Analysis
You have now navigated a series of guided projects, building your skills from the foundational concepts to more advanced, intermediate techniques. You have built a customer churn dashboard, an HR analytics report, and a sophisticated supply chain decision tool. This experience is the core of your new portfolio. However, the ultimate goal of this journey is to equip you with the skills and confidence to tackle any data problem independently. The final stage of your development is to move beyond guided projects and to start your own analyses.
This final part of our series will focus on this transition. We will discuss how to find your own compelling datasets and how to approach an independent project from scratch. We will also cover the crucial topic of how to effectively showcase your completed projects in a professional portfolio to attract the attention of recruiters and hiring managers. Finally, we will look ahead, discussing strategies for continuous learning and for staying current in the fast-evolving world of business intelligence.
Your journey with Power BI does not end with the completion of a few case studies. It is a continuous process of learning, experimenting, and refining your craft. The skills you have built are in high demand, and by effectively showcasing them and committing to ongoing development, you can build a successful and rewarding career in data analytics.
How to Find Compelling Datasets for Your Own Projects
The best way to demonstrate your passion and creativity is to work on projects with data that genuinely interests you. Finding your own datasets is a key step in becoming an independent analyst. Fortunately, there is a wealth of free, high-quality data available online. Government portals are an excellent starting point. Many countries and cities have open data initiatives that publish vast datasets on everything from public health and transportation to crime rates and economic indicators.
Data science competition websites are another fantastic resource. They host a wide variety of clean and well-documented datasets that are perfect for portfolio projects. You can find data on topics ranging from movie ratings and sports statistics to e-commerce transactions and scientific research. These platforms also have the added benefit of communities where you can see how other people have analyzed the same data.
Other sources include public APIs from companies, which allow you to programmatically pull data on topics like social media trends or financial markets. Academic repositories and non-profit organizations also frequently publish data related to their research. When choosing a dataset, look for something that is not only interesting to you but is also complex enough to allow for a meaningful analysis and storytelling.
Structuring and Presenting Your Portfolio
A portfolio is a curated collection of your best work, designed to showcase your skills to potential employers. It should be professional, well-organized, and easy to navigate. The most effective way to host your portfolio is on a personal website or a professional networking profile. For each project, you should create a dedicated page or section that provides a comprehensive overview.
Your project showcase should not just be a link to the final Power BI report. You need to provide context. Start with a clear project title and a brief summary of the business problem you were solving. Describe the dataset you used and your process, including the key steps you took in Power Query for data cleaning and the structure of your data model.
The centerpiece should be a high-quality image of your main dashboard, along with a public link to the interactive report itself (you can use Power BI’s “Publish to web” feature for public datasets). Most importantly, you must articulate the story behind the data. Summarize the key insights you uncovered and the actionable recommendations you derived from your analysis. This demonstrates not just your technical skills, but your business acumen and communication abilities.
The Importance of Storytelling in Your Presentation
When a hiring manager looks at your portfolio, they are not just evaluating your ability to use Power BI. They are assessing your ability to think like an analyst and to communicate effectively. The most critical skill to showcase is your ability to tell a compelling story with data. Every project in your portfolio should be framed as a narrative.
Your story should have a clear beginning, middle, and end. The beginning is the business problem or the question you set out to answer. The middle is your analytical journey—the charts, graphs, and findings that you uncovered as you explored the data. The end is the conclusion—the key insights and actionable recommendations that provide a solution to the initial problem.
When you describe your project, use this narrative structure. Do not just say, “I made a bar chart to show sales by region.” Instead, say, “To understand our geographic performance, I analyzed sales by region and discovered that the North region was underperforming by 30%, which led me to investigate the local marketing spend in that area.” This storytelling approach makes your work far more engaging and demonstrates your value as a strategic thinker.
Preparing for Power BI Interview Questions About Your Projects
Your portfolio is likely to be a central topic of discussion during your interviews. Be prepared to talk about each of your projects in depth. Interviewers will ask you specific questions to test your understanding of the work you have done. They will want to know why you chose certain visualizations, how you wrote a particular DAX measure, and what challenges you faced during the project.
Before any interview, you should review your own projects thoroughly. Rehearse a concise, two-minute summary for each one. Be prepared to explain your data modeling decisions. Why did you choose a star schema? What was the most difficult part of the data cleaning process? An interviewer might also ask you to critique your own work. “If you had more time, what would you add or change about this dashboard?” This question tests your self-awareness and your desire for continuous improvement.
You should be able to articulate the business impact of your analysis. Even for a personal project, you can frame the impact in terms of the potential value it could provide. Your ability to confidently and clearly discuss your past work is a direct reflection of your competence and will be a major factor in the hiring decision.
Conclusion
The world of business intelligence and data analytics is constantly evolving. Power BI itself is updated by Microsoft every single month with new features and enhancements. To remain a top-tier professional, you must commit to continuous learning. A great way to do this is to follow the official Power BI blog and to engage with the vibrant online community of users, experts, and Microsoft MVPs.
Participating in data visualization competitions is another excellent way to sharpen your skills and to keep your portfolio fresh. These competitions provide a creative outlet to experiment with new techniques and to tackle challenging datasets. They are also a great way to get your work seen by a wider audience and to get feedback from other experts in the field. A winning or even a well-regarded entry in a competition can be a fantastic addition to your portfolio.
Never stop being curious. The projects in this series are a starting point. The real journey is about applying these skills to new problems, learning from your successes and failures, and continuously pushing the boundaries of what you can do with data. This commitment to growth is what will ultimately define your success in this exciting field.