University of Kwazulu-Natal

Power Bi Usage Scenarios Advanced Data Preparation

Bonisiwe Shabane
-
power bi usage scenarios advanced data preparation

Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. This article is part of the Power BI implementation planning series of articles. The series focuses on planning to implement a Power BI experience inside Microsoft Fabric.

See the series introduction. Data preparation (sometimes referred to as ETL, which is an acronym for Extract, Transform, and Load) activities often involve a large effort. The time, skill, and effort involved with collecting, cleaning, combining, and enriching data depends on the quality and structure of source data. Investing time and effort in centralized data preparation helps to: There was an error while loading. Please reload this page.

In the world of business intelligence, technical skills alone aren’t enough—what truly sets a Power BI professional apart is the ability to solve real-world business challenges efficiently and strategically. Whether it's optimizing slow dashboards, handling multi-source data integration, or enabling secure report sharing, each scenario demands not just tool knowledge but problem-solving expertise. This article presents 15 practical Power BI scenarios often encountered by analysts, consultants, and business decision-makers. Each situation includes a detailed and actionable solution designed to help professionals handle performance, usability, security, and data governance with confidence. If you're preparing for interviews, building client solutions, or managing enterprise-grade reporting, these examples will give you the clarity and approach needed to succeed with Power BI in real business environments. To optimize performance, begin by analyzing the data model for inefficiencies.

Remove unnecessary columns, minimize the use of high-cardinality fields, and ensure appropriate data types are used. Aggregations should be introduced at the query level where applicable. For large datasets, implement features like incremental refresh or aggregated tables to reduce load. Apply filters at the data source or dataset level rather than relying solely on report-level filters. Avoid overly complex visuals or custom visuals that hinder performance. Evaluate the necessity of DirectQuery mode, and if not essential, switch to Import mode with scheduled refreshes to significantly enhance report speed.

Start by reviewing the data sources and Power Query transformation steps to ensure they align with the logic applied in the Excel file. Examine the applied filters and slicers in the Power BI report for any unintended exclusions. Compare aggregation logic (e.g., sum, average) between both tools. Use the data lineage view to trace data flow from source to visualization, checking for discrepancies in joins, calculated columns, or DAX measures. Ensure consistent time frames and granularity are used across both reports. Access to this page requires authorization.

You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. This article is part of the Power BI implementation planning series of articles. The series focuses on planning to implement a Power BI experience inside Microsoft Fabric. See the series introduction.

We encourage you to download the Power BI usage scenario diagrams if you'd like to embed them in your presentations, documentation, or blog posts—or print them out as wall posters. Because they're Scalable Vector Graphics (SVG) images, you can scale them up or down without any loss of quality. For more information, see Power BI usage scenarios. Power BI is highly regarded for delivering robust analytics and immersive reporting. Yet, as you progress beyond the basics, you’ll find a vast range of features and nuances waiting to be explored. Whether you’re honing your DAX skills, refining your data models, or seeking new ways to share insights with stakeholders, advanced tips can make a world of difference in your workflow.

In the following sections, we’ll walk through 72 tips that touch on every stage of the Power BI lifecycle—from pulling in data to distributing final dashboards. Each tip aims to solve a specific challenge you might face, with guidance on best practices, lesser-known tools, and strategic approaches for working more efficiently. Even seasoned Power BI users can pick up time-savers and new techniques here. And if you’ve ever needed a better system for exporting or scheduling your data-driven slides, keep an eye out for Tip #31, which highlights how Rollstack offers an automation strategy for recurring Power BI... Prepare to expand your Power BI skill set and enhance how your team interprets and acts upon your findings. Dataflows allow you to offload heavy transformations to the Power BI service.

Instead of reapplying the same transformations in multiple reports, create a dataflow once and reuse it, saving time and reducing duplication. Before building visuals, use the View data and Column profiling features in Power Query Editor to spot missing values, outliers, or schema inconsistencies. Early detection of anomalies prevents errors down the line. In today's data-driven world, organizations are increasingly relying on data analytics to drive informed decision-making. Power BI is a powerful business intelligence (BI) platform that enables organizations to collect, analyze, and visualize data from various sources. However, to fully harness the power of Power BI, it's crucial to implement advanced data preparation techniques.

Why Advanced Data Preparation is Essential Raw data, as collected from various sources, often contains inaccuracies, inconsistencies, and missing values. These issues can hinder the effectiveness of data analysis and lead to misleading insights. Advanced data preparation addresses these challenges by transforming raw data into a clean, organized, and structured format that is ready for analysis and reporting. Benefits of Advanced Data Preparation in Power BI Implementing advanced data preparation in Power BI offers several key benefits:

Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. This article is part of the Power BI implementation planning series of articles. The series focuses on planning to implement a Power BI experience inside Microsoft Fabric.

See the series introduction. Data preparation (sometimes referred to as ETL, which is an acronym for Extract, Transform, and Load) often involves a significant amount of work depending on the quality and structure of source data. The self-service data preparation usage scenario focuses on the reusability of data preparation activities by business analysts. It achieves this goal of reusability by relocating the data preparation work from Power Query (within individual Power BI Desktop files) to Power Query Online (using a Power BI dataflow). The centralization of the logic helps achieve a single source of the truth and reduces the level of effort required by other content creators. Dataflows are created by using Power Query Online in one of several tools: the Power BI service, Power Apps, or Dynamics 365 Customer Insights.

A dataflow created in Power BI is referred to as an analytical dataflow. Dataflows created in Power Apps can either be one of two types: standard or analytical. This scenario only covers using a Power BI dataflow that's created and managed within the Power BI service.

People Also Search

Access To This Page Requires Authorization. You Can Try Signing

Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. This article is part of the Power BI implementation planning series of articles. The series focuses on planning to implement a Power BI experience inside Microsoft Fabric.

See The Series Introduction. Data Preparation (sometimes Referred To As

See the series introduction. Data preparation (sometimes referred to as ETL, which is an acronym for Extract, Transform, and Load) activities often involve a large effort. The time, skill, and effort involved with collecting, cleaning, combining, and enriching data depends on the quality and structure of source data. Investing time and effort in centralized data preparation helps to: There was an ...

In The World Of Business Intelligence, Technical Skills Alone Aren’t

In the world of business intelligence, technical skills alone aren’t enough—what truly sets a Power BI professional apart is the ability to solve real-world business challenges efficiently and strategically. Whether it's optimizing slow dashboards, handling multi-source data integration, or enabling secure report sharing, each scenario demands not just tool knowledge but problem-solving expertise....

Remove Unnecessary Columns, Minimize The Use Of High-cardinality Fields, And

Remove unnecessary columns, minimize the use of high-cardinality fields, and ensure appropriate data types are used. Aggregations should be introduced at the query level where applicable. For large datasets, implement features like incremental refresh or aggregated tables to reduce load. Apply filters at the data source or dataset level rather than relying solely on report-level filters. Avoid ove...

Start By Reviewing The Data Sources And Power Query Transformation

Start by reviewing the data sources and Power Query transformation steps to ensure they align with the logic applied in the Excel file. Examine the applied filters and slicers in the Power BI report for any unintended exclusions. Compare aggregation logic (e.g., sum, average) between both tools. Use the data lineage view to trace data flow from source to visualization, checking for discrepancies i...