Welcome to Week 2 of the QuickViz Challenges!  These challenges are a fun way to practice your data visualization skills, experiment with Power BI and Microsoft Fabric, and share your insights with the community. Whether you’re a beginner or a seasoned pro, these challenges are designed to help you build confidence and creativity with real datasets.

Data chaos is one of the biggest hidden costs in any organization.
When your Power BI reports are slow, inaccurate, or difficult to maintain, the root cause is almost always the same: a weak or inconsistent data model.
This article shows you how to transform scattered operational data into a clean, scalable, high-performance Power BI data model the same principles used in real-world global operations.

Welcome to Our November 2025 Newsletter!

We’re excited to bring you fresh insights this month as we begin a brand-new series on Scenario Analysis in Power BI.

Starting with this edition, I’ll be sharing a collection of articles that explore different approaches to scenario analysis and their practical applications in real business cases.

In this first article, we’ll focus on Simple Scenario Analysis — integrating What-If Parameters to dynamically adjust key variables and visualize how changes impact outcomes directly within Power BI.

Resources and files for this article are available for download here: https://1drv.ms/f/c/357fb5c8090fe1b2/EraKe-FIlR1EpTL1HYTpAREBYFALs9GKTMh7MnWebL6mWg?e=05Qg4J

In this analysis, let’s create a group (using Home → Enter Data) to organize all the measures we’ll be using.

Have you ever tried explaining a Power BI issue and immediately realized you can’t share the dataset behind it?

Maybe the data is confidential, maybe it’s messy, or maybe you just don’t want to hand-craft a new example every time. Before you know it, you’re exchanging files, fixing paths, and losing momentum...

My solution? Generate a sample dataset using Power Query only!

Many Power BI reports slow down because of date handling mistakes. This post explains why auto date/time hurts performance and how to build one proper calendar table for faster, accurate reports.

While developing a BI report, sometimes information is spread across multiple columns and other times it is packed into rows. There are three powerful Power Query transformations come into the picture, which are Pivot, Unpivot and Transpose.

In this blog, l will walk you through what they are, why they are useful and how they different.

Welcome to Week 1 of the QuickViz Challenges! 🎉

These challenges are a fun way to practice your data visualization skills, experiment with Power BI and Microsoft Fabric, and share your insights with the community. Whether you’re a beginner or a seasoned pro, these challenges are designed to help you build confidence and creativity with real datasets.

Power Query functions are the backbone of efficient and reusable transformations in Power BI.

In this hands-on post, we’ll explore three practical examples, from simple text cleanup to a dynamic pivot and automatic INR currency conversion.

In this aricle we delve into how to use additional column context with TimeIntelligence functions of DAX.

Do you want to participate in a dataviz contest that's bite-sized? Join us for our new QuickViz challenges. We'll build a single visual at a time!

Join us for a fun and friendly competition where we're building a notebook! Use Fabric Data Science Notebooks to uncover insights about how cities evolve, sustain, and innovate for the future.

Join us for a dataviz contest created to level up both your skills and your career! Build a Power BI report that represents you and your career, add to your dataviz portfolio, learn from your peers, and have some fun!

Wanting some accountability for studying for your Fabric certification exam.  Read on for details on how to join a study group.

Join us for the first dataviz contest made just for students! Build a Power BI report, start your dataviz portfolio, learn from your peers, and have some fun!

When you start building a report on Power BI platform, you can quickly see lines connecting tables in the Model View. It looks very simple but behind those lines lies the secret of how Power BI understands your data. What do these actually mean?