Cleaning, Calculating, and Communicating: The Analyst’s Power BI Workflow

Have you ever opened a raw dataset and immediately felt overwhelmed? How easy was it for you to understand the data? My guess is you often found it messy and probably wondered to yourself, “How is this kind of data supposed to relay insights and help in business decision-making?”

Other than Excel, there is another tool called Power BI that allows you to load messy data, clean it, model it, visualize it, and transform it into business insights that decision-makers can understand and act on.

This article will show you what the analysis workflow in Power BI looks like as we unlock our analyst minds and showcase our skills of not only coming up with visually engaging yet insight-driven dashboards, but also being able to turn chaos into clarity.
Let’s begin!

Terms you will find in this article.

1. Power Query - A data transformation and preparation engine from Microsoft that enables users to import, clean, reshape, and combine data from various sources without needing advanced coding skills.
2. Data Analysis Expressions (DAX) - A formula expression language used in Microsoft Power BI, Power Pivot in Excel, and Analysis Services to create custom calculations, measures, and calculated columns.
3. *ETL (Extract, Transform, Load) process - * A data integration methodology that collects raw data from multiple sources, cleans and reshapes it, and loads it into the Power BI engine for analysis.

What is Power BI?

Power BI is a business intelligence tool developed by Microsoft that enables analysts to transform raw data into interactive, decision-ready dashboards. It allows analysts to clean raw data, calculate meaningful metrics, and communicate insights in a way decision-makers can actually use.

Before opening Power BI, great analysts ask one critical question What decision is this analysis meant to support? Many dashboards tend to fail because they start with visuals instead of purpose. A good analysis begins with clarity (What do we really expect at the end of this analysis?):

• Are we trying to increase revenue?
• Reduce costs?
• Improve customer retention?
• Identify underperforming products?
• Understand customer engagement?
• Increase safety in air travel?
• Plan for reasonable maintenance schedules?
• Decrease flight departure and arrival delays?

When the goal is clear, everything else becomes easier. The data you work on, the metrics you calculate, and the visuals you design should all serve that one objective. Power BI is just the vessel. The destination is always an impactful business decision.

NOTE - The general workflow in Power BI follows a logical order, often summarized by the ETL process (Extract, Transform, Load).

Loading data in Power BI

Loading data is the first step in analyzing data in Power BI. When you open Power BI, this is the first thing you will see. To load data, click on get data, then depending on your file type, choose the appropriate file to load. It may take some time depending on your data size.

A pop-up will then appear where you can Load data, Transform Data, or even Cancel if you maybe uploaded the wrong data set. Now, since we have to now clean the data, our next button to click is Transform Data(black button). This will now take us to our next step, **Power Query**, where we will prepare our data for analysis.

*NOTE - * In an instance where you have already cleaned your data using Excel and now want to visualize your data using Power BI, you will click the Load (Red) button. This will take you directly to your worksheet area to start your calculations and visualizations.

Preparing Data with Power Query

Real-world data is rarely analysis-ready. That’s why the first technical step in Power BI is almost always Power Query. This is what analysts use Power Query for:

• Data connection
• Transforming and shaping data (e.g., fixing incorrect data types such as text vs. numbers vs. dates)
• Combining Data (e.g., merging related tables)
• Automating processes
• Loading data

For example, an airline safety and management dataset might have:

• Flight dates stored as text
• Revenue with currency symbols
• Flight crew names are spelled differently across files
• Duplicate Flight IDs

If you don’t fix these issues first, your analysis will be wrong, no matter how good your DAX or visuals are. Think of Power Query as the foundation of a house. If the foundation is weak, everything built on top will crack and eventually crumble.

Data Modelling in Power BI

Power BI will almost always automatically create relationships between the tables in your dataset, based on common fields across tables. Now, this brings us to our next step in Power BI workflow analysis, Data Modelling.

It is good practice not to fully trust Power BI’s automatic modeling, as it can sometimes create incorrect relationships. Before working on your calculations, ensure that your modeling is correct, with your relationships well identified. To understand the schemas and modeling in Power BI, check out this article that will help you understand it in detail Schemas and Data Modelling in Power BI

Turning Data into Metrics with DAX

Once the data is clean and the models are correct, analysts move to the next phase, which is calculating insights using DAX (Data Analysis Expressions). DAX is the language Power BI uses to create:

• Measures (dynamic calculations like totals, averages, ratios)
• Time intelligence (month-to-date, year-to-date, growth %)
• Rankings and comparisons

For example, instead of just showing Total Flight Trips, an analyst might calculate:

• Average Travel Delays
• Profit Margin
• Month-over-Month Growth
• Top 10 flight accidents by cause

This is where raw data becomes business intelligence. A table of numbers tells you what happened. DAX measures help explain how well it happened and why it matters. Good analysts also keep DAX readable and logical. Complex formulas are powerful, but only if they can be understood and maintained.

Designing Dashboards That Drive Decisions

At this stage, we have successfully loaded our messy data into Power BI, transformed our data in Power Query, modelled our data, calculated metrics to get a deeper understanding of our data, and finally, we are at visualizing our data. The workflow becomes structured and logical.
This step, communication, is now a step closer to finalizing our analysis in Power BI, turning calculations into dashboards that tell a story. A strong Power BI dashboard:

• Answers specific questions
• Highlights what matters most
• Makes insights obvious at a glance

This means:

• Choosing the right visuals (not just the fancy ones)
• Using layout and hierarchy to guide the viewer’s eye
• Limiting clutter
• Using consistent formatting and colors (Best advisable to use colors relating to the type of dataset you have, e.g. in an agricultural dataset, you can use green to represent nature and maybe a touch of brown)

Instead of asking users to search for insights, great dashboards present them. For example:

• A KPI card for Total Flight Trips
• A trend line showing growth in flight safety over time
• A bar chart ranking the top causes of flight accidents
• A slicer to explore by region or cause

The goal isn’t to impress, but to inform and facilitate great business decision-making.

What Great Analysts Do Differently

The best analysts don’t stop at insights. They go one step further and translate them into recommendations, not just Flight safety was increased in Region B, but Flight safety improved in Region B, with major accidents decreasing by 78% after targeted safety measures were implemented based on identified root causes. This suggests the measures were effective. We should review how they were implemented and consider replicating the approach in other regions.. This is where Power BI becomes a decision engine, not just a reporting tool. Here’s an example of action-oriented analysis:

Suppose an airline recorded 12,000 flights last year. After cleaning and modeling the data:
• You discover that 62% of delays happen during peak weather months.
• 45% of major incidents are linked to maintenance scheduling gaps.
Instead of simply reporting these numbers, the recommendation becomes:
**“Reallocate maintenance checks before peak weather months and reinforce crew training during high-risk periods.”**

NOTE - Insight without action is just information. Action is what creates value.
 

Common Power BI Mistakes (and How to Avoid Them)

Making mistakes while working with Power BI — or any tool — is part of the learning process. Even experienced analysts make mistakes. Mistakes are part of the learning process. What matters is identifying them early and improving from them. Some of the most common include:

1. Building dashboards before understanding the business problem
2. Always start with the question, not the visuals.
3. Overloading dashboards with too many charts
4. Fewer visuals = clearer message.
5. Writing complex DAX that no one understands
6. Keep formulas readable and documented.
7. Ignoring performance and refresh issues
8. Optimize models and ensure reliable data updates.
9. Designing for yourself instead of the user
10. Always think about who will use the dashboard and how, assuming they are not tech-savvy.
11. Not using a star schema.
12. Poor model design can significantly reduce performance. Always aim for a clean fact table connected to dimension tables.

The Skills That Separate Good Analysts from Great Ones

Power BI is a technical tool, but great analysis is a blend of technical and human skills. Great analysts combine:

• Power Query + DAX + Data Modeling
• Business thinking
• Communication and storytelling

They understand that:

• Data is about people
• Dashboards are about decisions
• And analytics is about impact

Pros and Cons of Power BI

Just like every other software you can use out there, Power BI has its own pros and cons.

      1. Pros

• Cost-Effective - Offers a free, functional desktop version, with affordable Pro licensing compared to competitors.

• Microsoft Ecosystem Integration - Seamlessly connects with Excel, Azure, SQL Server, and Office 365.

• Powerful Visualization & Data Handling - Features a wide range of interactive, custom visual tools and strong data modeling capabilities.

• Constant Updates - Microsoft frequently updates the platform with new, AI-driven features.

• Large Community Support - Extensive online resources, forums, and documentation are available.

   2. Cons
  

• Steep Learning Curve - While easy for basic tasks, mastering DAX (Data Analysis Expressions) and complex data modeling requires significant technical expertise.

• Performance Issues - Can become slow when processing, loading, or refreshing large or complex datasets.

• Limited Customization - Flexibility for customizing visuals can be restrictive compared to tools like Tableau.

• Dependency on Windows/Cloud - Primarily designed for Windows, with limited or no native, full-featured editing capabilities on other operating systems.

• Licensing Complexity - The licensing structure can be confusing, and, for some, the cost of scaling (Premium) is high.

Conclusion

Finally! We now have a clear understanding of the Power BI analysis workflow. The workflow is structured and logical — and with consistent practice, it becomes second nature. Platforms like Kaggle provide high-quality practice datasets to sharpen your skills.

The analyst’s workflow can be summarized in three powerful steps:
Load -> Clean -> Model -> Calculate -> Communicate

Power BI supports every stage of the modern analytical workflow:

• Power Query cleans the chaos
• Data modeling creates structure
• DAX builds intelligence
• Dashboards communicate insight

Tools alone don’t create impact — analysts do.
The real value lies not in reporting what happened, but in guiding what should happen next.

ALL THE BEST IN YOUR POWER BI EXPLORATION