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

Fri, 13 Feb 2026 10:04:17 +0000

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.

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.
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.
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:

Building dashboards before understanding the business problem
- Always start with the question, not the visuals.
Overloading dashboards with too many charts
- Fewer visuals = clearer message.
Writing complex DAX that no one understands
- Keep formulas readable and documented.
Ignoring performance and refresh issues
- Optimize models and ensure reliable data updates.
Designing for yourself instead of the user
- Always think about who will use the dashboard and how, assuming they are not tech-savvy.
Not using a star schema.
- 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.
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   2. Cons
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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

MS Excel for Data Analytics: A Beginner-Friendly Introduction

Fri, 23 Jan 2026 20:51:23 +0000

Howdy! Ever heard of Microsoft Excel? Ever wondered what its use is? Worry not, let me simplify it for you. Microsoft Excel is one of the most poowerful and beginner-friendly tools for data analytics. Before people move to advanced tools like Python or SQL, they often start with Excel, and for good reason. It helps you organize data, clean it, analyze it, and visualize it all in one place. How cool is that?

Whether you are tracking sales, survey responses, expenses, or social media performance, Excel can turn your raw numbers (data) into meaningful insights.

What You Will Learn from This Article

By the end of this article, you will be able to:

Understand what Microsoft Excel is and how it is used in data analytics
Identify rows, columns, and cells in a worksheet
Enter and organize data in table format
Use basic Excel functions like SUM, AVERAGE, COUNT, and IF
Clean data by sorting, filtering, and removing duplicates
Create Pivot Tables to summarize large amounts of data
Build simple charts to visualize trends and comparisons
Understand how Excel can be used to create basic dashboards for reporting
Gain a strong foundation to start working with data confidently, even as a beginner.

Excel Terms You Will Encounter (Excel & Data Analytics)

Here are simple explanations of key terms used in this article:

Cell - A single box in Excel where a row and column meet (example: A1).
Column - A vertical group of cells labeled with letters (A, B, C…).
Row - A horizontal group of cells labeled with numbers (1, 2, 3…).
Worksheet - One page inside an Excel file where you enter and analyze data.
Workbook - An Excel file that contains one or more worksheets.
Dataset - A collection of related data arranged in rows and columns.
Formula - An equation used in Excel to perform calculations (example: =A1+B1).
Function - A built-in formula in Excel that performs a specific task (example: SUM, AVERAGE).
Range - A group of selected cells (example: A1:A10).
Filter - A tool used to display only the data that meets certain conditions.
Sort - Arranging data in a specific order (A–Z, smallest to largest, etc.).
Pivot Table - A tool that summarizes and analyzes large datasets by grouping and calculating data automatically.
Chart - A visual representation of data, such as a bar chart or line graph.
Dashboard - A single page that shows key data insights using charts, numbers, and summaries.
Data Cleaning - The process of fixing or organizing data so it is accurate and ready for analysis.

All right, let’s get into understanding MS Excel.

1. Introduction to Excel

Definition

Microsoft Excel is a spreadsheet software developed by Microsoft that helps people store, organize, calculate, and analyze data using tables made of rows and columns.
It allows users to:

Perform calculations with formulas and functions
Sort and filter information
Create charts and graphs
Summarize data using tools like Pivot Tables

Simply put, Excel turns raw numbers into useful information that helps with decision-making.

Excel is a spreadsheet program made up of:

Rows – horizontal lines (1, 2, 3…)
Columns – vertical lines (A, B, C…)
Cells – where rows and columns meet (like A1, B2)

Each cell can contain:

Text (Names, Categories)
Numbers (Sales, Scores)
Dates (1/1/2026, 02-11-2026)
Formulas (calculations), among many others.

Example Dataset

Let’s say we have this simple sales dataset:

Date	Product	Quantity	Price	Total Sales
1/1/2026	Pen	10	15	150
1/2/2026	Book	5	70	350
2/3/2026	Pencil	7	25	175
1/4/2026	Rubber	3	10	30

*NOTE: In this table, the Total Sales column is calculated by multiplying Quantity × Price.*

This kind of table is the starting point for data analysis.

2. Excel Functions (Basic Calculations)

Functions are built-in formulas that help you perform calculations easily.

1. SUM – Add Numbers

Adds a range of numbers.

=SUM(C2:C5)

This adds all values from cell C2 to C5.

*Use case: Total sales, total expenses, total quantity sold.*

AVERAGE – Find the Mean

Calculates the average of numbers.

=AVERAGE(D2:D5)

*Use case: Average score, average daily sales.*

COUNT – Count Numbers

Counts how many cells contain numbers.

=COUNT(A2:A5)

*Use case: Number of transactions recorded.*

COUNTA – Count Non-Empty Cells

Counts cells that are not empty (text or numbers).

=COUNTA(B2:B5)

*Use case: Number of customers or products listed.*

IF – Logical Function

Performs a test and returns different results depending on the outcome.

=IF(C2>10, "High", "Low")

This checks if the value in C2 is greater than 10.

* Use case Categorizing sales as High/Low, Pass/Fail.*

3. Data Cleaning in Excel

Before analysis, data often needs cleaning.
Common cleaning tasks:

Removing duplicates
Data -> Remove Duplicates
Sorting data
Data -> Sort A to Z
Filtering data
Data -> Filter

Filtering lets you view only certain data, like:

Only sales for “Pen.”
Only records from January

4. Pivot Tables (Powerful Data Summaries)

Pivot Tables help you summarize large data quickly without complex formulas.

You can answer questions like:

Total sales per product
Sales per month
Quantity sold by category

Steps to create a Pivot Table:

Select your data
Go to Insert -> PivotTable
Choose where to place it
Drag fields into Rows, Columns, and Values

Example:

Rows -> Product
Values -> Sum of Total Sales

Excel will automatically calculate total sales for each product.

5. Charts in Excel

Charts help turn numbers into visuals that are easier to understand.

Common chart types:

Column Chart – Compares categories
Line Chart – Shows trends over time
Pie Chart – Shows proportions

How to insert a chart:

Select your data
Click Insert
Choose a chart type

*Example: Sales by product displayed as a column chart.*

6. Dashboards in Excel

A dashboard is a single page that shows key insights using charts, numbers, and summaries.

A simple Excel dashboard might include:

Total Sales (big number)
Sales by Product (chart)
Sales by Month (line chart)
Top Product (text or card)

Dashboards help decision-makers understand performance quickly.

Tools used in dashboards:

Pivot Tables
Charts
Slicers (interactive filters)

Conclusion

Excel is a powerful starting tool for data analytics because it helps you:

Organize data in tables
Perform calculations using functions
Clean and filter messy data
Summarize information with Pivot Tables
Visualize trends using charts
Build dashboards for reporting

Mastering Excel gives you a strong foundation before moving into advanced analytics tools like Power BI, Python, or SQL.

ALL THE BEST IN YOUR ANALYTICS JOURNEY!