DEV Community: Judy Mokaya

Behind the Dashboard: How Analysts Turn Data Problems into Business Decisions

Judy Mokaya — Sun, 08 Feb 2026 15:10:45 +0000

Most people imagine data analysis as clean tables and perfect charts.

In reality? Data shows up late, incomplete, duplicated, and slightly wrong and the business still wants answers.

This is where Power BI analysts earn their keep: not by making data pretty, but by turning chaos into clarity and insight into action.

Messy Data Is the Starting Point, Not a Failure

Real business data is messy because real businesses are messy.

Sales happen before systems update. Dates are entered manually. Names don’t follow rules. Some fields are just… empty.

Before anything meaningful can happen, analysts step into Power Query and start asking:

What can we trust?
What needs fixing?
What should be flagged instead of “corrected”?

Cleaning data isn’t about forcing perfection, it’s about creating consistency so decisions aren’t based on noise.

Why this matters:

If the data foundation is shaky, every insight built on top of it is questionable.

DAX Is Where Data Becomes Insight

Once the data makes sense, the next question is never “What is the total?”

It’s “Compared to what?”

This is where DAX comes in.

With DAX, analysts move beyond basic totals and start answering real questions:

Are we improving or declining?
Which products are actually profitable?
Is growth coming from volume or discounts?
Where are we losing money quietly?

DAX allows metrics to respond to time, filters, and context exactly how the business thinks.

Why this matters:

Decisions aren’t made on static numbers. They’re made on patterns, trends, and comparisons.

Dashboards Are Tools, Not Art Projects

A dashboard’s job is not to impress, it’s to inform.

Good Power BI dashboards:

Focus on the questions stakeholders care about
Make problems visible quickly
Reduce the need for explanations

Analysts design dashboards with intention:

KPIs show if things are on track
Trends reveal direction
Breakdowns explain why something is happening

Every visual earns its place.

Why this matters:

When leaders understand the story immediately, they act faster and with more confidence.

When Insights Start Driving Action

The real win isn’t the dashboard, it’s what people do after seeing it.

Power BI insights help teams:

Shift sales focus to high-performing regions
Adjust pricing and discount strategies
Catch operational issues before they escalate
Track goals without chasing reports

Because dashboards refresh automatically, conversations move from “What happened?” to “What should we do next?”

Why this matters:

Timely insights prevent small problems from becoming expensive ones.

The Analyst’s Real Job: Translation

At the end of the day, analysts are translators.

They translate:

Raw data into meaning
Business questions into metrics
Numbers into decisions

Knowing Power BI, DAX, and data models is important but understanding context is what makes those skills powerful.

The best analysts don’t just show numbers.

They help people understand what the numbers are asking them to do.

Final Thoughts

Power BI is not just about reports and dashboards.

It’s about helping businesses see clearly, decide confidently, and act deliberately even when the data is imperfect.

And in the real world, that ability is what turns analysts from report builders into trusted decision partners.

Schemas & Data Modelling in Power BI

Judy Mokaya — Sun, 01 Feb 2026 13:23:40 +0000

Overview

Great Power BI reports start long before visuals, they start with good data modelling. A well-designed model improves performance, simplifies DAX, and ensures accurate reporting.

This article briefly covers:

Fact and dimension tables
Star and snowflake schemas
Relationships
Why data modelling matters in Power BI

Fact and Dimension Tables

Fact Tables

Fact tables store measurable data such as:

Sales amount
Quantity
Revenue

They usually contain many rows and grow over time.

Dimension Tables

Dimension tables store descriptive data that give context to facts:

Customers
Products
Dates

Dimensions are used for filtering, grouping, and slicing data.

Example: Fact vs Dimension Tables in Power BI Model View

Power BI Model View showing one fact table connected to multiple dimensions.

Star Schema

The star schema is the most recommended modelling approach in Power BI.

Structure

One central fact table
Multiple dimension tables connected directly to the fact table

Why Star Schema Works Best

Simple and intuitive
Fewer joins → better performance
Easier and cleaner DAX

Star Schema Example

Power BI model with a clear star layout (fact table in the center).

Snowflake Schema

The snowflake schema is a more normalized version of the star schema.

Structure

Dimension tables are split into multiple related tables
Some dimensions connect indirectly to the fact table

Power BI Drawbacks

More complex relationships
Slower performance
Harder to understand and maintain

For Power BI, denormalized dimensions are usually better.

Snowflake Schema Example

Model view showing dimensions branching into other dimensions.

Relationships in Power BI

Relationships control how filters flow between tables.

Best Practices

Use one-to-many relationships
Prefer single-direction filtering
Avoid many-to-many unless necessary
Ensure dimension keys are unique

Bad relationships can cause incorrect totals and confusing visuals.

Relationships View Example

Power BI relationship settings dialog or Model View relationship lines.

Why Good Data Modelling Matters

Good data modelling:

Improves report performance
Ensures accurate aggregations
Simplifies DAX formulas
Makes reports easier to scale and maintain

Bad models don’t just slow reports, they can produce misleading insights.

Final Takeaway

Data modelling is the foundation of Power BI reporting. Understanding fact and dimension tables, using a star schema, and designing clean relationships leads to faster, more reliable, and easier-to-maintain reports.

Strong data models lead to trustworthy insights.

MS Excel for Data Analytics

Judy Mokaya — Sun, 25 Jan 2026 12:00:42 +0000

Introduction

Microsoft Excel is one of the most widely used tools for basic data analytics, especially for beginners. It is simple to learn, easy to use, and powerful enough to help you understand data, find patterns, and make data-driven decisions.

This article introduces how MS Excel can be used for basic data analysis, using beginner-friendly explanations and practical examples.

What Is Data Analytics?

Data analytics is the process of collecting, organizing, and analyzing data to extract useful information. For beginners, this often includes:

Organizing data into tables
Performing basic calculations
Sorting and filtering information
Creating charts for visualization

Excel is an excellent tool for performing all these tasks.

Understanding the Excel Interface

When you open Microsoft Excel, you will see:

Workbooks – Excel files
Worksheets – Sheets inside a workbook
Rows and Columns – Used to organize data
Cells – Where data is entered

Each cell is identified by a column letter and row number (for example, A1, B2).

Entering and Organizing Data

In Excel, data is best organized in a tabular format:

Each column has a header (for example: Name, Sales, Date)
Each row represents a single record

This structure makes data easier to analyze and visualize.

Performing Basic Calculations

Excel allows you to perform calculations using formulas. Every formula starts with an equals sign (=).

Common Excel Functions for Beginners

Sum – Adds values together

=SUM(B2:B10)

Average – Calculates the mean

=AVERAGE(B2:B10)

Count – Counts numeric values

=COUNT(B2:B10)

Sorting and Filtering Data

Sorting

Sorting helps you arrange data:

From smallest to largest
From A to Z
From highest to lowest

Filtering

Filtering helps you display only specific data, such as:

Sales greater than 5,000
Data from a specific date
Records from one category

Using Pivot Tables for Simple Analysis

Pivot Tables are one of Excel’s most powerful tools for data analytics. They help you summarize large amounts of data quickly.

With Pivot Tables, you can:

Calculate total sales per category
Count records
Compare values across groups

Visualizing Data with Charts

Charts make data easier to understand by turning numbers into visuals.

Common Charts for Beginners

Bar Charts – Compare values
Line Charts – Show trends over time
Pie Charts – Show proportions

Cleaning Data in Excel

Before analysis, data often needs cleaning. Excel helps with tasks like:

Removing duplicates
Fixing inconsistent text
Trimming extra spaces
Finding missing values

Why Excel Is Great for Beginner Data Analysts

Excel is ideal for beginners because:

It is easy to learn
No programming is required
Results are instant and visual
It prepares you for advanced tools like SQL and Power BI

Conclusion

Microsoft Excel is a powerful starting point for anyone interested in data analytics. By learning how to organize data, use formulas, apply filters, create pivot tables, and build charts, you can already perform meaningful data analysis.

With regular practice, Excel becomes more than just a spreadsheet tool—it becomes a data analytics companion.

Git & GitHub: A Beginner’s Step-by-Step Guide to Version Control

Judy Mokaya — Fri, 16 Jan 2026 19:37:38 +0000

Introduction

If you’re new to software development, you may have heard of Git and GitHub, but wondered what they actually do.

This guide explains Git in simple terms, why version control is important, and how to push, pull, and track your code on GitHub — all step by step.

What Is Git?

Git is a version control system. It keeps track of changes in your code over time.

Instead of manually saving multiple copies like project_v1, project_v2, etc., Git stores every change in a history. You can go back to any previous version anytime.

Why Version Control Is Important

Version control helps you:

Track changes in your code
Collaborate with other developers without overwriting work
Recover mistakes easily
Maintain a history of your project

Git makes development faster, safer, and more organized.

What Is GitHub?

GitHub is an online platform that stores Git repositories.

It allows you to:

Back up your code online
Share code with others
Collaborate on projects
Access your projects from anywhere

Think of Git as the tool to manage versions, and GitHub as the cloud storage for your Git projects.

How to Push Code to GitHub

Initialize Git in your project
Inside your project folder, run:

git init

This sets up Git in your project so you can start tracking changes.

Check File Status

git status

This shows:

Untracked files
Modified files
Files ready to commit

Add Files to Track
Add all your files to Git staging:

git add .

Or add a specific file:

git add filename

Commit Changes
Save a snapshot of your current work:

git commit -m "Initial commit"

Always use a clear, descriptive message for your commits

Connect to Your GitHub Repository
Create a repository on GitHub (do not initialize with README) and then run:

git remote add origin git@github.com:username/repository-name.git

Replace username with your GitHub username
Replace repository-name with your repository name

Push Code to GitHub
Send your local commits to GitHub:

git branch -M main
git push -u origin main

Check GitHub — your files should now appear online

How to Pull Code from GitHub

If you or someone else updates the repository, pull changes to your local machine:

git pull origin main

This keeps your local project synced with GitHub.

How to Track Changes Using Git

Check Status

git status

See which files have changes or are untracked.

View Commit History

git log

Shows all commits, authors, dates, and messages.

Typical Git Workflow

Make changes to files
git status → check changes
git add . → stage changes
git commit -m "Message" → commit changes
git push → upload to GitHub

Repeat this cycle whenever you work on your project.

Important Note for macOS Users

Git Bash is mainly used on Windows. On macOS (and Linux), you can use Terminal, which supports the same Git commands.

Conclusion

Git and GitHub are powerful tools for anyone working with files that change over time — not just developers.

They help you:

Organize projects
Collaborate easily with others
Keep your work safe from mistakes

Whether you’re coding, writing, designing, or managing any project, learning Git will make your workflow smoother and more reliable. Start practicing today and take control of your work history!

Git & GitHub: A Beginner’s Step-by-Step Guide to Version Control

Judy Mokaya — Fri, 16 Jan 2026 19:37:37 +0000

Introduction

If you’re new to software development, you may have heard of Git and GitHub, but wondered what they actually do.

This guide explains Git in simple terms, why version control is important, and how to push, pull, and track your code on GitHub — all step by step.

What Is Git?

Git is a version control system. It keeps track of changes in your code over time.

Instead of manually saving multiple copies like project_v1, project_v2, etc., Git stores every change in a history. You can go back to any previous version anytime.

Why Version Control Is Important

Version control helps you:

Track changes in your code
Collaborate with other developers without overwriting work
Recover mistakes easily
Maintain a history of your project

Git makes development faster, safer, and more organized.

What Is GitHub?

GitHub is an online platform that stores Git repositories.

It allows you to:

Back up your code online
Share code with others
Collaborate on projects
Access your projects from anywhere

Think of Git as the tool to manage versions, and GitHub as the cloud storage for your Git projects.

How to Push Code to GitHub

Initialize Git in your project
Inside your project folder, run:

git init

This sets up Git in your project so you can start tracking changes.

Check File Status

git status

This shows:

Untracked files
Modified files
Files ready to commit

Add Files to Track
Add all your files to Git staging:

git add .

Or add a specific file:

git add filename

Commit Changes
Save a snapshot of your current work:

git commit -m "Initial commit"

Always use a clear, descriptive message for your commits

Connect to Your GitHub Repository
Create a repository on GitHub (do not initialize with README) and then run:

git remote add origin git@github.com:username/repository-name.git

Replace username with your GitHub username
Replace repository-name with your repository name

Push Code to GitHub
Send your local commits to GitHub:

git branch -M main
git push -u origin main

Check GitHub — your files should now appear online

How to Pull Code from GitHub

If you or someone else updates the repository, pull changes to your local machine:

git pull origin main

This keeps your local project synced with GitHub.

How to Track Changes Using Git

Check Status

git status

See which files have changes or are untracked.

View Commit History

git log

Shows all commits, authors, dates, and messages.

Typical Git Workflow

Make changes to files
git status → check changes
git add . → stage changes
git commit -m "Message" → commit changes
git push → upload to GitHub

Repeat this cycle whenever you work on your project.

Important Note for macOS Users

Git Bash is mainly used on Windows. On macOS (and Linux), you can use Terminal, which supports the same Git commands.

Conclusion

Git and GitHub are powerful tools for anyone working with files that change over time — not just developers.

They help you:

Organize projects
Collaborate easily with others
Keep your work safe from mistakes

Whether you’re coding, writing, designing, or managing any project, learning Git will make your workflow smoother and more reliable. Start practicing today and take control of your work history!