DEV Community: Nkatha Gatobu

Messy data to actionable insights: HOW ANALYST USE POWER BI, DAX, AND DASHBOARDS.

Nkatha Gatobu — Fri, 06 Feb 2026 19:10:07 +0000

“This can’t be real data… right?” 😅

Welcome to real-world of analytics.

In this , we’ll walk through how data analysts actually work with Power BI — from messy data, to cleaning, to writing DAX, to building dashboards that are clear and understandable.

By the end, you’ll understand the full workflow and be able to build your own simple Power BI report with confidence.

The Analytics Workflow (Big Picture)

Most Power BI projects follow this flow:

Raw Data → Cleaning (Power Query) → Data Model → DAX Measures → Dashboards → Decisions

📊 Visual Flow Diagram (Conceptual)

[ Excel / CSV / SQL / APIs ]
            |
            v
       [ Power Query ]
            |
            v
       [ Data Model ]
            |
            v
            [ DAX ]
            |
            v
       [ Dashboards ]
            |
            v
     [ Business Actions ]

Step 1: Load Messy Data into Power BI

In the real world, data usually comes with problems:

❌ Duplicate rows
❌ Blank values
❌ Wrong data types (numbers stored as text)
❌ Inconsistent column names
❌ Multiple date formats

How to Load Data

In Power BI Desktop:

Click Home → Get Data
Choose Excel / CSV / SQL Server
Click Transform Data (don’t click Load yet)

This opens Power Query, where cleaning happens.

Step 2: Clean and Prepare Data (Power Query)

Power Query is your staging area.
Anything you fix here becomes repeatable and automatic.

Common Cleaning Tasks

Problem	Power Query Fix
Duplicates	Remove Rows → Remove Duplicates
Text with spaces	Transform → Format → Trim
Wrong data type	Change column type
Messy column names	Rename columns
Null values	Replace values / Filter

Example

Before

Customer   | Sales     | Order Date
 John Doe  | "1,000"   | 02/01/24
 John Doe  | "1000 "   | 2024-01-02

After

Customer  | Sales | Order Date
John Doe  | 1000  | 2024-01-02

Step 3: Create a Proper Data Model

A good data model = better performance + easier DAX.

Typical Tables

Sales (fact table)
Customers
Products
Date (calendar table)

Star Schema (Recommended)

Customers ──┐
            ├── Sales ── Date
Products ───┘

Modeling Best Practices

✅ One fact table, multiple dimension tables
✅ One-direction relationships
✅ Always use a Date table
❌ Avoid many-to-many unless necessary

Your DAX calculations depend on relationships to filter data correctly.

Step 4: Write DAX Measures (Beginner → Technical)

DAX = Data Analysis Expressions
This is how Power BI calculates metrics.

Start Simple

Total Sales =
SUM(Sales[Amount])

Total Orders =
COUNTROWS(Sales)

Total Customers =
DISTINCTCOUNT(Customers[CustomerID])

Slightly More Technical: Time Intelligence

Sales MTD =
CALCULATE(
    [Total Sales],
    DATESMTD('Date'[Date])
)

Sales Last Month =
CALCULATE(
    [Total Sales],
    DATEADD('Date'[Date], -1, MONTH)
)

MoM Growth % =
DIVIDE(
    [Sales MTD] - [Sales Last Month],
    [Sales Last Month]
)

DAX hack Tips:

Use measures, not calculated columns
Always create a Date table for time intelligence
Use DIVIDE() instead of / to avoid errors
Build and test one measure at a time

Step 5: Build Dashboards That Answer Questions

A good dashboard answers:

What is happening?
How is it trending?
Where should we act?

Example Layout

+----------------------------------+
| KPI Cards: Sales, Orders, Growth |
+----------------------------------+
| Sales Trend (Line Chart)         |
+----------------------------------+
| Sales by Product | Sales by City |
+----------------------------------+
| Filters (Date, Region)           |
+----------------------------------+

Visual Best Practices

KPI Cards → summary metrics
Line charts → trends
Bar charts → comparisons
Slicers → user interaction
Keep colors simple and consiste

If your dashboard needs explaining, it’s probably too complex.

Step 6: Turn Insights into Action

Dashboards are only useful when they change behavior.

Example

📉 Sales drop in Nairobi
➡️ Filter by product
➡️ Identify stock-outs
➡️ Fix supply chain
➡️ Sales recover

This is where data becomes impact.

Hands-On Practice

Exercise 1: Data Cleaning

Remove duplicates
Fix data types
Rename columns
Remove null rows

Exercise 2: DAX Measures

Total Sales = SUM(Sales[Amount])
Total Orders = COUNTROWS(Sales)
Average Order Value = DIVIDE([Total Sales], [Total Orders])

Exercise 3: Dashboard

Build:

1 KPI card
1 trend chart
1 bar chart
1 slicer

Power BI + DAX Cheat Sheet

Power Query

Task	Action
Remove duplicates	Home → Remove Rows
Change data type	Column header
Trim spaces	Transform → Format → Trim
Split column	Transform → Split Column

DAX

Goal	Function
Sum values	`SUM()`
Count rows	`COUNTROWS()`
Distinct count	`DISTINCTCOUNT()`
Apply filters	`CALCULATE()`
Time intelligence	`DATESMTD()`, `DATEADD()`
Safe division	`DIVIDE()`

Modeling

Use a Date table
Prefer star schema
Avoid bi-directional filters unless needed

lessons learnt so far.

Power BI is not about making pretty charts.
It’s about building reliable data pipelines that lead to better decisions.

The real skill is not just DAX or visuals —
it’s knowing what question the business is trying to answer.

If you’re new to Power BI:

Start messy
Build small
Improve one step at a time

You’re doing real analytics when your data isn’t perfect

power bi; for beginners on schema and snowflake.

Nkatha Gatobu — Mon, 02 Feb 2026 15:29:00 +0000

Power BI: Star Schema vs Snowflake Schema
Star Schema
A star schema is defined as the simplest data warehouse schema where one or more fact tables reference any number of dimension tables in a star-like structure.
Structure
• Fact Table: Central table containing business metrics and foreign keys
• Dimension Tables: Surrounding tables connected directly to the fact table
• Denormalized: Dimension tables contain all related data
*Example of star schema
*

Advantages
• Simpler Queries: Fewer JOINs required
• Better Performance: Faster query execution
• Easy to Understand: Intuitive structure for business users
• Optimized for Reporting: Ideal for Power BI and analytics
• Reduced Complexity: Minimal table relationships
Disadvantages
• Data Redundancy: Repeated data in dimension tables
• Storage Inefficiency: Larger storage requirements
• Update Anomalies: Potential data inconsistency
• Less Flexible: Harder to accommodate changes
Snowflake Schema
A snowflake schema is defined as a normalized version of the star schema where dimension tables are broken down into multiple related tables.
Structure
• Fact Table: Central table with foreign keys
• Normalized Dimensions: Hierarchical dimension tables
• Multiple Levels: Dimensions split into sub-dimensions

Example of a snowflake schema

Advantages
• Reduced Data Redundancy: Normalized structure
• Storage Efficiency: Smaller storage footprint
• Data Integrity: Better consistency
• Flexibility: Easier to accommodate changes
• Better for OLTP: Closer to operational databases
Disadvantages
• Complex Queries: More JOINs required
• Slower Performance: Reduced query speed
• Harder to Understand: More complex for business users
• Maintenance Overhead: More tables to manage
When to Use Each Schema
Use Star Schema When:
• Primary Use Case: Business intelligence and reporting
• Performance Critical: Fast query response needed
• Business User Focus: End users need simplicity
• Power BI/Tableau: Optimized for visualization tools
• Read-Intensive: Heavy reporting workload
• Data Marts: Department-specific analytics
Use Snowflake Schema When:
• Primary Use Case: Complex data relationships
• Storage Constraints: Limited storage capacity
• Data Integrity: High consistency requirements
• Source System: Mirroring normalized source data
• ETL Processes: Easier incremental loading
• Regulatory Compliance: Strict data governance
Power BI Considerations
Star Schema is Recommended Because:

DAX Optimization: Better performance with measures
Relationship Simplicity: Cleaner model relationships
User-Friendly: Easier for report consumers
Query Performance: Faster refresh and calculation
Best Practice: Microsoft's recommended approach
Reference Table showing the differences

Takeaways from power bi schema and snowflake:
• Star Schema = Performance + Simplicity
• Snowflake Schema = Storage Efficiency + Data Integrity
• Power BI prefers Star Schema for better performance
• Choose based on your specific use case and constraints

MICROSOFT EXCEL FOR BEGINNERS.

Nkatha Gatobu — Sun, 25 Jan 2026 06:21:35 +0000

Microsoft Excel (Ms Excel) happens to be a key toolset in any data analyst's skillset. It helps any data to be understood with ease. People are able to draw conclusions and make interpretations when it is presented in terms of sums, averages, making comparisons from period to period and through presentations in the form of charts and responsive dashboards.
Ms Excel is represented in rows (vertical), columns (horizontal) and cells where formulas of data manipulation are input.

Data on View Mode on Ms Excel

Ms Excel View on Opening a Dataset
The above image is a representation of Ms Excel - the rows, columns, cells and sheets of a dataset to be analysed.

Freezing of First Column
This helps the analyst not to lose track of the data that they are analysing. This allows easy manipulation of a large dataset without losing track of the data titles.
The process used to achieve this is as follows:

On the ribbon/ toolbar, click on View
Choose the item called Freeze Panes
For ease of data access, with a set of data titles, select freeze the first column.
Cursor on Ms Excel highlighting Frozen area
Spilling Over Columns
For data titles in the columns spilling over to other cells, rendering some data hidden in view could lead to the data not being well understood. It is good to auto-adjust the entire dataset for easier reading of titles for proper analysis and interpretation.
This can be done through:

Click on Home on the toolbar
Choose Format
Autofit Column Width; or

Click on the row and column intersection

Hover your cursor on the column until a cross with arrows on the left and right appears

Double-click on the area that this cross appears

The column splits are removed all across the dataset.

Removing Duplicates from the Raw Data
This is an important aspect that every person who intends to manipulate data for it to be easily understood needs to work on.
This will ensure that the raw data does not have any repetitions that could make the analysis poor.
The procedure used to achieve this is as follows:

Select the entire document; Ctrl + A
Select Data on the toolbar
Under data tools, select remove duplicates
Using Operator Functions in Ms Excel

These operators are used to help users analyze raw data for easier interpretation.
They include:

Addition (+)
Subtraction (-)
Division (/)
Multiplication (*)
Exponent (^)
The above operators are used to get averages/ mean, MAX and MIN values, counts/ sum and predictions from raw data with ease.

ADDITION OPERATOR

This helps to find the sum of a set of data.
This can be done using COUNTS, COUNTIF, SUM and SUMIFS.
=SUMIF(E2:E877,">100000") - For salaries equal to or above $100000
OR

=COUNTIF(G2:G877,"50") - For ages equal to or above 50 years
MAX AND MIN VALUES

This is one of the ways to analyse data.
We end up arriving at the maximum/ highest or minimum/ lowest values of raw data.
=MAX(D2:D22) - the highest value - e.g., age, salary, fares
OR

=MIN(F2:F22) - the lowest value - e.g., age, salary, fares
Using Lookup Functions in Ms Excel
These are divided into three:

VLOOKUP Function
This searches for data of relevance on the vertical items.

HLOOKUP Function
This searches for data of relevance on the horizontal items.

XLOOKUP or Index and Match Function
This indexes the column with the information that you want to know.

VLOOKUP in Use

Illustration One
How much is employee ID 10520s bonus?

=VLOOKUP(10520,A1:P877,15,FALSE)

The answer is $630.98
HLOOKUP in Use

Illustration Two
What is the marital status of ID 10102?

=HLOOKUP(10102,A1:AGS26,16,FALSE)

The answer to this is MARRIED.
XLOOKUP in Use

Illustration Three
What is the name/ID of employee earning $77498?

=XLOOKUP(77498,E2:E877,B2:B877,"NOT FOUND")

The answer to this is John.
PIVOTTABLES
Used to develop tables that interpret raw data to set conditions.

To create a suitable pivot table, this is the procedure:

Click Insert on the toolbar and select Pivot Table
How to Insert a PivotTable

Click OK to proceed.
PivotTable continued

On the right side under PivotTable Fields, drag and drop data title items into rows, column or filter sections to meet your analysis needs.
PivotTable Fields to the right of the screenshot

Developing Charts on Ms Excel for ease of Interpretation of Raw Data

This is used to interpret the analysed data.
Charts come in various forms and shapes - bars, pie chart and line charts.
The procedure used to get charts is as follows:
Click on the developed PivotTable
Pivot chart analyze
Pivot chart
Select the appropriate chart type from your available options

The developed PivotTable for analysis

PivotTable with Column, Row and Sum

Click on PivotChart on the Toolbar
Under PivotChart Insert the Appropriate Chart Type

Select the Chart Type to be displayed
Column Chart Displayed

For simple data analytics, Microsoft Excel is a helpful tool. It facilitates data organization, basic analysis, and straightforward result presentation. Ms Excel is a good starting place for novices learning advanced data analysis.

GIT FOR BEGINNERS;

Nkatha Gatobu — Fri, 16 Jan 2026 01:40:39 +0000

***LEARN TO TRACK, PUSH, PULL LIKE A PRO FOR BEGINNERS.
*
Are you stating on your journey on learning about git and github, below is a simple and detailed step by ste on how to get stared on you coding experience alone or with friends and collegues.

Have you ever worked on a document and wished you could go back to an earlier version?
Collaborated with others and ended up with conflicting changes? Version control solves these problems.
Git is the most popular tool for it. This beginner-friendly guide will help you understand the basics!

What is Version Control?

Think of version control as a super-powered "undo" button for your entire project. It's a system that:

· Tracks every change you make to your code
· Saves snapshots of your project at different points
· Helps teams collaborate without overwriting each other's work
· Lets you experiment safely (you can always go back!)

Understanding Git's Three Areas

Imagine Git has three workspaces:

Working Directory - Where you actually edit files
Staging Area - A "preview" area where you prepare changes
Repository - The permanent storage where Git saves your snapshots

Getting Started with Git

Step 1: Install and Set Up

Download Git from git-scm.com
Open your terminal/command prompt
Set up your identity:

git config --global user.name "Your Name"
git config --global user.email "your.email@example.com"

Step 2: Create Your First Repository

A repository (or "repo") is where Git stores your project's history.

# Navigate to your project folder
cd my-project

# Initialize a new Git repository
git init

Congratulations! You now have a Git repository tracking your project.

The Essential Git Workflow

Tracking Changes: The Basic Cycle

Here's your daily routine with Git:

# 1. Check what you've changed
git status

# 2. Add specific files to staging
git add filename.txt
# Or add all changes
git add .

# 3. Save a snapshot (called a "commit")
git commit -m "Description of what you changed"


**

## Understanding Branches: Parallel Universes for Your Code

**

## Think of branches as parallel timelines for your project:

· Main branch: Your stable, working code
· Feature branches: Where you develop new features without breaking the main code

bash

Create a new branch
git branch feature-login
Switch to that branch
git checkout feature-login
use the shortcut:
git checkout -b feature-login 8. ```

Working with Remote Repositories (GitHub/GitLab)

**
Remote repositories are copies of your project stored online (like on GitHub). This enables collaboration and backup.

Setting Up a Remote Connection


bash
# Link your local repo to a remote one
git remote add origin https://github.com/yourname/your-repo.git

Pushing: Sending Your Code to the Cloud


bash
# First push to a new remote repository
git push -u origin main

# Subsequent pushes (after the first)
git push

Pulling: Getting Updates from Others


bash
# Get the latest changes from the remote repository
git pull

git pull is downloading what others have shared.

Common Workflow: Making Changes with a Team


bash
# 1. Always start with the latest code
git pull

# 2. Create a branch for your work
git checkout -b my-new-feature

# 3. Make changes, add, and commit
git add .
git commit -m "Add new feature"

# 4. Push your branch to share it
git push -u origin my-new-feature

Dealing with Common Situations

"I made a mistake in my commit message"


bash
git commit --amend -m "New corrected message"

"I want to see my project's history"


bash
git log --oneline  # Compact view
git log --graph --oneline --all  # Visual history

"I accidentally added a file I don't want to track"


bash
# Remove from staging (but keep the file)
git reset filename.txt

# Stop tracking a file completely
git rm --cached filename.txt

Best Practices for Beginners

Commit often - Small, frequent commits are better than huge ones
Pull before you start - Always begin with the latest code
Write helpful messages - Explain "why" not just "what"
Use branches - Keep your main branch stable
Don't panic - Git keeps your work safe. You can almost always recover!

**_Remember: Every expert was once a beginner. The more you use Git, the more natural it becomes. Happy coding!

_**