Understanding Schemas and Data Modelling in Power BI

Joy Maritim — Mon, 02 Feb 2026 09:32:39 +0000

Data modelling is the backbone of effective reporting in Power BI. A well-designed model ensures not only accurate insights but also optimal performance. Let’s break down the key concepts every Power BI developer should master.

📖 What is a Schema?

A schema is the logical structure that defines how data is organized in a database or model.

It describes:

How tables are arranged
How they relate to each other
The rules governing those relationships

In Power BI, schemas help shape the data model that drives reporting and analysis.

📊 What is Data Modelling?

Data modelling is the process of designing how data should be structured, connected, and optimized for analysis.

In Power BI, this involves:

Identifying fact tables (numerical, transactional data)
Creating dimension tables (descriptive attributes like customer, product, date)
Defining relationships between tables
Ensuring the model is efficient, accurate, and easy to use

Good modelling ensures performance, scalability, and trustworthy reporting.

⭐ Types of Schemas in Power BI

⭐ Star Schema

The star schema is the most recommended design for Power BI.

At the center lies a fact table containing measurable business data (e.g., sales, revenue, transactions).
Surrounding it are dimension tables that provide descriptive attributes (e.g., customers, products, dates).
This structure resembles a star, with the fact table at the core and dimensions radiating outward.

Why it works:

Simplifies relationships
Improves query performance
Aligns with DAX best practices

❄️ Snowflake Schema

The snowflake schema is a variation where dimension tables are normalized into multiple related tables.

Example: A product dimension split into product, category, and subcategory tables.
While this reduces redundancy, it introduces complexity and can slow down performance in Power BI.

Best practice: Flatten dimensions where possible to maintain simplicity and speed.

🔗 Relationships

Relationships define how tables connect in the model.

One-to-many (1:*): Most common, linking dimensions to facts.
Many-to-many: Avoid unless necessary, as they can complicate calculations.
Single vs. bi-directional filters: Use single-direction filtering for clarity and performance, unless bi-directional is explicitly required.

📊 Fact vs. Dimension Tables

Fact tables: Contain numeric, transactional data (e.g., sales amount, quantity).
Dimension tables: Contain descriptive attributes (e.g., customer name, region, product details).
Together, they enable slicing and dicing of metrics across different perspectives.

Why Good Modelling Matters

A strong data model is critical for:

Performance: Reduces memory usage and speeds up queries.
Accuracy: Ensures calculations return correct results.
Scalability: Supports future growth without redesign.
Usability: Makes reports intuitive for end-users.

✅ Key Takeaways

Prefer star schema over snowflake for Power BI.
Keep relationships simple and directional.
Separate facts (numbers) from dimensions (descriptions).
Good modelling = faster, cleaner, and more reliable reporting.

💡 In Power BI, modelling is not just a technical step—it’s the foundation of trustworthy analytics.

#Mastering Excel like a Pro: An Absolute Beginner's Guide to Understanding Excel for Data Analytics.

Joy Maritim — Sun, 25 Jan 2026 19:11:37 +0000

Introduction to MS Excel for Data Analytics

What is Data Analytics?

Data analytics is the process of collecting, cleaning, analyzing, and interpreting data to discover useful information and support decision-making.

Data can come from:

Sales records
Student results
Business transactions
Surveys

Microsoft Excel is one of the most popular tools used for basic data analysis, especially for beginners.It can be used for data entry,calculations, analysis and visualization.

What is Microsoft Excel?

Microsoft Excel is a spreadsheet application used to:

Store data in rows and columns
Perform calculations
Analyze data
Create charts and reports

Excel is widely used because it is:

Easy to learn
Powerful
Available in most offices and schools

Advantages of Using Excel for Data Analytics

Easy to use for beginners
No programming required
Fast analysis for small to medium datasets

- Widely accepted in organizations

How Excel is Used for Data Analytics

1. Data Entry and Data Organization

Excel allows users to enter data in a structured format using rows and columns.

Examples of data that can be entered:

Names
Dates
Sales amounts
Scores

Each column represents a variable, while each row represents a record.

📸 Screenshot 1:

2. Data Cleaning

Before analysis, data must be clean.

Excel helps in:

Removing duplicates
Correcting spelling errors
Filling missing values
Formatting numbers and dates

📸 Screenshot 2:

3. Sorting and Filtering Data

Sorting helps arrange data in a specific order (ascending or descending).

Filtering helps display only the data you need.

Examples:

Sorting sales from highest to lowest
Filtering students who scored above 60

STEPS on How to Filter Data:
Select the Header row
Go to Data --> Filter
Click the drop-down arrow to filter values

STEPS on How to Sort Data:
Select your Data range
Go to Data --> Sort A to Z OR Z to A

4. Basic Calculations Using Formulas

Excel supports formulas that help perform calculations automatically.

Common formulas include:

SUM() – Adds numbers in a range for example =SUM(L2:L633)

AVERAGE() – Finds the mean of values for example =AVERAGE(M2:M633)

COUNT() – Counts entries for example =count(o2:o633)

MAX() and MIN() – Finds highest and lowest values respectively for example =max(L2:L633)

=MIN(L2:L633)

Turning Numbers into insights using Pivot Tables and Charts

Numbers alone can be a little tricky to interpret, but with just a few clicks, you can turn raw data into clear visual insights.

Pivot Table

Pivot tables are helpful in summarizing large data sets quickly and efficiently.

How to Create a Pivot Table

Click on a cell(eg E4)
Select Insert --> Pivot Table
Choose Location (New Worksheet)
Click OK
Using Pivot Table Fields, Select fields to add to the report
Rows: Categories(eg Region)
Values: Numbers to Calculate eg (Count of OrderID)
Columns: Comparison Fields
Filters: Limit displayed data

Example: Count of OrderID by Region

Charts and Data Visualization

Charts help to present data visually.

Key Chart Types and Uses
Column Chart: Vertical bars used for comparing values across categories.

Bar Chart: Horizontal bars, ideal for comparing large sets of categories.

Line Chart: Connects data points to visualize trends over time (days, months, years).

Pie Chart: Displays data as slices of a circle, showing proportions of a total.

Scatter (XY) Chart: Plots individual data points to show relationships or correlations between two variables.

Area Chart: Similar to line charts, but filled below the line to emphasize the magnitude of change over time.

Combo Chart: Uses two or more chart types (e.g., column and line) in one, useful for comparing different data types.

Histogram/Pareto: Used for statistical analysis to show data distributions.

Radar Chart: Compares multiple quantitative variables.

How to Create a Chart

Select range of Data
Go to Insert --> Charts
Choose chart type
Customize the Titles and labels

For Example: Use PivotChart to Represent Count of OrderID By Region

Summary

In this guide, you will learn how to:

Basics of Data Analytics and Excel
Data Cleaning [Removing Duplicates]
Sorting and Filtering Data
Basic Calculations using Formulas in Excel
Creating Pivot Tables
Building Charts for Data Visualization

Conclusion

Microsoft Excel is a powerful and beginner-friendly tool for data analytics. It helps users organize data, clean it, perform calculations, and visualize insights using charts.

For anyone starting a career in data analysis, Excel is an essential skill.

Author

Joy Maritim
Data Analyst

Forem: Joy Maritim