INTRODUCTION
When I started using Power BI, I only thought of visuals like charts and graphs. However, as I progressed, I discovered a great data dashboard is built on great data models.
Data Modelling is the process of organizing your data tables and defining how they relate to each other so Power BI can combine them into meaningful reports and dashboards. Good, designed data makes it easier and faster to maintain.
Why is data Modelling Important
Well-organized data makes it easier to manage data.
Reduction of the duplicates.
Ensures data consistency.
Understanding Relationships
Relationships allow the data table to give communication using fields.
For example,
Customer Table stores all information about a customer.
Product Table store product details
Sales Table stores all information about the transactions.
Power BI connects the information between the customer’s name and Customer Id rather than repeating them it connects the information using joins.
Going through relationships I discovered schemes. Scheme is the way tables are organized in databases. There are different types of schemes e.g. Star Schema, snowflake schema and Flat table.
Star Schema
A star schema is a data model with one central fact table and dimension table surrounding it.
Fact table
A table that stores events, transactions of what happened.
• Total sales
• average sales
• quantity sold
Dimension table
A dimension table describes the items in the fact table. The table contains descriptive information.
• The customer table describes the customer
• How much sales were made
The fact table sits in the center, while the dimension tables surround it—forming a star.
Dashboard designs
A good dashboard has to fit one page.
A dashboard should show critical information.
Update automatically when data changes.
Focus on data understanding and decision making.
Conclusion
Power BI taught me that a great report are built from a a great dashboard which is achieved by having great models.
Structuring a data into facts and dimension table and choosing star schema creates a powerful model.
Data cleaning is important because a clean data is the best.
Power BI has changed how I interpret and interact with data.