Power BI: The Craftsman’s Guide to Data Modelling

Data modeling in Power BI is the process of organizing and structuring data in a way that makes it easy for analyzing and visualizing. A well-designed data model can improve the performance and flexibility of your Power BI dashboards and reports, and can make it easier to explore and analyze your data.

Power BI data modeling: It is the process of organizing and manipulating data in a way which makes it easy to analyze and visualize. The data modeling features in Power BI allow users to create relationships between tables, define calculations and hierarchies, and create calculated tables and columns.

Here are some tips for data modeling in Power BI:

1. Normalize your data: Normalizing your data means breaking it down into smaller, more manageable pieces. This can make it easier to work with your data and can improve the performance of your dashboards and reports.
2. Use relationships to link tables: In Power BI, you can use relationships to link tables together. This allows you to create relationships between different pieces of data, which can make it easier to explore and analyze your data.
3. Use calculated columns and measures: Calculated columns and measures are calculated fields that are based on other fields in your data model. They can be used to create new fields or to summarize data in different ways.
4. Use hierarchies to organize data: Hierarchies are structures that allow you to organize data into levels. They can be used to create a hierarchy of categories or to create a drill-down structure for your data.
5. Time Intelligence: Power BI has built-in time intelligence which allows users to perform time-based calculations and analysis. For example, you can use time intelligence to create a year-over-year comparison of your data.
6. Data Types: Power BI supports various data types such as text, whole number, decimal number, date/time, and Boolean, etc.

By following these tips, one can create a well-designed data model in Power BI that makes it easier to analyze and visualize your data.