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The process of moulding and changing data in Power BI to produce a meaningful and dynamic representation of your data in reports and dashboards is referred to as modelling. It entails organising, improving, and structuring your data in order to provide better data analysis and visualisation. Here’s a quick rundown of modelling in Power BI:

  1. Data Sources:

Begin by configuring Power BI to connect to your data sources. Power BI can access data from a variety of sources, including databases, spreadsheets, cloud services, and others.

  • Data Transformation:

 Once you’ve linked to your data, you can use Power Query Editor to conduct different data transformation operations. Cleaning, filtering, combining, and manipulating data to prepare it for analysis is part of this process.

  • Data Modelling:

 In Power BI, data modelling is defining relationships between multiple tables in your data model. Relationships are classified into three types:

The most frequent connection is one-to-many (1:N), in which one item in one database connects to many things in another table.

N:1 (Many-to-One): The inverse of one-to-many, in which many things in one table correspond to one item in another.

Many-to-Many (N:N): A more complicated connection in which many things from one table link to many items from another table via an intermediate table.

         4     Calculated Columns and Measures:

              Using Data Analysis Expressions (DAX), you may build calculated columns and measures in       Power BI. Calculated columns are calculated for each row in a table, whereas measures are aggregate computations (e.g., sums, averages). DAX is a strong mathematical language that may be used to  create bespoke computations.

  • Hierarchies and Time Intelligence:

Hierarchies may be defined to aid with data drill-down and exploration. DAX’s time intelligence features assist you in performing date-based calculations such as year-to-date, quarter-to-date, and more.

  • Best Practises for Data Modelling:

To guarantee efficient and optimal performance, best practises such as defining suitable relationships, minimising the usage of calculated columns where measurements can suffice, and optimising data types should be followed.

  • Data Security: Data security capabilities in Power BI allow you to restrict access to certain data based on user roles and permissions. This gives you control over who can see and interact with certain aspects of the data.
  • Data Visualisation:

 After modelling your data, you can use Power BI’s drag-and-drop interface to build dynamic and intelligent data visualisations. Charts, tables, maps, and other forms of visualisation can be used.

  • Report Creation:

 When creating reports, you may include interactive elements like as slicers, filters, and drill-through actions to allow users to examine data in a variety of ways.

  1. Publish and Share:

 Once you’ve completed your reports and dashboards, you can publish them to the Power BI service or export them as files. Options for sharing include sharing with specific persons or groups, as well as embedding reports in websites and applications.

  1. Refresh and Schedule:

Data in Power BI reports may be refreshed on a regular basis to guarantee that the information is current. You can plan automatic refreshes based on the availability of your data source.