What is Power BI ?

Power BI is a powerful tool for creating interactive and informative reports and visualizations. However, to create effective reports, following best practices for Power BI development is important. This article will provide an overview of some key best practices for Power BI development, including creating a clear and well-defined data model, keeping the data fresh, using appropriate visualizations, using filters and slicers, organizing and labelling Report elements, documenting and commenting on your work, testing and validation, and security and access management. By following these best practices, you can create accurate and meaningful reports that are easy to understand and navigate and that effectively communicate the information they contain.

Throughout this blog post on certain pointers, we showcase Datagaps’ BI Validator.

What is BI Validator and where do you use it?

Datagaps’ BI Validator is a tool that can be used to validate and test the accuracy of data in Business Intelligence (BI) reports. It is designed to automate the validation process and can be integrated with other BI tools and platforms. It can be used to check for errors, inconsistencies, or outliers in the data and can also be used to perform data quality checks. It can validate data from a wide range of BI platforms, including Power BI, Tableau, and SAP BusinessObjects, and it can also be used to validate data from a variety of data sources, such as Excel spreadsheets, SQL Server databases, and cloud services like Azure SQL Database or Power Platform. The tool can be used to validate the data before it is loaded into a data warehouse or reporting system.

Try BI Validator – Free for 14 days.

Creating a Clear and Well Defined Data Model

There are several ways to achieve this:

• • Identify key pieces of information: Start by identifying the key pieces of information that you want to include in the report, such as sales, customers, products, etc.
  • Organize the data into logical tables: Once you have identified the key pieces of information, organize that information into logical tables, such as a table for customers, a table for products, and a table for sales.
  • Create relationships between tables: Once the tables are created, establish relationships between them, for example, a relationship between customers and sales or between products and sales.
  • Define the data structure: Define each table column’s data structure and data types. This will help to ensure that the data is accurate and consistent.
  • Use calculated columns and measures: Use calculated columns and measures to create new values based on the existing data, for example, total sales, average sales, etc.

Power BI Tables and Relationships

Keeping the Data Fresh

• • Data refresh: Schedule regular data refreshes. This can be done daily, weekly, or monthly, depending on the data source. This will ensure that the data in the report is up to date.
  • Data source connection: Connect the report to a data source such as an Excel spreadsheet, a SQL Server database, or a cloud service like Azure SQL Database or Power Platform. This will ensure that the report is always pulling the most recent data.
  • Direct Query: Use Direct Query to connect to the data source and retrieve the data as needed, this can be useful for scenarios where the data changes frequently, and you want the report to reflect the most recent data.
  • Live connection: Use a live connection to connect to the data source and retrieve the data in real time. This can be useful for scenarios where the data changes frequently, and you want the report to reflect the most recent data in real time.

By keeping the data fresh, you can ensure that the report is always accurate and up to date, making it more useful and reliable. Additionally, you can set up data alerts that will notify you of any issues with the data or the data refresh process.

Schedular

Using Appropriate Visualizations

• • Bar charts: Show the comparison of different values for a specific category. It’s a great option for showing the relative size of data points, such as sales by product or region.
  • Line charts: Show the trend of data over time. It’s a great option for showing how data has changed over time, for example, sales by month or year.
  • Pie charts: Show the proportion of different values for a specific category. It’s a great option for showing data distribution, for example, sales by product category.
  • Scatter charts: Show the relationship between two or more variables. It’s a great option for showing the correlation between different data points, for example, sales by price and quantity.
  • Maps: Show the location-based data. It’s a great option for showing data associated with a specific location, for example, sales by state or country.
  • Card visual: Show a single value or a data summary; it’s a great option for showing key metrics such as total sales, average sales, etc.

By choosing the appropriate visualization for the presented data, the report will be easier to understand, and the information will be effectively communicated. Additionally, combining different visualization types can enhance the understanding and analysis of the data.

Using Filter and Slicers

There are several ways to achieve this:

• • Filters: Allow users to narrow down the data that is displayed in the report by applying conditions to the data. For example, you could use a filter to only show data for a specific period or region.
  • Slicers: Users can interact with the report by selecting specific values, such as a product or a category. Slicers can be used to filter multiple visuals at once, making it easy for users to see the data that is most relevant to them.
  • Relative date slicers: allow users to filter the data by a relative range of dates, for example, last month, last quarter, etc.
  • Top N slicers: allow users to filter the data by the top N values, for example, the top 10 products or the top 5 regions.
  • Advanced filtering options: allow users to filter the data using advanced options such as “contains”, “starts with”, “ends with”, etc.

By using filters and slicers, users can quickly and easily focus on the most relevant data, making the report more interactive and efficient.

Organizing and Labelling Report Elements

There are several ways to achieve this:

• • Use a clear and logical layout: Group together related visualizations and information and use a consistent layout throughout the report. For example, you could group all of the visualizations related to sales on one page and all of the visualizations related to customers on another page.
  • Use clear and descriptive titles: Add clear and descriptive titles to all visualizations and charts, making it easy for users to understand what the data represents.
  • Use bookmarks: Create bookmarks that allow users to jump to specific parts of the report quickly. For example, you could create a bookmark for the sales data and another bookmark for the customer data.
  • Use drill-through: Create drill-through actions that allow users to explore the data in more detail. For example, you could create a drill-through action that allows users to see the details of a specific sale when they click on a data point in a visualization.
  • Use consistent formatting: Use consistent formatting throughout the report, such as the same font, colour scheme, and layout. It will help users to focus on the data and not be distracted by formatting variations.

By organizing and labelling report elements, you can make the report more user-friendly and easy to navigate, which can help users to find the information they need more easily, thus making the report more efficient and effective.

Documenting and Commenting Your Work

There are several ways to achieve this:

• • Annotating the report: Add annotations and comments to the report that provide context and information about the data, calculations, and visualizations being used.
  • Documenting data sources: Include information about the data sources used in the report, such as the location of the data, the data structure, and any transformations that were applied.
  • Documenting calculations and formulas: Include information about any calculations or formulas that were used in the report, such as how they were created and what they do.
  • Creating a report glossary: Create a glossary that defines the terms and measures used in the report, which can be helpful for users who are not familiar with the data.
  • Creating a report guide: Create a guide that explains how to use the report, including how to navigate, filter, and interact with the data.

By documenting and commenting your work, you can make it easier for others to understand and maintain the report, and it will be easier for users to use the report, as they will have a better understanding of the data and the context it is presented in.

Use Testing and Validation Techniques

There are several ways to achieve this:

• • Data validation: Compare the data in the report to the original data source to ensure that it is accurate and complete. This can be done using data validation tools, such as Power Query, or manually reviewing the data.
  • Data quality checks: Check for errors or outliers in the data that might impact the report’s accuracy. This can be done using data quality tools, such as Data Profiling, or manually reviewing the data.
  • Automated tests: Create automated tests that check the report for specific conditions or results. These tests can be run regularly to ensure the report functions correctly.
  • User acceptance testing: Involve end-users in the testing process by having them review the report and provide feedback. This can help identify any issues or confusion users might have with the report.
  • Performance testing: Test the report’s performance to ensure that it loads and functions quickly, even with large amounts of data. This can be done using performance testing tools, such as Power BI performance analyzer, or manually testing the report.

Using these testing and validation techniques, you can ensure the integrity of the data and the report, identify any errors or problems that might exist, and make necessary changes before sharing the report with others, ultimately ensuring that the report is accurate and reliable.

Security and Access Management

There are several ways to achieve this:

• • Row-level security: Allows you to limit data access to specific rows in a table based on the user’s role or group membership. This can be useful for scenarios where different users have different data access levels.
  • Custom roles: Allows you to create custom roles defining user actions on a report. For example, you could create a role allowing users to view the data but not edit it.
  • Data encryption: Power BI supports data encryption at rest and in transit, which helps to protect sensitive data from unauthorized access.
  • Azure Active Directory (AAD) authentication: Allows you to use AAD to authenticate users and control access to the report. This can be useful for scenarios where you want to integrate Power BI with other Microsoft services, such as SharePoint or Teams.
  • Conditional access: Allows you to configure access policies that are based on the user’s location, device, or other factors. For example, you could configure a policy that only allows users to access the report from a specific location or while connected to a specific network.

When used together, All of these options will provide a robust security and access management strategy, ensuring that the report is only accessible by authorized users and that sensitive data is properly protected, thus providing an extra layer of security to the report.

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Conclusion