1. Understanding of data and business context
The first step in effective data analytics is an in-depth understanding of the data and the business context in which it will be used. Knowing the sources of the data, its structure and business objectives allows you to design analytical models and tailor reports better to meet the needs of end users.
2.Application of ETL principles (Extract, Transform, Load)
Microsoft Power BI has a robust data preparation tool called Power Query. It is always a good idea to ensure the correctness and quality of the data right from the extraction and transformation stage. Carefully designed ETL processes ensure that data is adequately transformed and ready for analysis, eliminating problems at later stages.
3. Creation of coherent data models
A consistent data model is the foundation for any analysis in Power BI. What do good practices say in this regard? Use the right relationships between tables, avoid data duplication and ensure a logical structure. Relationship models should be clear and intuitive, making reports easier for other users to understand.
4. Optimising report performance
As data volumes and report complexity increase, Power BI performance can deteriorate. Monitoring performance and avoiding complex DAX queries or overloading reports with unnecessary visualisations is essential. It is worth using data compression techniques and filters at the query level.
5. Using DAX for advanced analysis
DAX (Data Analysis Expressions) is a formula language that enables the creation of advanced calculations and aggregations in Microsoft Power BI. Mastering the basic and advanced DAX functions, such as CALCULATE, FILTER or SWITCH, allows you to create more dynamic and detailed analyses.
6. Automation of data update processes
One key element of working with Power BI is automating data update processes. Thanks to refresh scheduling functions, reports are always based on the latest data. At the same time, regular refreshes reduce the risk of working with outdated information.
7. Using data hierarchies and drill-downs
Data hierarchies, such as year-month-day for dates, allow for more detailed data analysis at different levels. Drill-down functions enable report users to drill down into the data, making it easier to identify detailed trends and anomalies.
8. Maintaining visual consistency
Visual consistency is key to understanding reports. Reports must use a consistent colour scheme, format, and layout. Using the same styles and icons helps users interpret data and compare results.
9. Use of interactive filters and slicers
Microsoft Power BI offers many opportunities for interactivity through filters and slicers that allow report users to customise the data displayed dynamically. These tools are worth using to enable flexible data exploration tailored to your audience’s individual needs.
10. Ensuring security and access control
In Power BI reports, it is crucial to maintain appropriate security standards. Features such as Row-Level Security (RLS) allow you to restrict access to data based on user role. This can ensure that sensitive information is only available to authorised individuals. It is essential to test all functions and formulas thoroughly before releasing reports. Validating the results against other data sources or reports allows you to detect any errors that may affect the quality of the analysis. Regular testing of reports helps to maintain their reliability and relevance.
Best practices in data analysis
Following best practices in data analysis in Power BI is key to working effectively and flawlessly with this tool. Understanding your data, optimising performance, automating processes and ensuring visualisation consistency and security are all key elements that drive the quality of your analysis and reports. Remember to test your reports regularly and use modern DAX techniques to get the most out of Microsoft Power BI.
