Three approaches can be considered to optimize the data preparatory work and transformations using Power BI dataflows: centralizing data transformation, increasing data reuse, and enhancing performance.
Centralized Data Preparation: Dataflows provide an option for data transformational activities to be designed and developed as a sustaining process to be used in several reports within different workspaces. Once dataflows are constructed, different data transformation operations (for instance, cleansing, joining, or aggregation) can be executed just once, and the outcome can be stored in one location in the Power BI service. This helps in not doing the same transformations in every other report but also helps in uniformity of how the data is handled or consumed within the entire organization.
Data assets are reusable and modular: Dataflows facilitate the development of data assets that can be disbursed across several reports and teams. For instance, if your data is retrieved from various sources like SQL DBs, cloud sources, and even Excel sheets, this data can be merged and processed in one dataflow, and the output can be availed for direct use in accompanying several reports. This methodology makes it easier and more convenient to perform the same data preparation processes across different reports. Also, it allows different groups of people to share and use the right information without redoing the work.
Performance Optimization: Performance can be improved by using dataflows, especially for large datasets. Since dataflows are kept in Azure Data Lake, it implies that the transformation of data takes place in the center, hence enabling the processing of more data volumes than Power BI Desktop. Also, by making dataflows with scheduled updates, it is possible to load and store data in advance for use by the Power BI reports, thereby enabling access to already processed data, which reduces load time effectively. This configuration reduces or eliminates the need to query the primary sources of data in real-time, which is particularly helpful for enhancing refresh rates and general performance.
To conclude, Using dataflows in Power BI helps prepare and transform data for reporting purposes and enhances effectiveness through standardization and centralization. This enables the organization to maintain quality data and improve the processes of report generation within the organization.
