To design a Power BI data model without the need to repeat tables for both high and low levels of reporting, the emphasis should strive to develop more of a layered approach that uses filtering, aggregations, and hierarchies efficiently. Following are a few approaches that you can follow in order to accomplish this:
1. Use Aggregated Tables and Relationships: The first step is to create summary tables that include less detailed data, such as monthly or quarterly summaries instead of complete datasets. These summarized tables will be connected to detailed data with the help of one or more shared keys or relationships that allow detailed data when necessary. This helps in providing a very light model; however, there is a route provided for detailed data when it is really needed.
2. Implement Calculated Measures: Rather than keeping pre-computed aggregates spread across a diversity of tables, e.g., raw data and summary, dynamic DAX measures calculation can be put in place. For example, DAX functions such as SUMX, AVERAGEX, or COUNTROWS can be used to perform summary computations during a user selection based on any other dimensions included in the report. This leads to the elimination of pre-aggregated tables, hence reducing data duplication and model size. Also, measures can be sliced in or out using slicers or filters, and hence the ‘bridge’ between detailed and summary becomes seamless.
3. Use Hierarchies and Each and Every Drill Down Feature: This helps to summarize and drill down detail or summary through a single table/ visual, as Power Bi has hierarchies and drill-down functions. Implementing the model by specifying levels of hierarchy, e.g., Year Quarter Month Day, allows the users to open and close views of information without adding unnecessary tables. This method is beneficial from the users’ perspective and is also efficient since all levels of data fit the same table design.
I always improve Power BI models, balancing the aggregation levels to make them scalable for multi-level reporting without adding redundant information.
