I have recently noticed that it is very easy to carried away by the data I am analysing which makes me sometimes want to add more on the dashboard which causes overcrowded and unneccersaary information which becomes hard for people hence realised that:
Analysts can clean a messy data,DAX and dashboards using power bi best practises rto ensure the report is not over crowded and easy to be understood.The following are ways inwhch one can minimize Messiness:
Limit the number of visuals in dashboards and reports
Using too many visuals in a single report slows report performance as each visual requires data processing and rendering. Limit widget visuals to eight per report page and grids to one per page. Limit tiles to 10 per dashboard.
Limiting the number of visuals reduces the amount of data that needs to be fetched and displayed at once.
Also consider using Power BI’s new card visual, capable of displaying multiple cards within a single container. This improves report performance and minimizes the number of queries fired by consolidating information into a single query.
Use on-premises data gateway standard mode instead of personal mode
The personal mode imports data into Power BI, which can cause resource limitations and performance issues (particularly with large databases). On the other hand, the standard mode retains data in its original location.
As the standard mode doesn't import any data into Power BI, it is more efficient. Using this gateway minimizes data duplication, reduces memory usage, and avoids potential performance bottlenecks.
Use separate gateways for Power BI service live connection and scheduled data refresh
A Power BI service live connection (DirectQuery) maintains a real-time connection with data sources. Scheduled data refresh updates imported data at set times. Using the same gateway for both live connections and scheduled data refresh can overload it during refreshes, slowing live connections.
Separating the gateways ensures that both functions can work efficiently without affecting each other.
Use calculated measures and filters to limit complex measures and aggregations in data models
Complex measures and aggregations in data models can slow down query performance. Using calculated measures, which is computed during query execution, are more efficient than calculated columns. Applying filters can also aid in this process.
Push calculations to the source
Pushing calculations to the source is recommended as it offloads the processing to the data source, optimizing query processing. Being closer to the source can increase performance speed.
Use Star schema instead of the snowflake schema when possible
The star schema is a simpler and more denormalized data model compared to the snowflake schema. The snowflake schema involves more complex queries due to multiple related tables.
The star schema accelerates query execution and eases maintenance by reducing required joins. It improves query efficiency, reduces data redundancy, and simplifies report development.
Use slicers sparingly
Slicers are a great way of allowing users to navigate data, but they come at a performance cost. Each slicer generates two queries: one fetches the data and the other fetches selection details. Adding too many slicers drastically slows performance. Use the Filter pane to evaluate and remove unnecessary slicers.
Host reports and data sources in the same region
A Power BI tenant is the dedicated environment that houses an organization's data, reports, and dashboards. Microsoft operates data centers worldwide to provide and hosts its services. Each data center is in a specific region.
Hosting both tenant and data source in the same region minimizes network latency. This enables quicker data transfer, query execution, and thus faster data retrieval and report rendering.
Partition data and process multiple partitions in parallel for large semantic models
Partitioning is a technique that divides large tables into smaller subsets called partitions based on specific criteria. A columnar index is a type of database index that stores and organizes data by columns rather than rows.
Power BI uses columnar indexes, so longer, leaner tables perform better. The partitioning approach, where only necessary fields and tables are imported, improves data loading efficiency, and reduces resource consumption. This leads to faster report performance.
Use calculation groups to reduce redundant measures
Calculation groups allow you to define calculations (like time intelligence) once and apply them to multiple measures. This reduces the number of measures in your model and simplifies maintenance.
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