Build Power BI Data Quality Dashboards
Spot Data Quality Issues with Power BI
Organizations continue to struggle with data quality in their applications. Finding and cleaning core data elements can often be time consuming and difficult to organize. Business users need an easy way to see current issues in their data as well as make changes in source applications to see the impact of data corrections. Business Analyst can create Power BI Data Quality Dashboards directly against source data to uncover data issues.
In this Power BI Dashboard for Product Sales, I built data grids and graphs that show sales by Product Type, Retailer Type and Product Line. Power BI automatically groups the data on the content available in the data. Below are examples of data issues that popped out as I was building my dashboard.
Spotting Data Quality Issues
Product Type – The data shows sales for Eye wear and Eyewear, Latern and Laterns, Sun Screen and Sunscreen, and Watch and Watches.
Retailer Type – The data shows sales for Warehouse Store and Whouse Store, Outdoor’s Shop and Outdoors Shop, and Department Store and Depart Store
Product Line – The data shows sales for Mountaineering Equipment and Mountaineering Equipment
Cleaning Up Data Quality
Users that see issues in the data through Power BI can clean up the data by creating a data feedback loop. This loop could be users editing data in the source systems or working with IT do create processes to bulk clean up data. Once the data is refreshed the dashboard will reflect the updates. Data quality will improve over time by using the dashboards to make decisions. Users will spot inaccuracies in the data such as the 2 categories of Eyewear and “Eye Wear”.