How do you manage complex drill-through reports when users encounter incorrect or missing data in drilldowns

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How do you manage complex drill-through reports when users encounter incorrect or missing data in drilldowns?

I have a Power BI report with complex drill-through functionality, but users occasionally encounter incorrect or missing data when they drill down. I’m not sure if this is due to filter context or issues with relationships between tables. How can I manage and troubleshoot drill-through reports in Power BI to ensure accurate data appears at each drill level?
Nov 5 in Power BI by Evanjalin
• 5,440 points
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1 answer to this question.

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Step in the training process is necessary to achieve the desired functionality in Power BI transformation by gaining a drill-down Fidelityidually.

Understanding Filter Context and Visual Filters: The use of drill-throughs in Power BI relies on the context derived from the chosen visuals. Hence, it is important to ensure that all the relevant contexts have been established first. Take a step back to review the particular visualizations from which the users perform a drill-down, and make sure they deliver the anticipated filter context to the drill-through page. More often than not, some out-of-the-blue filters come along, which results in incomplete data displays. Within Power BI, one can specify ‘Edit Interactions’ and control which filters cut across visuals.

Relationship Setup and Data Model Review: Pay attention to the data model and the relationships among the various tables with regard to the relationship establishment direction. Relationships have also been a pitfall with incorrectly set relationships, e.g., using many-to-many relationships or using an inactive relationship can bring about voids in the drill-through reports or even deliver wrong statistics. You can test possible solutions, try other relationships, or even bridge tables to avert such problems. It is also important to check if there are any calculated columns or measures that may negatively impact drill-through even when that is not their main use.

Procedures Relating to Data Quality and Its Fill-In Alternatives: Where data is missing, examine data cleansing techniques that can help fill the gap, such as filling with the median values, interpolation, or filling up the gap with some other data. While data filling can be useful in improving the appearance of the information, caution should be exercised on the level to which the values filled may alter the conclusions made. Consider doing such evaluation at regular intervals, exercising it on trial cases to validate that the end product drill-through achieved is fit for the users’ purposes in the various cases.

These measures will help improve the quality of drill-through, but one must be careful with every single one. 

answered Nov 5 by pooja
• 4,590 points

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