Working with massive datasets in Microsoft Dynamics 365 Customer Insights-Data can be challenging. If you’re dealing with millions of records from different data sources, managing and filtering out unwanted data to enhance system performance is key.
Loading unnecessary data can create bottlenecks, slow down processing, and waste resources when creating segments, calculating measures, or building models. The result? Increased processing time and delays in generating valuable insights.
In this article, we’ll explore how you can filter data at the source to improve the efficiency and performance of your Dynamics 365 Customer Insights – Data, helping you save time and resources while delivering faster, more accurate insights.
Why You Need to Filter Data at the Source
When working with large datasets, time-consuming loading processes often lead to unwanted delays. Take, for example, one of our recent client projects. They were trying to create a segment to derive insights from a vast dataset, but the system was struggling to load all the data.
Here’s the issue:
- The system was loading the entire dataset, including irrelevant records, only to later filter out the data related to India (a small subset of the total data).
- Longer load times: The system took too much time to process all the data, even though the client only needed a fraction of it.
- Slower processing: Performance was further hindered during segment creation, model building, and measures calculation.
- Delayed insights: Since the system was processing unnecessary records, it was taking far too long to get the desired results.
The Solution: Row-Level Filtering at the Source
The 2025 Release Wave 2 of Microsoft Dynamics 365 Customer Insights – Data introduced a powerful feature: row-level filtering at the source. This new functionality allows you to filter out unwanted data before it enters the processing stage, optimizing your system’s workload and dramatically reducing processing time.
Here’s how this feature works for you:
Key Benefits of Row-Level Filtering:
- Filter Early, Process Less: Instead of loading entire datasets, the system only loads data that meets your filtering criteria, saving you time and resources.
- Targeted Processing: You’ll be able to filter data based on your specific needs before any heavy processing starts, reducing both processing time and system load.
- Better Efficiency: With only relevant data loaded into models, segments, or measures, the system can perform faster and more efficiently, resulting in quicker insights.
How Row-Level Filtering Works in Dynamics 365...Read More>>
