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Working with massive datasets in Microsoft Dynamics 365 Customer Insights-Data can be challenging. If you\u2019re dealing with millions of records from different data sources, managing and filtering out unwanted data to enhance system performance is key.<\/p>\n
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.<\/p>\n
In this article, we\u2019ll explore how you can filter data at the source to improve the efficiency and performance of your Dynamics 365 Customer Insights \u2013 Data, helping you save time and resources while delivering faster, more accurate insights.<\/p>\n
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.<\/p>\n
Here\u2019s the issue:<\/strong><\/p>\n The 2025 Release Wave 2 of Microsoft Dynamics 365 Customer Insights \u2013 Data introduced a powerful feature: row-level filtering at the source. This new functionality allows you to filter out unwanted data before<\/em> it enters the processing stage, optimizing your system\u2019s workload and dramatically reducing processing time.<\/p>\n Here\u2019s how this feature works for you:<\/p>\n Row-level filtering<\/strong> is applied to source tables<\/strong> during the unification, segment creation, or model building process. Here\u2019s how it improves performance:<\/p>\n By applying row-level filtering, our client saw a dramatic improvement<\/strong> in system performance. Instead of processing millions of rows, we were able to focus on only the high-value records that were relevant to their task. This reduced load times<\/strong> and processing delays<\/strong>, allowing them to generate insights more quickly and efficiently.<\/p>\n This approach also eliminated the need for post-load filtering, which could previously be time-consuming and resource-intensive. By automating this step, we were able to tell the system exactly which data to prioritize, streamlining the entire process.<\/p>\n Row-level filtering in Microsoft Dynamics 365 Customer Insights<\/strong> allows you to filter data directly at the source table level before it enters the processing phase. This means only the relevant data is loaded into your models, segments, or measures, improving processing time, performance, and resource usage.<\/p>\n When dealing with large datasets, loading unnecessary data can create bottlenecks, slow down system performance, and waste processing resources. Row-level filtering optimizes this process by ensuring that only the data that meets specific criteria is loaded, reducing load times and improving overall efficiency.<\/p>\n Yes, row-level filtering can be applied to high-volume source tables<\/strong> in Microsoft Dynamics 365 Customer Insights<\/strong>. It works across various data processes like unification, segment creation, and model building, helping you filter the data based on specific criteria (e.g., country, customer type, etc.).<\/p>\n No, row-level filtering doesn\u2019t permanently delete any data. It simply skips irrelevant rows<\/strong> during the data processing stage, which means the unwanted data isn\u2019t loaded or transformed. Your original dataset remains intact.<\/p>\n Dynamics 365 Customer Insights Data Row Filter is a quintessential feature for any organization working with high data volumes. Moving the filtering to the source processing layer bypasses the bottleneck of loading irrelevant data, resulting in quicker computation times, better resource utilization, and faster time-to-insights. Stop trying to load millions of unnecessary rows; start filtering at the source!<\/p>\n","protected":false},"excerpt":{"rendered":" Working with massive datasets in Microsoft Dynamics 365 Customer Insights-Data can be challenging. If you\u2019re 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\u2026 Read More »<\/a><\/span><\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[16,2361],"tags":[3248],"class_list":["post-42639","post","type-post","status-publish","format-standard","hentry","category-dynamics-365","category-technical","tag-dynamics-365-customer-insights-data"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/posts\/42639","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/comments?post=42639"}],"version-history":[{"count":0,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/posts\/42639\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/media?parent=42639"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/categories?post=42639"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inogic.com\/blog\/wp-json\/wp\/v2\/tags?post=42639"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
The Solution: Row-Level Filtering at the Source<\/strong><\/h3>\n
Key Benefits of Row-Level Filtering:<\/strong><\/h3>\n
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How Row-Level Filtering Works in Dynamics 365<\/strong><\/h3>\n
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Steps to add row filter in Customer Insights-Data:<\/strong><\/h3>\n
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![\"Dynamics](\"https:\/\/www.inogic.com\/blog\/wp-content\/uploads\/2025\/10\/3Dynamics-365-Customer-Insights-\u2013-Data.png\")
<\/p>\nReal-World Impact: Faster, More Efficient Data Processing<\/strong><\/h3>\n
Frequently Asked Questions:
\n<\/strong><\/h3>\n1. What is Row-Level Filtering in Dynamics 365 Customer Insights?<\/strong><\/h4>\n
2. Why is Row-Level Filtering Important for Large Datasets?<\/strong><\/h4>\n
3. Can I Use Row-Level Filtering for All Data Sources in Dynamics 365?<\/strong><\/h4>\n
4. Will Row-Level Filtering Delete Any Data?<\/strong><\/h4>\n
Wrapping Up<\/strong><\/h3>\n