{"id":9010,"date":"2021-06-24T09:00:43","date_gmt":"2021-06-24T16:00:43","guid":{"rendered":"https:\/\/www.microsoft.com\/insidetrack\/blog\/?p=9010"},"modified":"2026-04-03T12:56:44","modified_gmt":"2026-04-03T19:56:44","slug":"examining-microsofts-sap-transactions-with-microsoft-azure-anomaly-detector","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/insidetrack\/blog\/examining-microsofts-sap-transactions-with-microsoft-azure-anomaly-detector\/","title":{"rendered":"Examining Microsoft’s SAP transactions with Microsoft Azure Anomaly Detector"},"content":{"rendered":"

\"MicrosoftWe periodically update our stories, but we can\u2019t verify that they represent the full picture of our current situation at Microsoft. We leave them on the site so you can see what our thinking and experience was at the time.<\/em><\/p>\n

As part of our continuing digital transformation journey, our Microsoft Digital Employee Experience (MDEE) team is constantly looking for ways to improve our business processes and detect issues and anomalies before they become serious problems.<\/p>\n

Sometimes these failures happen sporadically within the application framework and often go undetected. Even if a user detects an anomaly, they need to decide how to react, which is time consuming.<\/p>\n

To do this, we\u2019re using Microsoft Azure Anomaly Detector to examine transactions across our SAP environment, which helps us identify issues before they become problems. In turn this enables us to proactively improve the performance, consistency, and reliability of our entire SAP landscape.<\/p>\n

[Unpack how we\u2019re optimizing SAP for Microsoft Azure<\/a>. | Discover how we\u2019re protecting Microsoft\u2019s SAP workload with Microsoft Sentinel<\/a>. | Explore how we\u2019re upgrading Microsoft\u2019s core Human Resources system with SAP SuccessFactors<\/a>.]<\/em><\/p>\n

Understanding the need for anomaly detection in SAP<\/h2>\n

At Microsoft, our SAP environment comprises many complex processes across multiple lines of business. To avoid having disparate environments and isolated monitoring and reporting data, we wanted to build a single codebase solution for monitoring and anomaly detection that each line-of-business can use with minimal code implementation.<\/p>\n

We wanted to build intelligence to detect anomalies and inconsistencies in business process flow to improve platform health. Improved platform health improves engineering service-level agreements (SLAs) and reduces revenue loss by being proactive rather than reactive.<\/p>\n

There were hundreds of areas that could benefit from anomaly detection in our SAP portfolio, but we wanted to identify a single area for our pilot project. In the Master Data Management (MDM) space, we create thousands of objects representing business entities such as customers and business partners.<\/p>\n

Most of these objects are created by using an application programming interface (API), and no human interaction is needed. However, it\u2019s extremely difficult to identify if issues related to MDM are occurring in upstream systems, so we needed a way to capture issues in advance, proactively and quickly.<\/p>\n

In the MDM space, we have SAP Master Data Governance (MDG) background processes, such Customer Master data creation, which run without any user interaction. Across various batch and scheduled jobs, process runtime varies based on data volume, time of day, time of year, and resource availability.<\/p>\n

Understanding the potential for issues in each process and the larger process environment involves several challenging questions, including:<\/p>\n