Improve Your Data Quality – A Hybrid Use Case with SAP Data Intelligence and SAP Information Steward
This blog introduces the expanded hybrid use case between SAP Data Intelligence and SAP Information Steward that provides users the ability to seamlessly leverage SAP Information Steward’s on-premise rules within SAP Data Intelligence Cloud, run the rules against support SAP Data Intelligence source, identify records that are not complying with the defined rules, remediate those records through SAP Data Intelligence’s data preparation, to improve and continually monitor and track the quality of the data.
In our ‘Improve Your Data Quality’ hybrid use case, existing SAP Information Steward customers’ can seamlessly leverage their existing data validation and quality rules and enforce the same data quality standards across the supported sources in SAP Data Intelligence. SAP Data Intelligence connects to SAP Information Steward on-premise instances using SAP Cloud Connector. After setting up your Information_Steward connection type in Data Intelligence’s Connection Management, you can then go to Metadata Explorer’s rules, click on Import, and you will see the Information Steward Connections tab. In the Information Steward Connections tab, you will see all your SAP Information Steward project, how many rules each project has, and you can also click on a project and see which rules are in that project. You can easily select which project rules to import into SAP Data Intelligence.
Check out this video, showing how quick and seamless we can share trusted rules from SAP Information Steward with SAP Data Intelligence.
Data Remediation with Data Preparation
Now that Data Intelligence has leveraged Information Steward’s rules, we can run those rules and identify records, failed records, that are not complying with the defined rules. We can have all the failed records saved to an SAP HANA connection. We can use a sampling of the failed records and easily correct the data that is failing a rule(s) using Data Intelligence’s data preparation. Data preparation intuitively allows users to correct, transform, enrich, standardize, aggregate, and join dataset. By creating a data preparation recipe that corrects the failed records, you can improve the quality of your data.
Automate Data Quality Monitoring by Scheduling Rulebooks
We have used rules from SAP Information Steward, identified failed records that don’t comply with the rules, used data preparation to correct the failed records – improving the quality and trustworthiness of our data, and we want to make sure we continually monitor the quality of the data, to do this we can setup a schedule task to run the rulebook automatically, this can be on a daily, weekly, or monthly basis. Setting up the rulebooks to run on a reoccurring basis means we will always be able to monitor the most up-to-date quality results so we can keep an eye on the quality and trustworthiness of our data.
SAP Data Intelligence Cloud August edition has many new features, capabilities, and enhancements beyond the few metadata and governance such as:
- Reusing existing rules substantial reduces the time to redevelop rules while gaining faster insight into quality and trustworthiness of newly added sources and applications
- Ability to use data preparation to remediate and improve data quality and compliance of the data. Effortlessly add the result of a preparation back into a rulebook to monitor quality and make sure that the results on the full dataset are improved
- Schedule rulebooks to automatically execute on a reoccurring basis, giving you the ability to always monitor the most updated rulebook results.
For additional information please check out:
- Webinar: SAP Data Intelligence Expert Series: The way forward with SAP Information Steward and SAP Data Intelligence
- SAP Data Intelligence – What’s New in DI:2107.
- SAP Data Services, SAP Information Steward, and SAP Data Intelligence: strategy, maintenance, and future vision of SAP Data Management products
- Data is Heading to the Cloud…Are you Ready?
- What’s New in SAP Data Intelligence – Central Blog Post
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