With the latest release of SAP S/4HANA 1909, SAP Master Data Governance received a comprehensive update of the data quality management capabilities that were already introduced with SAP S/4HANA 1809.
Collaboratively describe, catalog, and implement rules for data quality using a central rule repository
The rule repository of MDG allows you to manage your rules for master data quality in one single place. It provides you with a repository to catalog and define data-quality rules, including comprehensive descriptions of rules, business aspects, and contact information. Various statuses of rules and collaboration features, for example using SAP CoPilot, allow you to handle the complete lifecycle of rules. Furthermore, the implementation of rules in BRFplus is also done from this app. This gives you full transparency on business aspects, usages and the technical implementations of all rules.
Discover new data quality rules utilizing machine learning, analyzing your existing data
Besides entering already known data quality rules, data and business specialists can use mining runs to apply machine learning on existing master data. This will analyze how various aspects of the master data relate to each other and propose rules. The specialists can then collaboratively decide on the business relevancy of the proposed rules. Once accepted, data quality rules can be automatically created, including the transfer of the detected insights to the implementation of the data quality rule. Rule mining eases and shortens the discovery of rules and makes the qualification and implementation of data quality rules more efficient.
See also what kefeng.wang writes in his blog Using MDG Rule Mining to Improve Data Quality for more information!
Assess data quality by applying data quality rules on existing data
Data quality evaluation applies all rules to the active data in the system. The results of the evaluation are stored for later analysis. You can initiate evaluations ad-hoc, but of course also schedule evaluations, for example at weekly intervals.
Monitor progress of data quality initiatives and get insights for improvement
During data quality evaluation, the system stores the outcomes when applying the rules to your data. Furthermore, scores are calculated for each rule that indicate the share of good data in your system. You can group multiple rules in data quality dimensions. Data quality dimensions themselves belong to a data quality category. This allow you to define how you want to aggregate the scores on rule-level into higher-level KPIs for data quality reporting. You are provided with overview pages to monitor the current data quality situation and report on its trend. From each report you can drill-down to the lower-levels of your definition of data quality to identify root-causes of bad data.
Efficiently delegate or perform the remediation of data quality issues
Of course, you want to fix the issues in your data, once the data quality evaluation has detected it. From within the apps for data quality analysis, you can
Apply data quality rules at the point of data entry with central governance
With SAP S/4HANA 1909 you can use the same data quality rules for quality evaluation and for check in change request processing. This makes the definition process of rules efficient, as you only have to create a rule once. It also caters for consistent application and implementation of rules for both data quality evaluation and data entry. Please note that with the initial shipment of SAP S/4HANA 1909 this feature is only available for business partner master data (MDG-C, MDG-S, MDG-BP). We plan the availability for MDG-M with the next feature pack.
With SAP S/4HANA 1909, master data quality management with SAP MDG is not only available for product master data, but also for business partner data. Further domains are planned to be covered with solution extensions.
Master Data Quality Management with SAP Master Data Governance provides you with unique benefits:
What's New in Data Quality Management with SAP Master Data Governance on SAP S/4HANA 1909 (PPT)
Webinar recording featuring the new features of Data Quality Management with SAP Master Data Governance on SAP S/4HANA 1909
Documentation of configuration and usage – Visit http://help.sap.com/s4hana and make sure that you selected the S/4HANA 1909 version. Then navigate to Product Assistance -> Cross Components -> Master Data Governance. See the respective sections on Rule Management and Quality Evaluation beneath the nodes Configuration of MDG, Data Quality Management and Working with MDG, Data Quality Management.
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Summary
- Data quality evaluation to measure and analyze the quality of existing master data.
- Business Partner (including customer and vendor), Product, and customer-defined objects supported. Support of further domains by Solution Extensions to be announced.
- Rule mining applies machine learning to discover rules in existing master data.
- Validation of data in change request processing with the same rules used for data quality evaluation.
Rule Management
Collaboratively describe, catalog, and implement rules for data quality using a central rule repository
The rule repository of MDG allows you to manage your rules for master data quality in one single place. It provides you with a repository to catalog and define data-quality rules, including comprehensive descriptions of rules, business aspects, and contact information. Various statuses of rules and collaboration features, for example using SAP CoPilot, allow you to handle the complete lifecycle of rules. Furthermore, the implementation of rules in BRFplus is also done from this app. This gives you full transparency on business aspects, usages and the technical implementations of all rules.
Rule Mining
Discover new data quality rules utilizing machine learning, analyzing your existing data
Besides entering already known data quality rules, data and business specialists can use mining runs to apply machine learning on existing master data. This will analyze how various aspects of the master data relate to each other and propose rules. The specialists can then collaboratively decide on the business relevancy of the proposed rules. Once accepted, data quality rules can be automatically created, including the transfer of the detected insights to the implementation of the data quality rule. Rule mining eases and shortens the discovery of rules and makes the qualification and implementation of data quality rules more efficient.
See also what kefeng.wang writes in his blog Using MDG Rule Mining to Improve Data Quality for more information!
Data Quality Evaluation
Assess data quality by applying data quality rules on existing data
Data quality evaluation applies all rules to the active data in the system. The results of the evaluation are stored for later analysis. You can initiate evaluations ad-hoc, but of course also schedule evaluations, for example at weekly intervals.
Data Quality Analysis
Monitor progress of data quality initiatives and get insights for improvement
During data quality evaluation, the system stores the outcomes when applying the rules to your data. Furthermore, scores are calculated for each rule that indicate the share of good data in your system. You can group multiple rules in data quality dimensions. Data quality dimensions themselves belong to a data quality category. This allow you to define how you want to aggregate the scores on rule-level into higher-level KPIs for data quality reporting. You are provided with overview pages to monitor the current data quality situation and report on its trend. From each report you can drill-down to the lower-levels of your definition of data quality to identify root-causes of bad data.
Data Quality Remediation
Efficiently delegate or perform the remediation of data quality issues
Of course, you want to fix the issues in your data, once the data quality evaluation has detected it. From within the apps for data quality analysis, you can
- Directly fix single products or business partners by opening any of the apps assigned to you, for example Manage Business Partner Master Data.
- Delegate the correction to somebody else by sending the results of your analysis as a link to the app including filters and other settings.
- You can export the evaluation results in open office (XLSX) format.
- You can fix multiple products or business partners with MDG’s powerful mass processing capabilities.
- You can export selected business partners or products in open office (XLSX) format for offline editing and later import in the MDG mass processing app
Check of Data in Change Requests
Apply data quality rules at the point of data entry with central governance
With SAP S/4HANA 1909 you can use the same data quality rules for quality evaluation and for check in change request processing. This makes the definition process of rules efficient, as you only have to create a rule once. It also caters for consistent application and implementation of rules for both data quality evaluation and data entry. Please note that with the initial shipment of SAP S/4HANA 1909 this feature is only available for business partner master data (MDG-C, MDG-S, MDG-BP). We plan the availability for MDG-M with the next feature pack.
Domains
With SAP S/4HANA 1909, master data quality management with SAP MDG is not only available for product master data, but also for business partner data. Further domains are planned to be covered with solution extensions.
Benefits
Master Data Quality Management with SAP Master Data Governance provides you with unique benefits:
- A central repository for data quality rules, striving to cover every master data process in SAP Master Data Governance
- Re-use of rule definition and implementation for the purpose of data quality evaluation and check of data during entry with MDG’s change requests.
- With a few configuration steps (link help.sap.com) and no development at all you are ready to go with data quality management that is embedded into SAP Master Data Governance and SAP S/4HANA
- An easy way for rule implementation, based on pre-defined master data models, including for example value helps, plus the strengths of the BRFplus workbench and the robustness of its execution runtime.
- All data quality capabilities are accessible via the SAP Fiori Launchpad, making them available to all stakeholders in your organization embedded in their regular SAP S/4HANA work environment
- The possibility to drill-down from high-level KPIs, down to direct access to active master data in one single place, benefiting from the integration into SAP S/4HANA, for example due to the re-use of application authorizations when analyzing evaluation results
- Insight-to-action from where you analyze data quality findings to actually correct detected errors
Further Information
What's New in Data Quality Management with SAP Master Data Governance on SAP S/4HANA 1909 (PPT)
Webinar recording featuring the new features of Data Quality Management with SAP Master Data Governance on SAP S/4HANA 1909
Documentation of configuration and usage – Visit http://help.sap.com/s4hana and make sure that you selected the S/4HANA 1909 version. Then navigate to Product Assistance -> Cross Components -> Master Data Governance. See the respective sections on Rule Management and Quality Evaluation beneath the nodes Configuration of MDG, Data Quality Management and Working with MDG, Data Quality Management.
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