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Author's profile photo Andreas Seifried

Effective Master Data Quality Management

You need to effectively author master data in high quality for excellence in your business processes?

SAP Master Data Governance is the way to go for enterprise-wide master data management. Ever since the first release, data quality was “built-in”:

  • by the re-use of ERP business logic (nowadays S/4HANA business logic)
  • custom validations, with the choice to implement in BRFplus or code in ABAP
  • data enrichment using SAP products, your own code, or third-party offerings, for example for address validation
  • duplicate check to save you from the costs of unnecessary double-maintenance and the potentially caused inconsistencies

You can create your perfect data quality firewall with SAP Master Data Governance for data that enters your landscape via well-known channels, namely change request processing or master data consolidation, but comprehensive and effective data quality management is more than that.

Explicit and accessible definition of your data quality

Data quality is driven by the requirements of your business processes. Having an explicit definition of your data quality rules that is easily accessible not only to IT people, but also to process owners and all other stakeholders of master data, makes it possible to collaborate and to agree on your data quality standards. You need to be able to describe data quality rules in natural language, augment them with further information, for example the reasoning of the rule or the impact if data does not comply with the rule. Accessibility does not only mean that the rules are well described and organized, but also that there is the visibility down to the actual implementation and usage of each rule in your systems and processes.

For managing the quality of your data, you need to be able to measure it along your quality dimensions. You need to be able to define KPIs, their baselines, and their targets. Only then you will be able to see the current state of your data quality initiatives, their past achievements and where they are heading to.

Advancement in your definition of data quality

Anybody who ever dealt with setting data quality standards will certainly agree: Once your quality standards are defined it is already time to adapt them. Innovation in business processes mandate for changing your standards. Luckily, an explicit and accessible repository of your data quality rules is a solid base to further evolve and optimize your standards.

Measure and analyze your state of data quality

However, advancement in your definition of data quality also means that data of shiny quality in the past might not be flawless anymore. Work needs to be invested to identify and to improve existing data so that the actual state of the data can follow the changed standards.

While the advancements of the quality standards might be easily incorporated in the main authoring processes, reality proves that you can never consider all channels that bring data in your landscape. For some it might be just a matter of time and effort to cover them, but there are also situations in which there might be good reasons to accept data coming that needs to be improved later. Again, you need to be able to identify this data and to efficiently remediate its quality.

Furthermore, you need to know how the effectiveness and the progress of the measures you have taken for quality remediation. Do parts of your organization need further support to achieve the set targets? Knowing the current state of data quality is also the basis to set new goals and to define new initiatives for further advancements.

Improve and correct your processes and your data

Analyzing data quality issues gives you the required insights for improvements and corrections. Only if you know who, where and how bad data enters the system, you will be able to find yet uncovered data entry channels, to fix issues in your data entry processes, or to educate people how to meet the expected quality standards.

At the very end, somebody needs to do the job and fix the data errors. As this is typically a time-consuming task, efficiency and distribution of the workload is key.

What if …

What if you could achieve all this, including central governance and master data consolidation with one single product that is integrated in your SAP S/4HANA system?

If you agree that this is a desirable target, you are invited to have a look at the new master data quality management capabilities of SAP Master Data Governance on SAP S/4HANA 1809 and see how this can help your organization to achieve this target.

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