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Author's profile photo Markus Ganser

Continuously Assuring and Controlling Master Data Quality (Part One)

The Business Case for Continuous Data Quality Assurance

Continuous Data Quality Assurance for enterprise-critical data is indispensable for companies striving for sustained enterprise performance: Flawless business execution and trusted cross-company analyses depend on the quality of the underlying master data, and the challenge of continuously assuring premium master data quality as an ongoing activity is even higher when enterprises are operated on the basis of diversified system landscapes. Tackling this challenge is key to make the competitive edge.

The Situation Today

However today, it is still common that organizations have little, or no transparency at all into what the relevant data entities for a given business process are, nor do they have comprehensive capabilities to define and control required quality KPIs for these data entities. In such a setting, the negative impact of poor master data quality is only revealed when the damage is already done, i.e., through broken business transactions and shaky company analyses that produce wrong decisions. As long as organizations cannot statistically measure the quality of enterprise-critical data and their compliance with company terms, they are in a poor position when it comes to safeguarding and improving the overall performance.

The Way to Continuous Data Quality Assurance

To tackle the situation and accommodate this high-priority need, SAP features a comprehensive and sustained way to manage the data quality. Using this data quality approach, companies can:

  • Define the relevant metrics and set up the required quality rules that their critical enterprise master data need to comply
  • Subsequently monitor the compliance statistically and clearly visualize the prevailing data quality, and
  • Trigger follow-up actions if master data reveals quality issues.

These key activities are integrated into a collaborative end-to-end process that empowers data stewards and data administrators to exercise overall data control.
Using such an approach, companies can establish a closed quality loop around their enterprise master data management strategy.


Fig: SAP BusinessObjects Data Services and Xcelsius dashboards clearly visualize the prevailing data quality and trends. Defined quality dimensions can comprise (to name just a few):

  • Completeness (e.g., all mandatory fields contain data)
  • Conformity (e.g., all formats must match given patterns)
  • Validity (e.g., data must be in a valid range)

This data quality scorecard and remediation solution brings data, systems, and people together into one collaborative and coherent process flow. It combines SAP NetWeaver Master Data Management, SAP BusinessObjects Data Services and Xcelsius dashboards into a cohesive monitoring environment, and flexibly integrates with SAP NetWeaver Business Process Management to seamlessly trigger follow-up action if the data quality revealed forces to do so.  It is a perfect means to bring companies in good shape and keep them there on an ongoing basis.

Sounds interesting? Then stay tuned for part two of this blog series which will focus on architectural and implementation considerations of this scenario.

Best regards,


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      Author's profile photo Former Member
      Former Member
      Hi Markus,

      Thanks for writing on this critical data quality issue facing lots of orgs and being able to statistically measure it. I look forward to your future parts of this blog…