Stop The Rot: Embedding Quality Into Master Data Management Processes
Jennifer Millard
By Jennifer Millard

Organisations can increase their data quality by taking a pro-active approach to data quality by building in data quality checks into their master data management processes. In this blog we will see how SAP EIM tools facilitate this via a service-orientated approach.

Creating master data is a little like manufacturing a product. Organisations with world-class data management practices embed Quality Assurance within their Master Data processes to ensure quality at the point of entry. The idea is this: preventing defective data at source stops defects before they arise. This ensures higher quality data from the start.

This idea is nice in principle, but does it really stack up? And is there a technology that can facilitate it?

Master data management (MDM) systems typically come packaged with a set of validations designed to ensure a minimum standard of data quality. On their own, standard ‘out-of-box’ validations are not enough to ensure high data quality so MDM systems also typically offer the ability to develop custom validations. But should I develop data quality validations in my MDM system? Consider the following:

  • What if I want to change to a different MDM technology at a later date?
  • What if I want to report on the data quality within my organisation?
  • What if some of my master data is entered via another system and I want to implement data quality validations in this system too?

If you have answered yes to any of the above, developing validations in an MDM tool may not be the best option as it may prevent you from making the most of your data quality investment.

SAP BusinessObjects EIM tools can help organisations maximise their data quality investment

By exposing validations rules as a service, data quality checks can be integrated into a wide variety of applications.

For example, SAP’s Data Quality Management product uses SAP BusinessObjects Data Services to integrate address validations (Figures 1, 2 and 3) and duplication checks (Figure 4) into SAP ERP, CRM and MDG.

Figure 1

Figure 2

Figure 3


Figure 4


This can help organisations create a ‘data quality firewall’ to ring-fence their enterprise data from input sources. Once in place, a data quality firewall ensures a higher standard of data by preventing defective data at source. However, to be effective, a data quality firewall needs to cover the complete set of input sources for an organisation’s master data. As we can see from the following diagram, the technology that underpins a data quality firewall needs to be versatile enough to integrate into the organisation’s own systems as well as external portals.

Figure 5:
Create once. Re-use everywhere


The benefit of creating data quality validations in SAP BusinessObjects Data Services and Information Steward is that a single set of validations can be created once in a central repository and exposed as a service to be integrated throughout the enterprise landscape. This maximises the benefit of an investment in data quality, as rules can be used for a variety of purposes, including data quality reporting via SAP BusinessObjects Information Steward. TCO is also lowered by reducing development overhead and making the enterprise landscape simpler to manage.

To find out about our packaged service to install and configure SAP DQM please click here

Find out more about our services in Enterprise Data Management here

To learn more about SAP BusinessObjects Information Steward and SAP BusinessObjects Data Services, to see a demo and to understand how it can add value to your business please get in contact with us at

Jennifer Millard
By Jennifer Millard

Jennifer is AgilityWorks' resident Growth Hacker, working tirelessly with each business unit to bring you the latest and greatest technology and software innovations on the market. In her free time Jennifer is a keen cyclist, although she seems to spend more time falling off the bike than riding it.

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