SAP master data management: solving the dirty data dilemma

Published: 28-Sep-2023

The benefit from any IT system is dependent on the quality of the data that underpins it — and that primarily means the master data. For most organisations, this will be the core set of business objects upon which transactions are performed, such as customer and employee details, product lists, physical locations, purchase histories and so on

In SAP, maintaining master data quality has always been a challenge. Often, data has been entered and updated manually, which can be time-consuming and error-prone.

Often it resides in multiple silos, which can lead to duplication and inconsistency. And, in the main, data management is manual and periodic, being cleansed and updated only at set intervals or before major system upgrades or migrations.

But with each generation, SAP adds more and more functionality and pulls in more and more data. Businesses are making greater use of SAP as a data hub, too. All of which means that the already difficult task of managing master data (MDM) manually is becoming impossible.

Data quality therefore needs to become an everyday activity – an integral part of business-as-usual as Dan Barton (pictured), COO and co-founder, BluestoneX, argues — and outlines how organisations can deploy advanced policy based automation and role-aware workflows to implement continuous processes solve the dirty data dilemma and keep master data management up to date.

Data drift
Master data may be core to a business — foundational, even — but it has a tendency to drift. Addresses and prices change, products may become unavailable and, of course, purchase histories will evolve. These changes are typically event-driven and are often very repetitive or repeatable.

At the same time, our world is speeding up, becoming more digitalised and information-dense. This puts ever more pressure on core systems to keep up with the pace and depth of change.

SAP customers have felt this pressure more than many, owing to the complexity of SAP environments and master data models.

SAP master data management: solving the dirty data dilemma

And for those users migrating to modern applications, the journey to S/4HANA can be extremely resource-intensive, further reducing the focus on MDM.

SAP has responded over the years with the release of tools to help analyse and optimise how systems perform, the most significant of which is BTP, the SAP Business Technology Platform.

BTP has put process automation and optimisation firmly on the agenda, however, it is a toolkit, not a complete solution, and can be too complex and expensive for some SAP users, especially those with small or medium sized systems.

In essence, many SAP users are encountering the same challenge: the quality, consistency and integrity of their data stands in the way of business progress — and this is not helped by the way that master data is managed.

Process mapping and automation
Increasing volumes of data and demands for MDM mean businesses need better and streamlined processes.

Data needs to be centralised in a single repository; data models need to be simplified and MDM systems need to be integrated with other SAP and external systems to make it easier to establish automated processes for sharing data and maintaining consistency.

Instead of accepting that core data objects inevitably drift over time, streamlining and automating processes can help businesses learn why they drift and fix that.

In addition, ownership of data quality and MDM needs to be put in the hands of the business user – not IT.  Of course, IT teams do not normally want to get involved in business processes, and conversely, it is inadvisable for users to be manually editing master data.

This is where process automation comes in, allowing users to see only what is relevant to their role and to the task at hand.

Moreover, advanced policy-based automation allows participation in the MDM process to be widened safely — and by building it into ‘business as usual’ workflows, users may not even be aware that they are undertaking MDM activity.

Achieving continuous MDM
It is important to consider, firstly, that MDM does not have to be a "big bang" implementation. Many organisations may – and can – choose a more digestible, bite-size approach. Whichever methodology a business embraces, though, the key steps to success are the same.

It is imperative to start with understanding business as usual and derive from that the workflows and rules as they apply to master data. Then look for master data owners within the business – as mentioned, IT should be the enabler, not the owner of MDM.

Work with business-side users and prospective data owners to identify, standardise and automate role-aware workflows that build a continuous MDM process into the day-to-day operation of the business.

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The automated workflow is how we move MDM out of IT and onto the responsible business people. In particular, input and feedback from the users will be essential as an organisation goes through the process of identifying the areas and/or workflows to automate.

Finally, it is important to consider IT’s new role. IT still has both visibility and control, but as an MDM orchestrator, so it owns the rules not the data, and it enables the processes rather than executing them.

Governance and data quality will derive from automated MDM as a side benefit. By their very nature, properly programmed rules engines and workflows can ensure that governance is complied with and good quality is maintained, along with responsibility, tracking and auditing information.

Conclusion
Whether a business is an SAP user or not — but especially if it is — master data is crucial. Having a consistent, accurate and correctly-governed foundation of customer, supplier, product and other data is vital to the proper functioning of almost any organisation. Increasingly, it is also a legal and regulatory requirement. 

It is essential then to manage that master data — not as it’s been done in the past, via one-off cleaning or re-baselining exercises, done on a periodic or ad-hoc basis. Rather, tackle the root issues that can bring in inconsistencies and errors.

That means eliminating data silos and using process automation and the power of AI to seamlessly build routine MDM tasks, coupled with robust configurable business rules, into workflows. That way, master data should always be clean and on track, without overburdening business users.

Fortunately, with advanced policy based automation and role-aware workflows, MDM platforms can widen participation in the process of continuously keeping master data updated. With the right approach, IT can pass on responsibility for the master data to the business, while retaining control of the guardrails.

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