The benefit from any IT system is dependent on the quality of the data that underpins it – and that primarily means the master data. Most organisations rely on a core set of business objects, such as customer and employee information, product lists, geographic locations, and purchase histories, to perform business activities.
In SAP, maintaining master data quality has always been a challenge. In many cases, data is entered and updated manually, which is time-consuming and error-prone. Often it resides in multiple silos, which can lead to duplication and inconsistency. The majority of data management takes place manually and periodically, with data being cleansed and updated only 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, 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.
Master data may be vital to a business – foundational, even – but it has a tendency to go awry. Addresses and prices change, products may become discontinued, and of course purchase histories will evolve. These changes are typically event-driven and are often very repetitive.
At the same time, our world is speeding up, becoming more digitised 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, due to the complexity of SAP environments and master data models.
S/4HANA can be extremely resource-intensive for users migrating to modern applications, further reducing MDM’s relevance.
With the release of BTP, SAP’s Business Technology Platform, the company responded to this by developing tools to help analyse and optimise how systems perform. 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.
A major challenge facing SAP users is the quality, consistency, and integrity of their data, which is hindering their business progress – and master data management does not help with this.
Automation and Process Mapping
Increasing the capacity of data and demands for MDM mean businesses need more efficient, streamlined processes. For data sharing and maintaining consistency, it is necessary to centralise data in a single repository, simplify data models, and integrate MDM with other SAP and external systems to simplify the process. Instead of accepting that core data objects inevitably drift over time, streamlining and automating processes can help businesses learn why they drift and ultimately fix that.
In addition, the 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.
Additionally, advanced policy-based automation makes it possible to broaden participation in MDM safely – and users may not even be aware that they are engaged in MDM activities.
Achieving seamless MDM
It is important to examine, firstly, that MDM does not have to be a ‘big bang’ implementation, many organisations may – and can – choose a more digestible, play by play 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.
Business users and prospects need to work together to identify, standardise and automate role-based workflows that integrate a continuous MDM process into day-to-day operations. 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. The proper programming of rules engines and workflows can ensure compliance with governance, good quality, responsibility, tracking, and auditing information by their very nature.
Whether a business is an SAP user or not – but especially if it is – master data is crucial. In almost every organisation, a consistent, accurate, and properly-governed foundation of customer, supplier, product, and other data is essential. Increasingly, it is also a legal and regulatory requirement.
Master data must therefore be managed – not just cleaned or re-baselined on a periodic or ad-hoc basis, as it used to be in the past. Rather, tackle the root issues that can bring in inconsistencies and errors.
That means getting rid of data silos and utilising process automation and the power of AI to seamlessly build routine, manual MDM tasks, coupled with robust configurable business rules, into workflows. That way, master data should always be clean, clear 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.