Those involved in IT projects often only focus on data quality for specific purposes – when cleansing data during ERP migrations, for example. As soon as their systems are live, they tend to turn their attention to other matters.
Companies that neglect data quality management, however, soon find themselves working with inaccurate information. To find out how to do things right and what the advantages are, we sat down for a chat with Reiner Schaaf and Martin Nußbaumer, the spokesmen of the German-speaking SAP User Group’s work group for master data management, data quality, and data governance.
SAP.info: Your work group has been dealing with the topic of master data management in SAP products for many years, but you recently extended your focus to include data quality and data governance. Why are these subjects so important?
Reiner Schaaf: Generally speaking, every company collects and manages data; the better they do so, the more reliably they can control their business processes. High-quality data is essential for centralized reporting and consistent workflows, and if you’re looking to fine-tune your business processes to one another, there’s no way around master data management and the corresponding procedures, rules, and responsibilities.
Martin Nußbaumer: Improving processes is key. The resulting competitive advantage companies can gain enables them to respond faster both in and to the market – and benefit from their increased data quality in the form of fewer errors downstream.
SAP.info: How is your work group structured? How do your members approach your subject matter?
Reiner Schaaf: It’s very heterogeneous; our work group is a network platform that constantly evolves as it provides best practices and potential solutions.
Some companies are already using products in this field, while others are observing their experiences and trying to find their own orientation. This is why it’s important that our work group not focus too closely on particular products. Compared to larger midsize companies, which have an eye on the quality of their master data, it’s difficult to get smaller businesses interested in the subject.
In the case of the biggest corporations, meanwhile, implementations of master data management depend on their distributed IT landscapes and the wide variety of systems used for data storage.
Next page: Areas where companies need to take action
SAP.info: What’s stopping so many companies from taking a closer look at their information landscapes and taking action? Where do the potential difficulties lie?
Martin Nußbaumer: One of the obstacles we often see is that no one feels responsible for data quality. It’s not an institutionalized topic, so nothing happens until errors crop up somewhere. In our opinion, however, continuous data quality management is necessary, which means means creating positions responsible for it across the usual departmental lines. Data governance affects everyone, after all.
Reiner Schaaf: Ironically, having low-quality data also makes it extremely hard to measure the consequential expenses. You can’t always immediately attach dollars and cents to processes implemented and potential incorrect decisions made based on inaccurate information. This makes it difficult to calculate and justify corresponding investments in organizing data quality and individual projects.
SAP.info: Do you have a recommendation as to how companies can best approach the subject of data quality management? How can they benefit?
Martin Nußbaumer: A holistic approach is important. I’d recommend following the maxim “think big, start small.” Sustainable, long-term data management is essential, but you have to take it one step at a time. A specific project that succeeds quickly and clearly produces added value is a good basis for a company-wide rollout.
Reiner Schaaf: I’d start wherever it’s most urgent. Depending on your business area, it might be in supplier master data, putting together standardized product catalogs, or conducting a global spending analysis. The important thing is not to clean up your data once and then forget about it; otherwise, problems will just start cropping up again later. Here, SAP offers various scenarios for implementing long-term data quality management.
SAP.info: Mr. Nußbaumer, what do you think of SAP’s products and strategy in this area?
Martin Nußbaumer: SAP’s offerings in master data- and data quality management constitute quite a wide range of products. There’s no single master data solution that fits every company; it depends much more on each specific situation and existing system landscape. The challenge is figuring out which solution components best address the customer’s needs.
In heterogeneous landscapes, where data consolidation plays a key role, SAP NetWeaver Master Data Management is frequently the component of choice. Users often supplement it with SAP NetWeaver Business Process Management and SAP BusinessObjects Data Services to control processes, check data quality, and support data migrations.
Particularly for companies with less diverse system landscapes, a comparatively new application is very promising: SAP Master Data Governance functions as an add-on for SAP ERP systems and makes it possible to map master data workflows.