Data quality and master data management as a prerequisite for digitization and process optimization

Which brings us to the keyword "data. In view of the requirements of the digital transformation, data is increasingly becoming the focus of entrepreneurial interest. Companies today are data-driven. There is practically no process without data. In this context, master data is the prerequisite for data and analytics to provide added value in order to effectively improve processes and drive digitalization. Process management and data quality and master data management are therefore two sides of the same coin.

This is a problem that is mutually exacerbated: On the one hand, data quality often leaves a lot to be desired; on the other hand, many companies avoid master data initiatives because they are considered complex and costly. Nevertheless, doing nothing is not an option; the costs of poor data quality are too high for that; moreover, digitization and process optimization are made more difficult and growth is hindered. But there are ways to get a handle on data quality and master data management.

Survey status quo of data quality and make it transparent

Quite a few companies have a problem with their master data. With zetVisions' Data Quality Analyzer (DQA), they can get to the bottom of this problem and detect inconsistent, duplicate, incomplete and outdated data records in their SAP systems in the master data domains - customer/supplier (business partner), product and material master data. Validation rules can be used to create data quality rules that are combined into rule sets to check data for consistency, completeness, timeliness and uniqueness. The rule sets are used to assign a quality score to a master data record.

For each master data record, it is thus possible to evaluate whether it is well, sufficiently or poorly maintained. The weighting can be freely defined for each rule of a rule set. In addition, KPIs can be defined, i.e. "thresholds" at which data records are considered good, sufficient or poor. The rule sets are applied on a rotational basis so that trends can be displayed over a longer period of time.

On the way to Master Data Excellence

The Data Quality Analyzer, which can be expanded into a complete master data management solution (zetVisions SPoT), is the entry point for getting a handle on data quality. The ultimate goal is called Master Data Excellence (MDE). This is not just a software solution, but an extended service offering around the management of master data. Starting with the analysis, definition and implementation of the processes, through the setup of the software tool, to the subsequent change management, as well as the guidelines for planning, controlling and providing data (data governance), MDE offers a seamless end-to-end service for master data management. The steps on the way to MDE are shown in the following figure:

If the steps from the actual assessment of potentials to the development of the MDE operating model have been completed, the implementation follows. Generally, an implementation project takes place in several steps, which are shown in the following figure:

After the successful implementation of the PDC operating model, it must be monitored as part of a continuous improvement process. In zetVisions SPoT, ongoing monitoring of the implemented processes is possible for this purpose via integrated process monitoring, which provides clues as to where processes can be improved. The ongoing monitoring of data quality can in turn be carried out via the DQA.

Master data excellence is not primarily a technological issue, and therefore not a purely IT issue. In companies, it must be driven jointly by the business departments and IT. Experience shows that the success factors for MDM operating models include support from management, structured and targeted data governance, and process optimization. An MDM operating model interferes with traditional structures, processes and "territories". For this reason, accompanying change management is one of the success factors for turning those affected into participants and "taking them along" into the new world. Finally, a professional software solution can only ever have a supporting effect.

Learn more about "Data Quality and Master Data Management as a Prerequisite for Digitization and Process Optimization" at the "No SurpRISE with SAP" conference in a presentation by Andreas Stock, Head of Marketing & Presales at zetVisions GmbH, on June 2, 2022 at 10:00 am.