Master Data Excellence

The importance of master data management has changed, not least in view of the requirements of advancing digitalization, says Andreas Stock from zetVisions at the start of the E-3 interview. "To put it in a nutshell: MDM has become even more important. Ever since data was first collected, stored and processed, data quality has been a constant problem in the business world. So the topic is not new. What is new in recent years is that data is increasingly becoming the focus of corporate interest in view of the requirements of digital transformation."

According to the DataIQ Research Report, a third of the companies surveyed state that data quality is fundamental to the success of their digital transformation, with 54% seeing it as one of the core components. While 87 percent of companies understand that data quality is a fundamental or significant component to the success of their digital transformation, at the same time 68 percent strongly agree or agree with the statement that poor data quality has a negative impact on digital transformation.

"In the digital age, in which data is increasingly seen as a production factor, the pressure to provide high-quality data on a permanent basis is increasing," emphasizes Andreas Stock. The zetVisions manager explains that there is only one sequence of the growing challenges for data quality that needs to be remembered in order to develop the necessary "sense of urgency": "In data-driven environments, incorrect data leads to incorrect information and therefore incorrect decisions."

According to market researcher BARC, there are also aspects that force the organization to ensure reliable data. This includes the need to react flexibly to dynamically changing market requirements with transactions.

Effective master data management could be the decisive factor in minimizing integration costs. In addition, more and more cross-departmental analyses of integrated data records are required. Andreas Stock adds: "Data-driven companies in particular are dependent on quality-assured master data in order to optimize business processes and develop new - data-driven - services and products."

Jan Richter from KPMG sees the biggest challenges for successful MDM in the organizational aspects of corporate structures. "Organizational changes are difficult and employees are reluctant to part with - cherished - routines, even if they are inefficient." Richter sees the technical construct of companies as the second challenge: "The larger the organization, the greater the number of systems and data flows."

Thirdly, he refers to the business management processes: "If the organizational buy-in and the will for technical change are there, then the processes can be adjusted to reflect the new reality. I would consider this challenge to be downstream and independent of the other two and not at the same level. The reason for this is that experience has shown that there is quick agreement on overarching issues such as strategic importance and what master data should be used for. A systematic approach often allows a qualitative target image to be defined quickly."

Proven standard maintenance processes as well as standard roles and profiles help to build a future-proof MDE organization with effective governance structures. "The main challenge is the conversion project", emphasizes Jan Richter. "First of all, it is important to understand to a sufficient degree who is involved in MDM processes, what the specific requirements of each business unit are and where there are deviations from the previously defined standard - and why. Of course, it is a further challenge to accompany the transformation at group level rather than business unit level. This is because it is often necessary to understand in detail what the impact will be if MDM is changed from one day to the next and to predict how users will react if, for example, a certain field can no longer be used in the traditional way."

In plain language, according to KPMG manager Richter, this means: Where will certain information be stored in future, what impact will the change have on upstream and downstream systems and processes? Successful MDM therefore always includes extensive training for all affected employees.

The fact is that Master Data Excel-lence goes far beyond the management of data structures within an ERP system, as the latest study by the VDMA (German Engineering Federation) has shown: a key measure for optimizing sales measures and ensuring greater customer loyalty is the digitalization of processes and the targeted use of operational application systems.

The current study shows that by far the greatest challenge for companies in this context is data maintenance to ensure high data quality: 84 percent of survey participants rate the effort involved in entering, searching and maintaining data as high.

This aspect is particularly relevant as the maintenance of data has a direct effect on data quality and the usability of IT solutions. "Here
companies should start with this, because efficient use of the systems can only be guaranteed with high data quality", says Professor Claus Oetter, Managing Director of VDMA Software and Digitalization.

The solution is called Master Data Excellence. Jan Richter: "Master Data Excellence describes a proven, holistic approach and consists of the following five dimensions: firstly vision, strategy and metrics, secondly data governance, thirdly data model, fourthly processes and finally fifthly technological infrastructure."

And in the E-3 interview, Andreas Stock adds: "Perhaps the decisive difference is that the joint offering from KPMG and zetVisions on Master Data Excellence is not just a software solution, but an extended range of services relating to the management of master data. Starting with the analysis, definition and implementation of processes, through to setting up the software tool and subsequent change management as well as guidelines for the planning, control and provision of data - data governance - the partners provide a seamless end-to-end service for master data management."

MDM offerings seem to be a dime a dozen. Are there classifications and specializations here? "MDM offerings can be differentiated according to whether they are single-domain or multi-domain solutions", answers Andreas Stock. According to the Aberdeen Group, the most defining characteristic of the leading companies in MDM is their ability to manage multiple data domains simultaneously.

Multidomain MDM improves data efficiency in several ways: Firstly, the accuracy of the data is 8.7 percent and the completeness is 11.9 percent better than with non-multidomain MDM. "However, the advantages of multi-domain MDM become even clearer when it comes to the criteria of time to information and accuracy of decisions, which are important not least for effective BI", explains zetVisions manager Stock. When using multidomain MDM, 64 percent see improvements in time to information compared to only 35 percent with non-multidomain.

Organizations with only one version of the "truth" and a master data record for each key domain spend less time searching for information or confirming the reliability of existing data and more time on relevant analysis. "Multidomain MDM centralizes the entire master data management. All relevant data from purchasing to sales converge in a centralized system. This makes it possible to create a golden record for customers, products and suppliers, for example, and to recognize connections and correlations between these domains", says Andreas Stock, describing the challenges.

Master Data Management is a discipline in its own right and a golden record is the goal of MDM. "It's about having a single point of truth", says Andreas Stock and explains that it is not about master data that is scattered in various silos across the entire organization and sometimes shows more, sometimes less deviations.

"It's often enough about little things that cause confusion, require additional work, slow down processes - and generate costs, Andreas Stock knows from numerous successful customer projects. The question of whether a customer's name is "Bianca Peterhans" or "Bianca Hanspeter" is just as important as whether the address is "Osterbekstr. 90" or "90c". There are numerous examples of this for all master data categories. "The costs of poor data quality are significant", warns the zetVisions manager.

Master Data Excellence makes company-wide correlations and interactions visible and, as a result, often results in considerable potential savings in terms of time and costs. On the other hand, critical information for business decisions is generated. Decision-making efficiency is an important advantage of the aforementioned multi-domain approach to master data management. This makes it a key guarantee of success when it comes to basing decisions on facts and making better decisions through BI.

When it comes to the right time to start master data management, Jan Richter from KPMG says: "As in school, any time is the right time to do your homework. In practice, it is a good idea to use external momentum - just like in school. In the MDE context, this momentum is that of a transformation project, e.g. the S/4 transformation, or other digitalization initiatives related to master data, such as predictive forecasting and predictive planning. In practice, we see the following three triggers in particular that favor an MDC initiative: First, the realization that the current data quality leads to high opportunity costs, e.g. due to missed business opportunities. Data maintenance processes are often simply inefficient and therefore cost-intensive due to rework and clean-up work."

Basically, as soon as the amount of data and the number of affected systems have reached a certain mass, the effects can no longer be captured simultaneously.

According to Jan Richter, the high-level answer is: "As soon as certain data - e.g. product name, customer number, supplier address - is used in several systems but is not entered centrally, i.e. not always by the same person, and/or is entered in different systems, it is only a matter of time before a major data chaos arises."

"The switch to S/4 Hana is not a replacement for a master data management system", Andreas Stock knows from his successful professional practice. "Sustainability cannot be achieved by harmonizing and consolidating ERP systems alone. Especially if the data is located in a heterogeneous system landscape. At first glance, this may seem easier in a one-ERP landscape." A few years ago, a study by Omikron Data Quality showed that the target and actual status of data quality in ERP are far apart.

According to the survey, only one third of the more than 200 ERP users surveyed stated that the actual status of data quality meets the requirements for business applications. 29 percent noted certain deviations and 39 percent of respondents even reported major discrepancies.

"Against this background, there is a trend towards harmonization and consolidation in companies with the aim of reducing complexity, sources of error and costs and improving data quality by using fewer systems", emphasizes Andreas Stock in the interview. This goal is believed to be achievable with One ERP projects.

One-ERP refers to the plan to replace the heterogeneous ERP landscape with a single ERP system. This may also help in the short term, says Andreas Stock: "In any case, an ERP system is better for data quality than many ERP systems, but in the medium to long term the validation processes and data quality checks are missing, i.e. precisely the aspects that make up a master data management solution and that are necessary to ensure data quality in the long term. However, some shy away from the expense of such a solution."

When it comes to MDM, the process optimization steps are particularly important, as a master data initiative automatically involves evaluating and adapting business processes, as SAP CFO Luka Mucic repeatedly emphasizes. This process analysis and consulting is very resource-intensive, because only when the processes are clear and everyone knows who has data sovereignty, who maintains which data, which data is stored by whom, only then does the MDM software come into focus.

"Ultimately, it only maps what was previously defined on the process side. This illustrates a key difference between a One ERP project and a master data initiative: the latter analyzes the existing processes, adapts them to the required target status and defines processes, rules and standards for data management; the former consolidates several ERP systems into one, but generally leaves the processes as they are and does not define any binding data governance", explains Andreas Stock.

The SAP inventory customer is required to think in terms of algorithms and data structures. Anyone who deals with master data management will recognize that One-ERP has many advantages, but sustainable data quality cannot be achieved with the harmonization of various ERP systems alone.

"This requires an MDM solution", emphasizes zetVisions manager Stock, "which primarily takes the data governance aspect into account. If One-ERP has not yet been implemented and a heterogeneous ERP landscape is still in place, the question arises as to whether an MDM solution should be established, as this allows all existing ERP systems to be controlled with regard to standardized master data."