The model becomes reality

We recommend not to start with all master data domains - customers, suppliers, material, business partners, etc. - right away, but to start with one domain only. There are several options for the selection. One can tackle the domain with the biggest quality problems first or the one that either has the greatest importance for the company or promises the fastest success. This is an individual decision. After starting with one domain, the expansion to the other domains follows.

Data governance and process optimization

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. Our experience has shown that the success factors for MDM operating models include management support, structured and targeted data governance, and process optimization.

This is the only way to ensure that the importance of high master data quality for business and cost development is understood by all employees. Internal company guidelines for handling data are imperative.

Data governance defines uniform rules, processes and responsibilities for data entry, release and maintenance as well as data quality KPIs. It is not only the core processes in the company that need to be taken into account, such as purchasing, production or sales. The master data processes involved in creating, maintaining or deleting data must also be optimized.

An MDM operating model interferes with traditional structures, processes and "territories". Therefore, accompanying change management is one of the factors for success in order to turn those affected into participants and to "take them along" into the new world. Finally, a professional software solution can only ever have a supporting effect. Only after processes and authorities for data maintenance and release have been clearly defined can IT support be provided.

zetVisions SPoT 

Intelligent multi-domain
Master Data Management

• A central database for
  all master data domains
• Clear data governance
• Increased data quality
• Reduction of error sources through automated data maintenance
• Customized user interfaces 
• Customization of data models
• Flexible data transfer to SAP and non-SAP systems

Multi-domain solution

Regardless of our recommendation to start with one domain, companies should deploy a multi-domain MDM solution. This is a master data solution that covers multiple master data domains and centralizes the entire master data management in one platform.

This opens up new perspectives on the business process. Company-wide interrelationships and interactions become visible - and thus not infrequently a considerable savings potential in terms of time and costs. A multi-domain MDM thus creates the "one truth" for different master data domains across the entire business process.

In addition to integrated enterprise-wide data management, multi-domain MDM systems with data quality rules and lifecycle processes can also support data governance, i.e., uniform and binding frameworks, workflows, and responsibilities for handling data, maintaining it, distributing it, and so on. When using diverse single-domain data silos, it is naturally difficult to ensure company-wide compliance with defined standards.

In contrast, if there is only one source of master data, users have significantly less autonomy in developing definitions and rules for data because the cross-domain data architecture is binding and transparent. The result: effective governance principles and cross-functional collaboration between departments. Both together lead to more process efficiency and better resource allocation.

Ask questions

In addition to observing the success factors described at the beginning, it is important to first ask the right questions. Experience from numerous customer projects has shown us this. These include the following questions: What does master data mean in our context? How is this master data defined? Which master data needs to be aligned, and which should be transferred initially? What is global or local master data? What overlaps exist between this master data and the existing data pools? What should the target processes look like? Which systems should be connected? 

As a result, answering these questions by implementing a master data management solution leads to support for data governance. Since all systems use the same version of the master data, the data quality "automatically" improves and the "correct" data is always available on a daily basis. Lean processes without redundant, manual data entry in the various systems - and the associated coordination effort between departments - reduce complexity and cut costs.

Investment and change processes

Based on the processes and data governance guidelines defined in the MDE operating model, the design of the required creation and change processes is carried out as part of the implementation project for the master data management solution. Workflow-based processes are used on the one hand to ensure data governance and on the other hand to ensure smooth cross-departmental collaboration when creating or changing a master record across multiple departments or systems. In this way, they can also link processes across different systems beyond the system boundaries.

At an international company specializing in the manufacture of glass and glass ceramics, which uses zetVisions SPoT for the creation process for configurable products, the customer data and the individual product request are first recorded in the CRM system.

From there, the basic data is transferred to zetVisions SPoT, where it is pre-recorded as configurable SAP material. After completing all data required by the ERP system via departments such as production and product management, the master data record is transferred to the SAP system.

However, data transfer via preconfigured interfaces is not limited to the SAP world. In another application example, dealer data (such as names and addresses) recorded in the SPoT system of a motorcycle and sports car manufacturer is transferred to a content management system to enable customers to search for dealers on the company website.

Furthermore, processes provide the necessary transparency regarding the status of the request up to the release of a master record, since the request is broken down into individual process steps and workflow mechanisms make it possible to see at any time which phase the process is currently in.

The operating model of an MDM system also includes the integration of external services that enable master data to be checked for completeness and consistency. Examples of this are: checking the VAT identification number, address checks, and embargo list checks. These checks can be integrated via web service. 

Generally, an implementation project takes place in several steps, with each phase being completed with a milestone before the next project phase begins. Within a project preparation phase, the project scope is specified and a detailed project plan is created. A kick-off meeting including team training gets everyone on board.

In the subsequent target concept phase, which lays the foundation for the success of a master data project and is therefore the most important phase, individual objectives are defined in workshops and discussions; in addition, a needs-based concept for the IT-supported mapping of the company-specific data governance aspects, authorizations and the associated processes is developed. The definition of the data models as well as the interfaces should also take place in this phase.

The decision as to which master data should be managed in the central system is also part of this phase. In parallel, the MDM system is installed on the system landscape. In the subsequent implementation phase, the individual business concepts created by zetVisions are put into practice.

If necessary, an initial data transfer to the MDM tool can also be prepared here. Furthermore, there is also the opportunity to extensively test the settings or adjustments of the new system. During the production preparation phase, zetVisions supports the go-live planning. This phase also includes the training of the employees. They should not only be able to handle the system, but also be able to make adjustments to the system themselves (customizing).

Monitor and optimize

Data quality can be monitored using the zetVisions SPoT Data Quality Analyzer (DQA) - a flexible tool for analyzing and monitoring data quality in SPoT. With the DQA, KPIs and quality scores can be created to measure data quality or the achievement of individual data quality goals, and each can be weighted differently. The evaluation of the data quality objectives, over freely definable periods of time, is carried out graphically and in tabular form in data quality dashboards.