The royal road to the Golden Record

What are the success factors for a master data initiative?

Marco Wittigayer: A master data management initiative is not primarily a technological and therefore not a purely IT-Theme. It must be in Company jointly by the departments and by the IT be driven.

According to a PwC study, among other things, the support provided by the ManagementThe success factors include structured and targeted data governance and process optimization.

They should not be somewhere in the Company but should be located at the topManagement. This is the only way to ensure that the importance of high master data quality for business and cost development is recognized by all Employees is understood.

Internal company guidelines for dealing with Data are mandatory. Data governance defines uniform rules, processes and responsibilities for data entry, release and maintenance as well as data quality KPIs.

Not only are the core processes in the Company to consider how Purchasing, production or sales. Also the master data processes around the creation, maintenance or deletion of Data must be optimized.

This requires a great deal of persuasion on the part of Employees necessary?

Wittigayer: A master data initiative interferes with traditional structures, processes and jurisdictions.

Therefore, an accompanying change Management among 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 be supportive. Only after processes and authorities for data maintenance and release have been clearly defined can a IT-support can be provided.

How should Company proceed with a master data initiative?

Wittigayer: We recommend not to start right away with all master data domains -. Customerssuppliers, material, products, finances, etc. - but only with one domain for the time being.

There are several options for the selection.

You can tackle the domain with the biggest quality problems first, or the one that is either the most important to the Company or which promises the quickest sense of achievement. That is an individual decision.

After starting with one domain, the master data initiative is extended to the other domains, e.g. first the customer master data, then the vendor master data, and so on. This results in a thoroughly sensible division of the overall project into several phases.

Which MDM solution is more effective in your view: single or multi-domain?

Wittigayer: Regardless of our recommendation to start with one domain first, you should Company Deploy a multi-domain MDM solution.

This is a Master data-solution that covers multiple master data domains and centralizes all master data management in one platform. All relevant Data from Purchasing to sales converge in a central system and can be distributed from there to any target systems.

This opens up new perspectives for the Business process. Company-wide interrelationships and interactions become visible - and thus not infrequently a considerable savings potential in terms of time and costs.

Bring associated Master data of a domain together, a "golden record" can be created for e.g. CustomersThe result is a comprehensive overview of the domains, products and suppliers, and interactions between these domains can be identified. You get an all-round view of the Master data across all domains.

A multi-domain MDM thus creates the "one truth" for different master data domains across the entire Business process.

What other advantages do multi-domain solutions offer?

Wittigayer: 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 dealing with Data, their maintenance, distribution, etc. When using diverse single-domain data silos, it is inherently difficult to ensure enterprise-wide compliance with defined standards.

If, on the other hand, there is only one source for 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.

What is your approach to a master data project?

Wittigayer: We proceed in five steps, with each phase concluded with a milestone before the next project phase begins.

Within the project preparation phase, in coordination with the Customers the project scope is specified and a detailed project plan is drawn up. A kick-off meeting including team training gets everyone involved.

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, we define individual objectives in workshops and discussions; we also develop a needs-based Concept for the IT-supported mapping of the company-specific data governance aspects, authorizations and the associated processes.

The definition of the data models as well as the interfaces (data transfers) also takes place in this phase. The decision which Master data to be managed in the central system is also part of this phase.

In parallel, we install ZetVision's SPoT on the system landscape of the Customers.

What happens after this phase?

WittigayerIn the subsequent implementation phase, the individual business concepts are put into practice. If necessary, we also prepare an initial data transfer to SPoT here.

Furthermore receive Customers now have the opportunity to extensively test the settings or adjustments of the new system.

During the production preparation, ZetVisions supports the Customers during go-live planning. Of course, this also includes training the Employees. They should not only be able to handle the system, but also be able to make adjustments to the system themselves (Customizing) to perform.

In the final go-live phase, ZetVisions SPoT gets to work. After successful completion of the project, the handover takes place to our Support-Team.

What does your training concept look like?

WittigayerFrom the beginning of the project, we follow a train-the-trainer approach.Concept. The focus here is on the team that, in the first two phases of the project, has been the Customizing defined and receives full access to the system.

Already within an administrator training course, the administrators of the Customers with our support and can customize the system independently.

This means that the customer can rely on the in-house SAP-expertise, e.g., to implement the SPoT-Customizing and to carry out authorization management.

In addition, further training on individual aspects of the system will be conducted in the second phase of the project.

What is the goal of the trainings?

Wittigayer: During the training sessions, participants develop a basic understanding of how SPoT works and gain initial experience with data entry and evaluation.

The aim of the train-the-trainer concept is, in addition to introducing the solution, to enable the project employee to lead training courses independently. After project completion Customers introduce the end users to the system in a downstream training phase.

If required, ZetVisions also offers such user trainings on the desired topics or supports the implementation.

What happens after the implementation or after the project?

Wittigayer: Of course, even after the launch of ZetVision's SPoT, we remain by the side of our Customers.

After the go-live our Support not only during the Hypercare phase directly after commissioning, but also beyond that for all questions, wishes and problems.

The intensity of the care is determined solely by the requirements of our Customers determined.

What are your personal "lessons learned" from master data projects?

WittigayerIn addition to observing the success factors described above, it is important to ask the right questions first. This has been shown to us not only by experience from numerous customer projects, but also by implementing our solution for customer master data in-house.

The initial situation was characterized by three different data pools in which the internal Departments have created customer data, such as name, address, contact persons, for their purposes and thus multiple times.

Information that arrived in one "data pot" was not always passed on to the others, who should have known it. This sounds unspectacular at first, but unfortunately it is the nucleus for the typical, generalizable problems with the Management from Master data.

How does collaboration succeed across departmental boundaries?

WittigayerFirst of all, the departments had to discuss the definition and establish a uniform wording during the project.

For example, the finance department spoke of "debtors, Customers", whereas Sales & Marketing spoke of "accounts".

The question alone "When is someone Customer?" has triggered some discussions.

Against this thoroughly generalizable background, the typical questions that arise before and during a master data project include:

• What mean Master data in our context?
• How are these Master data defined?
• Which Master data need to be adjusted, which ones should be adopted initially at all?
• What are global or local Master data?
• Which system has the best Data quality for the initial data transfer to ZetVisions SPoT?
• What are the overlaps of these Master data with the existing data pools are there?
• What are the technical features of the systems?
• What should the target process look like?
• Who is responsible for data entry and maintenance?
• How should the release processes be structured?
• Which systems should be connected?

As a result, answering these questions by implementing a master data management solution leads to significantly better data governance through clearly defined responsibilities and controlled processes for data entry and release, as well as controlled and centralized data distribution to all receiving systems.

How does this affect the sources of error?

WittigayerPotential sources of error are reduced because it is defined who is allowed to maintain - or not maintain - which information where.

Since all systems use the same version of the Master data use, the Data quality "automatically" better, the right Data are always available on a daily basis.

Lean processes without redundant, manual data entry in the various systems - and the associated coordination effort between the Departments - reduce complexity and costs.

This summary can be applied to any other master data management topic.

Thank you for the interview!