Master Data Management is a strategic program!

In the end, both, the program and the software you're choosing, belong to a successful MDM program. The software is the enabler to implement what is to be achieved with the program.


If the goal is to be able to run clean analytics at the end, for which one needs a high-quality data foundation, then it is necessary to define rules and processes for handling data.


How do you start with this?


As mentioned in chapter 1.3, Gartner has developed a model for a successful MDM program that can be used in this way or adapted for your own program.


Source: Gartner, Use the 7 Building Blocks of MDM to Achieve Success in the Digital Age, Michael Moran, Bill O’Kane and Simon Walker, 9 March 2018


Starting with the vision and strategy, finishing with the infrastructure and thus with the software, both topics are represented in the headline.


1. Vision

The corporate vision defines what will be the objective over the next few years. From these objectives it is possible to determine concrete requirements for the company and the data.

For example, what is needed to expand the sales organization internationally? Is there a consolidated customer view on an international level, or is there a danger of duplicates?

In the vision phase, the basis for the subsequent steps of strategy formulation must be created. For this, the expectations of the stakeholders and the corporate culture have to be taken into consideration.


2. Strategy

The strategy for achieving the goals mentioned in point 1 is defined here. Which steps need to be implemented in the project? What does the project plan look like? Who is affected (individual or departments) or which systems?


The strategy should be fully transparent on how the program is implemented, who is involved, who has which responsibilities, what the costs will be and what results are expected.


3. Metrics

Results must be measurable. This also applies to an MDM program.


Once the strategic plan has been defined, it is necessary to determine how success and the activities will be measured for all affected areas.


Examples of frequently measured areas are master data (quality, availability, completeness and correctness), finances (costs and risks) or performance (duration, effort and time).


4. Governance

One goal of an MDM program is to provide up-to-date and trustworthy information. To ensure this, rules and responsibilities for the handling of data need to be defined. At the end of the day, information should only be accessible to authorized persons, on the other hand, all necessary information should be displayed as accurately as possible.  


GDPR considers this part more exactly, in order to avoid the risk to violate compliance.


5.  People

In this phase it gets very clear that MDM is not only the implementation of software. In this phase all persons involved in the program are being defined. Since processes and organizations often change with the implementation of an MDM program, it is important to continuously expand the list of responsible persons resulting from point 4 so that it is clear for each process who is responsible and making decisions.


6. Process

There should always be a beginning and an end for data lifecycles, which are defined in this phase. When does the process start and when is it finished or what happens thereafter?


Adapted to customer data, this could for example mean that with the first purchase a new customer record is created in the system. If the customer does not buy anything for three years, the record will be archived, or deleted. This would be an example for the handling of such a data record.

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