Master Data Management: Dos and Don’ts

We have now in a series of blog posts talked about the key topics within master data management. Maybe you already feel equipped and well enough informed to dive into MDM in your own company – or maybe you need the last piece of information to get started.

Af: Ida Eriksdotter og Helle Løngreen

13. September 2021

In this blog post, we have gathered some of the most important points for what you should be aware of in order to make MDM a lasting success in your company.

Initially, we would like to highlight a few important conclusions based on our previous posts in the series:

  • Insight, measurement, and improvement of data quality are essential factors to build trust in the data and analyzes that are made available in the organization. Often, however, companies do not know the state of their data quality issues and do not have a fixed set of measurements that can be followed up over time. Numbers speak for themselves, therefore remember to make sure to create insight into any quality issues at the beginning. This can also help set the level of ambition and objectives for your MDM program in general.
  • Master data is created and used widely in companies, which is why MDM becomes a task that expands across data sources, departments, and business areas. It is therefore important that you establish a management with ownership and responsibility for master data and create a form of collaboration and operating model that encourages the involvement of process-, IT- and business experts. Your operating model should ensure a common awareness of all the elements of MDM, including; strategy, processes, organization, governance, technology, content and quality - to realize the benefits of MDM.

Dos and dont's

There are without a doubt a lot of different experiences and opinions about what to do, when and how, but it will be up to the individual company's situation and ambitions to decide this.

Below, we have gathered some of the most important dos and don’ts regarding MDM.

DO: Define your master data requirements and data quality needs before purchasing a master data platform

A platform alone does not solve your challenges. You should know the scope of data domains, systems, integrations and know how the data quality challenges your business and growth before choosing the technical solution. Otherwise, you risk that the solution can’t handle the most pressing challenges.

DON`T: Implement a platform or master data organization without clear objectives and business case

If you fail to make a strategy for what you want to achieve and have a set of goals for success as well as support and funding for MDM supported by a business case it can lead to a lack of the right priorities and organizational support that can make the MDM initiatives fail. Master data is important for the entire company and implementing a good solution involves many stakeholders across the organization. In order to be successful with MDM, it is therefore of great importance that all stakeholders know the purpose, priorities and objectives of the initiatives, and that they know the meaning of the effort for both their work and the entire organization.

DO: Think scalability

A master data platform must be able to grow with your business. Maybe there are plans for market expansion, a more complex product hierarchy than you have today or something completely different that makes the rules of your master data more complex in the future. Even if you do not know it today, try to think generically and with possibilities for expansion when you choose your master data solution. If there is only one group of master data that your company is going to start with - for example, customer data - then it is very likely that you will add more groups over time.

DON`T: Choose the technology first and the resources later

As mentioned in a previous blog post, new technology does not solve all your challenges. By focusing primarily on the technical solutions to your master data challenges, you risk that MDM will generally be about IT rather than business value. To be successful, your employees must be committed. Commitment is an outcome of the right collaboration model, and that there is a governance organization with data owners, Data Stewards, etc., which focuses on solving master data challenges and understands the business value.

DO - Develop master data definitions and data quality rules in a cross-functional team

It is important that you from the beginning develop your master data definitions representatives from several departments. This is both to get everyone's approach to challenges and solutions but also to reduce the risk of disagreements about rules and definitions in the future. There are often different opinions on what is defined as good data quality. Therefore, it is important to reach an agreement across departments to achieve a successful implementation.

DO - Implement MDM governance in the already existing organizational structure as much as possible at the beginning

New role concepts, areas of responsibility and ways of working can take time to adapt, so if MDM governance is completely new to the organization, it may be a good idea at the beginning to place the new roles in the existing organizational structure. In this way, new roles are gradually introduced in a familiar environment making role assignment and anchoring easier. By adapting to existing work practices and gradually implementing the new MDM operating model, there is a greater chance of successful anchoring across the organization.

DO - Always implement KPIs

You need to know your starting point and your goal for where you want to go using numbers and timelines. This is because it makes it easier to get support from the company when you can present some numbers on how data is going to be improved and how it affects your business and ROI. It is not enough just to explain that master data is important. When presenting numbers on, for example, data quality and how it improves over time and what it means for revenue, bottom line, etc. makes the parties involved feel more responsible for improving and ensuring the quality of master data.

DON`T - Do not limit governance to assigning roles and responsibilities

To get your new governance implemented and go from theory to practice, you should ensure a good operating model and practical ways of working. The creation of an organizational chart and the preparation of role descriptions do not guarantee that the quality and integrity of data are automatically improved. It is your operating model with well-defined processes that ensure the correct use of tools and techniques to comply with agreed data policies that help you every day.


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