Dick Zeeman, CDMP Master, “Good predictive models stand or fall on data governance.”

“Good predictive models stand or fall on data governance.”

Summary
In this article, Dick Zeeman, Certified Data Management Professional (CDMP) Master, emphasizes the crucial importance of data governance for the success of predictive models. He explains that a solid data governance framework is essential to ensure the quality, reliability, and relevance of the data, which in turn improves the accuracy and effectiveness of predictive models. Zeeman stresses the importance of a holistic approach to data governance, in which collaboration between different stakeholders and departments is central to achieving a robust and sustainable data management process.

Data is no longer a by-product of processes, but rather an important asset that represents significant value for many companies. However, in order to be able to do anything with that data, it must be of good quality. And that is often where the problem lies. All the more reason to invest in data governance. Dick Zeeman of Data Talents therefore followed the Certified Data Management Professional (CDMP) training course at Connected Data Academy, as well as the Specialist follow-up training courses in Data Quality and Data Governance. Thanks to his high exam scores and impressive CV, he is now a CDMP Master.

“The CDMP Master certificate is very unique,” says Erik Fransen of the Connected Data Academy, part of Connected Data Group, an Open Line company. He is also a CDMP Trainer and secretary on the board of DAMA Netherlands. “No list is released, but as far as I know, there are only a handful of CDMP Masters in the Netherlands.” It is therefore not surprising that many clients are eager to benefit from his knowledge. After all, there are not many data governance consultants, and certainly no consultants who have reached Dick’s level.

Dick’s interest in data quality stems from the fact that he sees companies missing out on so many opportunities in this area. He explains: “Data is more than just a by-product of processes. You can get more value out of it than just financial BI reports. Think, for example, of predictive models that help you organize processes more efficiently. However, in many organizations, there is one major spoiler: data quality. Because if you are going to use data to carefully control processes, you have to be able to rely on that data being accurate.”

Training courses
For Dick, the importance of data quality was reason enough to follow two specialist training courses at Connected Data Academy in addition to the CDMP Fundamentals training course: one focused on data quality and the other on data governance. He says: “Central to our field is the DAMA DMBoK (Data Management Body of Knowledge). It’s not the most exciting book to read. By taking the courses at Connected Data Academy together with some colleagues from Data Talents, you get a much better feel for how to apply the DMBoK in practice. The trainers continuously translate theory into practice. They use examples provided by the students. This brings the material to life.”

In addition to acquiring new knowledge, Dick also uses the training courses to refresh his existing knowledge and put it into perspective. “I’ve been working in the data field for fifteen years. Just like driving a car, at some point you start doing things on autopilot. These training courses and the framework used help you to realize again why you are doing something. It is based on a common foundation and a common language, which makes it easy to translate examples from one domain to another.”

From data quality to data governance
If the data is of poor quality, it is usually because governance is not properly established, says Dick. “Governance means that you make policy. That policy is necessary because data quality does not stand alone. It goes hand in hand with questions such as: what data do we use in which processes? What is the quality of that data and what quality do we need to be able to make predictions? Who owns which data? You need someone in the business with sufficient mandate to make improvements and solve structural problems.”

An important reason for making processes more data-driven is the staff shortages in many sectors. After all, the more efficiently you deploy your available staff, the fewer people you need. “You want to make processes predictable and you want them to be executed ‘first time right’,” says Dick.

Predicting processes
Take a large logistics company, for example. To manage and predict their processes, they depend on a lot of data provided by external partners, such as suppliers and carriers. If one of those partners provides incorrect data, it has consequences for the entire chain, resulting in delays, additional costs, and dissatisfied customers. Dick: “If, for example, a large shipment of incoming goods arrives a day later than expected, you have scheduled staff at the wrong time, you cannot deliver orders to customers, and so on. That’s why it’s important to have a good overview of data quality. You do that by measuring that quality. Then you can intervene in time and also implement structural improvements by making the right agreements with your partners.”

This example clearly shows that data governance is not an IT exercise. “It’s a business issue. It’s about people, processes, and behavior.”

Finding balance
In addition to people and behavior, data governance also involves rules, procedures, and other things that people generally dislike. That is why it is always important to find the right balance, says Dick. “You don’t want to come up with extra rules or controls; you want to ensure at the source that data is recorded correctly and then automatically transferred to other processes. This usually involves redesigning processes and supporting IT systems.”

This brings us straight to the next issue: costs. Because changing processes or systems always involves costs. “But the returns are usually so much higher,” says Dick. That’s why the Connected Data Academy’s Data Governance training course also covers creating a business case. “Because ultimately, you convince management with a business case,” he knows.

Tips
Another topic covered in the training is defining the scope of a project. Companies generally generate enormous amounts of data, and the teams responsible for data governance and data quality are too small to tackle everything at once. Dick therefore advises: “Start with the processes where you experience the most bottlenecks.”

In the example mentioned, these are the dependencies between the various collaborating parties: supplier, transport company, distribution center. In order to better predict the processes and reduce the risk of disruptions, it is important to know how the data is used in the processes, but also who is responsible for it within the organization. Dick: “For all those data sources, you appoint an owner who is responsible for data quality. If the quality is insufficient, the data governance specialist helps the data owner come up with measures to improve it.”

Dick’s second piece of advice is: “Try to make the organization understand that data and technology are two different things. Data does not belong to the IT department, but to the business. Of course, there is a relationship between data and IT, but it starts with the data and never with the technology. In practice, I often see organizations starting with technology. They appoint the product owner of an IT system as the data owner. That is not right. I also hear this regularly from colleagues at Data Talents, and the subject came up frequently during the Connected Data Academy training courses. So be aware that it should be the business that owns the data. IT is supportive, but certainly not leading.”

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