How would you describe data insights to a novice?

Simply put, we build filters on top of a set of data so that this data becomes readable and interpretable. By releasing certain formulas and algorithms on such a set, you're able to compare data, for example. Of course, we always present this in a lovely way in dashboards so that even people who don't have knowledge of data can work with it. How we do this depends on the initial business demand. Sometimes it is purely a matter of presenting something intelligible in a report, other times a large infrastructure is built to get the data structured so that they can examine their own insights themselves.

What challenges are often the motivation for working with your team?

Data is everywhere, and there has been a growing awareness among many companies in recent years. Often it is just a matter of putting companies in the right position so they are able to work with their own data. We help them to get started and ensure that they reach a certain level of maturity so that they can work out and interpret insights for themselves. Of course, we'll always be available as a sparring partner as building a real data set in a company takes a lot of time.

We also often work with specific questions from business units that are getting to grips with a challenge and see a bright spot in data somewhere. We then dive into the detail and draw a path together. Such a path can take all kinds of turns – for example, we recently configured a project management tool for a customer because the tool was not sufficiently tailored to their needs. Our team is specialized in Microsoft's Data & AI range – we can get our teeth into in any issue.

I also think it's important to say that it does not have to be visionary. For example, you don't have to start working with artificial intelligence right away. Data can also be used on a small scale to be effective. What can be optimized in order to reap the benefits quickly?

How can data insights really make a difference?

For me, it's really about making a gut feeling concrete. People with a lot of business knowledge can often make the right estimates, but they still remain subjective. Data is a perfect reflection of reality, you are able to evolve towards decisions based on qualitative and objective data. I have to admit that data insights are often confirmation of what you already thought, but at least then you know for sure. In addition, data can really make a difference. Once I performed a project for a customer where they didn't have any data in the production process to prove what they were doing. However, they had already noticed that there was a correlation between a chemical process and external factors. After years of discussion with experts and after several (one-off) measurements, it still wasn't possible to determine the precise link. Only through constant data collection and the right formulas were we able to find out that there was indeed a correlation between temperature, atmospheric pressure and their chemical process. They were able to further increase their operational excellence. In itself, it was only a small detail compared to their entire process, but by measuring it and responding to it, they've been able to improve their production process by a few extra percents. It's not possible to fine-tune each element on gut feeling. Finding a hidden gem under a lot of raw data gives me a real boost, and I know that it often gives our customers a real competitive advantage.

Finally, a look to the future. Evolutions in the world of IT follow each other very quickly. Is this also the case in the data domain?

The world of data is changing rapidly, which is why I find it so important to actively participate in events such as dataMinds Connect. We also actively share knowledge within our team, meeting several times a year to discuss the projects and challenges that we face and see on a daily basis with customers. Of course, we are also in direct contact with each other through Teams, so a helping hand is never far away.

In 2020, I expect companies to take the next step to data lakes more and more. Until now, a lot of data has been collected, without anything really being done with it. Cloud storage is reasonably cheap and is therefore suited to storing large amounts of unstructured data, yet it just sits there. I see that many organizations are now daring to take that step so as to actually do something with the large amount of unstructured data. The challenges are huge, but the added value is all the greater!