Maarten, you are Craftzing's head of data. What does that role entail?

As a data strategist, I am the point of contact for business leaders and managers, guiding them in shaping their data strategy. Specifically, this means that I collaborate with clients to understand their business objectives, ambitions, and challenges. Then I find ways in which data can support those objectives. Tailored to each organization's needs, we explore the possibilities with the available data, what we aim to achieve with it, and the potential added value. Additionally, I act as the liaison between the client and the technical experts at Craftzing who work on data projects: data scientists, data engineers, data visualizers, product owners, and so on.

How do you start working on a data strategy?

To outline a data strategy, I start by addressing two key aspects. Firstly, I talk to the client to gain a clear understanding of their organizational goals. Additionally we conduct a thorough data analysis, we investigate and evaluate the client’s current data, systems, analyses, methods, knowledge, and applications together. Based on this, I assess what is required to achieve the set objectives. That could be various things: collecting new data, consolidating data silos, employing alternative analytical techniques, methodologically altering reporting flows, providing training to individuals within the organization…

What are some business objectives that data can be useful for?

When it comes to data, the possibilities are nearly endless. They range from reducing production costs to increasing sales, but data can also be leveraged to optimize machinery or better segment customers, among many other applications. Often, the potential benefits of data extend a lot further than one might initially think.

Can any organization use data to their advantage?

Absolutely! Every organization has the potential to make better decisions, gain insights, and take action using data, regardless of the industry and size. There are many misconceptions within companies when it comes to data: that data projects are costly, extensive infrastructure is required, a vast amount of data needs to be available, the data must be of extremely high quality, and so on. However, these assumptions are not necessarily true. You can work incrementally: identify which objectives can be supported with small steps or investments and build from there. We can hep with every kind of data strategy. Creating a step-by-step plan, guiding the client through the various stages, making tangible progress – that's how you turn data into a success story. Not coincidentally, that's exactly what we prefer to do: take real steps, together with the client.

What should an organization consider to make a data project successful?

There are many factors to consider. First and foremost, there’s the  people in an organization. What talent is available? Are they aware of the reasons and ambitions behind a data project? Do they grasp their own role in that? Equally, you need to understand the legal side of things: what is legally allowed and how should you handle data correctly? Infrastructure is also crucial: what systems does your organization use to store and process data accurately? Does your data system work in real-time or with monthly updates? Depending on this, you may or may not be able to support certain business objectives. Ultimately, what I would add to the list is methodology: what analytical techniques do you use? How do we interpret these techniques, and how do we present the results and insights in a way that enables people to make effective decisions based on that?

I think it shows that a data project involves a lot of knowledge and skills, many of which companies don’t necessarily have in house today. That’s perfectly fine, it just shouldn’t discourage them from working with data. Depending on your situation, you don't need all the expertise all the time. Those who want to temporarily enlist the help of an expert can always turn to parties like us Craftzing Data. We have a wide range of knowledge to support organizations when they need it.

What skills should a data strategist have or work on?

A skilled data strategist needs to be able to empathize with the needs and challenges within an organization. And they have to have a deep understanding of what is feasible or not feasible with data. Subsequently, they must be capable of devising a plan for the client, leveraging data intelligently to achieve the company's objectives. When necessary, these objectives should also be adjusted based on the insights derived from the data. Lastly, due to their intermediary role, effective communication skills are crucial. As a data strategist, you must be able to articulate complex matters in clear and understandable language.

Craftzing aims to significantly expand its current data team. Why, in your opinion, should someone interested in a 'data job' join Craftzing?

It’s our goal to provide comprehensive guidance to our clients. That's why we're hiring a wide range of data profiles. Our team includes data strategists, data scientists, data engineers, data visualizers, product owners, and more. We heavily invest in information exchange and knowledge sharing between all these experts, which offers many learning opportunities. Moreover, at Craftzing, you'll interact with diverse digital talents, including business strategists, designers, and software engineers. This enables you to broaden your perspective on data and digital, gain even more insightful experiences, and explore different angles. Additionally, because of our broad service portfolio, chances are high that you’ll be working on substantial and high-quality data projects.

The field of artificial intelligence is rapidly evolving. How do you, as a data expert, view AI solutions? Is this the next big thing that organizations should invest in?

My two cents: approach AI with caution. The major pitfall of many AI solutions is that they come from third parties. As a result, they might not be tailored to your specific needs, and you might have limited insight, let alone control.

For those thinking strategically about their organization, it's important to realize that an AI solution can be highly beneficial, as long as certain conditions are met. To ensure these conditions are controlled by you, it might be worthwhile to develop solutions in-house. Within your business context, with a clear goal in mind, based on a well-defined dataset, and with machine learning experts and data strategists you trust. That might sound more challenging than it actually is. If approached smartly, you’ll gain a competitive advantage for years to come.

What else keeps you busy apart from your role as a data strategist?

That's not an easy question, as the answer is quite extensive. One of my interests is reading and writing poetry. A few years ago I was appointed “city poet of Geel”. It might surprise you, but I see quite a few parallels between poetry and my work as a data strategist. When writing poetry, you observe a lot. You start pondering about a specific topic, what emotions it evokes, what associations we attach to it, where certain themes fit in our society, and how you can capture all of that in words. This exercise isn't all that different from what a data strategist does. Admittedly, with data, the questions might be more along the lines of: What role do data play in this organization? What is this organization looking for? Can we find a way to align data and objectives? Still, whether you're a data expert or a poetry enthusiast, the role of observation is quite similar. You observe a situation and then engage in the exercise of evaluating it from various angles. I think I’m a little bit addicted to that excercise. I find it challenging to wrap my mind around it and that’s exactly why both data strategy and poetry resonate with me.

By Michele Stynen

Digital Marketeer