Finaps: How we help companies embed their data strategies

What makes Finaps stand out?

Data is the 21st-century equivalent of gold and companies know it. Terms such as Big Data, Artificial Intelligence, Deep learning, Internet of Things and Streaming Analytics are thrown around like it’s nothing, but it’s not all that easy. Many companies think they should embed some form of data strategy to be able to implement these state-of-the-art technologies. They rush, trying to keep up with competition. Unfortunately, many of them fail. Fortunately, that’s where we as Finaps come in to save the day.

When Finaps was founded in 2010, all solutions were built in Mendix, a low-code platform to rapidly develop applications. But in the last few years, we have extended our scope. While we’re still implementing solutions with Mendix, we’ve also partnered with SAS, the leading platform in analytical software. Furthermore, we are using open source languages to keep improving our proposition. As we’re now building more data driven solutions, it’s becoming more and more clear to us why companies are struggling to embed these solutions into their strategy. This article aims to give you a deep dive into the following topics:

  • Why do many companies fail to form a data strategy on their own?
  • How do we help companies to form a data strategy?
  • What type of data driven solutions do we implement?

Embedding a data strategy, why many companies fail
To understand why many companies fail to embed a data strategy, it is important to understand its key levers. If, and only if a company knows how these levers interoperate, it can succeed. Unfor­tunately, many companies either rush their strategy or copy existing strategies –thinking it will work for them too– and fail miserably.

Every data strategy has three levers, all equally important for success:

  • People;
  • Processes;
  • Infrastructure.

It seems simple, but many companies still fail, why? Because most of them go through the same process when embedding their first data strategy. It all starts with their first encounter of Big Data, Artificial Intelligence or Applied Analytics. They hear the success stories and decide that they too need to embed analytics and decide on a data strategy. Due to the fear of missing out, they build a data-team right away and have them hacking away at possible solutions. Although this might sound like a good idea, it is a recipe for failure.

In short, companies adopting this failing approach go through four stages:

  • The company employs a data-team;
  • The team builds a proof of concept;
  • The proof of concept generates no direct revenue;
  • The company thinks the data strategy is a failure.

It’s easy to see why data strategy implementation didn’t work. Companies forget the two other key levers of a data strategy. To have a successful data strategy, one does not only need a data-team hacking away at solutions, but also processes and infrastructure. Many companies neither develop nor enforce these processes and do not dedicate resources to develop the infrastructure that is needed.

To be successful, at least the following needs to be in place:


  • Data governance: develop and enforce processes to ensure compliance of all steps in the data lifecycle;
  • Data quality: develop and enforce processes to ensure the quality and consistency of the data;
  • Data management: develop and enforce processes to ensure terminology and proper data usage.


  • Data content: ensure the infrastructure is in place to gather information that generates value;
  • Data access: ensure the infrastructure is in place such that all data is readily available.

Although these processes and infrastructure alone do not guarantee success, they do help to bring a proof of concept to production such that it can generate revenue. Generating revenue is key and will open the door to success.

Where does Finaps come in?
Empirical evidence shows that once the first data-driven solution is in production, the company will embed data-driven solutions more easily, realizes their data strategy is a success and will not abandon such initiatives. That’s where Finaps comes in.

Thus, our goal is to deploy a data driven solution to production as quick as possible, where it will start to generate revenue or business value. Meanwhile, we have to take the processes and infrastructure side of things into account. High over, there are six steps in this process, visualized in the figure below.

Step one is a brainstorm. Together with a client we’ll have a couple of sessions on their future and test the waters on their data strategy. It isn’t uncommon for them to shout: “I want something with AI!”. Well, everybody wants something with AI nowadays, so it’s our objective to understand the clients’ business and come up with a concrete problem to solve which impacts their bottom line. Because, as mentioned earlier, if it doesn’t positively impact the business, they will pull the plug.

Once the problem is defined, we’ll explore for possible solutions. We ask ourselves the question: “Is there a possibility that some available data will solve this particular problem?”. If so, we’re on the right track. If not, we go back to the drawing board until we find the fitting solution.

After the exploration phase, the same team will build a Proof of Concept (PoC). This PoC intends to validate the solution. In a very short period of time, we will develop an initial solution.

To test our initial solution, a pilot is setup. Because in theory, we might be able to predict ABC with accuracy X. However, this does not mean that it will instantly create business value. The business might not adapt to the new way of working or does not include the prediction in their decision-making process. Proper evaluation of this pilot is key: Do the benefits outweigh the costs?

Once the pilot is defined as a success by the business, it is time to develop the solution ‘for real’. Usually, the first step is to define the Minimum Viable Product (MVP). This can either be on top of the previously built PoC, or we start from scratch. Depending on the quality of the PoC and the use case.

After development, we deploy the solution to production. The continuous cycle of develop and deploy can begin, where new functionalities are constantly added to the initial MVP.

During all of this, we’re constantly in contact with the business. We’ll keep an eye on the processes and provide the infrastructure necessary to make the project a success. In the end, we’ll not only provide a solution that will make the company a little bit more future-proof than they were, we’ll also make sure there is a cultural change to embed the new way of data-driven work.

This is what we do best
We strive to deliver solutions of the highest quality. Our belief is that the only way to achieve that is through collaboration with our clients. By really listening to their wishes, understanding what they want to achieve and being honest about what is possible, we are able to develop solutions that will take their business to the next level. Be it through digital innovation, process optimization or applied analytics.

One example is our Smart Working Capital Assistant (SWCA). This mobile application harnesses the power of AI to provide commercial clients of ING with predictive insights into managing their working capital more efficiently. Predicting in- and outflow of cash, as well as developments that have an impact on financials, helps clients to plan certain transactions at a time that is more favorable for them.

Big Data 4 Small Babies is another example. We built a data driven algorithm to help doctors decide whether the administration of antibiotics to preterm infants is necessary or not.

These solutions are the product of much longer and more complicated projects. During these projects, we actively shaped the data strategy of both ING and the UMC Utrecht to create truly data driven solutions. Throughout the process we continuously consult with our clients about setting up proper infrastructure and processes along the way.

Still, our solutions are just a means to an end. Together with our clients we are exploring tomorrow’s possibilities, today. Transforming a world of data into a world of intelligence.

Come have a cup of coffee with us! We’re always looking for talented people. We strive to be an inspiring and fun place to work, where we are proud of the solutions that we deliver.

Emmaplein 10
1075 AW Amsterdam
+31 (0)30 6997040

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