Learn how we solve complex problems with advanced statistical techniques

Every now and then I come across someone who thinks that a touch of artificial intelligence is sufficient to make a silk purse out of a sow’s ear. The majority of entrepreneurs and managers, especially IT and innovation managers, however, take a more realistic approach towards big data and data analysis now, as many among them have already experimented with it in recent years and thus gained the necessary experience.

The hype and promise of mountains of gold are over. Some were left disappointed. They did make investments, but unfortunately couldn’t achieve the results they were hoping for. Nevertheless, they are still convinced that data can be of great value to their company and now they are racking their minds trying to figure out how they can successfully put data to good use.

Getting familiar with advanced analytics can start on a small scale

Developing a prototype can be a good first step for companies that are not yet active in the field of large-scale data analysis. In this way, companies can get to know the advantages of our advanced statistical techniques without having to invest a lot of money. You can call it an initiation. We also assist them in defining a data science strategy that fits their company and their objectives.

Some organisations have already reached maturity in the field of data analysis and may have very specific research questions. Therefore, we explore the further details together. Although such organisations may have their own data science department, they value our experience in converting ideas into results.

Complex problems can be solved by analysing large quantities of data

Our added value lies in the translation of strategic questions into concrete tools that drive better management decisions, for which we use a combination of traditional and modern statistical techniques. We can develop those solutions on an end-to-end basis, using our knowledge of data science (for example, by utilizing machine learning and AI) as well as our programming skills.

The projects are very diverse, which shows that different kinds of complex problems can be solved by processing and analysing large quantities of data. From strategic issues that management faces, to tactical issues during daily business operations. For example, data analysis can be used to make periodically recurring predictions and optimisations possible. Using the right algorithm, a mortgage lender can accurately assess how likely a potential customer will pay back their monthly instalments.

Smarter insights produce better results, and ensure control

Thanks to such a model, we can better assess situations and achieve better results; in the end, that’s what truly matters. But there’s more. Those insights ensure control, which is important for the management of an organisation. Does our solution, using advanced statistical techniques, help them make better informed decisions? If it does, it ensures stability and confidence.

I’m very pleased when I find out that clients continue to use the tools and models we have developed for them after we have left. When I come back a year later and see that the tool is an integral part of their daily activities, I can only be proud. It proves that the tool generates insights that our clients find useful and important, and that they are reaping the benefits from it.

When does an organisation benefit from advanced analytics? If management is only interested in knowing how many units have been sold last week, they can probably get away with a standard reporting tool. But if they want to accurately identify the key risk drivers influencing the number of units sold, or generate probability-weighted scenarios for the upcoming period… Well, those kinds of questions make us roll up our sleeves.