Machine learning techniques make it possible to obtain predictions based on vast amounts of data. By visualising these predictions, we make them more understandable and we give show the possible future scenarios. This information can in turn be used to your advantage in the decision making process.

With statistical analyses we can also create predictions. The main difference with machine learning is that these statistical analyses give more insight in the underlying mechanisms. We can together figure our which methodology works best for your business challenge.
Simply put; in case the focus is on predictive accuracy, we use machine learning. In case the focus is on getting an understanding of the underlying relations, we choose a statistical analysis.

What-if analyses are a combination of data visualisation, data analysis and machine learning. Such analyses can be interesting when you have several scenarios for the future and you wonder what the impact is of todays' decisions on the situation in a couple of days, weeks or months. We would then first analyse your data to understand the underlying relations. Thereafter we make predictions and possibly simulations to estimate the likelihood of several scenarios. Finally, the findings are visualised in such a way that allows you to make a well informed decision.

Predictive maintenance means that we use machine learning techniques to, for example, figure out at what exact moment maintenance is required for machines at your production facility or at your customers. The rationale behind this is that performing maintenance too often is inefficient and not performing maintenance often enough results in defects.
Together, we determine what data are necessary to perform predictive maintenance. This could mean that additional sensors have to be placed in order to have all necessary data available and make more accurate predictions.