Old school BI tries to find patterns in what happened. Users have access to smart reports that give them insights to make better decisions. A new specialization within information technology has emerged: data science. The data scientist takes BI to the next level.

Business intelligence specialists need two skills. Firstly, they must know the industry they work in, the regulations it faces, how data flows through the specific business processes and so on. Secondly, the BI consultant has to know how to capture and integrate that data. And set up complex data warehouses, OLAP cubes, smart reporting.

With big data, the BI specialist’s role has evolved further. Much larger data sets are available, also from multiple sources and multiple formats. New tools are emerging to gather and sort data faster. Batch data calculations that used to happen overnight now merely take an hour or even minutes. It allows data stewards to bring BI much closer to actual or even real-time business. Take decisions based on today’s insights, not the past fortnight’s.

Now let’s take it a step further and predict what tomorrow will bring and work based on that information. For instance, think about patient data being increasingly digitized: we will be able to use the data to determine how to best design treatments and allocate resources.

That’s where the new data scientist comes in. They gather and sort data and then use statistical methods to make predictions. The new data scientists therefore have strong programming skills, good insights into the available data sources and, above all, they are mathematicians, statisticians. That combination is very hard to find today.

Data science often sounds like the new IT buzzword. But the combination of big data and data science will lead to new services, interesting business cases and new advanced Analytics features. Predictive maintenance for instance. Imagine industrial companies that can predict asset failure in machinery – they would reduce maintenance costs and lengthen the life cycle of their machines. Isn’t every company interested in less capital expenditure? Or personalized marketing, quality optimization and so on. I know some people who’d love to make their sales and marketing smarter and more agile.

Me, for starters.