Alexander Beck is Managing Consultant at ec4u and responsible for data science and predictive analytics with a strong focus on customer analytics. We asked him some questions about this topic.

Data analytics interview with Alexander Beck


Hello Alexander, could you explain to us what data science is?

Data Science is actually an umbrella term for lots of methods. The underlying goal is to look into the future with data. In a way, this can be compared to a weather forecast as opposed to a weather report. A weather report is more comparable to business intelligence, that is, it reports on things that have passed, while the weather forecast looks into the future. The latter illustrates what is meantby the term data science.

Are you something like the weatherman of data?

If you want to put it very metaphorically, it’s actually not that far-fetched.

What role does the digitalization play in this context?

Digitalization is a very important factor in data science. This is because without digitalization, it will be very hard to make this technology applicable for companies. With pen and paper, you probably could have done data science in the 50s already, but for its actual practical use, digitalization is a key ingredient.

Who should use Data Science?

Who uses predictive analytics and data science?

Predictive analytics and data science are used in areas where huge amounts of data are created around recurring events, especially in marketing and other customer-facing business units. Other examples include trucks that drive from A to B, situations where products are taken from a distribution center to a supermarket, etc. Basically, data science is applicable in situations where many things happen repeatedly with high velocity leaving a data trail.

How can we help our customers?

My task is to advise customers on what they can expect from predictive analytics. Generally, this can be done in two different ways. The first is to have a look at the customer’s ideas on what to achieve with predictive analytics. Then, we conduct a feasibility study and see whether the client’s data is sufficient to realize his ideas.

The second approach is to start with the data and develop ideas together with the customer on what goals to achieve with analyzing and utilizing the data in customer analytics scenarios. My personal experience has shown that data often contains a highly creative potential and offers natural ways to make predictive analytics and data science successful for the customer. Once this phase is over, the actual implementation takes place. We don’t stop at talking about solutions, instead our ultimate goal is to build up and establish customer analytics solutions for the client.

The importance of business intelligence

Is predictive analytics possible without business intelligence?

If a customer already has well-functioning business intelligence in place, this a first important cornerstone to build predictive models. This is simply based on the fact that the customer has most likely intensively thought his database through. On top of that, predictive models can then be used to look into the future. Of course, there are still restrictive parameters that need to be evaluated. But chances are high that customers doing great in business intelligence will be successful in using predictive analytics as well.

Thank you, Alexander.

If you are interested, we have some other posts about customer analytics. Or read about our services regarding Customer Data, Predictive Analytics and Business Intelligence.