Data Science, what’s that?

Nelt avatar Nelt 20.12.2019.

Autor: Miloš Došlov, Head of SAP BI & Reporting excellence, Nelt Group

Data Science is a swanky word that will often crop in modern business conversations.  Everybody is talking about it, but each company has a slightly different understanding of what it actually means and what it is they need to do about it.

There are currently many off-the-shelf software solutions on offer for simplifying a number of company processes. One of the most important outcomes is collecting and generating various types of data which, through the process of monetisation, can generate great value for the companies themselves.

Experts also find it difficult to explain what Data Science approach actually means. My impression is that the implementation of these solutions is becoming more and more easy to understand.

If you still don’t know what I’m referring to, take a look at a few examples of practical application of this type of software.

  • Automatic face recognition from photographs found on social networks.
  • Information about the quickest route to your house is generated the moment you get behind the wheel of your car.
  • Food deliveries with guaranteed delivery time ordered using a mobile phone application.
  • Another interesting example is that of Liverpool Football Club who announced that they had nearly won the 2019 Premier League through simple use of advanced data processing to find and recruit underestimated football players.

Currently, it seems that more and more companies are opting for the database management model which, in this case, would entail the implementation and use of the so called Machine learning model – a model that learns and generates valuable information based on our data.

Experts believe that the next phase will involve artificial intelligence which will not only use our models to learn from, but will also be able to draw its own conclusions, thus generating an even greater value.

There are still those who believe that a Data Science Expert is a person who is in possession of all the necessary knowledge and experience. In my opinion, the truth is slightly different.  It is a set of skills that can be found in a number of people, depending on the type of project and its requirements. A particular set of necessary competencies and their complexity that young professionals need to dedicate themselves to, bring us to the logical conclusion that there are several professional Data Science profiles in existence.

My advice to young talented people, from my point of view, would be to pay special attention to developing transferable skills and cooperation. Assertive communication, presentation skills, ability to take feedback on board, collaboration….to mention a few.

The essential thing is being able to speak using the same language.  One cannot ignore the fact that this type of work is not done in isolation from the rest of the company and that the results of analysis and modelling need to be presented in a business context. The management will rarely, if ever, ask about the type of algorithm that has been used, or the level of precision of the model measured by the area under the ROC curve, and this is a fact that should not be ignored.

Based on my current understanding of trends and the experience I have in the business sector, I would say that data times are already here, that they are fast approaching and that we can only interpret certain segments. It would be pretentious trying to predict future services in such a dynamic environment.