The banking sector, the insurer and the Public Administration, benefited most from the rise of Data Science and the generation of value through data.
Everyone knows what a unicorn is, but no one has ever seen one. Almost the same could be said of the Data Scientist,one of the professional profiles most demanded by large companies today and which, given its high degree of specialization and the new discipline that it supposes, still shines by its absence.
Most of them are still being studied in universities or business schools and those who already work as data analysts are mostly mathematicians, statisticians or computer scientists, but do not usually have specific training in the area of business that concerns them.
That is why the New York Times has already defined it as ” the sexiest profession of the 21st century” and who are now dedicating themselves to this discipline, based on generating value from the data for proper decision making in business , strive to bring together, in their person and in their curriculum, advanced knowledge of analytics and business with the intention of helping companies to grow and save costs.
According to NBT CEO Borja Torres , during a practical workshop on Data Science held on Monday with VASS at Impact Hub in Madrid, ” the lack of this professional profile is causing major deficiencies in all companies “, since they do not have the ability to use for their own benefit all the data they already have from their customers.
The reality is that in the last 20 years, the number of people we have generated has multiplied by billions and, despite the fact that there is technology to treat and process them, ” what is lacking are people who are able to profit of them “. Suffice it to note that this trend will only increase in the coming decades: in 2006, 0.16 zettabytes (1 zettabyte equals one trillion gigabytes) of data was generated worldwide and it is expected that in 2020 this volume grows exponentially up to 40 zettabytes.
What to do with so much data?
The question is then, what to do with so much data and how to use them? Torres is clear: predict customer behaviors so that companies can grow and, above all, save costs.
This is especially useful for companies in the banking, insurance and public administration sectors (AAPP), since if they know how to convert data into knowledge and, hence, value information, strategic decisionswill be taken with a probability of accuracy close to 100%.
For example, a bank has in its historical figures about how many clients have a mortgage and how many have stopped paying the monthly fee on occasion. If these data are matched with the payroll, the contracting of other banking products and other socio-economic factors, a list of profiles of “twin” clients could be made that could behave similarly in the not too distant future. Having them identified, then, can be proactively offered by the bank some kind of personalized service to avoid these defaults.