In today’s post we will talk about Data Scientist, also known as Data Scientist. We are in a new era when it comes to digital strategy: the age of the user. Until not long ago the greatest marketing efforts were made in the information area, and the final decision and the closing of the purchase had great relevance the figure of the commercial.
Now, most users research the products and make the purchase decision in many cases, without the intervention of the commercial / seller in the case of online sales, and in those cases where there is an offline sale, the decision is already made by the consumer even before interacting with a seller.
Data Index
Currently, the main commercial work is carried out by the corporate website, traditional marketing channels (advertisements in the press, radio …), the own channels of social networks, recommendations and opinions on the network in channels not controlled by the company … generating for each contact or sale a significant amount of data.
Thus, andThere is a lot of data coming from various channels. Some of these data are sometimes “hijacked” by some departments of the company, and analyzed many times according to the intuition of those who manage it, without crossing with other data, and giving rise to contradictory conclusions with those of other departments.
In these cases the need arises for a figure within the company that coordinates the flow of information, structures it and helps the company to draw conclusions, and that is the data scientist.
1. The Data Scientist
In the current context, companies today tend to measure everything: data from the web, from the different channels opened in social networks, market research, CRM, ERP… an increasing amount of data that well analyzed can bring great competitive advantages to companies that know how to integrate them and take advantage of them.
This is where the Data Scientist comes into play: by combining his mathematical, programming and business knowledge skills, he is able to obtain specific and actionable knowledge, which will improve the efficiency of the processes analyzed.
A data scientistcompletes the flow of information from the most diverse data sources, structures it and transforms it into actionable data from which the company can gain a competitive advantage.
2. Requirements to be met by a data scientist:
- Training in mathematics and statistics.
- Mastery of programming and its different languages (R, Phython…).
- Knowledge of Data Mining and Machine Learning.
- High analytical capacity and problem solving capacity.
- Knowledge of the business and the sector on which you are going to carry out your work.
That said, we see that the Data Scientist is going to become a necessary strategic profile in new organizations, with a very specialized profile, far from the classic marketing consultant, although not so much from the technical digital analyst, and whose relevance will increase over time as the proper treatment of all the information provided by the Big Data.
Now, most users research the products and make the purchase decision in many cases, without the intervention of the commercial / seller in the case of online sales, and in those cases where there is an offline sale, the decision is already made by the consumer even before interacting with a seller.
