Data science and Business

The proposal of this post is to make a “call to action” to Data Scientists. It’s nice when others call you a scientist and visualize yourself as a problem solver with white coat and a lot of 1s, 0s and graphs in your computer screen. But, when it’s time to go out and get a job, how ready are you for business?

A Data Scientist is one of the many species of Scientist. A Scientist is somebody who is engage in a systematic activity to acquire knowledge and domain one or more areas of science. In a sample and some way restricted definition, a scientist may refer to an person who uses the scientific method for answer questions.

In the specific case of a Data Scientist mathematics, statistical and computer sciences are a must. The knowledge on this areas is acquire in sources as: academia, books, special courses, MOOCs and some others. Other skills as curiosity and creativity makes a data scientist outperform those how doesn’t own this skills.

With the aforementioned skills you may have someone capable of resolve scientific problems in an excellent way, with great efficiency and feasible results. This Scientists are going to take a problem and tackle it with the scientific method, then they will create a solution that answer the question which originates the problem.

At this point, a Data Scientist sounds great for laboratory experiments where you have control of factors, plenty of resources and indefinite time. But, what happens when you take this data scientist and move it to a business environment.

Data Scientist have the need of evolve to survive in business. They should develop abilities that are not common on scientific environments. Having a relevant career in business for Data Scientists, depends on how well they perform on this skills. To understand some of this abilities, one can mention:

  • A Data Scientist needs to understand that his personal research interests must stay apart when working in business environment. This doesn’t means that skills he has acquired in academic fields are not applicable at solving business problems, but the key aspect is the business not the research. He hasn’t been hired for experiment with business data for his personal academic researches, he is hired for helping company meet its business goals.
  • Communication. One would think Scientist are good at writing and in academic presentations, but a Data Scientist need more than that. They have the task of communicate their ideas, findings and results in the language of the business.
  • The above abilities should be followed by a good data visualization, that can tell a story by itself.
  • Disruptive and “influencer”. The implementation of new methods and tools for work is one of the task of the Data Scientist. Thus, being capable of showing that new methodologies are required and business effective, is imperative for a Data Scientist.

The above list isn’t an exhaustive one. Data Scientists have the hard job of evolve for the business, and meet business evolution for Data Science. Those who are ready for this evolution have a key advantage that should explode in their career as Data Scientist for business environments. And those who aren’t ready should hurry up, because there are to many players in the game, and the are few “bases”.