Understand the changes caused by BigData in the IT market:
The world of data analysis in business goes through a real revolution: the growth of Big Data. This new set of technologies is allowing companies to analyze and create solutions from a data volume that not long ago was unimaginable. This large amount of information can bring great insights for business, but it has very little utility if you do not receive advice from professionals who know what to do with them. This is where the data scientist comes in.
Understand what a data scientist does, why he will be a prominent professional in the next few years and why your company will have to board the Big Data revolution to stay competitive:
What does a data scientist do?
The data scientist usually has a degree in areas such as Mathematics, Computer Science, Physics or even Economics. He is a person who needs to have intimate knowledge of computing, mathematics and data analysis, but it is not enough. The data scientist needs to have the ability to produce insights and solutions from the analysis of a large volume of data; he should be able to “find a needle in a haystack”.
So, this professional must be curious. He is called a “scientist” because not only he conducts the analysis and presentation of this information, as a data analyst should, but he also needs to develop hypotheses, test them and find solutions to run away from the obvious. He must also have in-depth knowledge of the market where the company provides services and know how to focus on the client.
Why is the data scientist such a prominent professional?
When it comes to date scientists, normally the first example that appears in every conversation is the Physics PhD, Jonathan Goldman. He went to work at a professional social network LinkedIn in 2006, as it was already a prominent website (with almost 8 million people), but still had many difficulties in spreading up. The main problem was that users, despite the ability to invite friends to the tool, interacted very little and remained a short time on the social network.
From the analysis of a large volume of data, Goldman began to formulate a series of hypotheses and test them on the tool. The primary functionality that he tested was called “People you may know” and consisted of a small module that showed the name of three users of LinkedIn that the person probably knew. These suggestions were given from the intersection of information such as school where the person studied, a company in which they worked in the same period and other connections on the same network. The result: the box “People you may know” became the most clicked of the network and LinkedIn took off.
This example shows how to have a professional with high data analysis capabilities, creativity and ability to test ideas a little obvious but that can be critical to businesses. The story of Goldman and LinkedIn also shows how important it is that this professional has the autonomy to test the hypotheses, without having to go through the approval of dozens of executives in the chain of command.
The importance of delving into the world of Big Data
The relevance of implementing solutions in Big Data and of having a data scientist in your company should be pretty clear, but one question remains: are there a good number of these professionals in the market? The answer is no, data scientists are still extremely rare and disputed by companies charging high wages and being extremely difficult to maintain. This is because it is a very recent profession, that requires many different skills and there are still very few institutions that offer a specific training in Data Science.
Then, would it be better to wait for this market to consolidate and the formation of new professionals to be concluded? The answer, again, is no! Big Data offers an almost unfair competitive advantage to those that apply it in relation to other competitors, by providing the ability to analyze a huge volume of data. It gives the company the possibility to search for new ways to market and make projections from hard data, not simply for the experience and the “feeling” of executives.
Netflix, Zynga, Google, Wal-Mart, LinkedIn, American Express … All these companies understand the importance of the data scientist and are investing heavily in Big Data. So, the companies that learn to have this revolution aboard as soon as possible will be ahead of their competitors, who might simply disappear if they don’t accept it soon.
