It is totally natural and normal to feel anxiety when hearing these words: "Big Data", "Data Mining", "Data Science". Most of us, professionals in communication areas, were never taught "how to eat this data analytics stuff".
First, let's be clear that this is not a fad, nor is it a trend. Our professions are changing; the information sciences are converging with the social sciences, thus changing the rules of the game.
Getting down to business, what do these terms mean and why is it important to understand them?
Companies are floating on data. Every day billions of pieces of data are produced and stored, and at best "put to good use".
Around the clock, we are feeding databases of companies that make decisions based on our information. For example, Eugenia gets up at 5:30 a.m. and activates her smartwatch for her workout. While she goes through her routine she listens to music from her app, then goes to the supermarket and buys a yogurt and some fruit, and takes a few minutes to check her social networks. Conservatively, in less than two hours, he has fed the database of the company that developed his smartwatch, the music app, the supermarket, the yogurt supplier, the fruit supplier, the mobile device company, the social networks...
Simply put, Big Data is this, databases or data sets that are too large for traditional processing systems and require new technological processes to be used . To take advantage of this huge amount of data and make it useful for a company there are Big Data technologies.
It is the extraction of knowledge from databases in an automated way through technological processes. It can be fed from databases of Big Data systems. It works for us to understand causes of phenomena, as well as to predict future behaviors based on a mix of variables that impact the "probability of doing/not doing something".
For example, why did Eugenia go to that supermarket and buy that brand of yogurt? Was it because of proximity, because of her lifestyle, because she saw an offer in the morning, or because of her preference for that supermarket? From the data we know about Eugenia and thousands of other people, we can estimate a probability to know if her sister Marta will go to the same supermarket and buy the same yogurt. This is how data analysis becomes interesting.
They are the principles, processes and techniques that guide the extraction of knowledge from databases, that is, that guide the Data Mining processes, these processes must be treated in a systematic way, following clear and well defined stages.
To continue with Eugenia's example, by using Data Science principles in Data Mining processes, we were able to understand a little better why she went to that supermarket to buy that brand of yogurt.
Traditionally, business decisions have been made based on intuition. Creativity and exposure to diverse situations make us more assertive in strategic decision making, or so we believe.
With Data Analytics we can achieve that company data provide us with relevant information to make decisions with greater accuracy to solve business problems. This does not mean that intuition and creativity are not important, of course they are, but with the help of technology in the interpretation of data we can facilitate decision making.
Excerpt from the article published in the Costa Rican media El Observador, by Marian Bákit, CEO of IDEAS MCW and student of our Executive Master in Business Analytics.
