Publication

Big Data, Data Mining, Data Science, Data… what?

20 2019 September
INCAE Executive Education

It is totally natural and normal to feel anxiety when hearing these words: "Big Data", "Data Mining", "Data Science". Most of us communication professionals were never taught "How do you eat this data analytics."

Let's first make it clear that it is not a fashion and it is not a trend either. Our professions are changing; the information sciences are converging with the social sciences, thus changing the rules of the game.

Beginning lesson, What do these terms mean and why is it important to understand them?

Big Data:

Businesses are floating on data. Every day billions of data are produced and stored, and in the best of the cases “well used”.

24 hours a day we are feeding databases of companies that make decisions based on our information. For example, Eugenia gets up at 5:30 am and activates her smart watch for her exercise session. While doing his routine he listens to music from his app, then he goes to the supermarket and buys a yogurt and some fruits, and takes a few minutes to check his social networks. Being conservative, In less than two hours, it has fed into the database of the developer of its smart watch, that of the music app, that of the supermarket, that of the supplier that produces the yogurt, that of the fruit supplier, that of the mobile device company, that of social networks ...

In a simple way, 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 enormous amount of data and make it useful for a company there are Big Data technologies.

Datamining:

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 mixture of variables that affect 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 your lifestyle, because you saw an offer in the morning, or because of your 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.

Data Science or "data science":

They are the principles, processes and techniques that guide the extraction of knowledge from the databases, that is, they 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 having been exposed to various situations make us more assertive in making strategic decisions, or so we think.

With Data Analytics we can get company data to provide us relevant information to make decisions more accurately 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 data interpretation we can facilitate decision-making.

Extract 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. 

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