Edwards Deming once said, “In God we trust, all others must bring data”. Indeed, the economy of data is here. The amount of data generated by humans every day is enormous. More data was generated in the last five years than in the entire history of our civilisation. And this was possible because of the massive advent in computing technology. Data is the new oil. A buzzword that has been doing the rounds in academic and industrial circles for a long time is the term ‘Big Data’. Organisations are looking for ways to analyze big data to solve the biggest societal problems. Companies are doing it to improve their businesses.
This new data driven economy has given birth to a new profession: the Data Scientist.
Who is a Data Scientist?
The data scientist takes an organisation’s data and uses statistics , math and computing to make predictions or generate insights that may be used by a company to take important businesses decisions.
Given the potential of Big Data companies are spending a fortune on these professionals. Almost every company wants to have its own data science team. Thus this new profession means big money. Data Scientists are easily among the highest paid professionals today. Yes, Fortune 500 CEOs earn more, but for the remaining 99.9% of us, the Data Scientist is hard to beat. Their earnings typically start at upwards of five ciphers and increase with experience. The Harvard Business Review famously termed the Data Scientist as “The Sexiest Job of the 21st Century”.
What makes a Data Scientist so valuable?
Let’s take a look at some of the problems that a typical data scientist might be required to solve.
Saber-metrics: the application of data science to baseball. In the noughties, a second rate team in American baseball called Oakland As made it to the playoffs of the World Series by winning equal number of matches as the favorites Boston Red Sox. This was in spite of the fact that the As payroll was only a fraction of that of the Red Sox. A similar victory was scripted this year in the English premiere League by Leicester City. Both these teams stood out because of the use of advanced data analytics. Today almost all sports franchises have data analytics teams.
Retail: If you are a retail store owner, you may be bothered by questions like: How much shelf space should I give to product X. In which part of the shop should I stack product Y to improve consumption? What is the right product mix for my shop? All these questions can be answered by data science. That said, to laud data science is not to undermine the importance of subjective decision making. But Data does help us take more informed decisions.
Traffic Control: Traffic data all over the world is being analysed to arrive at efficient traffic control systems that make life easy for commuters. Several years ago, Southwestern Airlines reduced operational costs using sophisticated optimization algorithms.
Data scientists are trying to predict stock market prices, results of presidential Elections, the number of runs Kohli will score in the next IPL season and so on.
However becoming a data scientist is not quite as easy as it sounds. It requires a good understanding of programming and statistics along with good business knowledge. Data Scientists are rare and that justifies the demand for them.
Bottom line: The data economy is here and the data scientist is here to stay.
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