The Science behind Big Data
Every day, the amount of information available to solve some of society’s most vexing problems grows exponentially. By 2020, the amount of data in the digital universe will grow ten-fold.
"Big Data only becomes truly powerful when it is compiled, sorted, analyzed and manipulated -when it is translated into the language of business leaders and policymakers"
But for all its potential, data alone won’t change the way we distribute innovations, administer healthcare, conduct business or operate in the global economy. Data in its raw form is nothing but untapped potential.
Big Data only becomes truly powerful when it is compiled, sorted, analyzed and manipulated when it is translated into the language of business leaders and policymakers. And so the explosion of data has driven the emergence of fields built for the sole purpose of making data usable.
Today, we see entire disciplines and areas of business that have been born of the need to glean insights from vast amounts of, otherwise, indecipherable information. In particular, a new profession of Data Scientists has emerged to meet this growing need to bring structure to large quantities of formless data.
The job is still in its relative infancy the term “Data Scientist” was first coined in 2008 by the leaders of data analytics at LinkedIn and Facebook but even so, in seven short years the profession has exploded.
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In 2012, Harvard Business Review named the position, “the sexiest job of the 21st century.” This year it was Mashable’s hottest profession. With acknowledgments like that it should come as no surprise that students with Masters degrees of PhDs, but no work experience often come out of graduate school and receive six figure salaries. Experienced data scientists are commonly paid similarly to senior business executives.
Those salaries aren’t without warrant. Data science is now an essential business tool. According to recent research from Accenture, 87 percent of companies agree. They believe that within three years, big data analytics will redefine their respective industries and are spending on it, accordingly. In fact, 73 percent of enterprises are spending more than a fifth of their technology budget on analytics.
Moreover, there is shortage of qualified scientists to fill this growing demand. Companies are finding themselves expanding the search for talent to physics majors, engineering and applied mathematics, but that requires significant testing and screening to ensure they can adapt to the rigorous requirements of the data science field.
But why exactly does a Data Scientist need these skills? What exactly does a Data Scientist do? And what sets this profession apart from the more established mathematicians and statisticians?
The big difference is a Data Scientists’ ability to think like a business person they not only parse through vast and varied banks of data, but relay findings clearly to decision makers. As the industry defines it, data scientists have “the ability to communicate findings to business and IT leaders in a way that can influence how organizations approach business challenges.”
Another big difference is the number of highly technical and quantitative skills needed to be successful. There is the highest demand for machine learning skills, Python and Java development skills, open source analytics and data management.
At Experian, we are investing substantial time and resources into this burgeoning field.
We have years of experience harnessing the power of data. In fact, we have been gleaning insights from information to help our customers since before big data became a buzz word. Today, we are still using data assets to improve society. We are helping consumers, financial institutions, healthcare organizations, automotive companies, retailers and governmental organizations make more informed and effective decisions.
And we are committed to developing data science to improve our business. Our Data Labs are a prime example. These are staffed by teams of scientists with experience in stats and analytics. The unique combination of skills allows our labs to consider problems in new ways and identify previously undetected strategies. By analyzing billions of transactions and records, the Data Labs teams are improving the economy by solving strategic marketing and risk-management problems.
The future is bright and there’s still more we can do with data to drive growth and improve national policies. We’re working with the health care industry and others, from energy to automotive to the multi-family housing community and government to fully leverage data. But to do so we will need more individuals capable of interpreting data.
Going forward, data scientists will prove instrumental in using data for good.
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