The television show Star Trek gave us much to look forward to: teleporting, food “replicators” and that other far-fetched creation – the data scientist. The show also introduced us to Data, an android who could access every piece of information ever generated, while Spock himself wasn’t just a pointy-eared Vulcan, but the logic-loving prototype for a role that taps into the power of information in unprecedented ways.
The idea that people in leadership roles should specialize in the organization, visualization and translation of vast swathes of data is no longer limited to sci-fi buffs. Today, data scientists play a leading role in what to do at a fork in the road within organizations, says DJ Patil, Vice-President of Product at RelatelQ, who helped coin the phrase “data scientist” while at LinkedIn. “Companies need a Spock in the boardroom,” he adds.
Firms, both large and small, are heeding this suggestion, as they continue to grasp the strategic importance of “Big Data”. This is no longer just a buzzword: data, data science and data analytics are all crucial tools for everything, from understanding customers to optimizing supply chains. As such, companies, governments and other institutions are vigorously investing in data science techniques and expertise.
Within the last 20 years, the focus has shifted from data collection to data use. What can we learn from the data? What is it telling us? How do we organize, access and visualize data in a way that steers strategic action? And how will this change in the future as data and the algorithms designed to comb them become more powerful?
These questions are just a few that will be addressed by global industry and government leaders at the World Economic Forum Annual Meeting of the New Champions in Tianjin, People’s Republic of China, next month. Jeremy Howard, a Forum Young Global Leader and CEO at Enlitic, has been involved in data science for two decades and has witnessed first-hand the seismic shift in how society perceives information gathering. “Twenty years ago there were few systems to collect and get information from data,” he says. Furthermore, “there were no systems and no strategy for use of data” within organizations.
But all this changed with Google, which served as a role model for how data science research could not only be better conducted, but also how specific kinds of data could be more efficiently collected, understood and used to help businesses, says Howard. In particular, the company developed a software technique for processing huge troves of data called MapReduce, which broke tasks into many smaller components to perform among multiple machines,” says Kenneth Cukier, Data Editor for The Economist. The open-source version of this novel process – that has become very widely adopted – is called Hadoop.
“Nowadays, hundreds of organizations have transformed their industries with data science,” says Howard. “There are more tools now to process the data and gain insight from it, and this is exciting.”
The repercussions of the data revolution are rippling out through industries and societies. “We are able to datafy things we could never render in a data format before,” adds Cukier. “We can do this because of the lower costs of collecting, storing and processing data in a way that is almost unimaginable in the past.” Self-driving cars are one striking example. “We transformed the problem from one of explicitly teaching a car how to drive, to feeding in lots of the data and having the car figure out what to do in different situations,” he says.
In another sphere, AidData, an innovation lab that aims to make development finance more transparent, provided detailed maps based on GPS data of where financial aid was being sent in regions in Africa. It revealed a distinctive “mismatch” in where authorities thought aid was going and where it actually ended up. “Jaws dropped,” says Cukier. “In Kenya it showed that for the tens of millions of dollars of international development assistance flowing into the country, there were sectors and regions that weren't getting financial support.” The aid organizations hadn’t seen this before because the calculations and correlations just weren’t available in the past. Now, large data sets that previously didn’t exist, combined with new ways to analyse the data, have opened new channels of communication, action and capital.
The idea that people in leadership roles should specialize in the organization, visualization and translation of vast swathes of data is no longer limited to sci-fi buffsAlaina G. Levine