When Google burst onto the corporate landscape more than a decade ago, its distinctive name embodied ambition and mystery. A “googol,” after all, is a one followed by 100 zeros—an unimaginably large number. Yet the explosion of data today is rapidly making large numbers commonplace: Cisco’s latest Visual Networking Index estimates we will collectively generate 1.3 trillion gigabytes of data annually by 2016. How large is that? Imagine every word ever spoken by every person in the history of the planet. Then multiply by 200.
Mining “Big Data” like this presents a huge opportunity for companies to better understand customer behaviour at a fraction of the cost, and with previously unimaginable precision. Academic research already suggests that companies that use data and business analytics to drive decision-making enjoy up to 6% higher productivity than competitors who do not.
Yet capturing Big Data’s potential requires overcoming significant hurdles. One problem we increasingly see is companies that are simply buried in information. Somewhere amid the millions of gigabytes of data are customer insights that can transform existing businesses and create new ones, yet many executives have no idea where to look or even where to begin.
Companies need to hire employees with data expertise and experience, but that is easier said than done. A McKinsey Global Institute report last year estimated that the United States has a shortage of 140,000 to 190,000 people with the necessary analytical and managerial expertise, and a further 1.5 million managers and analysts are needed with the skills to understand data and make decisions from it. That makes your hiring and retention strategy even more important. And even when you have the right people, you must ensure they are not merely engaging in fishing expeditions: they need to gather the right data and use it the right way.
If you can clear these barriers, however, the potential of Big Data is enormous. For example, analyzing more information in more sophisticated ways is helping television executives make better programming decisions. One major network crunched years of minute-by-minute viewing and ratings data for itself and rival channels to better understand how shows succeed, as well as the probability of a program recovering after a ratings dip. By more accurately predicting ratings, the model informs programming and advertising pricing decisions.
Today, such data-driven approaches are everywhere. Retailers use tools such as mobile devices to build models of how consumers behave inside stores, then change the placement of products and price promotions to maximize sales. Pepsi used social networks to gather customer input for the creation of new flavours of its Mountain Dew brand. One U.S. transportation company analyzed data on worker availability to better match labour supply and demand, and cut its hiring costs by more than 10%.
All this data comes with risks. The information boom is enabled by more open access, through many devices on multiple networks. The more data flows freely between IT architectures, the greater the risk of accidental leaks of private data—or deliberate theft by hackers.
Executives must realize that data is a tool, not a panacea. Analytics can substantially improve decision-making, minimize risks, and unearth valuable insights that would otherwise remain hidden. Yet, as one CEO recently told us, “It doesn’t show what one is not doing.” Because internal data offers no knowledge about potential markets where a company does not yet compete, human intuition and experience still play key roles. Executives must ask the right questions and, if necessary, seek out data they don’t already have.
In an increasingly competitive world where companies spend millions of dollars looking for the slightest competitive advantage, marrying art and science in the world of Big Data will make all the difference.
Dominic Barton is the global managing director of McKinsey & Co.