Leading in the age of disruption

The year 2013 moved the world further into the Digital Age - a global epoch of changes whose likely impact on the world economy will be 2-3 times greater than that of the Industrial Revolution. Some 90 percent of the world's total data were created in the past two years. By 2020, the quantity of stored data could be 50 times greater than it was in 2010. Many pundits regard this massive explosion of data as the new oil, even a new asset class.

This profusion of data is being fueled by the near ubiquity of the Internet. Smart phones are set to connect an additional 2-3 billion of the world's citizens by 2020, with billions of machine sensors monitoring everything from tractors to jet engines, and further breakthroughs in computing power enabling massive increases in data storage and analysis.

In this environment, fluency in data management and analytics will be vital for successful organizations. A study published in 2011 by MIT's Erik Brynjolfsson and his colleagues found that companies using data-driven decision-making had a 5-6 percent higher productivity rate than those that did not. The ability to capture, organize, extract insights from, and transact with data has now become a core competency for every industry and across every sector.

The disruptions resulting from the new crucible of data and analytics are spreading across both the public and private sectors. Netflix, the popular video-streaming website, used its vast database of user searches, views, pauses, and reviews to design the made-for-the-Internet series "House of Cards." The series combined a popular director (David Fincher), actor (Kevin Spacey), and plotlines borrowed from a popular British show with the same title - all of which scored highly on Netflix's popularity metrics.

In other industries, too, data-driven decision-making in product development, marketing, and customer interactions is fast becoming the standard, supplementing (and in some cases replacing) intuition and experience. It is also streamlining supply chains, refining workforce schedules, and optimizing manufacturing processes.

More significant disruption is likely to occur across industries, as privileged access to proprietary data redraws competitive battle lines. Companies with deep data sets will increasingly have the ability to play in markets outside their traditional domains - and leaders already are seizing the opportunities. At Alibaba, the Chinese e-commerce company, small and midsize vendors in its network can also apply for credit. Alibaba has financed the working capital of 320,000 companies (more than $16 billion) using transaction data to underwrite the lending - and has done so far more efficiently than the average bank.

Governments, too, are sensing that data analytics can change their global standing. Singapore, for example, has a ten-year master plan that focuses on the development of a robust information and communications industry, including data analytics. More recently, the authorities launched an open-data initiative, making vast amounts of government data easily accessible.

And yet, despite many organizations recognizing the importance of data analytics, there is wide variation in how aggressively they have moved to embrace it. Early adopters, such as Amazon and Tesco, which quickly built up the requisite talent bases and experience, are now shifting gears to maximize the impact of analytics on their organizations (that is, exploring disruptive opportunities). Many more organizations, however, are still only conducting small-scale experiments and hiring their first data scientists.

The good news is that many companies will be able to accelerate the pace of change. Talent is one promising area. Tapping the potential of data analytics requires deep pools of advanced technical expertise. To be sure, workers skilled in data management and advanced analytics are in short supply, as are members of an emerging class of "translators" - those whose talents bridge IT and data, analytics, and business decision-making.

Previous Page 1 2 Next Page

Previous Page 1 2 Next Page