Thomas Davenport, in a recent Harvard Business Review article, says “Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0.” What does this mean for leaders of large organizations?
The Three Phases of Analytics
Davenport writes that the field of analytics has evolved during the past 60 years in three phases:
Analytics 1.0 was born in the mid-1950s and was referred to as “business intelligence.” It gave managers “the fact-based comprehension to go beyond intuition when making decisions,” Davenport says. It involved examining data from production processes and customer interactions. He notes that new computing technologies were key and were often custom-built.
Analytics 2.0 emerged in the mid-2000s when Internet and social media companies — Amazon, Google, eBay, etc. — “began to amass and analyze new kinds of information,” he says. This was referred to as “big data.” Davenport says big data differs from small data because big data comes from sources outside the company and were not generated solely by the organization’s own internal systems. It comes from sensors and social media as well as video and audio recordings. It could not be stored on a single server. Much of it has to be stored in the cloud.