Nearly every business is moving to become analytics-driven as more and more of them seek to support their digital transformations. According to , 53% of companies were using analytics as of the fourth quarter of 2017, up from 17% in 2015. Explosive growth like that makes it clear that most organizations have only just begun to utilize analytics. And there’s every indication that the use of big data analytics is going to snowball, with it soon being employed by every department and innumerable individual employees. Call it the shift from Analytics 1.0 to Analytics 2.0
With operating divisions and management demanding more and broader analytics to truly understand the state of the business, IT teams must provide the infrastructure that can support these much larger workloads. But IT must do so quickly—in weeks, not months or more. That means there’s no time for legacy self-integration—IT’s age-old bias for DIY—to support infrastructure deployment; it just doesn’t match the speed that the business demands in today’s highly competitive environment.
Previously, there were good reasons for IT teams to manage the acquisition, installation, integration, testing, and rollout of an analytics platform consisting of software, compute, storage, and other components. But that time has passed. Depending on skills, staff availability, and other issues, this process can take one to three months or more, and only then can the work on the analytics activities start. That’s a timeframe today’s businesses just can’t support.
Is there any doubt that the use of analytics is taking off? According to Dresner Advisory Services, 53% of companies are currently using big data analytics, up from just 17% three years ago. And in a recent book published by the Harvard Business Review Press, Competing on Analytics, the authors document how the use of analytics is determining winners and losers in the market. They further state that analytics-driven enterprises will eventually apply analytics to most of the functional areas of their business, using advanced predictive, prescriptive, and autonomous analytical tools for marketing, supply chain, finance, M&A, operations, R&D, and HR. And the rise of the “citizen data scientist” foreshadows a trend where virtually every employee will be using analytics daily.