“It is a capital mistake to theorize before one has data.” – Sherlock Holmes
As part of my job with the IBM Center for Applied Insights, I often spend hours on end –days even – with my head deep in data. I like to call it “data spelunking” – like a miner deep in underground caves, I explore, perform repeated analyses, and chisel away until data sets reveal their precious gems. The latest study undertaken by my team, The 2014 IBM Business Tech Trends Study, offered no end of data to examine. We surveyed over 1400 global IT and business leaders to uncover adoption and investment trends in cloud, analytics, mobile, and social. But it’s the deeper findings relating to big data and analytics that I find the most exciting.
We examined a subset of enterprises that consider the four tech areas to be critically important to their business success and adopt them ahead of rivals. These leading organizations – the Pacesetters – are getting more out of the technologies than others. For example, if they aimed to improve their workforce efficiency using Cloud, they did – at 10 times the rate of lagging enterprises. If they wanted to speed up innovation with Mobile, they did — at 6 times the rate of the laggards.
Given their superior ability to hit their business goals, we closely examined what distinguishes the Pacesetters’ approach to these tech areas. One characteristic hits close to home for those of us who live, eat, and breathe data:
For Pacesetter enterprises, analytics is their fuel
Pacesetters have built up strong analytical machinery: 89% say they have mature big data and analytics capabilities, and three quarters report having most of the analytics skills that they currently need. They’re not pulling back on the throttle, either: Almost two thirds plan to increase their investment in big data and analytics by 10% or more over the next two years, and 8 in 10 are plan to rev up their use of specialized new analytics such as social media analytics and mobile analytics.
But it’s not enough to have a solid analytical toolset and skills. Analytics, and their associated insights, don’t do your organization any good if you let them sit idle, gathering dust. They’re only useful if you use them for smarter decision making. Indeed, analytics is changing the nature of how these organizations operate:
For 7 in 10 Pacesetters, data-driven insights are integral to their decision making
Our study highlights a case study of an innovative healthcare company using sophisticated analytics for early identification of patients at risk of heart failure. You may also want to check out a recent MIT Technology Review report, Data and Decision Making, for more examples of how data and analytics are fundamentally altering business – and even personal – decision making. Did you know that LinkedIn offers “University Pages,” a tool that leverages their goldmine of data about the roles/companies individuals work in after graduating from various schools? With this tool, high school students eyeing a particular field or specialty can determine which colleges/universities have the best history of getting their graduates into those areas, and can use that insight to guide their choices.
Is there any tangible business benefit to basing decisions on insights, rather than one’s gut instincts? A 2011 study by Prof. Erik Brynjolfsson and colleagues at the MIT Sloan School of Management sheds some light: They carried out a study of 179 large firms, finding those that emphasize decision making based on data and analytics realized 5-6% higher productivity than would be expected, given their IT investments and usage. Brynjolfsson has said this productivity gain is “significant enough to separate winners from losers in most industries.”
Our examination of Pacesetter organizations – the vast majority of which now significantly weave analytical insights into their decision making – finds that 9 in 10 report gaining major competitive advantage through their initiatives in the four tech areas. Only 4 in 10 of the lagging companies can say the same.
Over a century ago, Sir Arthur Conan Doyle’s character Sherlock Holmes said, “It is a capital mistake to theorize before one has data.”
Now, it may also be a capital mistake for organizations to make decisions and take actions before they’re equipped with analytics-based insights.
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