A worrying proportion of the Global 9000 - the 9,000 public companies reporting a billion dollars or more in revenue per year - are doing nothing, about Big Data.
We recently commissioned an independent survey from King Research to find out what the Global 9000 is doing with Big Data, the challenges they are dealing with and what opportunities they see for generating value.
As might be expected for such a young technology, we found a mixed picture. Just over a quarter (26%) of large corporations are currently working on Big Data projects, while another third (34%) are in the evaluating and planning phase. But a very high four in ten - 40% - say they have not yet evaluated their Big Data needs. Or worse, have evaluated them but then decided not to proceed any further.
The research also gives us insight into the systemic problems at the root of what we might call The Reluctant 40%. Respondents who have decided against a Big Data project, or are still hesitating, say the major inhibitors are 'not enough staff with expertise' and the 'expected cost of Big Data initiatives'.
What's so concerning about this inertia? The problem is the wide range of benefits - the kind of exponential leap in understanding promised by better customer information gained through Big Data, bringing an end to wasted marketing efforts - is by definition out of their reach. That makes such inaction from these large global companies short-sighted, to say the least.
Another worry is that the 'reluctant' 40% may have customers in the 'convinced' 60% confidently going down the Big Data path. As this more confident cohort begins to extract returns from their Big Data efforts and become more and more sophisticated in dealings with customers, they will expect the same degree of savvy and care from their peers and suppliers. And here, again, is a potential red warning flag for the Big Data backwards-looking 40%.
After all, for the majority of the Global 9000 who are starting their Big Data projects, the biggest motivation is to get better customer experience analysis: customer insights, fraud prevention and analysis, market targeting, behavioural analysis, customer lifecycle analysis and operations improvement were commonly-cited Big Data project aims.
In a similar vein, the Big Data apps in use today to meet all these needs, as well as help with important ancillary operations, are customer experience analysis, customer insights, market targeting/decision, capacity forecasting, customer lifecycle management, fraud prevention and analysis, as well as network monitoring.
And our Global 9000 survey respondents say they see, or anticipate seeing, benefits including increased competitive advantage, superior customer targeting, improved efficiency and the ability to make better decisions, faster.
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