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.
All in all, it’s clear that understanding customers is the major motivation for these first-adopters. And this underlines a related point: maybe these pioneer started with the problem(s) Big Data analysis is intended to solve, rather than building Big Data architecture for architecture’s sake before deciding what to do with it. It’s well worth always reminding yourself that the big problem with Big Data is not where you put it. Instead, start by asking “What do I want to get out of my data? What problem am I trying to solve?”
In other words use that tried and true standard: Start with the business goal always uppermost in your team’s mind.
Organisations far-sighted enough to become early adopters for Big Data clearly recognise the enormous business opportunity attached to understanding their customers better. Of course, they are not going to be in a position to ever ‘read our minds’ as customers.
But the intelligence there to be gleaned about a specific individual from their purchasing behaviour, likes, interests, allied to an ability to match their profile to similar profiles from social networking sites, search engines or interest-specific social sites, as well as to mine sentiments gleaned from Facebook likes, positive Tweets, Yelp reviews and so on, will give these Big Data converts knowledge that will be deep, targeted and useful.
As a result, businesses will be able to be truly smart - able to predict their customer’s needs and make more appealing offers for products or services we really want to buy. This is a huge and welcome leap, and why underneath all the hype and froth Big Data is a Big Deal. But we need to get that Reluctant 40% back on board.
A factor that should be addressed is that some organisations are hesitating because they don’t yet feel Big Data is relevant to them. Vendors are having difficulty identifying successes. Competitive advantage is why.
Success with customer behaviour analysis can make such a huge difference that those who are indeed succeeding are withholding this secret from the rest of us. We have seen this for years in the Financial Services space; customers are very reluctant to reveal how they succeed with technology that makes a difference, precisely because they are succeeding!
The post 2007 slow economic recovery makes big corporate budget commitments difficult for the foreseeable future. Nevertheless, business leaders and senior managers must grasp the Big Data opportunity. We are saying to The Reluctant 40% that any perceived Big Data skills gap will be short-lived. Meanwhile, there are many useful tools that could help with any Big Data mission, easily offsetting any upfront costs of development.
Where there’s a will, if backed up by enough management support and leadership, there’s definitely a way. We need to get the laggard 40% back on the Big Data path- lest they face the risk of real business collapse through being too unfocused on Big Data and what it represents.
Posted by Jeff Morris, Vice President of Product Marketing for leading Open Source Business Intelligence (BI) specialist Actuate