Before you embark on a big data project, it's important to define the problems you're trying to solve.
5 Big Wishes For Big Data Deployments
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Quick, what pops into your head when you think "big data?" Hadoop, maybe. Terabytes and petabytes. Or everybody's favorite data trio, the 3Vs: volume, variety, and velocity.
But rather than focus on the goodies of big data -- the newfangled platforms and cutting-edge software -- it's smarter to determine the business problem you're determined to solve and what sources of data can assist your decision-making.
That's according to Steve Jones, director of strategy for big data and analytics for Capgemini, a global tech consulting and outsourcing giant with 125,000 employees in 44 countries.
In a phone interview with InformationWeek, Jones said that enterprises often run into trouble with big data projects when they focus on data rather than decisions, and when chief marketing officers (CMOs) run things without significant input from the folks in IT.
"I've been to companies where they're putting in a Hadoop cluster," said Jones. "They've got 10 or more terabytes of data, and then ask the question: 'Well, what are we going to do with it?'"
The moral here is to work out a big data strategy before you start stockpiling all of those bits. "Yes, Hadoop's cheaper. Yes, it can handle large volumes. Yes, you can do different analytics more cost-effectively," Jones said. "But if you don't know what you're trying to improve, you're really just shooting in the dark."
There's nothing wrong with exploring pros and cons of Hadoop and other big data platforms, but your project's goals must be clear from the start. "It's really about understanding where your business needs to improve and working out where these tools help, rather than starting with the technology and trying to find [where] it fits in," Jones advised.
One common challenge of big data projects is understanding the difference between correlation and causation. To paraphrase Wikipedia, a correlation between two variables doesn't necessarily mean that one causes the other. "CMOs start looking at these large-scale data sets and are looking for correlations," said Jones. "It's not the same as causation."
CMOs can always find correlations in big data. But is there causation? Marketers need to run closed-loop tests to find out, Jones noted, as one U.K. retailer did recently. "They saw a relationship between sales of goat cheese and sales of red wine. If goat cheese sales went up, so did red wine sales," Jones said. "To find out if there was a causation rather than just a correlation, they discounted goat cheese in a few stores."
The result? "They did indeed see sales of red wine go up," he said. "So they established a causal relationship between goat cheese and red wine. That was the closed loop."
Capgemini's big data advice to chief marketing officers makes sense in light of a recent prediction by Gartner analyst Laura McLellan: CMOs will outspend CIOs on IT by 2017.
Hence the need for CMOs to establish clearly defined goals during the big data planning phase. "What happens if we get this wrong?" Jones said. "What's the impact on the business? That's really what determines the rigor you need to put in."
Of course, Capgemini isn't alone in pointing out the challenges that enterprises face in creating value with big data. And it's always important to consider the self-interest of the person or company offering sage advice. But the notion of CMOs and IT working together on big data projects can't be a bad thing -- can it?
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