Big data is all the rage, and many organizations are hell bent on putting their data to use. Despite the big data hype, however, 92% of organizations are still stuck in neutral, either planning to get started "some day" or avoiding big data projects altogether. For those that do kick off big data projects, most fail, and frequently for the same reasons.
It doesn't have to be this way.
[Want more big data advice on staffing? Read Data Scientists: Stop Searching, Start Grooming. ]
The key to big data success is to take an iterative approach that relies on existing employees to start small and learn by failing early and often.
Big data is a big deal. According to Gartner, 64% of organizations surveyed in 2013 had already purchased or were planning to invest in big data systems, compared with 58% of those surveyed in 2012. More and more companies are diving into their data, trying to put it to use to minimize customer churn, analyze financial risk, and improve the customer experience.
Of that 64%, 30% have already invested in big data technology, 19% plan to invest within the next year, and another 15% plan to invest within two years. Less than 8% of Gartner's 720 respondents, however, have actually deployed big data technology.
That's bad, but the reason for the failure to launch is worse: Most companies simply don't know what they're doing when it comes to big data.
It's no wonder that so many companies are spending a small fortune to recruit and hire data scientists, with salaries currently averaging $123,000.
8 ways to fail
Because so many organizations are flying blind with their data, they stumble in predictable ways (including thinking that a data scientist will magically solve all their problems, but more on that below). Gartner's Svetlana Sicular has catalogued eight common causes of big data project failures, including:
Throughout this list, one common theme emerges: As much as we might want to focus on data, people keep getting in the way. As much as we might want to be ruled by data, people ultimately rule the big data process, including making the initial decisions as to which data to collect and keep, and which questions to ask of it.
Innovate by iterating
Because so many organizations seem hamstrung in their attempts to start a big data project, coupled with the likelihood that most big data projects will fail, it's imperative to take an iterative approach to big data. Rather than starting with a hefty payment to a consultant or vendor, organizations should look for ways to set their own employees free to experiment with data.
A "start small, fail fast" approach is made possible, in part, by the fact that nearly all significant big data technology is open source. What's more, many platforms are immediately and affordably accessible as cloud services, further lowering the bar to trial-and-error.
Big data is all about asking the right questions, which is why it's so important to rely on existing employees. But even with superior domain knowledge, organizations still will fail to collect the right data and they'll fail to ask pertinent questions at the start. Such failures should be expected and accepted.
The key is to use flexible, open-data infrastructure that allows an organization's employees to continually tweak their approach until their efforts bear real fruit. In this way, organizations can eliminate the fear and iterate toward effective use of big data.
When selecting servers to support analytics, consider data center capacity, storage, and computational intensity. Get the new Hadoop Hardware: One Size Doesn't Fit All issue of InformationWeek Tech Digest today (free registration required).Matt Asay is Vice President of Community at MongoDB. He was previously VP of Business Development at Nodeable. You can reach him at [email protected] and follow him on Twitter @mjasay. View Full Bio