The big data project failure rate is 60%, according to 2015 research from Gartner. A major contributor to this failure rate is political or people-oriented, rather than the technology. What can big data project managers do to avoid these project failures?
“To succeed, you must develop a viable strategy to deliver business value from a big data initiative,” according to Gartner research director Svetlana Sicular, as stated in a Gartner blog post. “Then map out and acquire or develop the missing and specialized skills that are needed. Once strategy and skill priorities are addressed, then you can move on to big data analytics.”
If you can’t demonstrate immediate value to the business from a project, your end users and managers will be scratching their heads and asking, “Sure, the project got installed and the application is working, but what are we getting from the our investment that is traceable to revenue expansion or the reduction of operating expenses?”
In an interview with insideBIGDATA, tech expert Robin Purohit said he believes that it can be all too easy for IT to get consumed in just completing a technology project.
“The one problem that especially plagued a lot of the early Hadoop projects, and still pops up now and then, is that setting up a big data analytics platform is seen as an end in itself,” said Purohit. “Without any clear understanding of pain in the organization that Hadoop might address, a cluster and its associated technologies are set up as a sort of science experiment…. the likelihood that the project will be declared a success under those conditions is virtually nil. Unless a particularly savvy project manager stumbles on a business use case, there just isn’t any way to call that kind of project a win.”
How to improve your odds of big data project success
1: Never start a big data project without specific cost reduction or revenue enhancement goals
2: Continuously assess the likelihood of project success
Last year, I visited with Teradata, a major provider of big data and analytics solutions. My Teradata acquaintance said, “Especially for experimental big data and analytics projects, our most experienced project leads intuitively sense when a project isn’t going to succeed, and they pull the plug.”
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