Requirements Management for Big Data Projects
Accurately defining project scope and subsequently identifying clear, quality and complete project requirements is a challenge for many IT projects. In fact, inaccurate scope and lack of quality requirements including ambiguous, incomplete, and/or contradictory requirements is a primary cause of schedule delays, cost overruns and ultimately project failure.
According to a recent Infochimps survey, 58 percent of respondents indicated that inaccurate scope was the primary cause for failure of big data projects. For big data projects, where “100 percent go over schedule, 100 percent go over budget, and half fail,” requirements management is a significant concern, since inaccurate scope is one of the three main causes of failure according to the CIO’s participating in this survey.
Given these grim statistics for big data projects, it is clear that something needs be done to address this concern; something must change in the way we manage requirements. But before we can fix it, we need to understand why it is happening. Infochimps CEO Jim Kaskade concludes that the reason inaccurate scope occurs so frequently is “because organizations just jump in without a real plan.”
So what tips can be applied to help you plan for success of your big data project?
1. Identify business goals and objectives. How often are project teams unaware of the
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