Aligning Tech & Business Requirements: 10 Questions to Answer Before Starting a Big Data Analytics Project

Long-time BI, big data, and analytics guru Neil Raden has released new paper called “10 Questions To Answer Before Starting a Big Data Analytics Project: Aligning Technology with Business Requirements,” and it’s available here, and for those of you who prefer listening/watching, he and I recorded a webinar discussing this exact topic, which you can watch on-demand here.

In a highly-covered space like big data, it’s really important to contribute content that helps organizations take a sober, grounded approach to big data projects. The reason: as big data analytics graduates from its novelty phase, funding for technology initiatives that utilize it are going to be held to the same stringent standards as other high-visibility technology projects.

With that in mind, people who are planning big data initiatives need to be as vigilant as their counterparts in other technology projects. They’ll need to inventory the things that can make projects fail, and avoid them. They’ll also need to look at BI’s project history and, though it is sometimes looked upon unfavorably, recognize and retain the things that were done right.

We hope and believe Neil’s paper will trigger conversations early in the process to test for alignment, and set you up for project success. Why the focus on alignment? Because it’s a fundamental driver of big data project success. You may think that clear requirements and IT-Business alignment is obvious. A “no-brainer.” Not so fast. One analyst firm says that by 2018, 90% of deployed data lakes will be useless. Why? Because they’ll be “overwhelmed with information assets captured for uncertain use cases.”

A well-defined, high value use case is absolutely critical to your team’s ability to deliver big data success. What’s a good use case? Let’s start with what’s NOT a good use case. Many of your peers have stumbled into these, embarking on an uncertain or low-value mission that all but ensured disappointment.

  • Ad hoc reporting – This is a style of information access, not a solution to a real business problem. What’s the value? Reduced reporting cycles for IT? That’s not a game-changer and won’t make your company more competitive.
  • Integrating social media with CRM data – This is a mechanical description and starts with where the data’s coming from, not with what a user wants to get out of it. What questions need to be answered by combining this data and what value will that deliver?
  • Big data sandbox – This is a friendly name for a doomed-to-fail data lake where people pretend that they’ll throw a bunch of information into one place and “watch the magic happen.” As demonstrated in Neil’s paper (link), “build it and they will come” didn’t work in data warehousing 1.0 and it doesn’t work in big data analytics.

Neil’s paper offers a lot of practical advice. Here are some tidbits:

  • Consider how you will assist your organization in adopting new processes born of learning from big data analytics.
  • Rather than starting with a hypothesis and trying to gather data to support it, try to “listen to the data” with an open mind.
  • Think about how you’ll address resource constraints if you have them. You won’t be able to do more with new technology until you can put resources behind it.

Neil is insightful, pragmatic, and occasionally cynical, but always with a great sense of humor. If you can answer all of Neil’s 10 Questions to Answer, with your business users and IT professionals “on the same page,” you have a great shot at success with your big data analytics project.


Join Datameer and Cloudera on June 21 for our upcoming webinar; with Datameer 6, we are completely re-imagining the user experience for modern BI, helping you deliver more results to your data-hungry business. Register now:

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