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Three Common Mistakes Scoping a Big Data Project

February 25, 2017

Three Common Mistakes Scoping a Big Data Project

In Big Data as in any other technology, theory is one thing, and practice another. You've done your homework on Big Data, read the white papers, and maybe even approached a few other VARs about collaborating on custom Big Data solutions. Now you're ready to start scoping a Big Data project with an actual customer. But before you dive in, there are some pitfalls you should know to avoid. Here are three common mistakes made when scoping a Big Data project.

1. Failing to start with a clear scope

Big Data is powerful, inevitable, and exciting, and at the start of a Big Data project, it can be easy to see it as a cure-all for an enterprise's business intelligence woes. Suddenly your customer will be able to extract marketing insights from social media, understand exactly what's happening in call centers, and extract actionable knowledge from all their silos of customer and transaction data!

Not so fast. Like anything else, Big Data implementations require clear use cases for effective design and deployment. Before you can recommend the most appropriate hardware, software, and supplementary services to your customers, you and they need to identify the actual business problem that the technology needs to solve, as Izzy Sobkowski, CIO of the New York City Department of Health and Human Services, told GovLoop. Having a clear scope is essential to successfully scoping a Big Data project.

2. Failing to put the right team and support together to make the project happen

The success and failure of any project hinges on the team that's tasked with it and the support system behind it. That holds true for Big Data, too. As you work with your customer on scoping a Big Data project, be mindful of what kind of team they need to bring on board. Will it include data scientists or  experts in Big Data analytics? How about additional network or systems administrators? Does their current IT team possess the needed expertise? And will anyone on the team need additional training or support that you can provide? You've got to know what people and resources your project needs before it can succeed, as IT Business Edge points out.

3. Failing to develop an effective architecture and procure the right hardware, software, and applications

Here's where #1 becomes critical. The Big Data ecosystem is already a large and complicated one, with multiple vendors and multiple developers offering multiple solutions for all levels of the Big Data stack. Which hardware and software are right for your customer's specific business use case? When scoping a Big Data project, you've got to not only know what your customer's need is, but how to solve that need for them. Distributors like Ingram Micro put a universe of options at your fingertips. You bring value to your customers not only through your specialization, services, and economies of scale, but also through your ability to sift through the options to put together exactly the right solution for them. If you can't, then all your work scoping a Big Data project will end up wasted.

Luckily, help is available if you need it. The Big Data ecosystem may seem overwhelming, but Ingram Micro's expert training, resources, and community can get you up to speed on the Big Data technologies you'll need.

What should VARs keep in mind when scoping a Big Data project? Let us know in the comments.