You may have been successful with a center of excellence for your business intelligence or analytics practice. But big data success requires a different approach, a bus.

Jessica Davis, Senior Editor

February 28, 2017

4 Min Read
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 More Data & Analytics Live at Interop ITX
More Data & Analytics Live at Interop ITX

So you want to invest in big data capabilities? You may be asking yourself who should be on the big data team, and what project should this team work on first? How do you put together the capability -- the team -- to tackle the big questions?

Organizations may find themselves hung up on these types of questions, whether they are just starting out with big data or even if they already have projects underway. Tamara Dull and Anne Buff recommend turning around the way you think about your organization's big data capability and staffing if you want to succeed in big data.

They should know. They've been thinking about how to succeed with big data projects for years.

Buff is Business Solutions Manager and Thought Leader for SAS Best Practices. Dull is Director of Emerging Technologies for SAS Best Practices. Buff and Dull spoke to InformationWeek in an interview about how organizations can avoid the stumbling blocks that trip up so many when they put together a big data practice, and they'll also address the topic during a session at Interop ITX, designed to help any organization that is starting or in the middle of a big data project.

"Big data is not new," said Dull. "It's a lot of data that we've had for decades, and we've been dealing with it already. What's new is all these technologies that have come on board -- a lot of them are open source -- that enable the capabilities of mixing and matching all of our data. You can take your social media data and mix it up with your CRM data and mix that up with your sales records.

"This is not BI 2.0," Dull said. "This is a bit different."

These efforts don't replace your BI and analytics programs. This is "in addition to," Buff said.

Part of the problem that organizations encounter when they start with big data is that the factors that lead to success with these projects are different from the factors that helped people succeed with business intelligence. Business intelligence and analytics practices are often housed in centers of excellence. But you don't want or need a big data center of excellence. What you need is a bus.

The big idea is that you need a different mix of people on your big data bus for each project. With a bus you can load those people on when you need them for a project. They only need to ride for as long as you need them for the project. And when the project is over, the team gets off the bus. Your bus is now ready for the next project and the next group of team members.

Your successful big data team is really an ad hoc team created for each individual project. Not all members are there for the whole project. What's more, members will likely change from project to project, as well.

You'll have certain roles always represented, of course. For instance, you always need a stakeholder or executive sponsor. But your stakeholder or executive sponsor on one project may be the chief marketing officer, while the next project may call for the CTO or CIO to be the stakeholder or executive sponsor. It all depends on what the project is. That's another important aspect of the bus.

Before you decide who to invite to ride your bus for any given project, you have to ask yourself why your business is pursuing the project, and then how you plan to achieve that initiative. Only then should you ask the question "who" should be on your bus.

"Who is running the big data team is going to shift depending on what the scope of the current project is," Dull told me. "It's not a 'once and done' effort [the way a BI Center of Excellence is]. You build your big data team and the people on your bus are going to change every time you pursue a different opportunity for the company."

This approach is more Agile, Dull said, and it calls for your team to change according to the project. In turn, that also means that teams will be a mix of employees, freelancers, contractors, and external service people, too.

Some functions or even entire projects may be outsourced, depending on the project. The very nature of big data projects is that some will die early and others will go the distance, said Buff. That's why it is critical to create a structure that allows your big data practice to be flexible and Agile.

Find out more about how to Get the Right People on Your Big Data Bus during Dull and Buff's session at Interop ITX in Las Vegas in May.

About the Author(s)

Jessica Davis

Senior Editor

Jessica Davis is a Senior Editor at InformationWeek. She covers enterprise IT leadership, careers, artificial intelligence, data and analytics, and enterprise software. She has spent a career covering the intersection of business and technology. Follow her on twitter: @jessicadavis.

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