Like any good sports team, your data team needs a leader, a common goal, and group chemistry.

Ron Bodkin, Founder & CEO, Think Big Analytics, a Teradata company

January 9, 2015

4 Min Read

"Data-driven" -- the simple term for businesses that make decisions based on data -- is a popular buzzword these days… and for good reason. Numerous studies show a clear correlation between a company's propensity to rely on data when making decisions and its profitability, ability to innovate, and worker engagement.

But those findings raise an obvious question: How does an organization actually become data-driven? The answer: While the right technology undoubtedly plays a role, even the best technology is useless without the right people to apply it.

[What do industry soothsayers predict for big data for 2015? Big Data: 6 Bold Predictions]

Just as in sports, a successful data-driven team needs to not just have talented players, but the right mix of players, the right culture, and the right coach. Let's take a closer at those four fundamental needs for building your own data-driven dream team.

1. Get individual data all-stars
The first step to building your data-driven dream team is to, quite simply, bring in folks who know data. This means people who have strong backgrounds in mathematics, statistics, science, and other relevant fields. On top of that, it also means hiring and developing people with the right aptitudes -- employees who are intellectually curious, play well with others, and are productive.

Sometimes you can spot these people within your ranks, while other times you'll have to tap talent from outside the company. In both instances, though, there's no shortcut to finding the right players for your data-driven team. Just as seeing one game or looking at one statistic doesn't prove someone is a great basketball player, there's no single question or qualification that proves someone is a data all-star. You need to really get to know internal and external candidates just as a coach would evaluate a player in a try-out.

This means exploring all areas of the job and the candidate's background, including actually hearing them talk about the work they've done and the work they'd like to do. There's no one magical interview question or quiz.

2. Make sure the team meshes
As you're evaluating individual talent, you need to also take a step back and make sure you're bringing in the right mix of team members and promoting cross-functional collaboration. Again, it's the same in sports: You shouldn’t build a basketball team of all point guards, and you also shouldn't always keep your point guards working separately from your post players.

For instance, organizations need some folks who are meticulous and numbers-driven, yet also need some who are creative and throw in crazy ideas. And most of all, companies need to make sure all these people are working together. How? The best way to build team chemistry among data scientists, engineers, and domain experts is by focusing all parties on the same goal or business outcome.

This won't always be easy, as each group prefers to focus on what they know best, and there will likely be differences of opinion. As a result, baby steps are a good plan of attack. Don't start with the most complex project first. Instead, begin with low-hanging fruit, so that success will serve as proof that the collaboration is paying off and will lead to a gradual increase in demand for new cross-team data strategies.

3. Build a culture
While starting with low-hanging fruit is a smart first step for some positive reinforcement, another key aspect for success is to build a culture where failure is OK. Being data-driven is useless if you aren't exploring new ideas. And if some of those ideas aren't failing, there's a good chance people are approaching problems with an answer in mind, then simply using data to support their intuition.

To avoid that, the data-driven dream team needs a culture that promotes the ability to try new things and truly let the data speak… and the first step to that end is democratic access to data.

4. Find the right "coach"
Still, organizations need to make sure a data-driven dream team doesn't become complacent or lapse into a street ball free-for-all. This exploration of new ideas still needs to come with a game plan and a focus on business outcomes. On top of playing a role in finding the right talent and building the right culture, the manager or "coach" needs to have the business expertise to know when to call a time out if the team is getting deeper and deeper into something that's interesting but not necessarily a good bet for helping the business move forward. Finding the right leaders and mentors for the team is critical for success.

This is because, ultimately, you shouldn't be data-driven for the sake of being data-driven. Instead, organizations should embrace the buzzword because of the benefits: more innovation, more engagement, and more profits.

Just 30% of respondents to our new Big Data and Analytics Survey say their companies are very or extremely effective at identifying critical data and analyzing it to make decisions, down from 42% in 2013. What gives? Get the The Trouble With Big Data issue of InformationWeek Tech Digest today. (Free registration required.)

About the Author(s)

Ron Bodkin

Founder & CEO, Think Big Analytics, a Teradata company

Ron Bodkin is the founder of Think Big, a Teradata company and provider of independent consulting and integration services specifically focused on big data. The company's expertise spans all facets of data science and data engineering and helps customers to drive maximum value from their data analytics plans. Previously, Ron was VP of Engineering at Quantcast, where he led the data science and engineer teams.

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