How To Build an Analytics Team

Analytics are in high-demand and many companies are answering that demand to fill the need. Even at Pace, our analytics team is growing and we are navigating the waters to build a dynamic group—hiring the right people at the right time. It can be difficult to build a team though, which includes finding the right technical components along with the right people.

While at the Strata + Hadoop World conference in NYC, I attended a presentation given by Q. Ethan McCallum and Brett Goldstein on the topic of “Building Your Analytics Shop, Step by Step.” After speaking with several companies to learn their approach for building an analytics shop, McCallum and Goldstein compiled several effective practices to share at the Strata + Hadoop World conference.

Diversity

First and foremost, you must gather a diversified team.. A good analytics team is comprised of a combination of talents and skill sets that make the biggest impact when combined. A team needs both technical and business knowledge in order to gather data and form insights.

For example, one of the hardest parts of working with data is gaining direct access, and this step can require an individual with highly technical skills. After gaining access to the data, gathering insights and meaningful analysis may need to be tackled by an individual who specializes in marketing or business. The key to a successful analytics team is to ensure that each team member brings a unique skill set to the table, which also enhances the capabilities of their team members.

Wait

Don’t be in a hurry to hire. Spend time determining your company’s specific needs and then gather the right people to fill those defined roles. Each team should look and work differently depending on the environment. Rather than looking to hire people with a specific set of credentials, look to hire a smart, curious team of people that will work in unison to tackle a problem.

Open Source

Building an analytics practice doesn’t have to be expensive. Of course, there are a variety of pricey products on the market, but you can also find many free options through open source software. For example, R can be used for statistical computing and graphics and can even prepare HTML presentations. Pandas, developed by Wes McKinney, allows for data analysis in Python programming language. And last but not least, Nordstrom Data Lab) was designed for simplicity and productivity.

Using open source software can seem challenging, if not impossible, without coding experience. But fear not, there is also a vibrant online community that can assist in learning these languages. Rather than filling your toolbox with expensive products, spend time with open source programs to see which is the right fit for your company.

People

Once companies get started with analytics, many can become excited after implementing only a few lines of code. However, it can be a red flag if companies assume their new computer programs can replace employees. While computers are beneficial, they can’t do everything. Computers are great at reporting, but they can’t gather business insights and make strategic judgment calls. It’s also important to keep in mind that computers need monitoring. Companies are mistaken if they think they can implement a program, and forget it. People always need to be part of the team, monitoring and providing analysis and actionable insights.

Building an analytics team can be time-consuming, but it’s well worth it when forming the right team that will yield long-term results. Don’t be afraid to be malleable and to learn from your mistakes along the way. If you are interested in joining the Pace analytics team, check out our open job listings.

What advice do you have on forming a great analytics team? Share your thoughts in the comments below or point us to tools and resources that are open source!

By Sarah Ingle

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