How to Create a Data Team

Data Basics, Scaling AI Lynn Heidmann

Hype around AI means that, more and more, businesses are dedicating huge sums of money to assembling large data teams and setting them loose, hoping they produce results on their own. Often, they are disappointed; so how can organizations thoughtfully build not just data teams, but productive ones?

Group of three people at a coffeeshop, two with laptops and one reading an article, there are notebooks and calculators too

Leverage Existing Skills

While it’s true that the title of “data scientist” hasn’t been around that long, relatively speaking, that doesn’t mean the role didn’t exist before in other forms. Some industries (like supply chain and logistics or banking and finance) have data-savvy staff that have been working with data for years… and they know the business side.

When looking to spin up a formal data team, don’t think that it’s just a matter of hiring the best external data scientists that money can buy. It’s also a matter of leveraging existing staff from across the company, whether or not they have formal training in data science. They can add their existing knowledge of processes and people to guide the team and make sure they are connected with all the lines of business. Upskilling employees with business profiles to enable enhanced analytical activities in this way is a critical element to building a successful data team.

In fact, hiring people — no matter how skilled in machine learning — that are disconnected from the rest of the company will only fail to produce magical data-driven insights and projects, but they also might go in directions that don’t make sense or align with the larger business objectives at all. 

Strike a Balance

In general, the most successful data teams (and, some argue, the most successful people) strike a harmonious balance between having fun and expressing creativity while thoughtfully evaluating the risks and rewards involved.

Part of what makes young, hopeful data teams successful is that they are eager and nimble; they have the will to succeed and they’re still small enough to be agile and think outside the box. The downfall of a young data team can, on the other hand, be too much fun and creativity and a lack of discipline, leading to risky solutions that hurt the business they are trying to help. Similarly, building a data team that is too hindered by rules and restrictions about what they can and cannot do probably won’t develop the results that the business is looking for, either.

Infographic with data team characteristics and how to nurture your data team from infantile to mature

Create a Larger Data Culture

In addition to this overall balance between creativity and risk taking for data teams, it’s important for the data team to evangelize a data culture throughout the entire company. Creating a data culture means sharing and encouraging the following ideas widely:

  • Being data-driven and making data-driven decisions is a shared responsibility among everyone at the company (it’s not just the responsibility of the data team). This can manifest itself eventually through the development of a self-service data team.
  • Automation, predictive analytics, machine learning, and AI are a positive and important evolution for the company, not a way to eliminate jobs or responsibilities from other employees.

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