As organisations increasingly rely on the insights gleaned from big data to make critical business decisions, the role of the data scientist has become crucial.
An experienced data scientist or effective data science team can turn data into actionable insights, which can make the difference between overtaking competitors and lagging behind.
“Experienced data scientists are a rare commodity and organisations should snap them up if they can find them,” says Ross Farrelly, chief data scientist, Teradata Australia and New Zealand.
“Creating a data science team is an important initiative for many organisations. However, it isn’t simply enough to employ a team of data scientists and leave them to it.
“To build a successful team, specific elements must be in place. You need to consider what you want your team to focus on, how you want them to perform, and how to get the most out of them.”
Going forward, Farrelly has identified seven traits of successful data science teams:
1. Executive sponsorship
Like any project, the data science team needs executive sponsorship for legitimacy.
For Farrelly, a good executive sponsor will serve as the champion and passionate advocate for the adoption of data analytics, as well as advocating for the data science team.
2. Map outcomes to core business objectives
“Data can be exciting and fascinating,” he says. “It can be easy for data scientists to get carried away with the options and take projects into new directions.
“However, these projects must match up with actual business objectives or risk failing to deliver a return on investment.”
3. Manage communications
Farrelly believes the data science team will only be able to gain valuable insights and explain them effectively if there are strong communicators in both the data science team and the business unit.
“It can be worth designating a representative from both teams to make sure communication flows clearly between them,” he adds.
4. Embed data scientists into the teams they support
“There is no better way for a data scientist to understand the business than to be embedded in the team needing insights,” Farrelly adds.
“Isolating data scientists from the business only serves to make it more difficult for them to understand the business objectives they’re supposed to be helping to achieve.
“Instead, they should sit with the team, absorbing knowledge and information that may not otherwise be apparent.”
5. Let the data scientist become the customer for a day
If the data scientist knows what customers want and how they interact with the organisation, Farrelly says they can be even better-placed to deliver value.
“Embedding the data scientist with the customer, even for a day, can yield valuable understanding,” he adds.
6. Build a team with varied skills
As Farrelly explains, diversity brings new ideas, approaches, and ways of looking at existing problems.
“It’s important to find data scientists with a range of different skills so each can bring a unique value to the team,” he adds.
7. Understand how to motivate the team
Farrelly says data scientists are often more motivated by intellectual challenge and peer recognition, although some may be more interested in financial rewards.
“Like any employee, it’s important to find out what motivates the team and then deliver that motivation for best results,” he adds.