The Teradata Data Analysis Index revealed that 88% of companies use analytics to make decisions based on data, but 86% have no plans to hire a data scientist.

The results suggest these organisations either already have a data scientist, which is unlikely, or they don’t understand the benefits it could bring to the business.

Role of data scientists

Data scientists can identify business problems and the right data to solve them as well as communicating the solution back to the executive team.

The Teradata Index found that 97% of organisations gather data from internal systems, 80% gather data online and 74% gather third-party data, so it’s clear that most organisations have access to a broad range of information. The survey results show that companies are mostly analysing this data to improve the overall customer experience. More than 3/4 (76%) of respondents are looking to reduce customer service issue and complaints, and 53% aim to improve customer loyalty and optimise the mix of marketing initiatives.

Organisations that consider appointing a data scientist or a team of data analysts may find that they can derive much deeper and more varied insights from their data. This will let them recommend improvements in areas of the business such as supply chain and logistics, product or service development, or customer acquisition.

As organisations increasingly rely on the insights gleaned from big data to make critical business decisions, the role has become crucial. An experienced data scientist or effective team can turn data into actionable insights, which can make the difference between overtaking competitors and lagging behind.

Experienced data scientists are rare and organisations should snap them up if they can find them. Creating a data science team is an important initiative for many organisations. However, it isn’t enough to employ a team and leave them to it. You need to consider what you want them to focus on, how you want them to perform, and how to get the most out of them.

7 traits of successful data science teams:

  1. Executive sponsorship

Like any project, a data science team needs executive sponsorship for legitimacy. 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.

  1. Map outcomes to core business objectives

Data can be exciting and fascinating. 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.

  1. Manage communications

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.

  1. Embed data scientists into teams

There’s no better way for a data scientist to understand the business than to be embedded in the team needing insights. They should sit with the team, absorbing knowledge and information that may not otherwise be apparent.

  1. 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, they can be even better-placed to deliver value. Embedding the data scientist with the customer, even for a day, can yield valuable understanding.

  1. Build a team with varied skills

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.

  1. Understand how to motivate the team

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.