Well OK, so there is an "i" in science, but being a data scientist is certainly not a lonesome job. Engagement with other team members is essential with data analytics work, so you never really work in isolation. Without the rest of the team, we would fail to ask all the right questions of the data so as to solve critical business issues. The hard-earned insights produced would also not be used or understood by the organisation we’re working with.

So, what are the key ingredients of a data science team that you should be looking for? It is, in fact, a group of employees with quite diverse roles. My SAS colleague Jennifer Nenandic highlighted these in her recent blog post, How to build a data science dream team. I’ve summarised the star players here.

The translator (AKA, the business manager)

The subject matter expert has lots of business acumen: an understanding of the issue from a business perspective. As the team focuses on one analytics effort after another, the translator’s role will change. Their role is to help the rest of the team understand the business context of the challenge. They are involved from the beginning of the project and helping to set the scene, right through to the end result when the results are presented. With the business context in mind, they can also help prepare and present the results as well as the return on investment from the project.

The analyser (AKA, the business analyst)

This person is involved in the team in a little more detail, and creates the link between the business context (perhaps from the translator) and the business data and source systems, to assist with data preparation and initial data exploration phases. Just like the business manager, they are a crucial link in helping the statistical modeller to understand the nuances and business definitions of the data source.

The preparer (AKA, the data management expert)

Next is the preparation and integration of the various data sources, and possibly even deploying the final analytics model. The team remains incredibly important as the preparer needs to make sure they partner with the analyser to ensure that all-important context is considered. The preparer can also arrange for the long-term use of the model, by creating a repeatable analytics model for use on a regular basis.

The modeller (AKA, the statistical modeller)

A role with many possible names – data miner, statistician, data scientist, or analytical consultant. In essence, the statistical modeller is responsible for performing the advanced analytics methods that build a statistical model. As the analytics initiatives begin to expand into higher levels of analytics (such as data mining, predictive analytics, and optimisation) and into areas showing more and more business value, this role is going to be crucial to the success of your team.

The modeller will be a core part of the team from the beginning of the project right through to the end. He/she will be working closely with the preparer and analyser to learn from the context they offer. Those discussions have to continue throughout the project, so the whole team can garner insights to take back to the business.

Add an IT specialist?

Interestingly, I also saw a LinkedIn discussion around Jennifer’s blog post that suggested an additional role: IT specialist. The group response seemed to suggest that these skills will help integrate the project into the rest of the company which I agree is very important. What seems to be the underlying theme of all the feedback is how crucial the management and leadership of the team is, with someone who can understand both scenarios.

Setting up for success

Every data analytics project is unique, so every project will have a unique team set-up. Depending on the project and resource available you might have one person covering more than one role at any one time, or conversely, multiple people working on one specific role. My advice on preparing your team effectively? Take time to establish what roles you will need as early as possible, and then form the team with the right mix of skills to fit the roles required, whether taking on multiple roles or sharing out the role between more than one person.

Finally, make sure the analytical insights can be correctly turned into appropriate actions and business decisions. This requires someone to communicate the insights back to the business, which is probably best suited to the person in the “translator” role. This pitfall was highlighted in my last blog post as the major barrier to analytical success.

You can find out more about the role of a data scientist from our recent research into What Makes a Great Data Scientist.