Five steps for building an in-house data analytics team

14 June 2018 4 min. read
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Data analytics is essential for companies to be more impactful and increase their operational excellence. Having the internal capacity to utilise available data can even make or break a modern-day firm. French management consultancy Sia Partners has outlined five steps for clients keen to get ahead of the curve and build an in-house data analytics team.

Sia Partners data experts Jeroen de Laat – a Senior Manager – and Nabi Abudaldah – a Supervising Consultant – have put together a five-step guide for organisations seeking to build their own data analytics teams. The guide is inspired by substantial field experience, working closely with clients to build successful and stable teams that thrive over time. 

An expert guide

The first step is to start with data challenges, not data platforms. “Most software vendors and advisory firms will tell you to invest heavily in platforms before actually practicing data analytics,” says De Laat. This leads to huge spending on revamping the IT infrastructure before the team even understands the particular problems they are trying to overcome.

“Start by framing your strategic challenges and goals first,” advises De Laat. “Analyse where you are, and where you want to be as a company. Or rather, where your customers and other stakeholders are and where they would like to be.” Most importantly, try to refrain from any large (technology) investment decisions at this point. “More often than not, you don’t need an infinitely scalable and state-of-the-art technology solution to be successful.”Five steps for building an in-house data analytics teamOnce the strategic challenges are identified, the next step, says Abudaldah, is to find and organise internal talents. “Free up those individuals from their current day-to-day responsibilities to work on data analytics cases. Simply hiring externals to ‘do the job’ isn’t desirable because of their relative distance to your problems and the temporary nature of their employment.”

Leaders will typically have an idea of who in their organisation is passionate about analytics and has some degree of competence. “Design a small and mixed team of business and technology specialists and give them clear goals and timelines,” says Abudaldah.

Once the in-house talent is identified the third step is drafting in external support. “While some internal talents might not fully be able to get out of their earlier responsibilities for example, external talents can go full-speed ahead to make a successful start.” This rounded approach helps avoid false starts and brings fresh perspective to the team.

With the data analytics team set up and raring to go, the fourth step is to identify a suitable challenge to tackle first. “Don’t take on your most complex or anticipated data challenge first,” says De Laat. “Setting up a team is already a challenge. If you also take on a highly complex problem to solve, it might become rather a burden to any new team.”

“Start with low hanging fruit,” Abudaldah adds. “Don’t assume that small problems are too easy or even boring to solve. In practice, it always turns out the other way around.” Both consultants advise starting with small challenges which, handled successfully, have immediate impact. This way the team can under-promise and over-deliver, cultivating a solid reputation and the legitimacy to pursue larger and riskier projects at a later stage. 

As with many complex organisational endeavours, the final step is scaling and professionalising the new environment. “At this point, you know what challenges are valuable to solve, which talent gaps you have, what tools you need, and which processes are required to manage these new ways of working,” says De Laat. “This is a far better position to be in when it is time to professionalise, rather than spending big earlier but without a clear picture of the role of the new data analytics team.”