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Build a data analytics team in 5 steps

Many of our clients are building analytics teams and have involved Sia Partners for assistance. Together with our clients, we’ve learned some valuable lessons that could help you avoid waste in your journey to setup a successful in-house data analytics team. While there are many intermediate steps and challenges, we’ve boiled them down to 5 broad steps as a guideline.

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. Much budget is spent on building technology platforms and reinventing large parts of the IT infrastructure before any problem has been truly understood. These - often large - investments end up not solving any of your problems nor making your organisation better off.

Therefore, start with your most urgent data challenges first (low hanging fruit). Analyse where you are, and where you want to be. Or rather, where your customers and other stakeholders are and where they would like to be. Focus on those issues, analyse these issues and then make rational investment decisions on what you need after you have discovered what the most urgent or valuable challenges are. More often than not, you don’t need an infinitely scalable solution that is state-of-the-art, including that huge price tag.

Let’s assume you have your strategic challenges defined, what is the practical next step?

Find and organise internal talents

Making analytics core to your organisation means organising your internal talents first. 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.

So, start by finding and organising your internal talents first. You probably have at least some people already within your organisation that are passionate and competent in data analytics. Design a small and mixed team of business and technology specialists and give them clear goals and timelines within a clear organisational structure of management and reporting.

Once you have a team of talented people organised, how do you make sure it works?

Get a flying start with experienced support

Now you have found a great team, support may be valuable. Change within large organisations comes with many challenges. To avoid a false start and quickly go from an initial group to a respected team, do ask for external help.

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.

Now your team is ready to go, what challenge should they tackle first?

Start with low hanging fruit

When you start out in data analytics, don’t take your biggest strategic challenges as your first challenge. Because setting up a team and defining how to work and where to get all inputs from to create outputs, 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. Also, don’t assume that small problems are too easy or even boring to solve. In practice, it always turns out the other way around.

Before pursuing the most ambitious problems and risking to over-promise-but-under-deliver, try turning it around. Start with small challenges and a solution with immediate impact that could confirm believers and convince non-believers of the true power of analytics. That is when your new team under-promises and over-delivers. A small and successful project gives legitimacy to undertake larger and more risky projects after.

Now you have solved a small problem in data analytics, what’s next?

Start scaling and professionalise the environment

While many would advise to go professional from day one, we advise it as a final step. Now that your team is able to tackle real problems for the organisation and its stakeholders, it is time to think about scaling up and professionalising. At this point, you know what challenges are valuable to solve, which talent gaps you have, what tools you need, which processes are required to manage these new ways of working, you have a much clearer picture of what needs to happen to make this process more robust.

Your lessons learned have been useful by-products of your entrepreneurship into data analytics and are also perfect inputs into a proper design of a process to scale and streamline your data analytics efforts.

We wish you all the wisdom and success in your data analytics journey!


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