Top Pitfalls to Building a Data Analytics Team

building a data team

Building a data analytics team which is effective can be fraught with challenges right from the onset. Leading up to our event with one of our partners Tableau, on ‘Building a Modern Data Analytics Team’, we will be highlighting some of the top pitfalls – to help you with building a data analytics team more efficiently and avoid what potentially could be very costly oversights. At a high level these mainly revolve around ensuring good timing as well as defining boundaries of ownership for important topics like Change Management and GDPR.

1. Senior Recruitment Timing

This is finely balanced, it is important to do a really thorough organisational design and to define the structure and roles required when building a data analytics team. However, due to the lead-time of recruitment for senior people, it can be easy to end up with lots of doers before the management has even arrived. Needless to say this can easily result in high costs with low output and direction for that business unit. Ensure you have your senior people in place at the start of the project, build your team of doers around them not the other way around.

2. Get an Agile coach

Whilst certainly not the only approach to the Software Development Life Cycle (SDLC), Agile is very well established and often seen as the standard approach for software development these days. But how far does your buy in go? Are your analytics teams trained in Agile? Are they committed? or just passively involved? One Head of Delivery for Analytics at a large UK manufacturer commented: “Agile implementation was important for us from day 1…. “Whilst we have implemented Agile, we only got an agile coach 3 months into the project – this felt early at the time but in reality we would have benefited if he was in the team from day 1 to cement processes from the start.”

3. Know your boundaries

Change Management could be so intrinsically linked to data analytics that the one could and should complement the other, however the question to ask and potential pitfall is; which should drive which?

Once that culture has been determined, it is important that the boundaries of which drives which be clearly defined. An excellent post written in the Harvard Business Review titled Data can do for change management what it did for marketing highlights very well that each are experts in their own field; Data Analytics and the age old Change Management teams both have a wealth of expertise which cannot be undermined by the other, nor should one be prioritised over the other. Although much of the outputs of data analytics can be used to underpin and drive decisions behind change, to allow for change management to be completely data driven may be a very costly mistake.

It is so important to draw clear boundaries for where the responsibilities of the team start and end for your business. Especially where analytics is used for transformation, are the analytics team responsible for the change itself or just the insight and tools? Should the change process be driven by those people who know how analytics can be used to drive change (i.e. the analytics team) or by those people who know how change should be delivered (i.e. Change Management & Transformation experts).

You have to ensure that all teams know their responsibilities and boundaries, and ensure that this carries no ambiguity in these important business operations.

4. Data Governance

GDPR, This is not mere paperwork or bureaucracy, and no business can afford to ignore regardless of size. To thoroughly understand data governance and clearly implement assignment of ownership is crucial – not only before May 2018, but right now. Some questions which must be answered in order to avoid falling foul of breaching GDPR compliance are set out below:

  • To what extent must Analytics take responsibility for data governance?
  • To what extent will Analytics need to resolve inevitable issues?
  • To what extent should it be owned by the business?

One thing is for sure – the return on investment from building a quality data team is undisputed.

We hope this raises awareness and brings to light some of the common pitfalls to building a data analytics team the right way.

We really look forward to discussing these and many other pitfalls in much greater detail in our event join hosted with Tableau, next week on the 27th September. Watch this space for more gems to be surfaced from the event.

Lawrence Freeman

Lawrence has over 15 years of hands on experience developing and actively contributing to the data community. He is currently Big Data Evangelist at Kubrick Group where he oversees Big Data agile projects as well as training and mentoring teams of developers in the bleeding edge of scale-out technology.

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