Designing analytics teamsMany of my recent ‘coffee conversations’, with leaders, have focussed on designing analytics teams. It sounds like many of you are wrestling, with how to design & resource the team you need, for 2018 and beyond.

For that reason, as the days begin to lengthen & we can start to dream of spring, we will focus on teams. Our theme for this month is designing and developing your teams. In this post, we focus on analytics or Data Science teams.

We have previously shared on the benefits of using a competency framework, as well as some brief thoughts on options for team design. Together with the results of our poll, on how you are resourcing your teams, this content has proven your interest in such advice.

To start of our team-focussed month, let me share some interesting blog posts others have published on this topic. Here are a few to help you quickly read around this important subject…

Designing analytics teams: Secrets of successful teams

To start us off, here is an interesting article from CIO magazine. In this post, Jeffry Nimeroff (CIO at Zeta Global) and a couple of other CIOs, share their advice on secrets of a successful analytics team.

There is much I agree with here. The value of diverse skillsets in such teams. Plus, the need for ruthless prioritisation, data quality focus and ongoing training. However, I disagree with the benefit of such close integration with IT team, as I have found that can lead to what I call ‘projectisation’. By that term, I mean the management of workload as projects, with too much inflexibility & insufficient creativity.

But, see what you think. Would these tips work for your analytics teams?

The secrets of highly successful data analytics teams

Effective data analytics can give companies a huge competitive edge, because business managers can gain new insights into trends and customer behaviors that might not otherwise be possible. To get the most out of their information resources, enterprises need to have a strong analytics team in place.

Designing analytics teams: IT-centric or not?

Continuing on the topic of closeness (or not) to IT department, bring me to my next post, from AltexSoft. They are a travel & hospitality technology consulting company; to use their own terminology.

They clearly have some experience in getting Data Science teams to work. In this post they share a useful perspective on  both the technical skills required & alternative design options. In particular, they compare 3 options: IT-Centric, Integrated & Specialised.

I’ve seen most success from businesses who pilot with a specialised team, embedded in a business team with relevant challenges. But a strong relationship with IT, not least to enable greater access to data, also matters. As such teams mature, their relationship with IT tends to become more formalised. So, this advice is worth

considering.

Similar considerations came up as a topic of conversation at last year’s Data Insight Leaders Summit. Our research for that, showed that the majority of de-centralised Data Science teams sat within IT. However, the majority of respondents were creating Data Science/Analytics centres of excellence.

How close are your Analytics or Data Science team(s) to your IT department? Have you considered the overlap in roles & skills required, as outlined in this article?

How to Structure a Data Science Team: Key Models and Roles to Consider

If you’ve been following the direction of expert opinion in data science and predictive analytics, you’ve likely come across the resolute recommendation to embark on machine learning. As James Hodson in Harvard Business Review recommends, the smartest move is to reach for the “low hanging fruit” and then scale for expertise in heavier operations.

Designing insight teams: pitfalls to avoid

For my next offering, let me share this advice from Lawrence Freeman. Lawrence has the trendy job title of Big Data Evangelist at Kubrick Group. In this post he shares his advice on 4 pitfalls to avoid when building your data analytics team.

Once again, my own experience accords with much that Lawrence proposes. Timing for recruitment, clarity of responsibilities & GDPR are all critical for successful teams.

I have less experience as to the importance of agile methodologies, but together with design thinking, they have helped others. In fact they were a key theme of last two FinTech CX conferences at which I led debate on customer insight.

So, only a short post, but useful pointers as to potential pitfalls to avoid as you design a team & approach that will work for your business.

Building a Data Analytics Team | Top Pitfalls | Kubrick Group

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.

Designing insight teams: an apprentice’s experience

There is nothing like asking your team, how a structure works for them, to get insight into issues you may well miss.

So, to close out this collection, of advice when designing your analytics team, let’s here from a team member.  Close to my home, over the last couple of years, the UK’s Office of National Statistics, have been building their Data Science Campus.

This has been a great development, both in terms of capability at ONS & increased sharing & data visualisation of their work. So, alongside their other content, which I often share on Twitter, I was glad to see this post. In it, Gareth Jones (one of their recent apprentices) shares his experience of working there.

It is a fascinating insight into two recruitment opportunities too often overlooked in debate on building such teams. Firstly, Gareth is not a graduate, let alone a graduate in Data Science. Rather, he has A levels in Maths., Physics & Chemistry, plus is still looking to continue his education. Under graduate potential can be too often overlooked, especially in those who have proven their drive to learn.

Secondly, Gareth is well past student age. He is a full decade older than other apprentices who have gone their straight from completing A levels. Mature students should be considered by more employers. Plus, with more & more experienced 50+ people looking to start a second or third career around that time, it makes sense not to be ageist.

It is so encouraging to hear the progress Gareth has made & the aspects of ONS team & environment that have helped him. So, I hope his personal story also helps you consider the human aspects of your plans:

Developing Data Analytics at ONS – Apprentice profile

Last year we recruited the UK’s first data analytics apprentices into our Newport-based multi-million pound Data Science Campus to work on some of the most exciting analytical and policy questions facing decision-makers and wider society. Here, we shine the spotlight on Gareth Jones, a Data Analytics Apprentice.

Designing analytics teams: What are your plans for 2018?

I hope those 4 posts helped you think over both options to consider & pitfalls to avoid, when designing analytics teams.

What about you? What are your plans for 2018? Are you considering any aspects or design options that haven’t been mentioned in these posts? If so, please do share your experience with our other readers.

I look forward to sharing more, on the topic of teams, this month. Have a productive time, working with your team.