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Why Non-Data Scientists can lead Data Science teams

I recently discovered a LinkedIn debate over whether or not non-data scientists could lead data science teams. The debate was polarised between those arguing for technical knowledge & those who focussed on leadership skills.

As well as being fascinating and relevant, I was glad to see one voice arguing for leadership. Andy Sutton is Head of Data & Personalisation for Endeavour Drinks in Australia. He was also my right-hand man for many years, while we created and lead great data & analytics teams.

Andy is both a skilled statistician & coder, as well as a very able leader & marketer. So, he is a useful voice to hear on this debate, as he can speak from both sides of the technical/leadership divide. Over to Andy to share his experience.

My Experience of Non-Data Scientists as Leaders

I felt compelled to write my first ever article following a conversation I was part of on LinkedIn last week. To cut a long story short, I commented on a post which was berating data science leaders who don’t have a data science background.

I didn’t agree with the perspective that only data scientists can lead a data science team, but faced a number of return comments suggesting I was wrong. This got me thinking about the world of leadership, management and followership and how this relates to my own career.

I’ve been fortunate to work for some amazing leaders in my roughly 20 years in data and analytics (Paul being one of them). On the surface, these leaders haven’t had a huge amount in common. I’ve worked for marketers, sales, directors, commercial managers, IT leaders and strategists “ so it’s fairly obvious that a technical background in data and analysis isn’t a prerequisite for me to be led by someone so what does it take?

What effective leaders need to offer their teams

This isn’t exhaustive but the beginnings of a list to which anyone reading this can add. Would be interesting to know whether we all have the same views “ or if different aspects appeal to different people

1) Vision

The ability to link the work of an individual to the work of the business. If you can make everyone in the business feel like they’re critical to the success of it then you’re on to a winning formula.

Too often I’ve seen teams with low morale because their role is seen as a necessary evil or BAU rather than talked up as contributing to the strategy. Is it any wonder people don’t feel motivated or inspired in their role if their leader doesn’t see their role as important.

2) Trust

Leaders who trust their team members employ them and then get out of their way to let them do their roles. Easier to say than do though! If you micro-manage then teams & individuals will lose the ability to think for themselves.

As an example, I once had a micromanager. I gave up trying to do my best work because I knew that however good a piece of work was she would find a way to add value to the output. So I started delivering at about 75% and left her to do the remaining 25%. Far better would have been to trust me to deliver the output and get the result she wanted.

3) Humility

The ability to know what you don’t know and ask your team to fill the gaps for you. Servant leadership is the new buzz word as we all move into an agile world “ but the best leaders have always known this.

I once had a leader who told me the first 6 months of leading a team he had no idea how to help them as they were far more technical than he was. He realised after 6 months that wasn’t his role.

His role was to sell what they did to the wider business, get positive feedback for them, generate more work and help them deliver it. He wasn’t there to jump in and help them “ and he didn’t know how to anyway.

What Else Do You Need from a Data Science Leader?

Even as I write this I’m sure there are far more bullets that others will think of “ but I wonder if technical ability will be in any ones list? I’m sure when I started out in analytics I would have put a technical manager as one of the bullet points “ but times have changed.

Part of this relates to the fact that the analytics world is moving so quickly in the age of Big Data, machine learning, deep learning, AI and the like that very few people with the technical skillset across all of these disciplines would also have the experience, relationships and vision to lead great teams.

This is a very wide generalisation, but some of the most technically competent analysts I’ve worked with have no desire to lead teams “ and would much rather be building and deploying their own models or algorithms. I’m sure there are data scientists and analysts who make great data science leaders “ but I’m equally sure there are data science experts who make useless data science leaders. Being a guru of data science is not a pre-requisite in my mind.

What do you think? What type of leader is needed?

Diolch yn fawr iawn (Nice to see Andy hasn’t lost touch with his Welsh identity down under’).

Paul has over 20 years experience of leading teams to generate profit from analysing  data. Over 13 years he’s created, lead and improved customer insight teams across Lloyds, TSB, Halifax and Scottish Widows. He’s delivered incremental profit of over £10m pa and improved customers’ experiences.

One of his specialisms is combining different technical areas into one holistic team, to deliver insights that can be acted upon to realise value.

Leading Customer Insight at the UK’s largest “Bancassurer”, he changed the role of Insight from being a reactive service into proactively shaping strategy. This meant guiding product & marketing teams, pioneering the use of behavioural economics and being a voice of the customer to the CEO and his top team.

Paul now works with organisations to help them realise more value from their customer insight teams. He coaches & mentors insight leaders, measures capability, guides strategy and transfers knowledge.

His work leaves teams enabled to drive valuable actions from insight themselves.

For more information about Paul or any of our content, please use Contact.

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