Can a non-expert lead a data team?

Most would say no. But is that feasible in your situation? What should you do?

Can a non-expert lead a data team?

4 minute read.

We say, “Yes” but …

Recently, we worked to help an International customer to find their data lead. We felt they needed someone to take ownership of their data management and data strategy – an internal hire before relying on external agencies like ours.

The recruitment process threw up several surprising insights.

Each curveball in the process lead to discussions on whether we were looking at this right.

Were we looking for too much in one individual? If we were, then how should we compromise? On what?

We re-considered:

– the importance of previous management experience,

– the importance of sports industry experience,

– some surprising (excessive?) salary requirements, and,

– the importance of technical expertise.

Each challenge helped everyone involved in the process re-imagine the role of the data team.

My initial reaction was also that the technical skills were the most important.

You need to know what you’re team is doing, right? Plus it is sole destroying to be a tech person managed by a non-tech person.

Or it can be …

I analogised to an iceberg.

There is a lot going on below the surface of an app or dashboard or (insert tech thing).

The non-tech person, for whatever reason, will not appreciate this.

One reason this becomes an issue is time.

More specifically commitments of time.

The non-tech person may not be able to estimate the time required to deliver a certain scope of works.

Predicting the time it will take to develop something is actually hard, even if you are up to speed on the tech.

If the non-tech manager over commits then it will put their team under undue pressure.

When many sports teams are suspect about the value data can offer anyway, setting unrealistic expectations isn’t great. It isn’t pleasant, nor is its sustainable.

Another issue is not TRULY understanding the importance of data rigour.

Data tools and systems get complex. Even for the simplest type of work.

Complexity isn’t an issue in and of itself, but it leads to more opportunities for mistakes.

Even the very best tech guru’s make mistakes.

We’re not talking fundamentals here, we’re talking the small things.

Like this:

What’s the difference between these two bits of data?

John
John

Nothing right? Wrong. What if I enclose the data in quote marks. See it now?

"John"
"John "

See the extra space at after the second John ?

Looks nothing. Yet, I once spent 2 days trying to find an issue with a tool we’d made, which was being caused by an issue like that.

Once I found it, it took about 10 minutes to sort. Unfortunately prior I hadn’t even been looking in the right place.

That issue, and others, are now imprinted in my “debug brain” for when things do not work.

I lost 2 days of my life doing that. Days I’ll never get back.

I don’t want to do that again. So now I add “extra” steps in our solutions to prevent these kinds of issues.

The message is this:

The danger is the non-tech manager doesn’t appreciate enough to build trust.

How can you justify spending 2 days solving a 10 minute problem?

“Are you rubbish?”

They might not say it but the question is often implicit.

Toxic.

Sole destroying.

But, then they’ve never done it.

How will they ever know?

How it can work.

Take our experience, and think about what you are expecting from your data team.

Think: What does your data team need to deliver?

Is it improving current reports?

Is it improving data capture?

What about data integration?

Want to build some predictive models?

What about external reporting and management needs?

Don’t worry about the HOW.

Focus only on when data COULD make a difference.

For sports, it is deliver fast, reliable and actionable answers to performance questions.

To compromise consider the components of the role.

The role of Data lead, as I see it, has the following main components:

– Requirements analysis (i.e. determine whats needed and develop project scopes)

– Strategic direction (i.e. prioritise whats needed, when including technology selection)

– Communication with partners (i.e. with suppliers, with customers, at conferences)

– Implementation of strategy (i.e. project manage improving tools, processes, research and reports)

– Developing others (i.e. mentoring, space for experimentation, support of individuals)

– Keeping current (i.e. new technologies, research and knowledge of the competition)

Each part has different importance at different times.

There is one fundamental though.

The Data lead has to ACE, Requirements Analysis.

Their ability to do this forms the foundation of everything.

This requires more of a traditional consulting skill set than deep technical background.

An inquisitive mind. A desire for learning. An ability to analogise. To listen. To always be asking questions. Then trying to confirm their understanding by trying to answer questions themselves. By talking with the team.

In sports it is asking questions of their coaches, practitioners and management teams.

It is about sales. About establishing requirements, setting expectations and delivering.

Find this first.

Then get your data lead exposed to data and tech …

… but with the sole aim of helping them improve their requirements analysis ability.

Training for these is good value and online.

If they have the aptitude and commitment, the non-expert will be up and running quickly.

Even if the data team are doing more of the doing, at least the lead will have been through some hands-on training.

Made some mistakes.

Gained an appreciation of why something simple might be more difficult in practise.

And much better appreciation of what their data team delivers.

So, the answer is:

“YES, a non-expert can lead a data team”

But give everyone a chance;

Don’t compromise on their requirements analysis ability.

 

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