BD #3 - Effectively managing data teams | Beyond Data

BD #3 - Effectively managing data teams | Beyond Data

Data teams can be hard to manage due to their wide scope of responsibilities and required skills. There's no understating the difficulty if you're a data professional that's been given management or leadership responsibilities for a data team. If you're not a data person - a business leader or senior manager from another background - then I can't imagine the confusion.

The thing is, many people and organisations are sold on the hype of data science long before they realise the reality. You hear about the big wins and advancements from such a company or the latest technology advances in some new blog and the FOMO kicks in.

I think the most important advice I can give is to get subject matter experts into the team. Whatever the current project your data and team are working on the most vital information they need for their insights and outputs to be useful is in the head of someone else in the business that knows the domain inside out.

If the current project is to deliver a dashboard to the accounts team, get someone from the accounts team into the project delivery. If it's to build a new model for the senior leadership, get a representative from that stakeholder group to sit in on weekly feedback sessions where they can answer questions and steer further development.

These things don't really mean anything concrete and are open to too much interpretation. Sure there's the famous Jobs quote about hiring smart people and not telling them what to do - but you need to give them some idea of what the goals are and what's important to the business.

Exploration, learning, and research tasks can feel very difficult to measure progress on. I've always been a big believer in artefacts to combat this. Make the measurable output a report or talk about what new insights or learnings have been made. Having a standard template can help. The measure then becomes delivering or contributing sections of these reports. Over time, the knowledge gained by those involved becomes builds into a shared understanding that the whole business benefit from - not just locked in someone's head.

If at all possible, convert your progress directly into terms of how much additional money or time the project earns or saves. Do this in temporal terms that are meaningful to them on a personal level too - think per week/month/quarter/year for the most impact.

Now a word of warning, this is probably the most difficult of these steps to achieve and sometimes it isn't possible. If you're struggling reach out to the community or myself (I'd love to hear), as I bet someone out there's faced a similar situation.

The above points are really about getting the right things in place to enable the team to go at full speed, without getting lost or going off track. One thing that will significantly hinder their progress though, is the reliance on others.

Whether this be needing approvals from management or relying on other teams to get the technologies spun up to deliver the project - it all introduces friction. Every touchpoint can become another bottleneck, or worse, a well-meaning suggestion from that collaborator turns into a huge distraction that derails progress.

Getting full autonomy can be difficult - but remember this is for the data TEAM, not the individual. Need IT to be involved to get tooling deployed? Sounds like someone from IT should be in the team. Need a senior manager to approve access or budgets? Sounds like they also need to be in the team. Having some people tied to the team on a partial basis also helps with some of the points from the first tip above.

And finally, if you've enjoyed this or know of someone that might find it useful I'd greatly appreciate you sharing it with them ????All the best.

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