Analytics team - Wins you can implement today - Part I

All data analytics teams have different issues: how to set up a clean data pipeline, how to maintain it, how to share dashboards (bonus points when there will be actually used), how to limit repetitive questions from being asked, how to pipe back the data to operational tools, …

But in the end (and as we could expect), all teams want to maximize their impact without growing the headcount exponentially. Tooling and technical skills are a very important part of the solution but the obvious one. Less obvious and still pretty often the bottleneck, communication and knowledge make sure your team has the expected impact.

If you’re in an analytics team (or if you are the analytics team!) and want to boost your impact, there is every chance that you should start by tackling communication and knowledge first. What do I mean? Here are some tips you can implement starting today!​

Communication is rarely cited as a criterion for being a good analytics team member. I beg to differ!

Part I — Communicate like a pro

Communication is rarely cited as a criterion for being a good analytics team member. I beg to differ! It’s crucial to have good “2-way communication” skills. Understand what is being asked, being able to reformulate, and then clearly express progress status and results.

Set up a Slack channel

Difficulty 1/5 — Costs 1/5 — Impact 2/5

It’s not much (and some of you already implemented it). But if you haven’t it’s actually a perfect way to open up communication. Coworkers could then ask their questions whatever they are: request, data location, etc… It’s important for coworkers to know where they can ping you if they need help. For you, it means a single place to look for limiting shadow DMs or email requests. Naming suggestion? #data-questions.

Send weekly emails about your priorities

Difficulty 1/5 — Costs 2/5 — Impact 3/5

The goal here is to communicate your team’s priorities. When you don’t, people lose trust in the process. As soon as you start, people’s complaints will die down peacefully. They might challenge prioritization but then it will be an actual opportunity to explain how you prioritize different requests (the ones with the highest impact). A few bullets points with the ongoing and the next topics might be enough to start here. Nothing shiny.

(Not a fan of emails? You might start a generic #data Slack channel or a dedicated #data-priorities one)

Find your relays

Difficulty 3/5 — Costs 1/5 — Impact 4/5

Relays would be people in the operational teams that are more interested in solving their issues using data than the average. They are the ones who want to do more with their dashboards and who love to share with others. Most of the time you can identify some of them in different teams.
Treat them carefully.
You should build a relationship with them. For example, you can create a specific channel with them where you send advanced tips or try out new things before you send them to other people in the org. They will act as proxies and train people around them with operational tricks.

Ideally, set monthly meetings with them, get feedback from what is needed in their teams. It helps prioritize the needs.

———

Again, for those who know me,​​ you know how much I believe an Analytics team is actually very close to an internal Product team. What does it mean for us here? Prioritize based on your users' (your coworkers) needs, communicate with your users, and find your hardcore users.

Ah and start early and iterate.

You want to know how you can boost data knowledge in your data team and in operational teams? It's in Part II of this segment!