How I supported my team to craft a team charter

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This week’s story is inspired by a session I held with my analytics team to establish our team charter. 

How do we not only maintain, but continue to innovate the quality of our craft as we scale? 

Gratefully and with excitement, our team is growing and we only see a trajectory where this will continue (and at pace) as the desire for analytics solutions that are tied to actionable insight, only increases. 

Beyond the data and tech (things like… structuring metrics layers, creating scratch databases, consistency of tools etc.) the other half of the equation is the people and process. 

Establishing our “rules to live by” in our work provides us with a set of standards for how we will operate as analytics professionals in every capacity. Regardless of how the work itself actually changes. 

More than that, the construction of our charter allows us to have a clear framework to point to if someone needs support in one of the outlined areas. In this way, they’re more than just rules for the work, they become group “norms” for what is acceptable by the team, for the team, in maintaining the quality delivery of our craft. 

We brainstormed exactly what to include alongside HOW we would hold one another to account in adhering to these norms. 

I couldn’t be more proud of the team’s active participation. While it may seem like a small exercise, this step is truly key in establishing the foundations of the kind of culture we want to foster as the team scales. 

Here’s what we came up with (in no particular order). 

1. Match Maturity
   This is, knowing the audience who is receiving the pack and tailoring the execution accordingly. Adoption of client or industry language and intuiting literacy levels.
2. 5 Second Rule
   If the audience can’t see the key takeaway in less than 5 seconds, chart it again. Always ask ourselves, “Can it be simpler?”
3. Don’t make me math
   Pretty self explanatory, always include totals, call out differences in visualisations. Never leave it to the reader to interpret the drop, the lift or the total.
4. Fastest path to value
   Idealist visions are great at highlighting what’s possible, however we work to build something useful that can be iterated and improved on over time to reach reporting nirvana.
5. Occam’s razor
   Often the simplest solution is the best solution. Don’t overthink it.
6. Solutions that scale
   Wherever possible (unless otherwise identified), aim first for a reporting solution that will allow scale. That means, adding new data sources, metrics and visualisations to an existing reporting solution.
7. Jargon Free
   If jargon or acronyms must be used, explain them the first time that they are used. Better still, just don’t use them at all.
8. Consider Colours
   Accessibility is often overlooked but it is important. Blue / orange, blue/red, blue/brown. Light and dark.
9. High Integrity, High Accuracy
   Check. 
   Cross-check. 
   Then, check it again. 
   Then, peer check it.
10. Final Step Peer Check
    Peer tick of approval before hitting any inbox.
11. Document Every Step (no black boxes)
    Document like you are having to pick up the analysis yourself. Always include the appendix or assumptions and detailed calculation methodologies of key metrics.
12. Be proud of every output
    This one speaks for itself. If we aren’t beaming with pride for what we’ve created, it’s not actually finished.

(is it just me or does this really give off wolf pack team vibes) 

Beyond establishing these sound bites for how we wish to live our days as analysts, we also discussed how we would enforce these rules with accountability within the team. 

Through the execution of one of our rules, “The Peer Check”, we have structured a set of steps to follow from which, we’ll use this as a checklist to ensure alignment. 

The final decision we came to as a team was to allow this charter to be viewed publicly across the business and by our clients. Through making our rules for operating public we hope to invite conversation, accountability, transparency and ultimately, an even greater respect for our craft. 

In a world where technology and data quality is often the first to blame, establishing ground rules for what “good” analysis looks like provides an additional layer of rigour. It’s about doing the best possible job with what we have and working as a team to deliver the best quality outcomes.

End.