Subscribe to receive the latest blog posts to your inbox every week.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Share
No items found.
In our fourth and final session in our Metric Tree Masterclass, we are looking ahead. Metric Trees are an undeniably powerful way for data teams to communicate your company’s growth model but how can they help your data team make some fundamental shifts?
In this session we’ll cover how metric trees can help your data team break free from the service trap, and start becoming a high-value team.
Watch the full conversation:
Or get the summary:
Key Takeaways
Data teams face a unique challenge. Unlike other teams, like sales or marketing, where the expectations of the team are clear and consistent across the industry, the role of the data team is ambiguous and rarely understood by the business. This prompted us to create a framework for high-performing data teams. After interviewing hundreds of data teams and business leaders over 6 months, we’ve found 4 fundamental principles for high-performing data teams.
The service traps. Business and data teams often operate like parent <> child relationships. This dynamic is exasperated by 4 traps:
Drowning the business with information - the org has access to more info than it can handle, which causes confusion, operational bloat and a fragmented understanding.
Answering every question the business asks - the business sees the data team as a source of information, creating a service dynamic that under-utilizes the skills of the data team.
Minimising time with stakeholders - data teams create clear boundaries between their operation and the wider business creating a transactional relationship.
Optimizing things the business can’t see - the data team spends time on projects with minimal or no ROI.
These traps are very easy to fall into, and you might realize you’re in them if you experience any of these:
Your dashboard: employee ratio is close to 1:1.
Your team feels swamped by ad-hoc requests and you feel unable to challenge the business about its value
Your team is spending most of its spare time developing new technical skills
Your data team is spending >40% of its time maintaining operational reporting and data quality
Your CEO/Exec team can’t list 3 ways your team has directly contributed to growth in the last quarter
Teams that have escaped the service trap follow 4 fundamental tenets:
Seeking operational clarity - the data team ruthlessly prioritizes and manages what the business sees, creating a common operational context and clear business priority
Solving business problems - the data team works with the business to directly define and solve the biggest challenges that limit growth
Minimizing time to decision - the data team ownership of improving the decision-making processes across every level of the business
Measuring yourself - the data team constantly measures its own operations to improve its speed, quality, and costs
Metric Trees fall into the first tenet of operational clarity. They are effective ways to clarify the operating model of the business and prioritize the actions that should be taken to improve performance. They also help to reduce information that can be repeated across many KPI dashboards.
However, there is more to operational clarity than metric trees. This includes:
Being precise and concise communicators
Having business owners for each key operational report that understands it and how it’s used.
Regularly depreciating assets so that the information available is accurate and trusted
At its core, operational clarity is about a mindset shift away from delivering numbers to making the business feel simple to the wider business. With that in mind, there are endless ways you could provide operational clarity beyond those examples.
To do this well often requires data teams to go beyond doing what’s asked of them, and really understanding the demands of the business and how it all works. Many data folks push back on this new responsibility seeing it as something business leaders are responsible for. Ollie warns you not to fall into that trap, and to assume ownership of filling in the gaps of understanding that you see.
One consequence of focusing on operational clarity is that you will likely be asked to help the business solve the problems you’ve identified. This is the second tenet and is important because you automatically know the value of the work that you’re doing and the business impact.
Self-service is often sold as a way to make the data team more efficient while helping the business make decisions quickly. Ollie argues this is fundamentally false. While it sounds good in theory, in practice it often leads to more confusion and even more questions on the data team. If instead you consider how to most quickly get to a good decision you would likely choose a different route. This is the third tenet.
The final tenet is to measure yourself, which sometimes does mean minimizing your querying costs, but Ollie suggests not getting too bogged down in those kinds of optimizations because ultimately the largest cost for most data teams is headcount, and cutting costs everywhere can further your image of being a service function.
The best teams focus on their positive impacts, not just their costs. What was the outcome of the work that’s been done? Do you share in that success?
Metric trees can be a great entry point for enacting these tenets. They are very visible and can demonstrate to the rest of the business how you want to start operating differently. If you do set out on this journey, focus on the small mindset shifts we’ve covered and take small steps - it can be a long journey, but well worth it!
.png)
