Data teams need to change. Last week I dove into the state of the industry and the three troubling trends that make me so convicted of this fact.
With better-than-ever backends, and AI by our sides we are perfectly poised to break free of the day-to-day drudgery and start doing things differently.
But what does that look like?
We believe there is a better way for data teams to work.
A way that puts the focus on adding value, not building assets for the sake of it.
A way in which data teams help their businesses understand how their work impacts the larger business.
And crucially, a way in which data teams help to achieve the company’s goals in partnership with the business, not in-service to the business.
The following are the principles behind the next generation of data team, with quotes and examples from some of our customers that are already experiencing the impact of this way of working.
In today's data-rich environment, the key is to achieve operational clarity by visualizing your business, not just your data. This means creating a common reality that allows teams to align on next steps and identify problems to solve.
While tools like metric trees can help structure this approach, they can be challenging to implement initially. A simpler starting point might be mapping out your onboarding funnel, which can provide immediate clarity on a crucial business process.
Through this process of visualising core metrics and their definitions, they are able to own something so much bigger than ‘the numbers.’ These teams are able to connect the dots for the entire organisation, to help everyone understand how the business is doing, and how they can help make it more successful.
“The more aligned your organisation is - and I don’t mean aligned around vision necessarily or strategy, I mean aligned around a common language, a common understanding, and common idea of what is and what isn’t - the more successful an organisation is…I think the data team’s responsibility is to ensure those connections are around something objective, like data, as opposed to something subjective, like team goals or team culture.” - Philip van Blerk, Director of Finance Data at Monzo
Example - Onboarding funnel > Onboarding dashboard
In this canvas, we can visualise the entire onboarding funnel for a mobile app and see the conversion at each stage. This makes it easier to visualise not only where we’re losing people, but why. With the context of what the user is seeing at each stage, we can more quickly identify where to focus our efforts.
Data analysts are naturally skilled problem solvers, and we need to elevate their role beyond just creating charts. By positioning analysts as strategic partners rather than task-executors, we can unlock their full potential to drive business value. This means engaging them in the problem-solving process from the start, using frameworks and collaborative approaches to tackle complex business challenges.
Sara Sabzikari, Data Analyst at Loqbox, has seen a resurgence of energy and ideas on her team when they’ve been freed up to work on problem-solving instead of being distracted by inefficient workflows and ad-hoc requests. Count has helped her team consolidate their workflow, and have space to think creatively about complex problems.
The key to delivering value lies in being obsessed with outcomes rather than just outputs. This means focusing on how quickly we can help the business make informed decisions, not just how fast we can generate insights. By embracing collaborative approaches similar to agile development (rather than waterfall methodologies), and working closely with stakeholders throughout the process, we can dramatically improve both our effectiveness and our relationships with the business teams we support.
Justin Freels, Data Engineering Manager at Accenture, found that a lack of understanding and a lack of trust were things most delaying decision-making. To fix that, he embraced transparency and collaboration when building data models. By working side-by-side with the business he’s able to explain his work, but also make sure they’re building the right things to maximised trust.
To keep ourselves focused on the right things, we can’t be satisfied with tracking our own efficiency metrics or even our cost savings. We have to measure our impact.
To measure impact effectively, data teams should focus on tracking several key aspects: understanding time allocation across projects (especially important given payroll costs), calculating ROI on infrastructure investments to prevent over-engineering, and monitoring the business outcomes of their contributions. Even with imperfect metrics, this measurement-focused approach helps teams continuously improve their operations and effectiveness.
The transition I wanted to make was from these analysts being task monkeys, if you like, to being value obsessed strategists. This is not an easy transition to make, but the idea here is that we're getting the analysts to think in a different way, to connect with their team in a different way. - Dan Redgate, Product Analytics Lead at Too Good To Go.
When you put these four values together, they work together to create a virtuous cycle.
Much like the Service Trap I mentioned in the previous article, the virtuous cycle is strengthened when these four values come together, except this time they create a positive feedback cycle - one win leading to another.
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The virtuous cycle has 3 loops:
More clarity → More problem solving. When there is greater operational clarity, it becomes much easier to identify, and agree upon, the biggest problems. Because the data team helped to identify those problems, they end up being asked to help solve them, which encourages analysts to be problem-solvers over chart-builders.
More clarity → Faster decision-making. When there is greater clarity and alignment across the business, decisions can be made more quickly and confidently.
Faster decision → Better measurement of impact. And with those decisions it means there is more value for the data team to measure itself against. Especially when it has been involved in those decisions and projects from the start.
Consider the following example:
You are asked to build a dashboard for the marketing team to show their key funnel metrics. Instead of doing that, you visualise the entire marketing flow in a diagram with live data.
With this new clarity, the marketing team is able to identify the key problem they need to tackle, instead of being very reactive to the latest ideas from management.
As you’ve now helped them identify this problem, you’ll likely be asked to help solve it, elevating your analyst into a partnership role.
And once you and the marketing team have implemented a solution to this problem, you’ll easily be able to track its impact and demonstrate it to the wider organisation.
It’s true you can do this regardless of the tools you use.
However, it’s worth asking whether your tools are helping or hindering your ability to work this way.
Does your BI tool make it easy to map out business metrics as a process flow, or tree, or does it keep you stuck with grids?
Does your BI tool make it easy to work side-by-side with the business or does it actively keep you in separate interfaces?
Does your BI tool make it easy for analysts to problem solve or just to build charts?
This is why we built Count - to help make this way of working possible.
Ours is the only BI tool that lets you visualise your business, not just your data; that enables true collaboration between data and business teams; and that is built for complex problem solving.
Building something this different hasn’t been easy. We’ve continually improved and developed Count for over 7 years, and we’re finally starting to see the work pay off.
Hundreds of our customers are already working in this way, already shifting the operating model of their data teams and focusing more on delivering value, not dashboards.
If this sounds like how you’d like to work, you can join the community of data leaders leading the charge here, or if you want to learn more about Count, get in touch!
