The Path to Data Maturity
How to help your organisation make better decisions that build better products
In my years of working with companies across different sizes and industries, I've noticed one consistent pattern: organisations that make better product decisions tend to be the ones where data is accessible, understood, and actionable for everyone. Yet this is the exception to the rule.
So many teams I see are still struggling with data silos, unclear metrics, and an overall lack of data awareness! And their leaders know it, too. In fact, one of the most common pain points I hear from leaders wanting to engage me as a coach and consultant is "We're not data-driven enough." But what does that actually mean, and just how can you systematically improve your organisation's data maturity?
I'd like to share my thoughts because it’s not something I often see referenced in other Product Ops publications - but it’s something I firmly believe is within the remit and responsibility of any great Product Ops leader. When done right, it can meaningfully transform not just your teams’ product decisions, but your entire company.
Data Maturity, Defined
Before diving into what you can do to increase your organisation’s data maturity, let's define what “data maturity” actually means. Contrary to popular belief, it’s not just about having sophisticated analytics tools or a team of data scientists. It's really about creating an environment where:
Data is visible - Everyone can see the right metrics at the right time
Data is accessible - People can self-serve answers to their questions
Data is actionable - Insights directly influence decisions and priorities
Data is trusted - Teams believe in the accuracy of what they're seeing
If you were so inclined, you could map these behaviours on a four-point scale:
Level 1: Ad-hoc - Some data exists somewhere, but it’s scattered across tools and teams. Decisions are mostly based on gut feeling or the loudest voice in the room.
Level 2: Defined - Core metrics exist, but they're hard to access. Data specialists are gatekeepers to insights.
Level 3: Managed - Clear metrics frameworks exist and most product managers can self-serve basic data. Some decisions are genuinely data-informed.
Level 4: Optimised - Data flows seamlessly through the organisation. Teams autonomously use insights to prioritise work and measure impact.
Truth be told, most organisations are stuck at Level 1 or 2, but desperately want the benefits of Level 4. And it’s not hard to see why!
Why Product Teams Struggle with Data
In a discipline that has touted itself as being data-driven since the dawn of time, it always struck me as quite curious that so few product organisations genuinely invest in a solid set-up. As a Product Manager I couldn’t believe how many teams and leaders were simply fine with having next to no meaningful insights to inform their decision-making - let alone have the ability to track whether what they were releasing actually hit the mark.
But “becoming data-driven” isn’t something you can just do on the side. It requires a concentrated effort by many people over a long period of time. It certaintly isn’t something you can fix by buying a new tool - or indeed, hiring data specialists. Data maturity at a higher level requires silos to be broken down, concepts to be aligned and agreed upon by several different parties, and the change management that comes with making decisions (and evaluating progress) in a completely new way. Which is exactly why Product Operations is so perfectly positioned to get this over the line.
But without concentrated effort, leaders and teams revert to doing the bare minimum - not because they don’t think it’s important, but because that’s all they have time for.
As teams and organisations evolve, there are many data traps waiting for them - all with their unique reasons for existing. In my experience, these traps need to be addressed and resolved before you do anything else:
Trap no. 1 - More data is always better: A classic quantity over quality fallacy. If nobody knows what the data actually shows and it’s hidden behind a million dashboards you need a PhD to understand, it doesn’t matter how much you track. Nobody can make sense of it.
Trap no. 2 - The black box of data definitions: When was the last time you verified that your concept of an active user is the same as your data team’s? Do you know how churn is actually defined? Chances are, someone who doesn’t even work at the company anymore and was tasked with setting up some metrics four years ago made an arbitrary decision on which you’ve been basing the success of your entire product
Trap no. 3 - You shall not help yourself: “I’d love to find out whether region influences purchase behaviour - I better create a ticket for the data team so that they can get back to me in 10-14 business days, by which time it will be too late to really do anything about it”. Sound familiar? It shouldn’t.
Bonus Trap: We’ve got the data - yes - but we’re not actually allowed to action it: You can be the most data mature company in the universe - if your Product Managers aren’t allowed to make informed decisions that steer product outcomes, good data practices won’t actually help you build better products.
Better Decisions, Powered by Data
Improving an organisation’s data maturity is no small feat - but as someone who’s been in the trenches numerous times, let me lay out what you’ll want to start thinking about as you tackle this force multiplier. In essence, you’re looking to cover four bases: Aligning on your basic definitions, creating access and visibility, building data literacy, and integrating data into your everyday work to make better decisions.
Easier said than done!
Having worked with teams at various maturity levels, let me outline exactly which questions I typicalls ask teams and leaders as we embark on this endeavour:
Building Alignment
What are the 3-5 north star metrics your entire product organisation cares about? How do you know your product is delivering what it’s promising? These should be directly connected to business outcomes and user value.
What are the data definitions for the core concepts related to your north star metrics? Is there consensus among departments? Do you have a data dictionary? When someone says “time to complete”, everyone in the organisation should have the same notion of what that refers to.
What happens when a new core metric needs to be established? Who is part of that conversation? Who is responsible for socialising it? Understanding what needs to happen in the future is just as important as retrofitting your existing metrics.
This alignment stage might feel slow and almost philosophical at times as you dissect which measurable user actions come together under which core concept, but it's absolutely foundational. Without it, all your other efforts will be built on quicksand.
Creating Access and Visibility
What is your single source of truth? Where will people go to get the insights they need? It’s imperative that commercial, marketing, and product teams don’t have their own data sources and definitions. Create one central place where anyone can find the metrics they need.
What’s your time to insight? How long does it take a Product Manager from having a query to answering that query by themselves? How many people are involved? The easier and more straightforward you make it for everyone to access the data they need, when they need it, the more likely it is that they will do so.
Be honest: How easy to read are your dashboards? Does everyone on your team understand the functionalities of your chosen data tools? Can you see important trends at a glance? Don’t underestimate the role good UX plays in making data practices stick.
I’ve said this before and I’ll say it again: If you want your organisation to adopt new habits, you need to make it easy for them to do so. Doing the ‘right thing’ cannot be more difficult than doing nothing - otherwise you’re fighting a losing battle.
Building Data Literacy
Do your teams understand basic analytical concepts? Do they know which visualisations will give them which kind of information? What about data pitfalls, and how graphs and charts can be misconstrued? Finally having eyeballs on your data is great, but looking at data and interpreting data are two entirely different ballgames. Make sure your teams are prepared.
When was the last time your product teams spoke about the data they see and how they interpret it? When was the last time they did this with commercial teams? Data is far less scientific and much more open to interpretation than people think - different people read very different things out of the same data set. Use that to your advantage.
Do you have a regular cadence for your product teams and data specialists to get together and learn from each other? When was the last time you asked a data specialist their opinion on measurable markers of customer habits? When was the last time they were involved in a discovery? Make use of the expert knowledge that’s already present in your organisation, and give your data teams a voice.
Don’t assume that everyone on your team has the same level of data literacy - in fact, don’t assume that even a basic level of data literacy is present. People’s comfort and experience with data varies wildly, and it’s your job to get everyone on the same level.
Connecting Data to Decisions
How will your Product Managers integrate what they learn into what they build? At which intervals and steps in the product development lifecycle (PDLC) will you prompt them to think about data? Assuming this will happen automatically as you get more data mature is naïve - habits need to be built proactively.
How well-versed are your teams in being hypothesis-driven? Are they thinking in informed bets, or gut-feel? What makes an idea progress to a discovery motion, and what makes a solution hypothesis progress to a prototype? And what role does quantitative data play in this? Be specific, and be explicit.
How comfortable is your organisation with the notion of failure? What happens when a feature gets released that misses the mark? The final frontier in your data maturity journey is the understanding that you will become a lot more intimately familiar with your failures - and that that’s a good thing.
Ultimately, connecting data to decisions is the actual goal in your data maturity journey. This is what you actually want to accomplish - everything else is simply a prerequisite. Once your organisation has settled into a continuous loop of being inquisitive > experimenting > launching > learning, that’s when you’re in the zone of genius.
A Note on Data Specialists & Data Departments
You might be reading this article wondering why I put so much emphasis on data maturity as something that sits within a Product & Product Ops leader’s remit, when there are so many underused data scientists and analysts who are far more technically advanced at this stuff than most of their Product peers.
To be extremely clear: I’m not advocating for data topics to be “taken away” from data specialists, nor would I ever dream to suggest that data specialists are no longer needed or that their skills are superfluous. Quite to the contrary, in fact!
When I was a QA tester, an engineer once told me “You should be happy our code produces bugs - without bugs you’d be out of a job!”
With a smile on my face I replied to him that were he able to produce bug-free code my job wouldn’t cease to exist - but we may finally be able to elevate our standard for what “quality software” actually means.
Imagine a world where your data teams are finally able to do the really cool stuff only they can do - without drowning in a sea of tickets flung over the fence at them from all angles of the organisation. Imagine they have the mental capacity to have meaningful conversations with your product team around what customer behaviour change looks like, and which data points are most relevant. Imagine they could become partners in crime - not a service centre, forgotten somewhere at the back of the open plan office.
So no - when I say that data maturity is in the hands of Product leadership I don’t say so because I am excluding data professionals. I say so because it is up to us to meet their standard, so that they can be freed up to do the work that will supercharge the entire company. It’s not up to them to cover for our inadequacies.
Start where you are
I hope this run-down has helped you better understand why data maturity is often a problem even in established product organisations, why it’s important to make it an explicit priority, and how to get started. As with any major change motion, improving your company’s data maturity isn't about reaching perfection overnight.
It's about making consistent progress from wherever you are right now.
If you're at Level 1 (Ad-hoc), focus on defining your core metrics and creating basic visibility.
If you're at Level 2 (Defined), work on democratising access and building data literacy.
If you're at Level 3 (Managed), focus on connecting data more directly to decision-making systems and closing the feedback loop.
What matters most is not the shiniest new tool, or the most comprehensive data governance process - what matters is creating an environment where everyone can make slightly better, more informed decisions today than they did yesterday.
And as always, remember: Data maturity isn't the goal in itself, it's simply a means to an end. As a Product Operations professional, your goal is to empower the organisation to build better products - by helping them make better decisions that deliver more value to your customers and your business.