Here's a pattern we see constantly: A data team leader tells us they're "operationally mature" — they've automated key processes, reduced manual work, and things run much smoother than they used to.
Then we ask questions like: "How long does it take to implement a pipeline change?"
Which leads to responses such as: "Well... two to three weeks. Sometimes a month if it's complex."
That's when the disconnect becomes clear. Teams that measure pipeline changes in weeks genuinely believe they're operating at a high level of maturity. Meanwhile, organizations at the actual top tier are measuring the same work in days or hours.
So, what's happening here? Why do smart data teams systematically overestimate where they stand?
This blog answers the above questions and more.
Before diving into why teams overestimate, let's clarify what we're actually measuring.
Data Maturity captures an organization's capability, effectiveness, and readiness to leverage data at any given time. It shows not just what data exists, but how well teams can turn that data into trusted insights, rapid decisions, and measurable impact.
Specifically, mature data operations can:
Deliver trusted insights consistently – teams across the business trust and use your data
Adapt quickly to change – systems pivot when priorities shift or new opportunities arise
Operate efficiently and at scale – processes are repeatable, well-governed, and not reliant on a few key individuals
Support advanced initiatives – AI and analytics projects are built on solid, reliable data foundations
In other words, Data Maturity reflects capability to support decision-making, innovation, and business growth, rather than simply counting technology or outputs.
You might have reduced pipeline deployment time and count that as progress—it is, but there's likely more you could do. You're measuring success against your own baseline, not against what's actually achievable.
You're not seeing the opportunity because you're looking backward instead of forward.
Where do your technical teams spend most of their time? If your answer is anything other than "solving valuable, complex problems," you're measuring the wrong thing — but you probably feel operationally mature because everyone is busy and productive.
You see your team working hard, tickets are closing, projects are progressing. That looks like maturity. Your team is engaged, competent, delivering. From the inside, it feels like high performance.
This is perhaps the most insidious reason teams overestimate their maturity: they simply haven't seen what the next level looks like.
You can't aim for a target you can't see. If you've never experienced or witnessed true operational maturity, you have no reference point for what's possible. Your current state easily becomes the ceiling of your imagination.
How often do temporary tools or fixes become long-term solutions? If you answered anything other than "rarely," here's what's happened: you've normalized technical debt. It's become "just how things are" rather than a sign of operational immaturity.
When workarounds become standard practice, you stop seeing them as problems. They fade into the background of "how we do things." One normalized workaround isn't terrible. But organizations typically have dozens. Each one seems small. Together, they create an environment where changes take too long, and problems become harder to troubleshoot — but because it happened gradually, you don't recognize it as dysfunction.
So how do you know where you stand without falling into the same overestimation traps?
Our Data Maturity Survey provides an objective baseline by measuring your operations across six critical dimensions:
When combined, these dimensions give you a clear picture of your current state, not where you hope you stand, or where you stood last year, but where you and your operations are right now.
A personalized maturity score benchmarked against hundreds of data teams
Specific gaps between your current state and operational maturity
Tailored recommendations on how to improve — from quick wins like automating repetitive processes, to strategic initiatives like strengthening governance frameworks, to capability-building moves like up-skilling your team