Most companies overestimate where they are
When you ask an executive how their company manages its data, the answer tends to be optimistic. "We have Power BI", "we use the ERP for everything", "the team runs reports every week". Fine. But none of those things answers the real question: are you making decisions with reliable and complete data, or with whatever someone managed to pull together in time?
Data management maturity doesn't measure the tools you have. It measures how far those tools let you decide with confidence.
The five levels, explained without jargon
There are several data maturity models, but they all converge on a similar scale. The practical version, the one that really matters for a company of 20 to 200 people, is this:
Level 1: Reactive. The data exists, but it's in manual spreadsheets, emails, and people's heads. Every report is a project. There is no single source of truth.
Level 2: Managed. There's some centralized system (ERP, CRM, some dashboard), but departments work with their own versions. The numbers don't always match across areas.
Level 3: Defined. There's a basic data architecture, the metrics are defined and consistent. Reports update automatically. Management can ask questions and get answers in hours, not days.
Level 4: Quantified. Data is used proactively to spot opportunities and problems before they escalate. There are basic predictive models running.
Level 5: Optimized. Decision-making is fully backed by real-time data. Improvements are continuous and systematic.
According to industry studies, the average European company sits around level 2.6. Most believe they're at level 3. The gap between that perception and reality is exactly where opportunities are lost and where management mistakes pile up.
How do you know what level you're really at?
There are three questions that, in my experience, place a company with fair precision:
First: If tomorrow your management team needs to know the real margin by business line for last month, how long does it take you to have it? If the answer is "it depends on who's available to cross-check the data", you're at level 1 or 2.
Second: Do the sales figures from the commercial side and the ones from finance always match? If there's ever been an argument in a meeting about which of the two numbers is the right one, you're at level 2.
Third: Can you spot a negative trend in a key metric before it hits the quarter's results? If the answer is no, or "sometimes", you're below level 3.
The problem isn't not having data. The problem is not knowing how far the data you have is telling you the whole story.
Why the jump from level 2 to level 3 is the most important
Level 1 to 2 usually happens on its own, as the company grows. Level 3 doesn't happen on its own. It requires a conscious decision: defining which metrics really matter, where the data comes from, who is accountable for its quality, and how the systems connect.
That jump doesn't need an in-house data team or complex infrastructure. It needs clarity about which decisions you want to be able to make, and building exactly what's needed to support them. Nothing more, nothing less.
A 50-person company that's had the business running for two years normally has enough data to be at level 3. The question isn't whether the data exists, but whether someone has taken the time to structure it.
What to do with this information
If, after reading this, you're clear that you're at level 1 or 2, the next step isn't to hire a data team or buy new software. It's to understand which concrete decisions you're making blind and which of them has the most impact on your business. From there, everything else is built logically.