I’m going to be honest with you: in many Data Centers the problem is no longer a lack of data, it’s that no one fully trusts it.
The scene is pretty typical: capacity meeting, DCiM dashboards on screen, open Excel files, someone holding a floor plan in PDF, conflicting opinions between IT and Facilities… and in the end, “the person who knows the room best” makes the call. It works, yes. But it’s getting harder and harder to sustain when the load grows, energy becomes more expensive and the business keeps pushing.
That’s what we mean when we talk about data maturity in your Data Center: how much order there is behind all that information, how much you trust it to make decisions, and to what extent it allows you to move towards a more automated and less heroic operation.
We’ve put togethe a data maturity test precisely for that: so that you can see where you stand and what it means if you stay there.
From “everyone with their own Excel” to a data-driven management model
In many projects we find the same starting point: a Data Center full of systems, but with information scattered like a jigsaw puzzle.
The inventory lives in an Excel file that only two people update. The “final” layout is in a PDF no one dares to change. Alarms are viewed in one system, energy consumption in another, changes are processed by email, and what’s really going on is understood by asking around in the corridors. From the outside, everything looks quite professional; from the inside, everyone knows that any major change requires almost a manual choreography.
When a DCiM comes in for the first time, the movie improves: at last there’s a place where you can see what you have, what state it’s in, how much energy you consume, how much capacity you’ve got left. We go into this in detail in articles like "How to choose the best DCiM", where we explain why choosing that tool well is what makes the difference between staying the same… or moving up a league.
But this is where the first trap appears: believing that just by installing a DCiM you’ve already “matured the data”. If key decisions are still being made outside the system, in spreadsheets, endless meetings or email chains, the DCiM runs the risk of becoming an expensive viewer. It looks great, but it doesn’t lead.
When data starts to lead and operations can breathe
The real turning point comes when day-to-day processes start to live inside the tool, not around it. We see it very clearly in projects where the "DCiM implementation": is done well: changes, moves, adds and deletes, tasks with third parties, capacity management… everything leaves a trace, everything is orchestrated and relies on a coherent data model.
That’s when very interesting things start to happen:
- The team no longer depends so much on certain people’s “memory”.
- Discussions about which data is the right one drop sharply.
- Human errors start to decrease, simply because the system itself guides you.
It’s also the moment when you can start using data for more than just a pretty picture: to measure, compare, justify. And if you also connect it to energy efficiency goals, the leap is even greater. We talk about this in detail in "DCiM as a path towards energy efficiency", where we explain why a DCIM-based management model is almost mandatory if you want to improve PUE and maintain certifications like ISO 50001 without dying in the attempt.
From there, conversations with management change. You no longer go in with “we think we should…”, you go in with “the data shows that if we don’t do this, the risk is this and the financial impact will be that”. You stop playing in the field of opinion and move into the field of evidence.
Stories that are already happening, from controlled chaos to optimized operations
When we talk about data maturity, we’re not speaking from theory, but from real projects. A good example is the ARSAT case, where we helped implement a DCIM-based management model that took their Data Center to the top of the “operations maturity pyramid”. They tell the story themselves in the article "DCiM at ARSAT leads to the optimization of its Data Center" ewer errors in daily operations, more visibility, and greater ability to anticipate problems before they affect the service.
Behind stories like that there’s no magic, there’s well-worked data:
- Integrity: what you see is what’s really there, not different versions depending on who you ask.
- Context: not just numbers, but relationships between equipment, energy, space, connectivity.
- Processes: what you do every day is recorded and feeds that data model.
And if you also connect it with a sustainability vision and good environmental practices, as we explain in, “MPGM and DCIM: the perfect duo for the sustainable future of DCs” data maturity stops being a “technical” topic and becomes a key piece of the company’s strategy.
And now, the uncomfortable question: what happens if you don’t do anything?
This is where we need to be clear.
Staying at a low level of data maturity, in a context of higher demand, greater complexity and increasing regulatory pressure, has very concrete consequences:
Higher likelihood of incidents that could have been detected earlier.
More invisible operating costs: oversizing, inefficiencies, duplicated work.
More dependence on specific people and less organizational resilience.
More difficulty in justifying investments in modernization, automation or sustainability.
Meanwhile, the Data Centers that do move forward, sometimes in small but steady steps, gain something very valuable: real decision-making capability based on data. Their teams work with more peace of mind, their leaders can better defend their decisions to senior management, and their infrastructure is better prepared for what’s coming.
The good news is that no one is asking you to go from zero to one hundred. The only essential thing is to know where you are today and decide how far you want to go.
How to really know what stage your Data Center is in
That’s why we created the data maturity test, It’s not an exam; it’s a structured conversation that helps us understand:
- How you currently manage information in your Data Center.
- What role DCiM (if you have it) plays in day-to-day operations.
- Where the biggest risks lie… and also the best opportunities for improvement.
From that starting point, you can map out a realistic path: from bringing order to your Excels to moving towards an operation supported by smart recommendations and, little by little, by automations that free the team from “firefighting mode”.
This is how we see it: either you manage your Data Center through data, or data turns into expensive noise.
Data maturity is, in the end, the story of how you choose to manage your critical infrastructure. And that story, whether you like it or not, you’re already writing. The question is whether you want the next chapter to be more chaotic… or a lot smarter 😉