At its core, data maturity is an organization’s ability to reliably use data for decision‑making and is enabled by strong governance, broad literacy, and the capacity to adapt as needs evolve. It is not just the presence of technology, but the readiness of people, processes, and culture to make data a trusted driver of action. Too many organizations, however, still see data maturity as a milestone. They think, “Once we’ve got the right tools, governance model, and staff, we’re set.” But real maturity doesn’t work that way. It’s not a box to check; it’s a muscle that needs to be strengthened over time. Like fitness, it takes consistent effort, discipline, and adaptation. Stop exercising it, and you lose progress.
Throughout this article, I’ll use operating room performance to show how data maturity functions more like a muscle — strengthened through consistent use, reflection, and iteration — rather than a milestone to complete. Many health systems assume dashboards are enough to track efficiency. But when first-case starts run late or block time goes underutilized, dashboards alone don’t create change. Organizations that actively flex their data muscle can quickly spot these patterns and act. Those that treat maturity as a one-time achievement end up reacting too slowly, losing valuable time, throughput, and revenue.
Just like setting a fitness goal, the key to any data maturity journey is knowing where to start and ways to build upon your routine; and it starts with governance.
Governance shouldn’t feel like red tape. Done right, it’s part of your culture. It’s something that speeds you up, not slows you down. It’s not just about compliance checklists; it’s about setting shared definitions, clear ownership, and accountability so staff can make decisions with confidence.
In the OR, governance ensures everyone is speaking the same language about what counts as an “on-time start” or what qualifies as a “fully utilized block.” Without that consistency, teams interpret performance differently, and delays or underutilization hide in plain sight. Strong governance also means leaders know where to go for trusted OR metrics and are encouraged to use them daily, not just during monthly committee meetings.
When governance becomes culture, it shifts from policing to enabling. Staff begin to trust the numbers, share a common understanding, and feel supported in acting quickly on data. Instead of slowing decisions down, governance becomes the very thing that keeps referral management and the broader organization moving at pace.
Once governance is in place, the next set of reps is data literacy. Data literacy is the difference between an OR utilization report sitting untouched and a perioperative manager noticing patterns of underutilized blocks and reallocating them proactively. When more OR staff and service line leaders know how to interpret and apply data, true democratization begins.
Democratization is one of the most powerful outcomes of data maturity. When staff at every level can confidently engage with data, information becomes infinitely more available and actionable. Efficiency stops being a management metric and it becomes a shared priority across surgeons, nurses, and managers. This is where the real momentum begins, when data moves from being centralized and reactive to distributed and empowering.
Once literacy and democratization take hold, the next step is sustaining energy through steady progress and visible results.
No one runs a marathon right out of the gate. You start with a 5k, then 10k, and keep building up to 26.2 from there. The same goes for the rhythm of building data maturity: small wins deliver incremental value while strengthening skills across the organization. For example, a small AI pilot in the OR might help identify the top causes of first-case delays or reveal unused block capacity. Those insights may not transform everything overnight, but they build credibility, trust, and curiosity for the next step forward. Each success, no matter how small, adds to organization confidence and strengthens the data muscle.
As these small wins compound, organizations naturally become ready to take on more advanced capabilities like AI, not as a leap, but as a natural next step in their maturity journey.
AI isn’t plug-and-play, and it’s not something you can prepare for by waiting.
You don’t develop AI-readiness by waiting. You build it by practicing.
Experimenting with AI proof of concepts is one of the clearest demonstrations of data maturity in action. Each test strengthens the organization's ability to evolve its data practices, linking data governance, literacy, and trust with real-world applications. In other words, AI experimentation becomes a living exercise in data maturity, where teams practice using data responsibly and iteratively, learning to balance innovation with discipline.
Going back to the OR example, AI can transform how leaders engage with efficiency data. Instead of waiting for a monthly report, an LLM layered on top of governed OR data allows leaders to ask in real-time, “Which surgeons most frequently contribute to first-case delays?” or “Which specialties are consistently underutilizing their blocks?” This self-service model can respond in seconds, drawing from trusted, governed datasets. It doesn’t replace the analyst teams — it amplifies them, encouraging end users to engage with data more naturally and consistently.
AI and data maturity reinforce each other. Mature organizations create better foundations for AI, while experimenting with AI strengthens the very muscles of governance, literacy, and trust that define maturity.
Once you’ve hit your goals, you can’t maintain your current state if you halt progress. Healthcare doesn’t stand still, nor should it. New regulations, treatments, and care models constantly shift priorities. Systems that have pumped up their data muscle can quickly pivot. For example, as value-based care becomes the norm, those with strong governance and democratized literacy can adapt referral workflows to align with new quality and financial incentives. For them, it isn’t a scramble; it’s just the next exercise in a routine they already know.
Data maturity isn’t a race to be finished; it’s ongoing. By embedding governance into culture, expanding literacy, celebrating incremental value, and embracing AI experimentation, organizations create a feedback loop that drives continuous learning and performance.
The ones that treat data maturity like a muscle — exercising it every day — will be the ones ready for whatever comes next. You don’t get ready by waiting. You get ready by practicing. The organizations building this discipline today are the ones that will thrive tomorrow.
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