Data maturity is one of those concepts that sounds abstract until you see it play out in a meeting. Someone asks a straightforward question — "What's our retention rate?" — and three people give three different answers. And they all turn out to be somewhat correct.
The reason for the mismatch? The data lives in different places, defined in different ways, with no shared understanding of what "retention" actually means.
Data maturity captures the gap between having data and using it well. And more broadly reflects how effectively your organization manages, governs, and exploits data across people, processes, technology, and culture.
This guide walks through what data maturity models are, why they matter, and how to assess where your organization sits on that spectrum. More importantly, it shows you what it takes to move forward.
What is a data maturity model?
A data maturity model is a framework for understanding how well your organization manages and uses its data. Think of it as a diagnostic tool that measures your current state, benchmarks you against best practices, and maps out what advancement actually looks like. It helps you figure out where you stand today and what it takes to get better.
Data maturity isn't a single capability you either have or you don't. It's a spectrum—a combination of technology, processes, people, and culture all working together.
The frameworks differ in what they emphasize most, with some prioritizing governance structures, others focusing on analytics capabilities, and newer ones putting infrastructure and AI-readiness at the center. Choosing the right framework comes down to what your organization is actually trying to solve.
Why data maturity matters
Data maturity matters because it directly affects how well your organization can turn information into action. When data practices are immature, teams end up wasting time chasing down numbers, second-guessing reports, and making decisions based on gut feel rather than evidence. As maturity improves, so does everything that follows: teams move faster, people trust the numbers, and the organization can take on more sophisticated analytical work.
Organizations with mature data practices generally spend less time hunting for numbers and reconciling conflicting reports, which frees analysts to focus on actual analysis instead of data archaeology. And when metrics are defined consistently and trusted across the organization, decisions happen faster because nobody's stuck debating whether the numbers are even right in the first place.
There's also a compounding effect worth considering. Mature data organizations tend to adopt new capabilities like AI and machine learning (ML) more successfully because they've already built the foundation those technologies depend on. Without clean, governed, well-documented data, advanced analytics projects tend to stall or produce unreliable results. The organizations that invest in maturity early are the ones positioned to move quickly when new opportunities come along.
The five stages of data maturity
Many data maturity frameworks describe a progression from ad hoc to optimized, often broken into multiple stages. The underlying pattern stays the same: organizations start with informal, reactive data practices and gradually build toward something more systematic and strategic.
What's useful about understanding these stages is that they help clarify what "getting better" actually looks like for your team, and where the common sticking points tend to show up. Here's what each stage looks like in practice:
Stage 1: Initial/ad hoc
At the initial or ad hoc stage, data management happens mostly informally, with analysts reacting as needs arise. There's no standardized process for how data gets collected, stored, or analyzed, so success depends on individual heroics.
Reports take days when the one person who knows the data is on vacation. Executives make decisions based on intuition because nobody trusts the numbers. And when someone asks, "Where does this metric come from?" the answer is usually a shrug or a 45-minute archaeology expedition through old spreadsheets.
This is also the stage where AI tools cause problems rather than solve them. Without clean data structures or documented context, AI agents confidently generate analyses built on whatever mess they find, and nobody has governance in place to catch the errors. Organizations at this stage that adopt AI analytics often see the worst of both worlds: faster answers that are just as unreliable as before.
Companies typically move beyond this stage after a crisis: a major decision goes sideways because someone used bad data, a key analyst leaves with all the institutional knowledge, or stakeholders adopt AI tools on their own and generate numbers that conflict with official reports.
Stage 2: Emerging
At the emerging stage, teams start to recognize that data has strategic value. Initial governance structures are forming, and some standardization exists, but departmental silos remain strong. There's a growing awareness that the current approach isn't sustainable, even if nobody's quite sure what to do about it yet.
This is also when foundational context work starts to matter—and not just for humans. Well-modeled data becomes the foundation that AI tools depend on. Naming conventions matter more when an LLM is trying to figure out which tables to join; the agent will find your data, but if it's a mess, it builds messy analyses on top of it. Documentation stops being optional, too. Teams used to let docs slide because they could answer questions when they came up. Now AI agents answer those questions, and they need the same context you'd give a human: What does this field actually measure? When was this table last updated? What are the known data quality issues?
You'll also start seeing pockets of good practice scattered around the organization. Maybe the finance team has solid data hygiene, but marketing and sales are still operating in separate universes. Consistency is the main challenge at this stage, since what works in one department doesn't naturally transfer to others.
Companies typically move from emerging to defined after cross-functional projects start failing, when teams can't agree on definitions, or leadership starts asking questions that pull data from multiple departments at once.
Stage 3: Defined
At the defined stage, data management processes are documented and standardized across the enterprise. Formal governance structures are in place, cross-functional data teams have clear responsibilities, and analytics capabilities are repeatable. Things start to feel organized rather than improvised.
You've got a data dictionary that people actually reference. New hires can ramp up without a three-week oral history from senior analysts. When the CEO asks about customer acquisition cost, everyone works from the same definition, and the conversation moves forward instead of getting stuck on methodology.
This is also when rules files start filling in the blanks about how your business actually works. These capture the things you'd mention to an analyst in passing: "Q4 revenue always spikes because of year-end deals" or "exclude accounts created before 2020 from cohort analysis because of the data migration." That institutional knowledge used to live in people's heads. Now it needs to be written down somewhere both humans and AI agents can find it.
Most organizations stay at the defined stage for a while. They move to managed when they want to go from consistent reporting to proactive optimization, or when they're ready to adopt advanced analytics that need tighter quality controls to work reliably.
Stage 4: Managed
At the managed stage, governance is actively enforced and monitored, integrated with business processes rather than just documented somewhere. Quantitative metrics track quality, usage, and impact across the organization. Advanced analytics run enterprise-wide with confidence.
Policies aren't enough anymore. You're measuring whether people follow them and catching issues before they become problems. Data quality becomes measurable and improves over time because you can see where the gaps are.
Semantic layers become essential at this stage—not just for human analysts, but as the source of truth for AI. When anyone can create their own metrics, you need a clear definition of what "active user" or "ARR" actually means. Semantic layers ensure consistency across hundreds of analyses you'll never personally review. Reusable analyses also become building blocks: AI agents can look at past work and extend it rather than starting from scratch. But this only works if those analyses are clear enough for an LLM to understand and build on.
Organizations typically reach the optimized stage when leadership wants data to drive strategy, not just support it. That's when they're ready to treat data as a competitive asset rather than an operational necessity.
Stage 5: Optimized
At the optimized stage, continuous improvement is embedded throughout operations. Data strategy proactively drives business innovation rather than responding to it. Enterprise-wide data sharing is normalized, and everyone treats data as a strategic asset without being reminded.
Data quality is simply how everyone works. Teams experiment with new data products and capabilities as a matter of course, building on a foundation they trust. People across the organization self-serve answers through AI—asking questions in natural language and getting trustworthy results because the underlying context, definitions, and governance are already in place.
The focus shifts from advancing to sustaining. Few organizations truly reach this level, and staying here takes constant effort because the bar keeps moving.
How to assess your data maturity
Assessing your data maturity means honestly evaluating where your organization stands across the capabilities that matter. The DAMA DMBOK framework is one of the most widely recognized references for this, and the core process is straightforward. Here's how to approach it:
Pick a framework and assemble a team that understands both technical infrastructure and business context
Evaluate governance by asking whether your rules and standards are actually followed or just documented
Assess technology by looking at platform sophistication and whether systems can talk to each other
Examine culture by checking if leadership genuinely supports data-driven decision-making
Measure data quality by considering when a decision last stalled because nobody trusted the numbers
Track progress over time so you can see whether investments are generating returns
These dimensions work together, and weakness in one tends to limit progress in others. Calendly shows what the "evaluate governance" step looks like when done well: their Go-to-Market analytics team built a standardized metric library that serves as company-wide KPI documentation. Instead of just documenting standards and hoping people follow them, they created a trusted reference that teams actually use to resolve conflicting reports and ramp new hires. That's the difference between governance that exists on paper and governance that shapes how people work.
Data maturity in the age of AI
AI and machine learning have expanded what data maturity means. Traditional frameworks focused on data management and governance, but modern assessment also needs to account for ML systems, AI governance, and infrastructure for operationalizing models at scale.
The core principles don't change, though. AI doesn't replace governance, quality, infrastructure, and culture; it extends them. Organizations struggling with basic data management won't solve those problems by adding AI, but organizations with strong foundations can use AI to accelerate their maturity significantly.
The right tooling can make a real difference in how quickly teams move through these maturity stages. Platforms that bring data work together in one place, rather than scattering it across disconnected tools, help teams build the consistency and trust that maturity requires.
Hex addresses these challenges as an AI analytics platform where data teams and business users work side-by-side in one secure workspace. It brings SQL, Python, and no-code capabilities together in a unified workspace, so analysts aren't jumping between six different tools just to answer one question. By unifying deep analytical work, governed context, and conversational self-serve, data teams move faster, empower their organizations, and deliver more impact.
Features like Semantic Modeling help you define metrics once and use them everywhere, addressing the "three people give three different answers" problem that keeps data teams stuck at early maturity stages. And with AI built natively into the platform through features like Threads, Notebook Agent, and the Modeling agent teams can accelerate their analytics workflows while maintaining the transparency and auditability that mature data organizations need.
Whether you're assessing your current maturity or building the infrastructure to advance it, the path forward starts with understanding where you are. Sign up for free or request a demo to see how Hex can support your data maturity journey.