Do different departments report conflicting numbers? Are your teams making critical decisions based on gut feelings because they don’t trust the data? These are common frustrations, and they are often symptoms of a deeper issue. Instead of patching individual problems as they arise, a data maturity assessment helps you diagnose the root cause. It systematically evaluates your entire data ecosystem—from governance and quality to your team’s skills and your underlying technology. This holistic view reveals why these issues keep happening and what foundational changes are needed to build a reliable, trustworthy data practice. It’s the first step to moving from a reactive, chaotic environment to one where data is a dependable asset.

Key Takeaways

• Go Beyond the Tech: A true data maturity assessment evaluates your entire data ecosystem—people, processes, and strategy—to give you an honest baseline for building a practical improvement plan.
• Translate Insights into a Roadmap: An assessment’s real value is the strategic roadmap it produces. This plan should outline concrete steps, balancing quick wins that build momentum with a long-term vision for transformation.
• Select the Right Model for You: There’s no single best framework; the right one aligns with your specific industry, resources, and business goals. This ensures your assessment provides relevant benchmarks and a truly useful path forward.

What is a Data Maturity Assessment?

Think of a data maturity assessment as a check-up for your organization’s data health. It’s a comprehensive evaluation that shows you how effectively your company uses data to make decisions and drive business outcomes. Instead of guessing where you stand, an assessment gives you a clear, objective picture of your current capabilities—pinpointing both your strengths and the areas that need improvement. It’s the essential first step in moving from simply collecting data to creating a culture where data-driven insights are at the heart of every strategy.

This process isn’t just about auditing your technology. A true assessment looks at the entire data ecosystem, including your people, processes, and strategy. It helps you answer critical questions: Do we have a clear vision for our data? Are our teams equipped with the right skills and tools? Is our data reliable and accessible? By understanding where you are on the data maturity spectrum, you can build a practical, actionable roadmap. This roadmap guides your investments and efforts, ensuring you’re focusing on the initiatives that will deliver the most value and help you achieve your strategic business goals.

Key Components

A thorough data maturity assessment evaluates several core areas of your organization. It’s not just about the tech; it’s about how everything works together. The assessment typically examines your data practices through a few key lenses. First is People and Culture, which looks at the data literacy of your teams, executive sponsorship, and whether you have a culture that encourages data-driven curiosity. Next is Process and Governance, which covers how you manage data quality, security, and compliance. Finally, it reviews your Technology and Infrastructure, including your data architecture, tools, and platforms, to see if they can support your ambitions.

The Business Benefits

Conducting a data maturity assessment delivers tangible business advantages that go far beyond a simple report card. By identifying weaknesses in your data management practices, you can directly address issues that lead to inefficiencies and poor data quality. This clarity allows you to create a targeted investment plan, ensuring you spend money on the tools and training that matter most. Organizations with higher data maturity consistently gain a competitive edge because they can react faster to market changes, understand customer behavior more deeply, and innovate more effectively. Ultimately, it aligns your entire organization around a unified data strategy, turning data from a simple asset into a powerful driver of growth.

How It Improves Decision-Making

A data maturity assessment directly improves decision-making by building a foundation of trust in your data. When you have a clear understanding of your data’s quality, accessibility, and governance, your teams can confidently rely on it to make informed choices. The assessment process helps break down data silos and establishes a single source of truth, so everyone is working from the same information. This shift moves your organization away from relying on gut feelings or anecdotal evidence and toward a culture of making strategic, evidence-based decisions. With reliable data at their fingertips, leaders can better forecast future trends, optimize operations, and personalize customer experiences.

What Are Data Maturity Models?

Think of a data maturity model as a roadmap for your organization’s data journey. It’s a framework that helps you understand where you currently stand with your data capabilities and shows you the path to becoming more data-driven. Instead of guessing what to improve next, these models provide a structured, step-by-step approach to assess everything from your data governance policies to your analytics tools. They break down the complex world of data management into clear, progressive stages.

Using a data maturity model helps you pinpoint specific weaknesses and strengths in your data ecosystem. It gives you a common language to discuss progress with stakeholders and helps you build a realistic plan for improvement. Whether you’re just starting to collect data or you’re looking to implement advanced AI, a maturity model provides the clarity needed to move forward effectively. There are several established models out there, each with a slightly different focus, but all share the goal of guiding your organization toward greater data proficiency.

Gartner’s Analytics Maturity Model

One of the most recognized frameworks is Gartner’s Analytics Maturity Model, which outlines five distinct stages of analytical capabilities. It starts with the “Initial” stage, where analytics are often chaotic and reactive, and progresses all the way to “Optimized,” where data is central to driving automated and predictive business decisions. This model is particularly useful because it focuses on the evolution of analytics practices themselves. It helps you see how your organization can move from simply describing what happened in the past to predicting what will happen in the future and prescribing the best course of action.

CMMI Data Management Maturity Model

The Capability Maturity Model Integration (CMMI) is a framework that emphasizes continuous, measurable improvement in your data management processes. Originally developed for software engineering, its principles have been adapted to help organizations systematically enhance how they handle data. The CMMI model encourages you to look at your data management practices as a set of processes that can be defined, managed, and optimized over time. By following this model, you can build a robust foundation for data governance and ensure your practices become more consistent and reliable as your organization grows.

Data Management Maturity Model (DMM)

The Data Management Maturity Model (DMM) is another structured framework designed specifically to help organizations evaluate and improve their data management capabilities. It provides a comprehensive set of best practices across key data management disciplines, such as data strategy, data quality, and data operations. The DMM is an excellent tool for conducting a detailed assessment of your current state and identifying actionable steps for improvement. Using a Data Governance Maturity Model like the DMM helps you align your data strategy with your business goals, leading to better operational efficiency and more trustworthy data.

Industry-Specific Frameworks

While general models are helpful, sometimes you need a more tailored approach. Industry-specific frameworks are designed to address the unique challenges and regulatory requirements of sectors like financial services, healthcare, or media. These models allow you to benchmark your data practices not just against a general standard, but against your direct competitors and peers. They incorporate industry-specific key performance indicators and compliance standards, providing a more relevant and actionable assessment. This helps you align your data strategy with the specific demands and opportunities within your market, giving you a competitive edge.

The Key Dimensions of a Data Assessment

A thorough data maturity assessment looks at your organization from multiple angles. It’s not just about the technology you use; it’s also about your strategy, your people, and your processes. By examining each of these dimensions, you get a complete picture of where you stand and a clear path forward. Think of these as the pillars that support your entire data practice. If one is weak, the whole structure is at risk. A balanced approach ensures you’re building a sustainable, data-driven foundation for growth.

Data Strategy and Governance

Your data strategy is your high-level plan for how you’ll use data to achieve your business goals. Governance, on the other hand, provides the rules and procedures to execute that plan effectively. This dimension assesses how well-defined your data policies are and whether you have clear ownership and accountability. As one report notes, “assessing data governance maturity helps organizations benchmark their current practices, identify gaps, and implement improvements.” A strong strategy ensures everyone is pulling in the same direction, while solid governance builds trust in your data and keeps it secure and compliant.

Data Architecture and Infrastructure

This dimension covers the technical foundation of your data ecosystem—the systems, tools, and pipelines that store, process, and move your data. Is your architecture scalable and flexible enough to handle future needs? Are your tools integrated, or are they creating data silos? A well-designed data architecture is the backbone of your entire operation. It’s what makes everything else possible, from daily reporting to advanced AI applications. A robust data architecture is essential for effective data management, enabling your organization to streamline data flows and ensure you get the right information to the right people at the right time.

Data Quality and Standards

You can have the best strategy and technology in the world, but if your data is inaccurate, incomplete, or inconsistent, your efforts will fall flat. This dimension evaluates the reliability and trustworthiness of your data. It looks at whether you have processes in place to clean, validate, and maintain data quality over time. As industry experts point out, “data quality remains the foundation—and the bottleneck—of effective data management.” Without high-quality data, your analytics will be flawed, your decisions will be misguided, and your team’s confidence in the data will erode.

Analytics and Insights

This is where your data starts to create real value. This dimension assesses your ability to turn raw data into actionable insights that drive business decisions. Are you stuck in basic reporting, or are you using more advanced techniques like predictive analytics? Many organizations find that “descriptive analytics still dominates—at a cost, as organizations struggle to move beyond basic reporting to more advanced analytics.” The goal is to move up the value chain from simply describing what happened to predicting what will happen and prescribing the best course of action. This is how you can leverage data to find new opportunities and gain a competitive edge.

Data Culture and Skills

Ultimately, becoming data-driven is about people. This dimension looks at the human side of your data practice, including data literacy, employee skills, and leadership buy-in. Is your team equipped with the right skills to work with data? Does your company culture encourage curiosity and data-informed decision-making? Organizations often face a “Data Literacy Challenge” alongside other hurdles related to people and skills. Fostering a strong data culture requires a commitment to training, clear communication from leadership, and making data accessible to everyone who needs it. It’s about empowering your entire team to use data confidently in their day-to-day work.

How to Choose the Right Assessment Model

With so many data maturity models available, picking the right one can feel overwhelming. The key is to remember that there’s no one-size-fits-all solution. The best model for your organization is the one that aligns with your specific context—your goals, your current state, your industry, and your resources. Choosing a framework that doesn’t fit is like using the wrong map; you might be moving, but you won’t get where you want to go. A generic model might tell you that you’re behind, but it won’t tell you how to catch up in a way that makes sense for your business.

Think of this selection process as the first step in your data strategy. It’s about finding a tool that provides a clear, relevant, and actionable path forward. The right model will give you a baseline, help you set realistic goals, and show you the specific steps to take to improve. It moves the conversation from a vague sense of “we need to be more data-driven” to a concrete plan. By carefully considering the factors below, you can select an assessment model that not only measures your maturity but also helps you build a practical roadmap for growth that will resonate from the C-suite to your analytics team.

Identify Your Business Goals

Before you can measure your data maturity, you need to know what you’re aiming for. What does success look like for your business? Are you striving to be a market innovator, or is your primary focus on operational efficiency? Your overarching business goals should be the North Star for your data strategy. A good assessment model allows you to tailor the evaluation to these objectives. For example, some models let companies select their specific business goals and then measure maturity based on what’s most important to them. This ensures the recommendations you receive are genuinely useful and directly support your strategic vision.

Evaluate Your Current Capabilities

You can’t plan a route without knowing your starting point. A crucial step in choosing a model is to conduct an honest evaluation of your current data capabilities. Where are you strong, and where are the gaps? Assessing your data governance maturity helps you benchmark your current practices, identify weaknesses, and pinpoint areas for improvement. This initial self-assessment will help you select a model that meets you where you are. If you’re just starting, a complex, high-level model might be discouraging. Instead, look for a framework that provides clear steps for foundational improvements.

Consider Your Industry’s Needs

Every industry has its own unique data challenges and opportunities. A media company’s data needs are vastly different from those of a healthcare provider. Many organizations struggle with inconsistent data quality, manual reporting, and decision-making based on intuition rather than facts. Your chosen assessment model should reflect the specific demands of your sector. An industry-agnostic model might overlook critical nuances, while a framework designed with your field in mind will provide more relevant benchmarks and insights. At DAS42, we have experience across many different industries and know how to apply the right lens to your assessment.

Factor in Your Resources

Finally, be realistic about the resources you can commit to this process. This includes your budget, your team’s availability and skills, and your existing technology stack. Some assessment models require significant investment in tools and external consultants, while others can be implemented with a more modest internal effort. The goal is to find a model that you can realistically execute and sustain. Leveraging a data maturity model should help you better manage your data resources, not drain them. Choose a framework that aligns with your capacity and provides a clear return on your investment of time and money.

How to Conduct Your Assessment

Once you’ve selected a model, it’s time to put it to work. A data maturity assessment isn’t just a technical audit; it’s a strategic project that requires a clear plan and buy-in from across your organization. Think of it as creating a detailed map of your current location so you can chart the best course to your destination. A methodical approach ensures you gather the right information, involve the right people, and produce findings that lead to real action. Following a structured process helps you move from simply having data to using it as a strategic asset.

Set Clear Objectives

Before you begin, you need to know what you’re trying to achieve. What does success look like for your data initiatives? Your objectives will guide the entire assessment process, from the questions you ask to the metrics you track. A Data Maturity Assessment helps you understand how well your organization is using data and AI, giving you a clear picture of where you stand and how to improve. Are you aiming to enhance customer personalization, optimize supply chain logistics, or improve your predictive analytics capabilities? Defining these goals upfront ensures your assessment is focused and that the results directly support your broader business strategy.

Choose Your Methods and Tools

You don’t need to start from scratch. There are several established data maturity models that provide frameworks for evaluating your practices. These models can guide you in selecting the right tools and methodologies for your specific needs, whether you’re focused on data governance, analytics, or overall data management. The key is to choose a framework that aligns with the objectives you’ve already set. Your assessment might involve a mix of methods, including stakeholder interviews, workshops, technical audits of your data architecture, and surveys to gauge data literacy across different teams. This tailored approach ensures the assessment is relevant and comprehensive.

Gather the Right Information

A thorough assessment requires a holistic view of your organization’s data ecosystem. This means collecting information across three key areas: Purpose, Practice, and People. For Purpose, review your data strategy and governance policies. For Practice, examine your data architecture, tools, and quality management processes. For People, evaluate the data skills and cultural attitudes within your teams. Gathering data from all these areas is crucial for a comprehensive evaluation. This approach ensures you understand not only what technology you have but how people are using it to drive business goals, giving you a complete picture of your data landscape.

Communicate with Stakeholders

A data maturity assessment is a team sport. It’s essential to involve stakeholders from across the business—from the C-suite to the analysts on the front lines. Effective communication ensures everyone understands the purpose of the assessment and can contribute their unique perspective. Schedule regular check-ins, share preliminary findings, and create clear, accessible summaries for leadership and funding partners. When stakeholders are engaged throughout the process, they are more likely to champion the changes and initiatives that come out of your final roadmap, making implementation much smoother.

Analyze the Results

After gathering all the information, the next step is to analyze it to identify where you are on the maturity scale. This analysis helps you benchmark your current practices, pinpoint specific gaps, and highlight your strengths. The goal is to translate your findings into a clear, actionable story. Where are the biggest opportunities for improvement? What are the quick wins you can tackle now? This analysis is the foundation for your data-driven roadmap, turning your assessment from a simple report into a strategic plan for the future, as demonstrated in our client success stories.

What Are the Data Maturity Levels?

Think of data maturity as a journey with distinct stages. Every organization starts somewhere, and understanding these levels helps you pinpoint where you are now and what steps to take next. Most models break this journey down into five levels, moving from a chaotic, reactive approach to a proactive, data-driven culture. Each level builds on the last, creating a stronger foundation for using data effectively. Recognizing your company’s current stage is the first step toward building a realistic roadmap. It’s not about judging where you are, but about getting a clear picture so you can plan your path forward. Let’s walk through what each of these five levels looks like in practice.

Level 1: Initial

At the Initial level, data management is often an afterthought. Think of it as the “wild west” of data—processes are unpredictable, inconsistent, and handled on an individual, ad-hoc basis. There’s no overarching strategy, so different people or teams might be doing things in completely different ways. At this stage, the organization is purely reactive, often scrambling to find data when a problem arises rather than using it to prevent issues. The U.S. Department of Labor’s Data Management Maturity Model describes this level as lacking a structured approach, leading to inconsistent practices and a general lack of awareness about the importance of data.

Level 2: Repeatable

When an organization reaches the Repeatable level, you start to see pockets of progress. Some processes are established and can be repeated, but they aren’t yet standardized across the entire company. For example, the marketing team might have a solid process for tracking campaign data, but the finance team is still using manual spreadsheets. There’s a growing awareness of data’s importance, but efforts are siloed and depend heavily on the skills of a few key individuals. This stage highlights some of the common challenges leaders face when trying to scale their data efforts from individual successes to a company-wide strategy.

Level 3: Defined

This is where a real shift happens. At the Defined level, your organization has documented and standardized its data management processes. Everyone follows the same playbook, which means practices are consistent and can be replicated across different departments. You’re no longer just repeating processes; you’re actively defining them as the official way of doing things. This stage involves creating clear guidelines, establishing best practices, and fostering a more collaborative approach to data. It’s about moving from “this is how I do it” to “this is how we do it,” creating a unified and reliable data environment for the whole organization.

Level 4: Managed

At the Managed level, you’re not just following processes—you’re actively measuring their effectiveness. This stage is all about monitoring and control. Your organization has established key metrics to assess things like data quality, accessibility, and performance. You can answer questions like, “How accurate is our customer data?” or “How quickly can we generate this report?” This level emphasizes the critical role of data governance and accountability, ensuring that your data is not only managed but also trustworthy. This allows your teams to make critical business decisions with a high degree of confidence in the information they’re using.

Level 5: Optimized

The final stage, Optimized, is where data becomes a true strategic asset. At this level, your organization isn’t just managing data well; it’s continuously improving its data practices based on performance metrics and feedback. Data is fully integrated into the business strategy, driving innovation, efficiency, and a significant competitive advantage. Teams use advanced analytics and predictive modeling to not just understand what happened, but to anticipate what will happen next. Decision-making is proactive and data-informed at every level, creating a culture that constantly seeks to refine its approach and find new ways to leverage data for growth.

How to Create Your Data-Driven Roadmap

Once your data maturity assessment is complete, you have a clear picture of where you stand. But how do you get to where you want to go? The answer is a data-driven roadmap. This isn’t just a project plan; it’s a strategic guide that translates your assessment findings into concrete, actionable steps. It connects your current capabilities to your future ambitions, ensuring every initiative serves a larger purpose.

A well-crafted roadmap breaks the journey into manageable phases, allowing you to demonstrate value quickly while building toward transformative change. It aligns technology, people, and processes, ensuring everyone is moving in the same direction. Think of it as the bridge between insight and impact. By outlining clear priorities and timelines, you can secure stakeholder buy-in, manage resources effectively, and turn your data vision into a reality.

Define Short-Term Wins

The best way to build momentum for a long-term data strategy is to score some early victories. Short-term wins are focused, high-impact projects you can complete in the next three to six months. These successes demonstrate the value of your data initiatives, build confidence among stakeholders, and energize your team for the journey ahead. Your assessment is crucial here; you can’t identify quick wins if you aren’t fully aware of your most pressing data issues. Look for the “low-hanging fruit”—the problems that are relatively easy to solve but offer significant returns. This could mean automating a manual report, cleaning up a critical dataset, or launching a pilot analytics project for a single department.

Set Medium-Term Goals

With a few wins under your belt, you can shift your focus to more substantial objectives. Medium-term goals are the core of your roadmap, typically spanning six to 18 months. These initiatives build on your early successes and begin to address the more complex gaps identified in your assessment. This is your chance to benchmark your data management and governance practices against industry standards and make significant strides. Examples include implementing a new data governance framework, migrating a key application to a modern cloud platform, or developing a predictive model to reduce customer churn. These goals should be specific, measurable, and directly tied to key business outcomes.

Establish a Long-Term Vision

Your long-term vision is the north star of your data strategy. It answers the question: “What will our organization look like in three to five years when we are fully data-driven?” This vision should be ambitious yet achievable, inspiring your team and guiding your strategic decisions. It’s about transforming how your business operates, not just making incremental improvements. Your assessment helps shape this vision by highlighting the foundational gaps that need to be closed to reach your ultimate goal. This could mean creating a seamless 360-degree view of your customer, fostering a company-wide data culture, or becoming an industry leader through AI-powered innovation.

Track Your Progress

A roadmap is only effective if you monitor your progress against it. Establishing clear key performance indicators (KPIs) is essential for keeping your initiatives on track and demonstrating their value. Tracking progress helps you move from making decisions based on intuition to relying on hard facts. You should measure both project milestones and business impact. Key metrics could include improvements in data quality scores, time saved through automation, user adoption rates for new analytics tools, and the ultimate ROI of your data projects. Regular check-ins and transparent reporting keep stakeholders informed, maintain momentum, and allow you to make adjustments to the roadmap as needed.

Plan for Continuous Improvement

Achieving data maturity is a journey, not a destination. Your roadmap should be a living document that evolves with your business. The data landscape is constantly changing, and your organization will face ongoing challenges related to culture, funding, and skills. A successful strategy includes a plan for continuous improvement. This means investing in ongoing data literacy training for your teams, creating forums for sharing best practices, and periodically reassessing your data maturity to identify new opportunities. By embedding a cycle of assessment, planning, and refinement into your operations, you ensure your organization remains agile and continues to generate value from its data for years to come.

How to Overcome Common Challenges

Moving up the data maturity curve is a significant undertaking, and it’s natural to encounter a few bumps along the way. Most organizations face similar hurdles, from tight budgets to team skepticism. The key is to anticipate these challenges and have a clear plan to address them head-on. By understanding the common obstacles, you can build a more resilient and effective data strategy that prepares your organization for success. Let’s walk through some of the most frequent challenges and the practical steps you can take to overcome them.

Addressing Resource Limitations

Many organizations struggle with limited budgets and personnel when it comes to investing in their data initiatives. If you’re facing resource constraints, the best approach is to be strategic. Instead of trying to do everything at once, focus on small, high-impact projects that can deliver a clear return on investment. These early wins can build momentum and justify further investment down the line. A phased implementation allows you to manage costs and scale your efforts over time. Partnering with a data consultancy can also provide access to specialized expertise and managed services without the long-term overhead of hiring a full-time team, giving you the support you need to get started.

Managing Cultural Resistance

Technology is only one part of the equation; people are the other. It’s common to face resistance from employees who are accustomed to existing processes and may not immediately see the value in new data practices. To manage this, communication is everything. Clearly articulate the “why” behind the changes, focusing on how a data-driven approach will make their roles easier and more impactful. Involve key team members from various departments in the planning process to foster a sense of ownership. By creating internal data champions and demonstrating the benefits through pilot projects, you can gradually shift the organizational culture toward one that embraces data.

Solving Data Quality Issues

You can’t build a strong data strategy on a shaky foundation. If your data quality is inconsistent, any insights you generate will be unreliable, and decision-making will continue to rely on intuition rather than facts. The first step is to conduct a thorough audit to understand the state of your data. From there, you can establish clear data governance policies that define standards for data entry, storage, and usage. Implementing automated validation and cleaning processes can help maintain data integrity over time. Fixing data quality isn’t a one-time project but an ongoing commitment that pays dividends by ensuring every decision is based on accurate, trustworthy information.

Closing Skills and Literacy Gaps

Having access to data isn’t enough if your team doesn’t know how to use it. A lack of data literacy can prevent employees from effectively interpreting information and applying it to their work. To close this gap, invest in targeted training that is relevant to different roles and skill levels. Provide your teams with user-friendly dashboards and tools that make data accessible and easy to understand. More importantly, foster a culture of curiosity where employees feel empowered to ask questions and explore data. When everyone in the organization has a baseline understanding of data, you create a powerful network of analytical thinkers.

Implement a Change Management Strategy

Overcoming these challenges isn’t about tackling them one by one in isolation. It requires a holistic change management strategy that ties everything together. A formal plan ensures that you address the technical, cultural, and educational components of your data transformation in a coordinated way. This strategy should outline your communication plan, stakeholder engagement, training programs, and methods for gathering feedback. By thoughtfully managing the transition, you can guide your organization through the necessary changes smoothly and effectively. A well-designed strategy helps ensure that your data modernization efforts stick for the long term, becoming a core part of how your business operates.

Related Articles

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Frequently Asked Questions

How is a data maturity assessment different from a typical IT audit? Think of it this way: an IT audit checks if your car’s engine is running correctly and has all the right parts. A data maturity assessment asks where you’re trying to drive, if you have the right map, and if your driver knows how to get there. While an IT audit focuses on the health and security of your technical infrastructure, a data maturity assessment takes a much broader view. It evaluates how your people, processes, and technology all work together to use data to achieve actual business goals.

Can we conduct a data maturity assessment ourselves, or should we bring in an expert? You can certainly start the process internally, and a self-assessment can be a great way to get the conversation started. However, bringing in an outside expert provides an objective, unbiased perspective that’s hard to achieve from within. A specialized consultancy can benchmark your practices against others in your industry, identify blind spots your team might miss, and help you build a much more effective and actionable roadmap based on years of experience.

This sounds like a huge project. What’s the first practical step we should take? If you’re feeling overwhelmed, start small. Forget about assessing the entire organization for a moment. Instead, gather a few key people from different departments and ask one simple question: “What is one critical business question we can’t answer right now because we don’t have the right data?” This single question can reveal gaps in your data, processes, or tools and provides a focused, tangible starting point for a larger conversation.

How long does a data maturity assessment typically take? The timeline really depends on the size and complexity of your organization. For a smaller company or a single department, a focused assessment might take a few weeks. For a large enterprise-wide evaluation, it could take a couple of months. The process involves interviews, workshops, and technical reviews, so the goal is to be thorough without causing disruption. A good partner will work with you to define a scope and timeline that fits your needs.

How often should our organization conduct a data maturity assessment? This isn’t a one-and-done activity. Think of it as a regular health check-up. A comprehensive assessment is a great idea every 18 to 24 months to recalibrate your strategy and measure progress. In between those major assessments, you should conduct smaller, informal check-ins every six months or so to ensure you’re staying on track with your roadmap and adapting to any new business priorities or technological changes.