Does finding reliable data in your organization feel like a treasure hunt with no map? When teams spend more time arguing over whose numbers are correct than making decisions, it’s a clear sign of data chaos. This confusion isn’t just frustrating; it holds your business back. The solution isn’t another dashboard or a new piece of software, but a clear understanding of where your processes are failing. A data governance maturity model assessment provides that clarity. It acts as a diagnostic tool, helping you pinpoint weaknesses and create a practical roadmap for building a data foundation that everyone can trust.

Key Takeaways

• Use a Maturity Model to Create Your Strategic Roadmap: A maturity model replaces guesswork with a clear, five-level framework. It helps you accurately assess your current data practices and build a targeted plan to transform data into a reliable business asset.
• Turn Assessment into Action with a Structured Plan: A successful assessment is a structured process, not a simple audit. By choosing the right framework and gathering cross-functional input, you can identify specific gaps and create a prioritized action plan that delivers measurable results.
• Make Governance Stick with a Focus on Culture and Consistency: Lasting change requires more than just a plan; it requires a cultural shift. Secure early stakeholder buy-in and commit to regular reviews to ensure your data governance program remains effective and evolves with your business.

What is a Data Governance Maturity Model?

Think of a data governance maturity model as a report card for how well your company handles its data. It’s a framework that helps you measure the current state of your data management practices and gives you a clear roadmap for improvement. Instead of guessing where you stand, a maturity model provides a structured way to assess everything from your data quality and security to your policies and team capabilities. It replaces ambiguity with a clear, tiered system that shows you exactly where you are and what steps you need to take to get to the next level.

This isn’t just about checking boxes; it’s about building a solid foundation for a data-driven culture. By understanding your maturity level, you can create a targeted data governance strategy that addresses your specific weaknesses and builds on your strengths. This process ensures your data becomes a reliable asset for growth and innovation, rather than a source of confusion. It’s the first step toward making your data work for you, not against you.

Key Components of a Maturity Model

At its core, a maturity model provides a scale to measure your progress, typically from Level 1 to Level 5. Level 1, often called “Initial” or “Unaware,” is where most companies start. It’s characterized by a lack of formal processes, where data management is chaotic, inconsistent, and handled on an ad-hoc basis by individuals. As you move up the levels, you introduce more structure, consistency, and strategic oversight.

The model evaluates several key areas, including data quality, security, policy enforcement, and stakeholder roles. It provides a clear set of criteria for each level, so you know exactly what it takes to advance. This structure helps you pinpoint specific gaps and create a practical plan to move from a reactive state to a proactive, and eventually, an optimized one.

The Business Impact of Data Governance

Using a data governance maturity model does more than just organize your data—it has a direct impact on your bottom line. A strong data governance framework helps you avoid costly problems like data breaches, compliance fines, and poor business decisions based on inaccurate information. When everyone in the organization trusts the data they’re using, you see better collaboration and more confident decision-making across all departments.

Ultimately, improving your data governance maturity turns your data into a strategic asset. It allows you to build a 360-degree view of your customers, improve predictive analytics, and drive real business value. It’s the difference between having data and having data that works for you, providing the clarity needed to stay competitive.

Popular Frameworks at a Glance

You don’t have to build a maturity model from scratch. Several well-established frameworks can guide your assessment, each with a slightly different focus. Some of the most recognized models include the DAMA DMBoK, Gartner’s Data Governance Maturity Model, the IBM Data Governance Maturity Model, and the CMMI Data Management Maturity (DMM) Model. Each of these provides a comprehensive structure for evaluating your practices.

Choosing the right framework depends on your industry, company size, and specific goals. For example, some models are more focused on risk and compliance, while others emphasize operational efficiency. Exploring these options will help you find the best fit for your organization and set you on the right path toward data excellence, a journey we’ve guided many of our clients on.

The 5 Levels of Data Governance Maturity

Understanding your organization’s data governance maturity is the first step toward improving it. Think of these five levels as a roadmap. They help you pinpoint where you are now, what the next stage looks like, and how to get there. Each level represents a significant shift in how a company perceives and manages its data, moving from a state of disorganized information to one where data is a strategic asset that drives business decisions.

Most organizations start at the lower levels, where data management is chaotic and reactive. As they mature, they introduce formal processes, define roles, and begin measuring their efforts. The ultimate goal is to reach a state where data governance is so ingrained in the company culture that it happens naturally, supporting continuous improvement and innovation. By identifying your current level, you can create a targeted data strategy that addresses your specific weaknesses and builds on your strengths, ensuring your journey toward data maturity is both efficient and effective.

Level 1: Initial (Unaware)

At this foundational level, there’s a general lack of awareness about the importance of data governance. Data management is chaotic and inconsistent, with no formal processes or standards in place. Different teams or individuals handle data in their own way, leading to data silos, poor data quality, and a lack of trust in reports. According to the U.S. Department of Labor, this stage means “there are no clear ways to manage data, or processes are done randomly by individuals without a common plan.” If your teams are constantly questioning the accuracy of their data or spending more time finding information than using it, you’re likely at Level 1.

Level 2: Developing (Reactive)

Organizations at Level 2 have started to recognize that their ad-hoc approach to data isn’t working. There’s a growing awareness of the need for data governance, but efforts are still largely reactive. Instead of preventing data issues, teams are constantly putting out fires. You might see some basic data definitions emerge or a few individuals tasked with “cleaning up” data, but there’s no comprehensive strategy. As DATAVERSITY notes, organizations at this level “begin to recognize the importance of data but lack comprehensive strategies.” It’s a step in the right direction, but the approach is fragmented and lacks the structure needed for real progress.

Level 3: Defined (Proactive)

This is where things start getting serious. At Level 3, an organization moves from a reactive to a proactive stance. Formal data governance practices are developed and implemented, with clear policies, standards, and procedures. Roles like data stewards and owners are officially defined, and a governance committee may be established to oversee the program. The focus shifts to actively improving data quality and making data easier to find and use. According to KORTX, this is when “clear plans for using data are set up, focusing on making sure the data is good quality and easy to find.” This stage is about building the foundation for a truly data-driven culture.

Level 4: Managed (Service-Oriented)

At Level 4, data governance is a well-oiled machine. The practices established in the previous stage are now consistently applied and, most importantly, measured. The organization has established key performance indicators (KPIs) to track the effectiveness of its data governance program and can demonstrate its value to the business. Data is treated as a shared service or a product, with clear accountability and service-level agreements. Automation is often used to enforce policies and monitor data quality, ensuring that governance is efficient and scalable. This is the point where data governance becomes a reliable, enterprise-wide function.

Level 5: Optimized (Business-Driven)

This is the pinnacle of data maturity. At Level 5, data governance is no longer a separate IT or data team initiative; it’s fully integrated into the business strategy. The processes are not just managed but are continuously improved based on feedback and changing business needs. The entire organization understands the value of well-governed data and actively participates in maintaining it. As KORTX describes it, data governance is “always improving and fully connected to business strategy.” At this stage, data is a true strategic asset, fueling everything from operational efficiency to groundbreaking predictive analytics.

How to Measure Your Maturity: Key Metrics

To understand where you are on the data governance maturity spectrum, you need to look at specific, measurable areas of your business. Think of these metrics as the vital signs of your data health. They give you a clear, objective picture of your current state and help you pinpoint exactly where to focus your improvement efforts. A thorough assessment looks beyond just technology; it examines your people, processes, and policies to provide a holistic view. By tracking these key metrics, you can move from simply guessing about your data governance effectiveness to knowing for sure.

Data Quality and Management

At its core, data governance is about trust. Can you rely on your data to make critical business decisions? Measuring data quality and management is the first step to building that trust. This involves looking at metrics like data accuracy, completeness, consistency, and timeliness. Are customer records free of errors? Are all required fields in your sales data populated? Maturity models help organizations “figure out how good their data governance is right now” and “provide a plan to get better.” By establishing clear benchmarks for data quality, you create the foundation for a data modernization strategy that delivers reliable insights and real business value.

Policy and Standards Compliance

Strong data governance relies on clear rules and consistent enforcement. This metric measures how well your organization adheres to both internal data standards and external regulations. You can track this by monitoring the percentage of data assets with assigned owners, the number of documented policies, and the rate of user adherence to those policies. Good governance isn’t just about avoiding penalties; it helps “avoid problems like data leaks or wrong information,” ensuring your operations run smoothly and your reputation remains intact. A solid data governance framework establishes these policies and provides the structure needed to maintain compliance across your entire organization, building a culture of accountability.

Technology and Infrastructure

The right tools can make or break your data governance program. Your technology and infrastructure should support your goals, not stand in their way. This metric assesses the capabilities of your tech stack, including data catalogs, quality monitoring tools, and access management systems. A mature organization leverages automation to enforce policies and streamline workflows. At the highest level of maturity, “processes are smart, automated, and used consistently.” Evaluating your tools helps you identify opportunities to automate manual tasks, improve efficiency, and scale your governance efforts with the right technology partners.

Stakeholder Engagement

Data governance is a team sport, and its success depends on company-wide participation. This metric gauges the level of awareness, buy-in, and active involvement from people across the business. You can’t build a data-driven culture from an IT silo. It’s crucial to “get information from all the people who work with data, from those who build data systems to those who use data for reports.” To measure engagement, you can track participation in data stewardship programs, survey user satisfaction with data resources, and monitor the adoption of governance tools. High engagement shows that your organization truly values data as a strategic asset, a key theme in many successful customer projects.

Process Effectiveness

Well-defined processes are the engine of an effective data governance program. This metric evaluates how efficient and streamlined your governance operations are. How long does it take to resolve a data quality issue? Is there a clear, documented process for onboarding a new data source or fulfilling a data request? Inefficient processes create bottlenecks and discourage user adoption. The goal is to “create a step-by-step plan to fix problems and get better,” which requires having repeatable and optimized workflows in place. By measuring process effectiveness, you can identify areas for improvement and build a scalable governance machine that supports agility and innovation, a core focus of our strategic approach.

Security and Risk Management

In an environment of increasing cyber threats and privacy regulations, protecting your data is non-negotiable. This metric focuses on how well your governance program safeguards sensitive information and mitigates risk. Key indicators include the number of data security incidents, the percentage of sensitive data that is properly classified and encrypted, and your team’s readiness to respond to a breach. As industry experts note, “having clean and organized data is crucial for making sure the information is accurate, trustworthy, and useful.” A mature data governance program integrates robust security controls and risk management protocols, ensuring that your data is not only valuable but also secure.

Find the Right Assessment Framework for You

Once you understand the levels of maturity, the next step is to choose a framework to guide your assessment. Think of these frameworks as different lenses for viewing your data governance practices. There isn’t a single “best” option; the right choice depends entirely on your organization’s specific needs, industry, and strategic goals. For example, a company in a highly regulated industry like financial services might prioritize a framework that emphasizes compliance and risk, while a fast-growing tech startup might focus on one that supports scalability and process improvement.

Choosing the right framework is a critical step in building a successful data governance strategy. It provides the structure for your assessment, helps you ask the right questions, and gives you a clear benchmark against which to measure your progress. Below, we’ll walk through five popular models. As you read, consider which one aligns most closely with your company’s current challenges and future ambitions. Each offers a unique perspective, so focus on finding the one that feels like the most natural fit for your team.

The DAMA DMBoK Model

The DAMA Data Management Body of Knowledge (DMBoK) is one of the most comprehensive frameworks available. It’s incredibly thorough, covering all major areas of data management, from data architecture and modeling to data quality and security. Because of its wide scope, the DMBoK is an excellent choice for organizations that want a complete, 360-degree view of their data practices. It provides a common vocabulary and a holistic structure that can help you identify gaps across your entire data ecosystem. If your goal is to build a truly enterprise-wide data governance program from the ground up, this model provides a solid and exhaustive foundation.

Gartner’s Maturity Model

If you’re looking for a model that gets straight to the core issues of ownership and quality, Gartner’s framework is a great place to start. It focuses heavily on establishing clear data ownership, defining data quality standards, and ensuring compliance with internal and external rules. This model is particularly effective for organizations struggling with questions like, “Who owns this data?” or “Can we trust these numbers?” It helps you assess your current state in these critical areas and provides a clear path for improvement. Its practical focus makes it a popular choice for businesses that need to make tangible progress on foundational data governance challenges quickly.

The IBM Data Governance Model

The IBM Data Governance Model is all about practicality and measurement. It emphasizes creating clear data policies, standardizing your processes, and defining key metrics to track your success. This framework is ideal for organizations that are results-oriented and want a structured, repeatable approach to improving their data governance. By focusing on tangible outcomes and continuous monitoring, it helps you move beyond theoretical planning and into active implementation. If your team thrives on clear goals and measurable progress, the IBM model provides the tools and structure to make your data governance initiatives successful and sustainable.

The CMMI Framework

The Capability Maturity Model Integration (CMMI) framework takes a unique approach by connecting data management directly to broader business process improvement. It doesn’t just look at data in a vacuum; instead, it treats data management as a key component of your organization’s overall operational excellence. This model is perfect for companies that are already committed to continuous improvement methodologies. It provides a structured path to not only enhance your data governance capabilities but also to ensure those improvements translate into better business performance. If you want to integrate your data strategy with your overall business strategy, the CMMI framework is an excellent choice.

The EDM Council Model

Developed by the Enterprise Data Management (EDM) Council, the Data Management Capability Assessment Model (DCAM) is another incredibly comprehensive framework. It assesses the entire data journey, from initial setup and data management strategy all the way through to how data is used for analytics and decision-making. Originally designed for the financial services industry, its principles are now widely applied across various sectors. The DCAM is a robust choice for any organization that wants a detailed, end-to-end evaluation of its data capabilities. It’s especially useful for identifying weaknesses anywhere in the data lifecycle and ensuring your data program is built for long-term success.

How to Conduct Your Maturity Assessment: A Step-by-Step Guide

Once you’ve chosen a framework, it’s time to put it to work. A data governance maturity assessment isn’t just a technical audit; it’s a strategic exercise that helps you understand your current capabilities and chart a course for the future. Following a structured process ensures you get a clear, unbiased view of where you stand and what you need to do next. Think of it as creating a detailed map that shows you exactly how to get from where you are to where you want to be. This process is foundational to building a successful data governance strategy that aligns with your business objectives and drives real value.

Step 1: Plan and Prepare

Before you begin, a little planning goes a long way. Your first move is to assemble a small, cross-functional team to lead the assessment. This group should include experts from IT, key business units, and compliance to ensure you get a well-rounded perspective. Next, define what you want to achieve. Are you aiming for better data quality, stronger compliance with regulations, or more efficient analytics? Setting clear goals will keep the assessment focused and ensure the outcomes are tied to real business value. This foundational work is critical for getting the buy-in and clarity you need to move forward effectively.

Step 2: Gather Information

With your team and goals in place, it’s time to collect data. The goal here is to get a complete picture of your current data governance practices. You can do this through a mix of methods, including one-on-one interviews with key stakeholders, workshops with different teams, and company-wide surveys. Don’t forget to review existing documentation, such as data policies, process maps, and technical diagrams. Talking to people across the organization—from the C-suite to the analysts on the front lines—is essential for uncovering how data is truly managed day-to-day, beyond what’s written down in a policy document.

Step 3: Analyze Your Current State

This is where you connect the dots. Take all the information you gathered in the previous step and compare it against the criteria in your chosen maturity model. This analysis involves honestly scoring your organization against the model’s standards to determine your current maturity level. For example, you might find that your data quality processes are at a “Defined” level, while your policy enforcement is still “Reactive.” This step provides an objective baseline, giving you a clear and detailed snapshot of your strengths and weaknesses across every dimension of data governance.

Step 4: Identify Key Gaps

After you’ve mapped your current state, you can clearly see the gaps between where you are and where you want to be. This isn’t about pointing fingers; it’s about identifying opportunities for improvement. Your analysis might reveal a lack of formal data ownership, inconsistent standards across departments, or technology that isn’t meeting your needs. Pinpointing these specific gaps is crucial because they form the building blocks for your improvement plan. Many organizations find that this step brings incredible clarity, as seen in various industry case studies where identifying core issues was the first step toward transformation.

Step 5: Develop Your Action Plan

Now it’s time to turn your findings into a concrete plan. Create a prioritized roadmap that outlines specific, actionable steps to address the gaps you identified. For each action item, assign a clear owner, set a realistic timeline, and define what success looks like. It’s often best to approach this in phases. Start with some “quick wins” to build momentum and demonstrate value, while also planning for larger, more strategic initiatives that will drive long-term change. This roadmap becomes your guide for evolving your data practices and achieving a higher level of maturity.

Step 6: Monitor Your Progress

Data governance is a continuous journey, not a one-time project. Once your action plan is in motion, it’s important to regularly check in on your progress. Schedule periodic reviews to track your initiatives and measure their impact. At first, you might want to re-assess your maturity every six months to ensure you’re staying on track. As your program matures, you can shift to an annual review. This creates a cycle of continuous improvement, allowing your data strategy to adapt and evolve along with your business, ensuring it remains effective and relevant over time.

Best Practices for a Successful Assessment

Conducting a data governance maturity assessment is more than just a technical audit; it’s a strategic initiative that sets the foundation for your company’s future. To get the most out of the process, it’s not enough to just follow the steps. You need to approach it with the right mindset and practices. Think of these as the ground rules that turn a good assessment into a great one—one that creates real, lasting change instead of just another report that gathers dust. By focusing on people, planning, and persistence, you can ensure your assessment delivers clear, actionable insights that drive your business forward.

Engage Stakeholders Early

Data governance isn’t just an IT project; it touches every part of your organization. That’s why it’s critical to get buy-in from everyone who works with data, from the engineers building your data pipelines to the marketing team using reports to understand customer behavior. Start by identifying key stakeholders across different departments and involve them from the very beginning. Their insights are invaluable for understanding the current state of your data practices. Getting information from a wide range of users helps you build a complete picture and ensures the solutions you develop will actually work for the people who need them. This collaborative approach also builds momentum and makes everyone feel like a part of the data modernization journey.

Keep Documentation Structured

A successful assessment produces a clear roadmap, not a confusing list of problems. To get there, you need a structured approach to documentation. From the start, create a clear, step-by-step plan for how you’ll identify issues and map out improvements. Think of it as building a phased plan that you can execute over time. This keeps the process organized and prevents you from getting overwhelmed. Your final report should clearly outline your current maturity level, the gaps you’ve identified, and a prioritized list of actions to take. This structured documentation becomes a living guide for your team, making it easy to track progress and communicate the plan across the organization.

Plan Your Resources

Achieving data governance maturity requires a real investment in people, technology, and time. Before you begin your assessment, take stock of the resources you have and what you might need. This could mean hiring data experts, investing in new tools, or dedicating team members’ time to the initiative. As the U.S. Department of Labor noted in its own assessment, improvement requires investing in fundamentals like hiring data experts and standardizing data management. Being realistic about your resource needs from the outset will help you build a practical and achievable action plan. Understanding these requirements is a key part of developing a successful cloud strategy that supports your governance goals.

Manage Change Effectively

Implementing a new data governance framework is a significant cultural shift. People are often comfortable with their existing workflows, so you need a solid change management plan to bring them along. A big part of improving is changing the culture so that everyone, from data collectors to leaders, sees data as a valuable asset. Communicate openly about why these changes are necessary and what the benefits will be for both the company and individual teams. Provide training and support to help employees adapt to new processes and tools. When you manage the human side of the transition effectively, you turn potential resistance into enthusiastic adoption.

Conduct Regular Reviews

Data governance is not a one-time project; it’s an ongoing commitment. Your business needs, technology, and data will constantly evolve, and your governance framework must adapt along with them. Plan to review your data governance maturity regularly—perhaps every six months when you’re starting out, and then annually once the program is more established. These regular check-ins allow you to measure progress against your roadmap, identify new challenges, and adjust your priorities. Consistent reviews ensure that your data governance program remains relevant and effective, helping you maintain momentum long after the initial assessment is complete.

Foster a Data-Driven Culture

Ultimately, the goal of a maturity assessment is to help foster a truly data-driven culture. This means creating an environment where data is treated as a core strategic asset and decisions are consistently informed by reliable insights. This cultural shift doesn’t happen overnight. It’s a continuous process that grows as your organization’s needs, technology, and understanding of data mature. By engaging stakeholders, planning carefully, and committing to continuous improvement, your assessment becomes the catalyst for this transformation. It’s the first step toward building a company where high-quality, well-governed data is at the heart of everything you do, a cornerstone of effective predictive analytics.

Address Common Challenges Before They Start

Knowing what potential roadblocks lie ahead is the first step to smoothly guiding your data governance journey. A maturity assessment will highlight areas for improvement, but the path to implementing those changes can have its own set of challenges. By anticipating these common hurdles, you can create a strategy that addresses them from the very beginning, setting your team up for success. Think of it as creating a roadmap that not only shows the destination but also points out the tricky intersections and steep hills along the way.

Technical Implementation Hurdles

Integrating new data governance practices with your existing systems can feel like a complex puzzle. Some governance models are difficult to implement and require significant time and specialized skills. Because data regulations and business needs are constantly changing, your models will need frequent updates to stay relevant. It can be a real challenge to connect new governance policies with established workflows without disrupting operations. A successful data modernization strategy focuses on flexible, scalable solutions that can adapt alongside your business, ensuring your technical framework supports your governance goals instead of holding them back.

Organizational Resistance to Change

One of the biggest challenges you’ll face isn’t technical—it’s human. Employees are often comfortable with their current routines, and new processes can be met with skepticism or resistance. Overcoming this requires more than just a mandate from leadership; it requires a cultural shift. You can get ahead of this by communicating the “why” behind the changes and demonstrating how better data governance benefits everyone. Good planning, clear communication, and comprehensive training are essential. The goal is to help everyone, from data collectors to executives, see data as a strategic asset that can make their jobs easier and the business stronger.

Working with Resource Constraints

Improving your data maturity is an investment that requires time, budget, and skilled people—resources that are rarely unlimited. To move forward, you’ll need to make a clear business case for the necessary investments, whether that’s hiring data experts or adopting new technology. Start by identifying the highest-impact, lowest-effort initiatives to build early momentum and demonstrate value. For organizations without a deep bench of in-house expertise, partnering with a consultancy can provide the necessary skills and guidance. This approach allows you to access specialized knowledge and manage costs effectively as you build your internal capabilities.

Maintaining Momentum Post-Assessment

Completing your first data governance assessment is a major milestone, but it’s just the beginning. The real work starts when you begin implementing your action plan. It’s easy for initial enthusiasm to fade as daily priorities take over. To keep the momentum going, you need to treat data governance as a continuous improvement cycle, not a one-time project. Schedule regular reviews—many organizations reassess every six months for the first couple of years, then annually. These check-ins help you track progress, celebrate wins, and adjust your strategy as needed, ensuring your data governance program continues to evolve and deliver value.

Ensuring Long-Term Sustainability

For data governance to stick, it has to become part of your company’s DNA. It’s a continuous process that grows with your business, not a project with a defined end date. A phased approach is often the most effective way to implement lasting change. By breaking down your action plan into manageable stages, you can make steady progress without overwhelming your team. This iterative method allows you to learn and adapt as you go, ensuring your governance framework remains relevant as your business needs, technology, and understanding of data mature. This commitment to ongoing improvement is what turns a good data governance initiative into a sustainable competitive advantage.

Related Articles

• Six reasons why companies fail at data governance
• 5 Essential Elements of Any Modern Data Governance Strategy
• Data Governance Varies, Data Cataloging a Priority

Frequently Asked Questions

How long does a maturity assessment typically take? The initial assessment itself can be completed in a matter of weeks, depending on the size and complexity of your organization. The real investment is in the work that follows. Think of the assessment as creating the map; the journey to a higher maturity level is an ongoing process. The goal is to build a sustainable program, not just to complete a one-time audit.

What’s the most common mistake companies make when starting this process? The biggest misstep is treating data governance as a purely technical or IT-led initiative. When you don’t involve people from across the business—sales, marketing, finance—you miss the full picture of how data is actually used and what the real pain points are. Success depends on collaboration, so bringing stakeholders to the table from day one is essential for creating a plan that people will actually adopt.

Our company is small. Is a formal data governance maturity model still necessary? Absolutely. The principles of understanding and improving how you handle data are valuable for any business, regardless of size. Your assessment and framework might be simpler than a massive enterprise’s, but the process is just as critical. It helps you build good data habits early on, creating a solid foundation that will support your company as it grows.

Do we need to hire an expert or can we do this ourselves? You can certainly lead an assessment internally, especially if you have team members with experience in this area. However, bringing in an expert partner can provide an objective, outside perspective that’s hard to get from within. A consultant can also accelerate the process by bringing established frameworks and experience from other projects, helping you avoid common pitfalls and get to a solid action plan more quickly.

What’s a realistic goal for our first year? Don’t aim for perfection. The goal isn’t to jump from Level 1 to Level 5 in a year. A fantastic and realistic goal is to make a solid move from one level to the next. This could mean going from a chaotic, unaware state to having a reactive plan in place, or moving from a reactive state to defining clear policies and roles. Focus on building a strong foundation and celebrating the incremental progress that builds lasting momentum.