By Ruben Buitelaar · Framework Developer & Data Strategy Expert
Table of Contents
A data maturity model is a framework that describes the progressive stages an organization moves through as it develops its ability to leverage data for decision-making, optimization, and innovation. It provides both a diagnostic tool—showing you where you are today—and a roadmap—revealing the path to where you want to be.
In the fast-paced business world of today, organizations struggle to capture maximum value from their data. While leading organizations increasingly rely on data for their decision-making processes, others fail to fulfill this data-driven ambition. The urgency to transform has never been greater, but organizations often lack the knowledge to change their old, intuition-based way of working.
This raises the fundamental question: How do we build a data-driven organization?
The answer lies not in implementing disconnected analytics projects, but in understanding maturity as a journey—one that requires simultaneous advancement across technical capabilities, organizational culture, and strategic alignment.
Research consistently shows that companies emphasizing data-driven decision-making demonstrate higher performance. Yet the path from intention to execution remains unclear for most organizations. A data maturity model bridges this gap by:
Establishing your starting point
(as-is situation)
Defining your destination
(to-be situation)
Illuminating the path forward
(strategic roadmap)
Without knowing what you're heading toward, you can never develop a plan to get there. Without understanding your current capabilities, you can't prioritize the right investments.
"The problem is not why organizations should become data-driven—that's well understood. The challenge is what to become and how to become it."
The Scanraven Data Maturity Model was developed through rigorous academic research at Leiden University, following established procedures for maturity model development in IT management. Unlike many industry frameworks that lack formal design methodology and rely on anecdotal evidence, this model features:
Systematic literature review of 8+ existing maturity models
Iterative development through multiple validation phases
Meta-evaluation by practitioners across industries
Statistical validation of all stages and dimensions
Assessment by 1,000+ organizations via our free assessment tool
The model synthesizes known theory and best practices from business intelligence, analytics, big data, and organizational transformation into a comprehensive, holistic framework.
Most data maturity models focus narrowly on analytical capabilities or technology sophistication. They treat analytics as an isolated activity within the organization. The Scanraven model takes a different approach:
Equal emphasis on technical and organizational factors
Includes Integration and Empowerment dimensions that position analytics within broader organizational context
Each stage provides specific recommendations, not just descriptions
Every component has been statistically validated for relevance and clarity
Data maturity is not a binary state—it's a progressive journey. Organizations move through distinct stages, each characterized by specific capabilities, challenges, and opportunities. The Scanraven model defines five stages:
The Foundation Stage
Growing interest in using data to support decision-making
Desire to move from intuition-based to evidence-based decisions
Basic reports showing historical performance
Data stored in spreadsheets on local computers or siloed databases
Ad hoc governance (if any)
Determine which data needs to be visible
Source and report critical data
Create basic dashboards with the most important metrics
Identify core competencies where analytics can make the biggest impact
Define basic responsibilities and decision domains
Proliferation of spreadsheets with no single source of truth
Data residing in functional silos
Lack of skills to plan and manage a data program
Uncertainty about where to start
Organizations in the Reporting stage focus on describing what happened. The goal is to visualize existing data and create the foundation for an analytical future.Read the complete Reporting stage guide →
The Insight Stage
Analytics has an official place in organizational strategy
Data stored in a central data warehouse with ETL processes
Analytical models created to predict future events
Data scientists trained in data mining techniques
Growing executive sponsorship
Determine which data is relevant for deeper analysis
Define more advanced KPIs and track them over time
Create analytical models backed by statistical methods
Train or attract data scientists to dive deeper into data
Professionalize the data infrastructure
Cultural resistance from those who "have always worked this way"
Need for clear direction from leadership
Struggle to translate insight into action
Growing complexity of data management
Organizations in the Analyzing stage focus on diagnosing why things happened. The birthplace of insight shifts from the manager reading a report to the analytics department.Read the complete Analyzing stage guide →
The Integration Stage
Large-scale data collection (user behavior, customer interactions)
Machine learning models predicting future behavior
Real-time dashboards revealing operational insight
Analytics integrated into production systems
Automated integration of insight into business processes
Collect data at scale to feed data-driven models
Use customer data to personalize experiences
Create processes to rapidly detect and deal with anomalies
Automate the use of analytical insight into existing processes
Focus on turning data into actions, not just insight
Reduced urgency after initial success
Technical complexity of real-time integration
Need for streaming data architecture (push vs. pull)
Balancing accuracy improvements against diminishing returns
Organizations in the Optimizing stage focus on predicting what will happen. The goal is to operationalize insight—integrate it automatically into business processes.Read the complete Optimizing stage guide →
The Democratization Stage
Third-party data augmenting internal data sources
Self-service BI tools available across the organization
Internal metrics tracking process quality and performance
Decentralized data scientists with domain knowledge
Analytics embedded in products ("smart products")
Create clear data governance strategy that scales
Distribute analysis closer to relevant departments
Scale analytics with self-service BI capabilities
Combine domain knowledge with analytical knowledge
Enable rapid integration of analytical features into products
Ensuring data quality with broader access
Training non-technical users in statistical interpretation
Maintaining governance while enabling agility
Retaining analytical culture as it spreads
Organizations in the Empowering stage focus on prescribing how to make things happen. Data-driven decision-making becomes everyone's responsibility, not just the analytics team's.Read the complete Empowering stage guide →
The Transformation Stage
Data is among the most valuable organizational assets
Autonomous analytics services assist model building
Innovation accounting measures new venture success
AI/ML capabilities at the leading edge
Culture of continuous improvement deeply ingrained
Continue seeking new data sources from new products
Leverage unstructured data (voice, images)
Track new ventures using innovation accounting methods
Invest in platforms that accelerate analytical model development
Use analytics to make decisions at both macro and micro scales
Staying ahead as competitors catch up
Maintaining innovation velocity
Balancing exploration with exploitation
Ensuring ethical use of advanced capabilities
Organizations in the Innovating stage focus on discovering what they can make happen. Continuous improvement becomes second nature, and analytical transformation becomes a journey that never ends.Read the complete Innovating stage guide →
Maturity isn't one-dimensional. Organizations must develop across multiple dimensions simultaneously to achieve true data-driven success. The Scanraven model defines ten dimensions—five technical and five organizational—that together provide a holistic view of data maturity.
Data is the fuel for all data-driven activities. This dimension assesses how you source, store, manage, and govern your data assets.
Storage: From spreadsheets → siloed databases → data warehouse → data lake → distributed edge processing
Sources: From operational systems → purpose-collected data → behavioral data → third-party data → unstructured data
Governance: Data quality, security, privacy, lifecycle management
Without quality data, even the most sophisticated analytical capabilities produce unreliable results. Data governance becomes critical as volume and variety increase.
If data is about input, metrics are about measuring output. This dimension assesses how you define, collect, and use performance indicators.
Sophistication: From plain measurements → aggregated trends → composite metrics → analytical KPIs → predictive indicators
Timeliness: From after-the-fact reporting → on-demand → real-time → predictive
Usage: From reporting → insight generation → optimization → empowerment → innovation
The right metrics communicate organizational priorities, enable performance management, and prevent focus on trivial tasks. Poorly designed KPIs can incentivize counterproductive behavior.
The people in your organization and the analytical skills they possess form a vital part of the analytical process. This dimension assesses both specialized analytical capabilities and general data literacy.
Analytical Skills: From planning/management → visualization → data mining → machine learning → AI development
Focus: Describing (what happened) → Diagnosing (why) → Predicting (what will) → Prescribing (how to) → Innovating (what's possible)
General Skills: Basic data literacy across the workforce
Human capital is the greatest asset of an analytical organization. Skills gaps are the most common barrier to data maturity advancement.
Technology is the catalyst for data-driven activities, playing a role in every step of the analytics process: collecting, extracting, storing, analyzing, visualizing, integrating, and connecting.
Infrastructure: From ad hoc scripts → basic platform → scalable infrastructure → modular architecture
Capabilities: From reporting → analysis → optimization → personalization → rapid innovation
The right technology enables speed and scale. Poor technology choices create technical debt that constrains future advancement.
Integration is often overlooked but crucial—it's how analytical insight flows back to the business and turns into action.
Method: From manual reports → automated dashboards → production system integration → distributed analytics
Action Capabilities: From viewing reports → identifying problems → predicting outcomes → prescribing actions → macro/micro decisions
Fantastic reports are worthless if no one reads them or no resources exist to take action. The faster insight turns into action, the more value is created.
Leadership is a critical factor determining the success of an analytical transformation. Without executive sponsorship, analytics initiatives struggle for resources and organizational support.
Activities: From learning → initiating → encouraging → leading by example → embracing transformation
Attitude: From interested → exploring → convinced → committed → passionate
Leadership provides the runway for analytics to take off. A strong advocate (champion) is required to set efforts in motion and allocate necessary resources.
Culture plays a crucial role in the acceptance and adoption of a data-driven strategy. It stretches beyond the analytics department to encompass the entire organization.
Attitude: From unaware → skeptical → interested → engaged → actively promoting
Adoption: From no initiative → inhibited adoption → experimenting → continuous improvement → sustainable advantage
Culture is a sustainable competitive advantage because it cannot be easily replicated by competitors. Leadership provides the runway, but culture keeps analytics flying.
Strategy determines what role analytics will play in the organization and aligns data initiatives with business objectives.
Role of Analytics: From absent → ad hoc → official → cornerstone → driving force
Strategic Focus: From no strategy → ad hoc initiatives → formalizing processes → radical improvement → innovation/transformation
Without strategic alignment, analytics efforts become scattered and fail to deliver meaningful business value.
Agility focuses on how to adapt, execute, and bring strategy to life. Analytical transformation requires a massive paradigm shift—from intuition to evidence-based reasoning.
Process Maturity: From non-existing → informal → formalized → optimized → facilitating innovation
Roles & Responsibilities: Clear decision rights and accountability structures
Successful strategic execution requires clarifying decision rights and defining who is responsible for specific decisions. Without clear responsibilities, insights get ignored and change gets blocked.
Empowerment refers to enabling all employees to engage in analytics—putting the right tools, techniques, and information in the hands of workers so they can discover opportunities and continuously improve.
Tools: From no access → basic reports → dashboards → experiment management → self-service BI → autonomous analytics
Education: From no knowledge → identifying relevant data → data literacy → scientific method → basic analytics → capturing maximum value
Information: From unavailable → ad hoc requests → scheduled reports → on-demand → real-time → pushed/anticipated
Data-driven organizations give employees access to data and analytical capabilities. Combined with the right knowledge and mindset, these unlock opportunities for innovation and continuous improvement.
Numerous data maturity models exist, each with different focus areas and design philosophies. Understanding their differences helps you choose the right framework for your organization—or understand why a more comprehensive approach is needed.
| Aspect | Industry Models | Scanraven Model |
|---|---|---|
| Validation | Often anecdotal, grey literature | Academic methodology, statistical validation |
| Documentation | Limited, proprietary | Fully documented design process |
| Dimensions | Typically 3-5, technology-focused | 10 dimensions, balanced technical/organizational |
| Actionability | General principles | Specific recommendations per stage/dimension |
| Integration & Empowerment | Usually absent or implicit | Explicit dimensions with detailed guidance |
| Assessment | Qualitative or expensive consulting | Quantitative, self-service, validated questions |
Most traditional maturity models—particularly those rooted in business intelligence—effectively end at Stage 3 (Optimizing). They focus on building analytical capabilities within dedicated teams.
The Scanraven model's Empowering stage represents a fundamental shift: moving from "having analytics" to "being analytical." It's the difference between:
A company with a data science team → A company where everyone thinks analytically
Analytics as a department → Analytics as a capability
Insight delivered to decision-makers → Decision-makers creating their own insights
This distinction matters because sustainable competitive advantage comes not from having analytics tools, but from an organization-wide culture of data-driven decision-making.
Change management starts with understanding where you are. A maturity assessment:
Interviews conducted by maturity model experts
Accurate; takes nuances into account
Time-consuming and expensive
Harder to quantify and benchmark
Structured questionnaire with scoring model
Fast to conduct; no expert required
Easily quantified and comparable
May miss situational nuances
Recommended: A quantitative assessment provides an accessible starting point. If results indicate areas of concern, follow up with deeper qualitative investigation.
Our assessment tool is designed around several key principles:
Speed: Complete in approximately 30 minutes
Accessibility: No expert knowledge required
Validation: Every question statistically validated for importance and clarity
Actionability: Results include specific recommendations for your maturity level
How It Works:
What You'll Receive:
Overall maturity score and stage placement
Score per dimension with radar visualization
Dimension-by-dimension breakdown of your current capabilities
Specific action items relevant to your maturity level
Strategic roadmap for advancement
Assessment alone doesn't create change—it informs strategy. Here's how to translate assessment results into a practical improvement plan:
Your dimension scores reveal your maturity profile. Common patterns include: Technology-heavy (high Technology and Data scores, lower Culture and Leadership), Strategy-light (good capabilities but lacking strategic direction), Skills-constrained (strong leadership support but execution bottleneck), Integration gap (good analysis but insight doesn't reach action).
Not all dimensions require equal attention. Prioritize based on: Business impact (which improvements would most affect strategic goals?), Dependencies (which dimensions are prerequisites for others?), Feasibility (which improvements can you realistically achieve?).
Don't try to jump from Stage 1 to Stage 5. Each stage builds on the previous: Reporting → Analyzing (focus on insight, not just visibility), Analyzing → Optimizing (focus on integration, not just analysis), Optimizing → Empowering (focus on distribution, not just optimization), Empowering → Innovating (focus on transformation, not just empowerment).
For each priority dimension: Define 2-3 specific initiatives, Assign clear ownership, Establish measurable outcomes, Schedule check-in reviews.
Over-investing in technology
Organizations often focus on technological factors while underinvesting in organizational dimensions. Our research shows this imbalance is common—and it explains why many analytics programs fail despite strong tools.
Skipping stages
Each stage builds capabilities needed for the next. Attempting to implement Empowering-level self-service BI without Optimizing-level integration infrastructure creates frustration.
Neglecting culture
Technology can be purchased; culture must be cultivated. Leadership and culture dimensions require sustained attention over time.
Focusing on big wins only
Small improvements compound. A culture of continuous improvement delivers more sustainable results than occasional major initiatives.
Use the maturity assessment as an initial diagnostic to understand your current capabilities, identify priority improvement areas, communicate data maturity status to stakeholders, and justify investment in data initiatives.
Use the full model as a blueprint for transformation to define your target state, build a multi-year roadmap, align initiatives with stage-appropriate priorities, and create shared vocabulary across the organization.
Deploy across multiple business units or teams to compare maturity profiles across departments, identify centers of excellence and areas needing support, enable knowledge sharing between teams, and standardize data practices organization-wide.
Repeat assessments periodically to track advancement, measure improvement over time, validate that initiatives are working, adjust strategy based on progress, and celebrate wins to maintain momentum.
Data maturity describes your organization's ability to leverage data for decision-making, optimization, and innovation
The journey consists of five stages: Reporting → Analyzing → Optimizing → Empowering → Innovating
Ten dimensions capture the full scope: Data, Metrics, Skills, Technology, Integration (technical) and Leadership, Culture, Strategy, Agility, Empowerment (organizational)
Most organizations are in early stages and often over-invest in technology while under-investing in organizational factors
Assessment provides the foundation for strategic planning and progress measurement
The Empowering stage—distributing analytical capabilities across the organization—is what distinguishes data-driven organizations from organizations that merely do analytics
Understanding your current state is the first step toward transformation. Our free assessment takes 30 minutes and provides:
Overall maturity score and stage placement
Dimension-by-dimension breakdown
Personalized recommendations
Strategic roadmap based on your results
Or explore the framework further:
Data Scientist & Product Manager
Ruben Buitelaar is the founder of Scanraven and the creator of the Data Maturity Model. He developed this framework as part of his Master's thesis research at Leiden University's ICT in Business program, where it was academically validated and has since been used by over 1,000 organizations worldwide.
With a background in software engineering and data strategy, Ruben combines technical depth with practical business understanding. His work focuses on helping organizations bridge the gap between knowing they should be data-driven and actually becoming data-driven.
Credentials:
Data Scientist & Product Manager
MSc ICT in Business, Leiden University
Creator of the Data-Driven Maturity Assessment (1,000+ organizational assessments)
Founder, Scanraven
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