Data Platform Maturity Models: Essentials for Success

In the first article of this three-part series on data platform modernization, we explained why modernizing data infrastructure is essential—not optional—for organizations that want to unlock the full value of their data. We also showed how legacy systems stall growth, while modern data platforms drive innovation and create a competitive advantage.

However, modernization is only the first step. To maximize these investments, organizations must actively assess and enhance their data platform maturity. In this article, we’ll build on that foundation to help data leaders understand and improve their platform maturity through data platform maturity models. 

By using these models to pinpoint their data platform's current state, data leaders gain the insights and context they need to implement a comprehensive data platform strategy—a topic we’ll explore in the final article of this series.

We’ll begin by explaining how maturity differs between data platforms and organizations and touch on specific modeling methodologies, based on the concerns we covered in our initial article. However, we’ll first lay a foundation by reviewing data platform maturity at a conceptual level, including the different stages that define it. 

Data platforms: Distinctions and stages of data maturity

At its most basic level, data platform maturity measures how sophisticated and capable an organization's data platform is in supporting data-driven decision-making, analytics, and innovation. It also shows how (or if) an organization is able to collect, manage, analyze, and use data as an emergent source of revenue. 

Like its conceptual elder sibling, organizational data maturity, data platform–specific maturity models offer structured frameworks that help organizations evaluate and improve their data capabilities.

That said, while organizational and data platform maturity are interconnected, they are distinct concepts:

• Organizational data maturity: Broadly applied to the business as a whole, organizational data maturity encapsulates an organization’s overall ability to leverage data effectively, represented as distinct and sequential stages. The modeling process itself includes cultural, strategic, governance, and operational aspects of data use across the entire organization. As a result, models leveraged at the org level measure the impact data has on high-level decision-making and overall business strategy.

• Data platform maturity: Alternatively, platform maturity modeling zeroes in on the technical infrastructure, tools, processes, and operational capabilities of the data platform itself. Platform maturity modeling, in turn, assesses a given platform’s ability to support data collection, storage, integration, processing, analytics, and delivery, ultimately turning proprietary data into a valuable revenue source.

This is a critical distinction data leaders need to embrace, especially during the upcoming data platform strategy phase. However, data management—and adjacent stakeholders, whose support is crucial in this phase—may not fully grasp the differences between these concepts. 

To effectively shepherd data platform maturity forward, data leaders must keep organizational leaders aligned on the platform-specific focus of their initiative. And it’s imperative to remember that maturity stages are not grades or achievements. 

As defined below, platform maturity is a journey. Everyone starts somewhere, with continual progress being the ultimate and most important goal. That said, the stages start where most businesses and organizations naturally do, at the beginning. 

1. Initial: Ad-hoc processes

The data platform is rudimentary or non-existent. Data is siloed in disparate systems with no centralized infrastructure.

Key characteristics:

• No formalized data architecture; reliance on manual processes for data movement
• Minimal or no use of ETL/ELT pipelines; raw data is often inaccessible or unstructured
• Limited storage solutions, such as spreadsheets or isolated databases
• Basic or absent analytics capabilities; any reporting is manual and reactive

Advancement criteria: Establishing a foundation for centralized storage and basic access

2. Managed: Basic standardization

The platform begins to support systematic data collection and storage, but integration across systems remains limited.

Key characteristics:

• Introduction of centralized storage solutions (such as relational databases or early-stage data warehouses)
• Implementation of basic ETL pipelines to automate some data movement
• Emergence of governance frameworks for access control and security
• Reporting tools provide descriptive analytics but lack scalability

Advancement criteria: Building on foundational infrastructure for structured data collection and governance

3. Defined: Integrated infrastructure

The platform supports integrated data pipelines and reliable analytics through formalized governance and scalable architecture.

Key characteristics:

• Implementation of modern architectures, cloud-based platforms, or hybrid solutions
• Data integration across multiple systems enables consistent access to unified datasets
• Governance frameworks include metadata management, lineage tracking, and compliance enforcement
• Self-service analytics tools empower business users, and diagnostic analytics become more common

‍Advancement criteria: Enabling scalable integration and reliable access to high-quality data

4. Optimized: Advanced analytics enablement

The platform is optimized for advanced analytics, including predictive modeling and machine learning (ML) workflows.

Key characteristics:

• Adoption of modern technologies like real-time streaming platforms (e.g., Kafka) or advanced cloud-native solutions (e.g., Snowflake)
• Operationalized ML models integrated into workflows (ML Ops)
• Real-time processing capabilities supporting dynamic decision-making needs
• Advanced governance ensuring high-quality, secure, compliant datasets at scale

Advancement criteria: Supporting predictive analytics and real-time decision-making through advanced architecture

5. Innovative: Proactive intelligent platform

The platform becomes a proactive enabler of innovation through seamless integration of AI and ML into business operations.

Key characteristics:

• AI and ML models embedded into core processes such as personalization engines or automated decision-making systems
• Fully automated pipelines with continuous monitoring ensure reliability at scale
• Real-time insights drive operational agility, and predictive models evolve into prescriptive analytics
• Governance frameworks addressing ethical AI use, bias mitigation, and explainability standards

Advancement criteria: As a means of stage maintenance and continuous platform improvement, driving innovation by seamlessly embedding intelligence into operations

Data platform maturity model comparison

Although platform maturity stages remain more or less consistent, the methodologies for modeling them can vary widely. This variation benefits data leaders by offering methodologies that emphasize different priorities and provide unique perspectives on platform maturity. 

The following are some of the most commonly used models, along with their specific focuses and applications.

Data monetization maturity model (Revelate)

This model evaluates an organization's readiness to monetize data by assessing two key dimensions: data maturity (internal sophistication in managing data) and externalization (the ability to package and sell data products or services). The model helps organizations identify where they stand on the path from basic data management to full-fledged data commercialization.

For a VP-level leader who’s focused on turning data into a revenue stream, this model directly addresses the progression from internal data optimization to external monetization opportunities like licensing, marketplaces, or embedded analytics.

Pros:

• Focuses on monetization-specific outcomes, such as creating data products or services
• Provides a clear roadmap for advancing from internal use to external revenue generation
• Highlights the importance of scalability and quality in monetizable data

Cons:

• Less focused on technical infrastructure specifics (like pipelines and governance)
• May require supplementary tools for assessing operational readiness

Data platform operational maturity framework (phData)

This framework assesses a data platform’s operational maturity across four pillars: strategy alignment, center of excellence, core operations, and advanced analytics enablement. It identifies gaps in platform capabilities and provides a prioritized roadmap for improvement.

This tool is ideal for leaders who are looking to ensure that their platform can support the advanced analytics and scalable operations that effective data monetization requires. It also emphasizes aligning platform capabilities with business objectives like revenue generation.

Pros:

• Provides a comprehensive focus on operational readiness and scalability
• Includes advanced analytics enablement, which is critical for monetization through ML or predictive modeling
• Offers actionable insights with a prioritized roadmap for platform improvement

Cons:

• Requires significant effort to implement due to its comprehensive nature
• May not directly address external monetization strategies without adaptation

Gartner data maturity model

A five-stage framework that evaluates an organization’s overall data management and analytics capabilities, from initial awareness to full integration into business strategy. It emphasizes aligning data initiatives with organizational goals.

While it’s broader than just platform maturity, this model helps leaders ensure that their platform investments align with strategic goals like generating revenue from data. It also provides high-level guidance on integrating analytics into the decision-making processes that drive monetization.

Pros:

• Widely recognized and trusted framework with extensive industry adoption
• Emphasizes business alignment, ensuring that platform investments contribute to revenue goals
• Provides clear progression steps for advancing maturity levels

Cons:

• High-level guidance may lack specificity for technical implementation
• A broad focus may require customization for platform-specific assessments

TDWI data management maturity model

This model evaluates six dimensions of data management maturity, including governance, integration, and analytics readiness. It benchmarks an organization’s capabilities against industry standards and provides actionable insights for improvement.

For leaders who aim to monetize data, this model ensures that the foundational aspects of governance, integration, and quality are in place. These are critical prerequisites for building trust in externalized or commercialized datasets.

Pros:

• Provides a holistic assessment of governance and integration capabilities
• Helps identify gaps in foundational areas critical for monetization readiness
• Benchmarks progress against industry standards

Cons:

• Does not explicitly address external monetization strategies
• Focuses more on internal optimization than outward-facing commercialization

DELTA Plus model (Acceldata)

This model focuses on five key areas—data, enterprise focus, leadership, targets, and analysts—to measure how well an organization leverages its data assets. It also emphasizes building robust infrastructure and leadership alignment to drive business value from data.

The DELTA Plus Model is particularly useful for leaders who need to assess both cultural readiness and technical infrastructure as they prepare their platforms for external monetization opportunities like licensing or productizing data insights.

Pros:

• Balances cultural aspects (like leadership buy-in) with technical requirements
• Encourages alignment between infrastructure investments and business goals
• Supports scaling platforms to handle complex monetization use cases

Cons:

• May require additional frameworks to address specific monetization mechanics
• Is less prescriptive about advanced analytics enablement

Moving on maturation: Putting a platform maturity model into practice

To ensure the successful implementation of any platform maturity modeling, data leaders should endeavor to follow a structured process that aligns organizational goals, fosters collaboration, and leverages available resources. 

While specific processes will vary depending on the needs of the data environment, the following steps outline a practical framework to begin moving forward:

1. Outline overall goals clearly

Start by clarifying what you want to achieve by implementing the assessment itself. For instance:

• Are you looking to optimize data governance processes, improve data quality, enable advanced analytics, or monetize your company’s data as a revenue stream?
• Are these goals aligned with broader business objectives to ensure buy-in from stakeholders?
• How will your organization’s current data capabilities align with its desired level of data maturity, as this will guide your choice of framework?

2. Recognize key players in the process

Identify key stakeholders who will contribute to or be impacted by the assessment:

• Include data engineering teams to assess platform architecture and pipelines.
• Engage business leaders to ensure alignment between platform capabilities and strategic goals.
• Involve governance specialists to evaluate compliance and data usage policies.
• Ensure that team members have sufficient data literacy to interpret findings from the assessment and provide input on platform capabilities.

3. Choose a framework, then make it your own

Select a methodology and work its unique aspects and benefits into your modeling strategy.

• For example, if you’re using the Data Monetization Maturity Model, focus on externalization capabilities like creating data products or licensing data sources.
• Alternatively, if you’re using the TDWI model, emphasize foundational elements like data governance, integration, and data lineage tracking.
• Ensure that your chosen framework accounts for critical platform functionalities such as scalability, automation, and integration as they relate to your current state.

4. Conduct a baseline assessment

Evaluate your current platform capabilities against your chosen framework’s criteria:

• Assess foundational elements, such as your organization’s data catalog completeness, pipeline reliability, and governance structures.
• Use metrics like time-to-insight, percentage of integrated systems, or completeness of data lineage tracking to benchmark your platform's current state without yet prioritizing improvements.
• Identify strengths and weaknesses in areas like scalability, automation readiness, or advanced analytics enablement.

5. Identify gaps and document everything

Analyze gaps between your current state and desired maturity level:

• Focus on foundational issues, such as poor data quality or incomplete governance frameworks that hinder progress.
• Highlight areas that directly impact value creation (like integrating siloed data sources or enhancing real-time processing capabilities).
• Thoroughly document all findings in a clear format, including your current level of maturity, key gaps, and areas for improvement.

Data platform strategy: Shifting from information gathering to definitive action 

Data leaders who take the time they need to holistically understand the concept of data platform maturity, choose the right methodology, and implement accordingly haven’t, at this point, unleashed the potential of their organizational data. But they’re ready to. With the right data platform strategy, they can turn the information they gain in the assessment phase into action.  

In the final article of this series, we’ll explore how to develop and implement a data platform strategy—a practical, results-driven approach that empowers data leaders to translate maturity assessments into real business impact. 

Ready to take the next step? Explore data platform strategy with Gable and learn how to transform your data maturity into measurable success.

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