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Model 73 of 74 · Emerging & Specialist

Data Monetisation Maturity Assessment

Assess your organisation's ability to create value from data assets.

Pillars & areas

Identify & value

Discover

  1. 01Data Asset Identification & Valuation
  2. 02Value Proposition & Business Models

Prepare & govern

Enable

  1. 03Data Quality & Readiness
  2. 04Governance, Ethics & Privacy

Deliver & scale

Monetise

  1. 05Technology & Delivery Capability
  2. 06Commercial Execution & Scaling

Levels

  1. 1ExploratoryInformal recognition
  2. 2ProductisedDefined revenue streams
  3. 3ScaledCore business capability

01 Data Asset Identification & Valuation

Does the organisation know what data it holds and what that data is worth?

0/4 answered

DA-1How does your organisation maintain an inventory of its data assets?

Think about whether there is a central register of what data exists, where it lives, and who owns it.

DA-2How does your organisation assess the financial or strategic value of its data?

Consider whether anyone has attempted to put a value on your data assets, or whether data is treated as a cost centre byproduct.

DA-3How well does your organisation identify data that could generate value beyond its original purpose?

Think about whether teams actively look for secondary uses of data, or whether data stays locked within the process that created it.

DA-4How does your organisation track and classify the sensitivity and shareability of its data?

Consider whether each dataset has a clear classification that determines what can be shared, sold, or used externally.

02 Value Proposition & Business Models

Has the organisation defined how it will create and capture value from data?

0/4 answered

VP-1How clearly has your organisation defined its data monetisation strategy?

Think about whether there is a documented plan for how data will generate revenue or measurable business value.

VP-2How diversified are your organisation's data monetisation models?

Consider whether you rely on a single approach (e.g. selling raw data) or have explored multiple models such as insights-as-a-service, embedded analytics, or data-enhanced products.

VP-3How well does your organisation validate demand before building data products?

Think about whether data products are built based on what the market actually wants, or on internal assumptions about what might be useful.

VP-4How does your organisation price its data products and services?

Consider whether pricing is based on cost, value delivered, market rates, or a combination — and whether it is revisited regularly.

03 Data Quality & Readiness

Is the data reliable, timely, and structured enough to be monetised?

0/4 answered

DQ-1How does your organisation measure and manage data quality?

Think about whether there are defined quality metrics (accuracy, completeness, timeliness) and whether they are actively monitored.

DQ-2How well is your data integrated across systems and sources?

Consider whether data from different systems can be easily combined, or whether every integration is a manual, project-by-project effort.

DQ-3How does your organisation ensure data is ready for external consumption?

Think about whether data intended for customers or partners goes through a packaging process — cleaning, documenting, formatting — or is delivered as-is.

DQ-4How does your organisation handle data freshness and timeliness for monetised products?

Consider whether customers receive data that is hours, days, or weeks old — and whether freshness expectations are defined and met.

04 Governance, Ethics & Privacy

Are data monetisation activities governed responsibly, ethically, and in compliance with regulations?

0/4 answered

GE-1How does your organisation govern what data can be monetised and under what conditions?

Think about whether there is a formal approval process for monetising data, or whether individual teams make these decisions independently.

GE-2How does your organisation ensure compliance with privacy regulations when monetising data?

Consider whether privacy impact assessments are standard practice, or whether legal review happens only when someone raises a concern.

GE-3How does your organisation address ethical considerations in data monetisation?

Think about whether anyone asks 'should we?' not just 'can we?' — particularly around fairness, consent, and potential for harm.

GE-4How does your organisation manage third-party data usage and downstream risk?

Consider whether you control or monitor what happens to your data after it reaches a customer or partner.

05 Technology & Delivery Capability

Does the organisation have the platforms, tools, and skills to build and deliver data products?

0/4 answered

TD-1How mature is your organisation's data platform for supporting monetisation?

Think about whether you have a scalable platform for preparing, packaging, and delivering data products, or whether everything is built manually.

TD-2How does your organisation deliver data products to customers and partners?

Consider the delivery mechanisms — are customers getting emailed CSV files, or do they have API access, portal logins, or embedded integrations?

TD-3Does your organisation have the specialist skills needed to build and scale data products?

Think about whether you have people with data product management, data engineering, and commercial analytics skills, or whether these are gaps.

TD-4How does your organisation monitor the operational health of its data products?

Consider whether you know when a data product is down, slow, or delivering incorrect data before your customers tell you.

06 Commercial Execution & Scaling

Can the organisation sell, grow, and scale its data products effectively?

0/4 answered

CE-1How does your organisation go to market with data products?

Think about whether there is a defined sales and marketing approach for data products, or whether they are sold opportunistically through existing relationships.

CE-2How does your organisation measure the financial performance of its data products?

Consider whether you can answer basic questions like: how much revenue does each data product generate, what does it cost to deliver, and is it profitable?

CE-3How does your organisation retain and grow its data product customer base?

Think about whether you actively manage customer relationships for data products, or whether retention is left to chance.

CE-4How effectively does your organisation scale successful data products?

Consider whether a product that works for one customer can be efficiently delivered to fifty, or whether each new customer requires significant custom effort.

Self-assessment report

Data Monetisation

Answer the questions above and your report builds here: an overall score, a score and target for each area, and the three changes that matter most.

About this model

About the Data Monetisation maturity model

Every organisation sits on data that could generate revenue, reduce costs, or create competitive advantage — but most never move beyond talking about it. The gap between "we have valuable data" and "we earn money from data" is bridged by capability, not ambition. This model assesses whether your organisation has the foundations to systematically create, deliver, and scale value from its data assets.

Data monetisation is not limited to selling data externally. It includes any deliberate effort to extract measurable value from data — whether through new revenue streams, enhanced products, improved decision-making for partners, or reduced costs through data sharing. The organisations that succeed treat data as a product, not a byproduct.

24 questions across 6 key areas, assessing your organisation's ability to identify, value, govern, build, and commercialise data products. Takes approximately 10-15 minutes to complete.

Scoring is based on 3 maturity levels per question. Your overall score reflects the average maturity across all areas, giving you a clear picture of where your data monetisation capability is strong and where it needs investment.

Who this assessment is for

  • Chief Data Officers and data leadership teams
  • Product managers responsible for data-driven products
  • Strategy and innovation leaders exploring new revenue streams
  • Data engineers and platform teams building data infrastructure
  • Commercial and partnerships teams involved in data licensing or sharing
  • Privacy, legal, and governance professionals overseeing data use

The three maturity levels

  • Level 1 — Exploratory: The organisation recognises that its data has potential value, but there is no systematic approach to realising it. Data assets are poorly catalogued, quality is inconsistent, and any monetisation efforts are ad-hoc or experimental. There is no dedicated strategy, team, or platform — value from data happens by accident rather than design. Governance around external data use is informal or absent.
  • Level 2 — Productised: Data monetisation is a deliberate activity with executive sponsorship. The organisation has identified its most valuable data assets, defined one or more monetisation models, and built the basic infrastructure to deliver data products. Governance and privacy processes are in place for new initiatives. A small but dedicated team manages data products, pricing is documented, and financial performance is tracked. The focus is on proving commercial viability and building repeatable processes.
  • Level 3 — Scaled: Data monetisation is a core business capability embedded in the organisation's strategy. Multiple monetisation models operate simultaneously, supported by a purpose-built platform that enables rapid product creation and delivery. A cross-functional team manages the full lifecycle from discovery to customer success. Governance, ethics, and privacy are automated where possible. Financial performance is managed at the product level with full P&L visibility. The organisation continuously discovers new data value opportunities and scales successful products with minimal marginal cost.

The 6 areas assessed

  1. Data Asset Identification & Valuation — Does the organisation know what data it holds and what that data is worth? Covers data cataloguing, valuation methodologies, secondary use discovery, and classification for shareability.
  2. Value Proposition & Business Models — Has the organisation defined how it will create and capture value from data? Covers strategy definition, model diversification, demand validation, and pricing.
  3. Data Quality & Readiness — Is the data reliable, timely, and structured enough to be monetised? Covers quality measurement, integration, external-readiness packaging, and freshness management.
  4. Governance, Ethics & Privacy — Are data monetisation activities governed responsibly, ethically, and in compliance with regulations? Covers approval processes, privacy compliance, ethical review, and third-party usage controls.
  5. Technology & Delivery Capability — Does the organisation have the platforms, tools, and skills to build and deliver data products? Covers platform maturity, delivery mechanisms, specialist skills, and operational monitoring.
  6. Commercial Execution & Scaling — Can the organisation sell, grow, and scale its data products effectively? Covers go-to-market approach, financial tracking, customer retention, and scaling efficiency.

How this model relates to other frameworks

  • Data-as-a-product practices (popularised by data mesh) — treating data sets as products with owners, users and quality standards
  • DAMA-DMBOK2 (Data Management Body of Knowledge)
  • EDM Council DCAM (Data Management Capability Assessment Model)
  • Open Data Institute (ODI) Data Spectrum and Data Ethics Canvas
  • Infonomics (Douglas Laney) — data valuation and asset management principles

Frequently asked questions

What is the Data Monetisation maturity model?

Assess your organisation's ability to create value from data assets. It is an original, simplified model with three maturity levels (Exploratory, Productised and Scaled), assessed through 24 questions across 6 areas: Data Asset Identification & Valuation, Value Proposition & Business Models, Data Quality & Readiness, Governance, Ethics & Privacy, Technology & Delivery Capability and Commercial Execution & Scaling.

How long does the Data Monetisation assessment take?

Most people finish in 10–14 minutes. Each of the 24 questions has three concrete answer options. Your progress is saved in the page URL, so you can bookmark it and come back later.

How is the score calculated?

Each answer scores 1 (Exploratory), 2 (Productised) or 3 (Scaled). An area's score is the average of its answered questions, and the overall score is the average of the area scores, so every area counts equally. Scores run from 1.0 to 3.0.

Is my data stored or shared?

No. Your answers are never sent to a server. They are stored only in the page URL, which you control. Copy the link to share results with colleagues, or export a PDF or CSV report.

How should I use the results?

Start with "Where to focus" in your report. It lists the questions with the biggest gap to the target you set for each area, and shows what the next level looks like in practice. For a fuller picture, ask several people from different teams to complete the assessment and compare their links. Differences in perception are often as revealing as the scores themselves.