How to Build a Data Monetization Capability Using Data Liquidity

A data monetization capability is the ability of an organisation to turn data assets into measurable financial value, repeatedly. A single report or product is a project. A capability repeats the result for the next question, product and customer without starting from zero. Data liquidity, the ease of reusing and recombining data, decides how cheaply each repetition starts. The article What is Data Monetization? defines data monetization and covers its routes, types, risks and legal considerations.

Key takeaways

• A data monetization capability repeats value creation. One successful project does not make one.
• According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 9 January 2017, data monetization has 3 routes: improving processes, wrapping products with information, and selling information.
• Data liquidity is the ease of reusing and recombining data. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, more liquid and more numerous strategic data assets accelerate data monetization.
• According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 November 2025, organisations with high data liquidity reported 14 percent higher customer experience and time-to-market scores in a 2024 survey of 349 respondents.
• Building starts with one dataset, measured and improved, and then the method is repeated.

What are the 3 approaches to data monetization?

According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 9 January 2017, data monetization follows 3 approaches, described by Wixom and Ross in How to Monetize Your Data. The 3 approaches are:

• Improving: using data to improve internal processes and decisions.
• Wrapping: adding information to core products and services.
• Selling: offering information products to new and existing markets.

According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 9 January 2017, each approach requires different capabilities, and it is usually better to pick the most promising approach and start there.

Why do most data monetization efforts stall after the first use case?

In most cases, efforts begin with a use case. A team picks a promising idea, such as a pricing model or a customer dashboard. The team collects the data the idea requires and builds it. A use-case start ties the work to a visible outcome.

The weakness appears after the first success. The next idea needs data from the same fragmented systems. Access is requested again. Files are exported and reformatted again. Definitions are agreed again. The first project proved that value exists. The first project also showed that every further project carries the full cost of reaching the data.

As the number of ideas grows, that cost grows with it. The business ends up with separate projects and no data monetization capability. What would the fifth and the twentieth use case cost if each began with the same weeks of preparation? According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, most company data has low data liquidity, because it is trapped in local processes, locked in closed platforms, duplicated or poorly defined.

How does data liquidity lower the cost of each new use?

Data liquidity lowers the cost of each additional use of a data asset, in most cases. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, data liquidity is "the ease of data asset reuse and recombination", as Wixom and Piccoli define it in the research briefing Build Data Liquidity to Accelerate Data Monetization. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, more liquid and more numerous strategic data assets accelerate data monetization. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 November 2025, the Caterpillar briefing states:

"Organizations with big aspirations for leveraging AI technology increasingly need data assets with high data liquidity." >— Wixom, Rodriguez, Piccoli and Beath, MIT CISR Research Briefing, November 2025

The full definition is in what data liquidity is and where the idea comes from.

Reuse means one asset serves more than one purpose and more than one user. Recombination means the asset joins with other assets, now or later. Most valuable uses combine several sources. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 19 December 2019, PepsiCo packaged its shopper insights into PEPWORX, which helped users optimise store-level assortment and total store space, and retailers granted access to some of their retail data for the work.

According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 November 2025, the 2024 MIT CISR Data Monetization Survey, with 349 respondents, found that organisations with high data liquidity (89 respondents) reported 14 percent higher customer experience and time-to-market scores than organisations with low data liquidity (86 respondents). The same survey found data sharing that was 28 percent easier and a 24 percent greater data monetization impact. Evidence of this kind indicates association, and not proof of cause. The research is detailed in Data Liquidity Levers at Caterpillar by Wixom, Rodriguez, Piccoli and Beath.

What does each approach ask of data liquidity?

A data monetization capability can use all 3 approaches, and each puts a different dimension of data liquidity under pressure. The table below is my own reading of the 3 definitions, and not a finding from the research.

In most cases, the further a business moves toward external sale, the more the cost of onboarding a new user decides whether the offer is viable. A dataset that needs a negotiation, a custom export and a manual delivery for every customer cannot be sold at scale. The worth of its content does not change that.

How can you build a data monetization capability using data liquidity?

To build the capability, raise the data liquidity of one important dataset first, and then repeat the method for the next. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 November 2025, Caterpillar manages data liquidity through 3 levers: data architecture, data preparation and data permissioning. The 6 steps below follow those levers:

1. Choose one dataset tied to a decision or product that already has a user.
2. Measure its data liquidity. Record how long a new legitimate user waits for access, how many uses it supports without custom work, whether it shares identifiers and formats with the datasets it joins, and how many manual steps sit between source and user.
3. Assign one owner and one system of record for each important record. The owner and the system of record create a single source of truth.
4. Define shared identifiers and definitions, so the dataset joins with others without re-engineering. Shared identifiers and definitions are the architecture lever.
5. Automate the movement that people currently do by hand, such as exports, copies and re-formatting. Automated movement is the preparation lever.
6. Set access rules, including vetting for regulated or personal data. Access rules are the permissioning lever.

According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 November 2025, the 3 levers work together, and a strong data platform still produces low data liquidity if employees struggle to obtain access permissions. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, Fidelity Investments, a large company building a data monetization capability, aims, through a four-year programme, to consolidate more than 100 data warehouses and analytics stores onto one platform, and has defined more than 3,000 data elements in a central catalogue.

According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, Mihir Shah of Fidelity Investments states the aim in one sentence:

"We want to create long-term data assets for creating value." >— Mihir Shah, Fidelity Investments, in Wixom and Piccoli, MIT CISR, 2021

A small or midsized business can apply the same logic to one dataset first.

Does high data liquidity guarantee monetization?

No. Data liquidity makes a data asset easier to use, and it does not guarantee that anyone has a worthwhile use for it. A dataset with high data liquidity and no user who wants it creates no value. A valuable dataset locked behind slow access creates little. The distinction matches tools versus assets: owning something differs from being able to put it to work.

A data monetization capability requires both. The capability depends on users who want the data and on data that reaches them at a low cost in time and effort.

What are the limits of this approach?

The approach has 3 limits.

• No accepted measure of data liquidity exists yet. The measurements in step 2 are a structured judgement and not a score.
• The survey figures show association between data liquidity and outcomes. The figures do not prove that data liquidity caused the outcomes.
• The comparison of the 3 approaches is my own reasoning from their definitions. My reading comes from an accounting background, where an asset is judged by how readily it can be put to use.

The sources are MIT CISR and MIT Sloan Management Review research and the academic literature on data liquidity. Large enterprises dominate the cases. For a smaller business building a data monetization capability, the single-dataset starting point in step 1 is the practical scope.

Related project: one client intelligence dashboard built from fragmented SEO, call-tracking, CRM and local-search data. Learn more about data liquidity work for growing businesses, or contact me with the dataset you want to start with.