Data Liquidity: How Easily Can Your Data Be Reused and Combined?

Key takeaways

• Define data liquidity: Data liquidity is the ease with which a data asset can be reused and recombined.
• Separate reuse from recombination: The factors that make data easy to reuse differ from the factors that make it easy to combine.
• Distinguish data liquidity from value: High data liquidity speeds up a use that already exists and does not create the use.
• Assess one dataset first: Measure the wait for access, the uses supported, the shared identifiers and the manual steps between source and user.

What is data liquidity?

Data liquidity is the ease with which a data asset can be reused and recombined. 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", a definition that places the emphasis on how ready the data is for the people who want to use it, and not on how much data exists or how valuable it is.

Most businesses measure their data by quantity and quality. Businesses count records, check for errors and track how many sources feed a report. A business can score well on all 3 measures while still finding that a new question takes weeks to answer, because getting the data to the person who needs it, in a form that fits with other data, is slow and manual. Data liquidity names that second problem.

Where does the term come from?

The term comes from finance, where financial liquidity describes how easily an asset can be turned into cash. Cash is liquid in the financial sense, because it can be deployed in any transaction immediately. According to Washington University in St. Louis (Olin Business School) and George Mason University (School of Management) research, April 2012, finance research studies the relation between asset liquidity and stock liquidity, which treats financial liquidity as a property of an asset that can be examined on its own.

For an accountant the logic is familiar. Financial liquidity does not measure how much an asset is worth. Financial liquidity measures how readily the asset can be put to use, and a business can hold valuable assets that it cannot deploy when a use arises. Data follows the same reasoning. 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, and each of those conditions places obstacles in the way of anyone who wants to put the data to work. As with financial liquidity, data liquidity is a continuum and not a yes or no.

Does high data liquidity mean valuable data?

No. Data liquidity makes a data asset easier to use, and it does not guarantee that the use is worthwhile. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, as strategic data assets gain higher data liquidity and grow in number, data monetization accelerates. An accelerator speeds up a use that already exists, and it does not create the use. The article What is Data Monetization? explains how the benefits created by data convert into financial returns.

As a hypothetical illustration, take a public web interface that returns random quotes attributed to a celebrity. The data has extremely high data liquidity, because anyone can reach it in seconds and combine it with anything, and few would claim that it creates much value. The reverse also exists: a dataset of great value to a business, locked in a system that takes weeks to access. Value and data liquidity are separate questions, and a business assesses both.

What does reuse mean?

Reuse is the ability to use the same data asset again, for more than one purpose and by more than one user. According to Stanford University research from the Graduate School of Business, September 2020, data is nonrival: a person's location history, medical records and driving data can be used by many firms simultaneously. Nonrival means that using the data does not consume it, so in theory it can be reused without limit.

In practice the cost of reuse varies a great deal, and it depends on how the asset is structured and governed. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 November 2025, data permissioning, which means establishing guardrails for acceptable data use, is one of 3 practice areas that raise or limit data liquidity. A dataset built for one team may need ad-hoc coordination for every new user, extra file formats, or a delivery schedule that someone maintains by hand. What does it cost a business each time a new team asks for the same dataset and the process starts again?

What does recombination mean?

Recombination is the ability to combine a data asset with other data assets, or to combine it again in a different way. Many valuable uses of data involve joining several sources, as the PepsiCo case shows. 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 in one case PepsiCo's syrup data was combined with retailer data about purchases from other providers.

Recombination is not always possible, and it is often expensive even when it is. Data assets are representations of the world, so they change as records are added, removed or updated. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, company data is often locked in closed platforms, duplicated, incomplete or poorly defined, and each of those conditions is resolved before the data can be joined with other data. Different standards add to the cost, because 2 systems that store the same fact in different formats are mapped to each other before they are joined. A data warehouse is the familiar answer to this cost: a repository that combines many source systems through transformation processes. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 20 May 2021, Fidelity Investments aims, through a 4-year programme, to consolidate more than 100 data warehouses and analytics stores onto one platform, and it has defined more than 3,000 data elements in a central catalogue, which shows how much work recombination can take.

Why are reuse and recombination separate dimensions?

Reuse and recombination are separate because the factors that make data easy to reuse differ from the factors that make it easy to combine, and an improvement in one does not guarantee an improvement in the other. 2 common patterns illustrate the difference. The 2 patterns are my own reasoning, and not a finding from the research:

• A dataset that is easy to reuse but hard to recombine. Departments or regions that capture data locally, to keep coordination costs low and preserve autonomy, often skip shared standards. The local data is easy for its own users to reuse, and combining it across systems later requires expensive re-engineering and standardisation.
• A dataset that is easy to recombine but hard to reuse. A highly standardised dataset joins readily with others, yet its owner may require a formal request, a negotiation about the use case and a defined service level before anyone is granted access.

A business that measures only one dimension can believe its data liquidity is high when half of the problem remains.

How can you tell how high your data liquidity is?

No single measure of data liquidity is in common use in the sources reviewed for this article, so any assessment here is a structured judgement and not a score. 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, compared organisations with high data liquidity (89 respondents, mean composite score 3.7 out of 5) and low data liquidity (86 respondents, mean composite score 1.9 out of 5), which shows that data liquidity can be assessed through a composite of survey questions. A practical way to start is to take one important dataset and ask questions about each dimension:

1. Measure how long a new, legitimate user waits between asking for the dataset and using it.
2. Count how many different uses the dataset already supports, and how many needed custom work.
3. Check whether the dataset shares identifiers, definitions and formats with the datasets it is usually joined to.
4. Record how many manual steps, such as exports, copies and re-formatting, sit between the source and the person who uses it.

The answers show where the obstacles are. A long wait for access points to a reuse problem, and a high number of manual steps between systems points to a recombination problem. Both can be reduced, usually by agreeing which system owns each record, defining shared identifiers, and automating the movement that people currently do by hand.