What is Data Monetization?

Data monetization is the act of turning data into money by creating and capturing financial value from data assets. Data monetization serves one purpose, converting the benefits that data creates into financial returns, and pursues one goal, measurable economic value. Methods of data monetization are 3 routes, improve, wrap and sell, applied internally or externally. Requirements of data monetization are data quality and governance, infrastructure and a clear use case.

Benefits of data monetization are new revenue, such as selling market intelligence; lower costs, such as automating financial reporting; and higher prices or customer retention, such as adding personalized recommendations to a product. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 17 August 2023, data monetization occurs when organizations convert the benefits created by data into financial returns.

Key takeaways

• Identify conversion routes: Improving operations, wrapping products with data-driven features and selling information turn data into financial value.
• Assess data monetization risks: Security, privacy, value leakage, provenance, customer trust, data quality, ethics, return on investment and data silos affect the value a business can capture.
• Choose data monetization types: Internal data monetization extracts value without sharing data externally, while external data monetization shares data assets with other parties.
• Verify legal considerations: Health information, privacy law frameworks, consent, licensing, cross-border transfers and permitted uses affect how data can be commercialized.
• Measure financial benefits: Data monetization produces cost reductions, revenue, customer retention and competitive advantage through identifiable business outcomes.

Academic definition

Data monetization is the commercialization of data and information assets through business models that convert those assets into economic value. According to University of Ottawa research from the Faculty of Engineering, 29 November 2022, Joan Ofulue and Morad Benyoucef examined this concept in Data monetization: insights from a technology-enabled literature review and research agenda, published in Management Review Quarterly. The systematic literature review by Ofulue and Benyoucef examined 54 articles published between 2013 and 2022.

The Ofulue and Benyoucef definition of data monetization describes the same economic activity as the MIT Sloan School of Management Center for Information Systems Research (CISR) definition but places more emphasis on commercialization models, infrastructure and organizational challenges. Both definitions concern turning data into financial value rather than simply collecting, storing or analyzing information.

Data assets

A data asset is a collection of information that an organization controls or can lawfully use to support decisions, operations, products or economic activity. Examples of data assets include customer records, transaction data and operational logs. Commercial value of a data asset depends on how accurately it represents something useful and whether that information can be applied to a business problem.

The same data asset can be monetized internally or externally. Transaction records, for example, can improve a retailer's inventory decisions or support an aggregated market information offering for suppliers. The distinction comes from how the information is used, not the format in which it is stored. The article Types of Data: 11 Types, How to Set Them Up and How to Maintain Them explains the broader categories of data and how to organize them.

How does data monetization work?

Data monetization works through 3 routes: improve, wrap and sell. Each route creates a benefit first, and that benefit converts into financial value through lower costs, higher prices, more sales, retained customers or direct payment.

Improving uses data to make existing work more efficient, such as using demand forecasts to reduce excess inventory. Wrapping adds data-driven capabilities to an existing product, such as delivery predictions that make a service more attractive. Selling provides information directly to a paying customer, such as a regularly updated industry dataset. In every case, collecting more data creates no financial return by itself; the return depends on whether the resulting benefit is captured.

Conversion routes

The 3 conversion routes differ in what changes inside the business and how the financial return is captured.

Improve takes 2 forms: process improvement, where data makes work cheaper or faster, and customer insight, where customer data segments customers by industry, preferences or demographics. Improving and wrapping do not require a separate data product to be sold. The value of improving and wrapping is captured through the existing business, whereas selling requires a buyer who pays for access to information.

Examples of data monetization

The table lists 5 industries, the conversion route each uses, what the data does and how money is captured.

Airlines and manufacturers are internal examples built on the improve route, retailers and banks are external examples built on the sell route, and insurers show that wrap sits across both types.

Steps

Data monetization follows 5 steps, from identifying useful information to expanding activities that produce measurable financial returns.

1. Identify data assets. Inventory available datasets, document their sources and select assets for further assessment if their quality, rights and potential applications can be established. Name the data product built from each asset (a dataset, an insight or a feature) and its user, internal or external.
2. Validate demand. Test an internal business problem or external customer requirement, if the expected benefit can be translated into lower costs, additional revenue or a measurable customer outcome.
3. Launch the offering. Deploy an operational improvement, product feature or information service, if data access, delivery infrastructure and permitted uses are established.
4. Measure results. Compare costs, revenue, adoption and customer behavior against a baseline, if the benefit can be attributed to the data-driven activity.
5. Scale what works. Expand successful use cases to additional products, customers or operating units, if recurring returns exceed the costs and risks of expansion.

The 5 steps separate creating value from capturing it. A dashboard can improve understanding without reducing costs, and a dataset can attract interest without generating payments. Measuring the financial outcome establishes whether an activity is data monetization or simply another use of data.

Prerequisites

There are 3 prerequisites of data monetization: data quality and governance, infrastructure and a clear use case. Data quality and governance establish whether information is accurate, consistent, appropriately accessible and subject to defined controls. Governance names a data owner for each data asset and a cross-functional group of finance, operations, product and legal that agrees on its use. Without data quality and governance, inaccurate data can produce unreliable decisions or unusable offerings, although the required level of accuracy depends on the application.

Infrastructure consists of the storage, processing, integration, access control and delivery systems (file, API or dashboard) that make information accessible for its intended use. Without suitable infrastructure, even valuable data remains difficult to apply, although a small internal use case may require only a database and a reporting tool. A clear use case defines who receives the benefit and how it becomes financial value. Without a clear use case, data projects can consume resources without producing a measurable return. The article Data Liquidity: How Easily Can Your Data Be Reused and Combined? explains how data reuse and recombination affect the ability to create further applications.

Pricing and contract design

An information offering can be priced through a subscription, a per-use charge or an agreed share of the customer's savings. Pricing connects the payment to the value the buyer expects to receive. TRIPBAM, a travel spend management provider, priced its offerings using an estimate that customers could save 2 to 3 percent of total travel spending.

Other pricing models include flat licensing fees, tiered access, payment per API request, usage-based charges and enterprise agreements. The contract determines which information is delivered, how frequently it is updated, who can use it and what happens when access ends. A price can be commercially attractive without covering the cost of maintaining the dataset, so recurring delivery costs and permitted use matter alongside customer value.

What are the risks of data monetization?

There are 8 main risks of data monetization: security, privacy and regulatory risk; value leakage and weak IP protection; loss of data provenance; erosion of customer trust; degraded data quality; ethical and bias concerns; low return on investment; data silos and limited scalability.

The risks of data monetization create legal consequences, such as a claim arising from unauthorized data sharing; financial consequences, such as losing the commercial value of a proprietary dataset; and reputational consequences, such as customers withdrawing trust after discovering unexpected uses of their information. The risks increase when a data initiative expands from one internal process to many users, partners, products and jurisdictions.

Security, privacy and regulatory risk

Security, privacy and regulatory risk is the possibility that collecting, processing or sharing data exposes information improperly or conflicts with applicable requirements. Partnerships that exchange data introduce additional security, privacy and contractual risks, while new applications of existing data can bring different regulatory obligations.

As more partners receive access, more systems become involved in handling the information. As datasets expand and operations cross jurisdictions, the number of potential exposures and applicable requirements can increase. Access controls, encryption and documented sharing permissions reduce exposure if they are enforced across every participating system. For example, sharing customer-level purchase histories with an analytics provider can create unnecessary exposure when aggregated figures would satisfy the same business requirement. The applicable obligations are discussed under What are the legal considerations of data monetization?

Value leakage and weak IP protection

Value leakage is the loss of economic value when another party gains the ability to use, reproduce or commercialize data beyond the intended arrangement. U.S. intellectual property law was not designed specifically to protect data as such, so contractual rights often play an important role in controlling its use. Some cloud and IT service agreements permit providers to use customer data for their own purposes.

The risk grows as additional vendors, partners and customers gain access to information or derived outputs. Narrowly defined usage rights and restrictions on reuse reduce value leakage if agreements cover the original information and relevant derivative products. For example, a supplier might license a proprietary pricing dataset to an analytics provider and later discover that the agreement permits the provider to incorporate those records into a competing commercial product.

Loss of data provenance

Loss of data provenance is the inability to establish where information originated, how it was transformed and which usage restrictions apply. Loss of data provenance occurs when datasets from different systems are combined without preserving their source records, licenses or processing histories. Without provenance, determining ownership, assessing permitted uses and maintaining compliance becomes difficult and costly.

As more sources and partners contribute information, tracing individual records becomes more complex. Source identifiers, transformation histories and license metadata preserve traceability if they remain attached to the relevant data throughout processing. For example, a market intelligence platform might combine public records with commercially licensed company information and later become unable to distinguish which records can legally be redistributed.

Erosion of customer trust

Erosion of customer trust is the decline in confidence that occurs when people believe their information is being used in ways they did not expect. A business may collect data to provide a service and later discover an opportunity to commercialize the same records for a different purpose. The financial opportunity does not establish that customers accept the additional use.

As a company introduces more products and partnerships, unexpected data reuse becomes harder for customers to understand or control. Clear explanations and meaningful preferences protect trust if the actual uses remain consistent with what customers were told. For example, users of a fitness application may object when their activity histories are incorporated into third-party advertising profiles without clear disclosure.

Degraded data quality

Degraded data quality is the loss of accuracy, consistency, completeness or relevance that makes a dataset less useful for its intended purpose. Degraded data quality often begins when businesses hoard information without defining ownership, validation rules or retention policies. Adding records increases storage and processing costs without necessarily improving the decisions produced from them.

As data volumes and sources increase, duplicate records, outdated values and inconsistent definitions can spread into reports and commercial products. Validation rules, assigned ownership, and scheduled data review maintain quality if errors are detected before downstream use. For example, combining customer records from several sales platforms without deduplication can produce inflated customer counts and misleading market reports.

Ethical and bias concerns

Ethical and bias concerns are the risks that data-driven systems produce unfair outcomes, exploit information asymmetries or reinforce discrimination. Ethical and bias problems can emerge when automated decisions depend on historical datasets containing unequal representation or when customer profiles are used for unfair targeting.

As an automated decision system serves more customers, an error or bias can affect larger populations without individual human intervention. Representative testing and outcome monitoring reduce unfair treatment if disparities lead to changes in the underlying data or decision rules. For example, a lending model trained on historically unequal approval decisions might repeat those differences when assessing new applicants.

Low return on investment

Low return on investment is the outcome in which the revenue or saving from a data offering is smaller than the cost of building and maintaining it. Low return on investment occurs when an offering is built without validated demand, so the data product does not match what the internal user or buyer needs.

As more offerings are built this way, the costs of data preparation, infrastructure and support grow without matching revenue. Validating demand before launch limits the loss if the buyer or internal user confirms the need before data preparation begins, which is why the Steps section places validation ahead of launch. For example, a company can build a supplier dashboard from sales data and find that suppliers already receive the same figures from their own systems.

Data silos and limited scalability

Data silos are datasets held in disconnected systems that cannot be combined or reused without manual work. Data silos occur when departments store the same information in separate tools with different definitions, so each new offering becomes a one-off build.

As customers and systems increase, the delivery cost of each offering rises and growth stops producing proportionally higher returns. A single source of truth for each data asset limits this if every offering reads from it. For example, customer records in a sales tool and a billing tool with different identifiers are matched manually before any aggregated product can be delivered. The article Data Liquidity: How Easily Can Your Data Be Reused and Combined? explains how data reuse and recombination affect scalability.

What are the different types of data monetization?

There are 2 main types of data monetization: internal and external. Internal data monetization occurs when a company extracts value from its data assets without sharing those assets with other parties. External data monetization occurs when a company shares its data assets with outside parties, including suppliers and customers, to create economic value. Internal data monetization is also called indirect data monetization, and external data monetization is also called direct data monetization.

Internal examples include reducing inventory costs through forecasting, improving staffing through operational analysis and supporting management decisions through performance dashboards. External examples include licensing datasets, providing paid analytics to suppliers and selling industry intelligence subscriptions. A business can use both types at the same time because the distinction depends on how data is used and who receives it.

Internal data monetization

Internal data monetization is the process through which a company extracts value from its data assets without sharing those assets with other parties. Internal data monetization is used in operations, finance and management to improve efficiency or support decisions. Unlike external data monetization, the return comes from existing business activities, such as reducing manual processing costs or improving purchasing decisions.

Setting up internal data monetization

To set up internal data monetization, identify a business process with measurable costs, map the data used in that process and establish a baseline for performance. Connect relevant systems, define data access and build an operational report, forecast or automated decision process if the expected improvement can be tested against that baseline.

Setup costs come from data preparation, integration, infrastructure, employee time and process changes. A complex analytics platform adds little value if the underlying operational problem can be solved with a smaller reporting or automation system.

Maintaining internal data monetization

To maintain internal data monetization, refresh source data, validate calculations, monitor system performance and review whether the expected operational improvement continues. Update data definitions, access permissions and decision rules if business processes or source systems change.

Without regular maintenance, dashboards become outdated, forecasts lose accuracy and automated decisions can depend on conditions that no longer exist. Maintenance costs also grow when multiple departments create separate versions of the same data instead of using consistent definitions.

External data monetization

External data monetization is the process through which a company shares its data assets with outside parties, such as suppliers or customers, to generate economic value. External data monetization differs from internal data monetization because information or access crosses organizational boundaries. Examples include selling an industry dataset to another business and licensing recurring access to company intelligence records.

Setting up external data monetization

To set up external data monetization, identify a buyer with a measurable information problem, establish which data can be shared and define the product that addresses that problem. Choose a delivery method, pricing structure and access agreement if the information provides enough value to justify payment.

Setup costs include data acquisition, cleaning, rights verification, API or portal development, security controls, documentation, sales and customer onboarding. Commercial demand is tested separately from technical feasibility because a useful dataset is not automatically one customers purchase.

Maintaining external data monetization

To maintain external data monetization, update the information, monitor delivery reliability, manage customer access and maintain records of licensed use. Review data sources, contracts and customer requirements if new data providers, buyers or jurisdictions become involved.

Failure to maintain freshness or contractual controls can reduce customer confidence and increase disputes over permitted use. Recurring infrastructure, support, data licensing and compliance costs also affect whether subscription or usage revenue produces a sustainable margin.

Delivery models of external data monetization

External data monetization can be delivered through several models that differ in what the buyer receives, in which format, and how the information is consumed.

Delivery models can overlap. A subscription describes how access is paid for, while data-as-a-service describes how information is delivered. The same dataset can be sold through an API, distributed on a marketplace or incorporated into an analytics service.

Types mapped to conversion routes

Internal and external data monetization describe who receives the data-driven value, while improve, wrap and sell describe how that value is converted into money.

Wrapping can create internal value for the business while providing additional information-driven functionality to its customers. The Conversion routes section explains the financial mechanism behind each route.

Data monetization in healthcare

Data monetization in healthcare can involve internal use of clinical and operational information to improve scheduling, resource allocation, purchasing and patient service delivery. External data monetization may involve information offerings shared with authorized recipients, but clinical and patient information cannot be treated as an unrestricted commercial asset.

For example, a hospital can analyze purchasing records to reduce supply costs or provide an appropriately governed information product to another organization. External disclosure involving protected health information requires evaluation of the applicable authorization rules and exceptions. The Health data and HIPAA section explains the specific restriction on selling protected health information.

What are the legal considerations of data monetization?

There are 7 legal considerations of data monetization: health data and HIPAA; privacy law frameworks; consent and purpose limitation; ownership and licensing contracts; cross-border transfers; legal review of foreseeable use cases; ethical principles.

Under the U.S. Department of Health and Human Services' HIPAA Omnibus Final Rule, published on 25 January 2013 and effective on 26 March 2013, a covered entity or business associate generally may not sell protected health information without an applicable authorization. The restriction appears in 45 CFR 164.502(a)(5)(ii), subject to the regulation's specified exceptions.

Health data and HIPAA

A sale of protected health information generally means a disclosure in which a covered entity or business associate directly or indirectly receives remuneration from or on behalf of the recipient in exchange for that information. The rule restricts such disclosures unless a valid authorization applies or the transaction falls within a specified exception.

The exclusions from the regulatory definition of a sale include qualifying disclosures for public health, research, treatment and payment, business associate activities, disclosures to the individual, and disclosures required by law. Other narrowly defined exceptions also exist, and some depend on limitations on remuneration. An excluded sale is not automatically an unrestricted disclosure; the underlying use or disclosure independently satisfies the applicable HIPAA requirements.

Privacy law frameworks

A privacy law framework is the set of laws that governs how personal data is collected, shared and sold. The United States has no single comprehensive federal privacy law, so regulation follows sectors and states: HIPAA governs health information, the Gramm-Leach-Bliley Act (GLBA) governs financial information and the Children's Online Privacy Protection Act (COPPA) governs personal information of children under 13. State laws add further requirements, such as the California Consumer Privacy Act (CCPA), amended by the California Privacy Rights Act (CPRA). The European Union regulates through the General Data Protection Regulation (GDPR).

The frameworks share obligations including notice that data is sold or shared, the consumer right to opt out of the sale or sharing of personal information, the right to delete and limits on sensitive data. As more jurisdictions are involved, more frameworks apply to the same dataset. Mapping each dataset to the frameworks of the places its data subjects live controls this exposure if the dataset is sold or shared. For example, a subscription dataset with customers in California and the European Union falls under both the CCPA and the GDPR.

Consent and purpose limitation

Consent is an individual's informed agreement to a specified use of information. Purpose limitation means information collected for one reason is not automatically available for every other commercial activity. Repurposing or selling data for another purpose may require a new permission or another valid legal basis, depending on the applicable rules.

For example, providing an email address to receive a purchase receipt does not itself establish permission for its sale to advertising brokers. Explicit informed consent identifies the relevant activity, while withdrawal and opt-out mechanisms allow individuals to exercise applicable choices. Consent and opt-out controls also establish boundaries between the information needed to deliver a service and additional commercial uses.

Ownership, licensing and contracts

Data licensing determines which rights an organization receives or grants over information and its permitted uses. The first check is whether the business owns the dataset or only licenses access to it. The second is whether a partner agreement defines scope of use, payment, duration and sharing limits. The third is whether any open-data licenses impose conditions on attribution, redistribution, modification or commercial use.

Data partnerships can also change the significance of contractual representations, compliance commitments and termination rights. A contract for hosting data, for example, may contain different permissions from an agreement allowing a partner to develop products from that data. The actual rights depend on the language of the agreement, not the label assigned to the commercial relationship.

Cross-border transfers

Cross-border data transfers occur when information is sent, accessed or processed across national or jurisdictional boundaries. Moving data from an internal database into a third-party data lake can introduce new transfers and potentially trigger data localization or export restrictions.

Not every international transfer is restricted in the same way. The requirements depend on the categories of information, the jurisdictions involved, the recipient and the processing activity. Mapping the storage locations, access paths and onward transfers helps control cross-border exposure if those records are checked before new vendors or hosting regions are introduced.

Legal review of foreseeable use cases

Legal review of foreseeable use cases is the examination of current and reasonably anticipated uses of information before a data initiative becomes difficult to change. Reviewing only the first application can leave later products or partnerships dependent on rights that were never obtained.

The review covers data ownership, licenses, consent, confidentiality, regulatory obligations and permitted onward sharing. Legal review takes place during identification and validation, before launch contracts and infrastructure choices commit the organization to a particular model. Future uses can then be compared against the permissions and restrictions established for the original activity.

Ethical principles

Ethical data monetization applies transparent and fair treatment to the collection, analysis and commercial use of information through 4 principles: protect privacy, obtain consent, disclose purposes and ensure equitable use.

• Protect privacy. Limit exposure of personal or confidential information to the uses and recipients for which appropriate permissions exist.
• Obtain consent. Establish meaningful permission when consent is the applicable basis for collecting, sharing or commercializing information.
• Disclose purposes. Explain how information is used, which parties receive it and which commercial activities it supports.
• Ensure equitable use. Evaluate whether automated decisions and commercial targeting create unfair outcomes for particular groups.

The 4 principles address the gap between a technically possible use of data and a use that respects the interests of the people represented in it.

What are the benefits of data monetization?

There are 4 main benefits of data monetization: lower cost, new revenue, higher customer retention and competitive advantage. According to MIT Sloan School of Management research from the Center for Information Systems Research (CISR), 16 October 2023, an average of 51% of data monetization returns comes from improving, 31% from wrapping and 18% from selling.

Improving and wrapping together account for 82% of returns through better work operations and customer experiences. Lower cost appears when data reduces manual processing; new revenue appears when buyers pay for information; retention improves when data-driven features make a service more useful; and competitive advantage develops when a company makes better decisions or delivers a better experience than alternatives.

Returns by route

The table shows the average distribution of data monetization returns across the 3 conversion routes.

The 51%, 31% and 18% shares describe the average allocation reported in the MIT Sloan School of Management Center for Information Systems Research (CISR) findings, not a guaranteed return distribution for an individual company.

Cost and efficiency

Cost reduction through data monetization is the decrease in operating expenditure achieved by using information to perform existing work more efficiently. Microsoft reduced the time its financial analysts spent gathering data by 30 percent within fifteen months, illustrating how an improvement in information access can reduce manual effort.

The 30 percent reduction is an example of the improve route because the value arises inside an existing business process rather than through selling a dataset. Time saved does not automatically equal a proportional reduction in payroll expense; the financial return depends on whether the recovered capacity reduces costs or produces additional valuable work.

New revenue from information offerings

New revenue from information offerings is income received when customers pay for access to data, insights or recommended actions. Selling accounts for 18% of average data monetization returns. Healthcare IQ, a healthcare data and analytics provider, estimated that a hospital with $100 million in spending could achieve at least $8 million in price savings through its data assets and tools.

The $8 million figure represents estimated customer savings, not revenue earned by Healthcare IQ. The $8 million estimate illustrates why an information offering can support commercial pricing: the buyer pays for information because applying it may create an economic benefit greater than its cost.

Product value and customer retention

Product value and customer retention through data monetization refer to the additional usefulness and continued customer relationships created by embedding data-driven features in an existing product. Wrapping accounts for 31% of average data monetization returns, with financial value captured through higher prices, additional sales or customers who continue using the product. Retention shows as lower customer churn.

For example, a logistics software provider can introduce predictive delivery estimates into its existing customer portal. Customers receive a more useful service without purchasing a separate information product, while the provider can capture value through stronger retention or a higher-priced service tier. The result depends on whether customers recognize and use the additional feature.

Competitive advantage and customer experience

Competitive advantage through data monetization is the ability to use information to deliver better business outcomes or customer experiences than competing organizations. A company that understands purchasing behavior can allocate inventory more accurately, personalize relevant product recommendations and respond to demand changes faster. Accurate inventory allocation, relevant recommendations and faster demand response can strengthen customer experience while reducing wasted resources. Repeated use of reliable data also creates a data-driven culture in which decisions are tested against observable results rather than assumptions. Competitive advantage depends on how effectively the organization applies information, because access to the same data does not guarantee the same performance.