Data creates the most economic value when an organization treats it as a reusable input rather than a possession that must remain locked away. Unlike a rivalrous good, a dataset can often support several permitted uses at once. That does not make data limitless or automatically profitable: quality, relevance, processing cost, rights, security and governance determine whether potential value becomes measurable value.
What an abundance mentality means in data economics
A rivalrous product is depleted when someone consumes it. Data is often different. The OECD describes data as something that “can be used repeatedly, by different actors and at the same time without being depleted or eliminated.” The same approved dataset might support internal planning, a customer-facing service, academic analysis and a public program without the original records disappearing.
An abundance mentality therefore asks, “Which additional safe uses can this data support?” rather than only, “How do we keep competitors from accessing it?” It is a strategy for finding reuse and spillovers, not an instruction to publish everything. Data still has finite usefulness: stale, inaccurate or poorly documented records may create no benefit, and every use can require collection, cleaning, storage, computing, integration and oversight.
Where the economic value can appear
Value is distributed across an ecosystem, not necessarily captured by the organization that holds the data. OECD’s 2019 review separates several channels:
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- Direct value: the holder may reduce operating costs, improve decisions or create a product.
- Indirect value: suppliers, partners and users may become more productive when they can access or combine the data.
- Induced value: wider economic activity can result from new services, research, innovation and public decisions.
Sharing can increase total value while changing who receives it. In some circumstances, giving others access can reduce the holder’s producer surplus even as users and the wider economy gain. That distribution question belongs in the business case alongside revenue and efficiency.
What the OECD estimates do—and do not—say
The OECD’s 2019 synthesis reports studies estimating that value for data users could be 10–20 times the value to data holders, and value for the wider economy could be 20–50 times the holder’s value. These are estimates assembled from studies with different definitions and contexts, not multipliers to apply to a particular company, dataset or country.
The same review reports rough study ranges of 0.1%–1.5% of GDP for public-sector data and 1%–2.5% when public- and private-sector data are included, with a few studies reaching 4%. They indicate possible scale and spillovers; they are not a forecast or guaranteed return.
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Why data has no single price
There is no reliable, context-free market price for most data. A dataset’s value depends on who will use it, for what purpose, how current and accurate it is, whether it can be linked to other information, and what permissions and controls apply. Much data is generated and used inside organizations rather than traded, so an observed transaction price may not exist.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsOECD work on measuring data and data flows identifies several approaches, each with limitations:
| Approach | What it can show | Important limitation |
|---|---|---|
| Observed market transactions | Prices paid for comparable data products or licenses | Many datasets are never sold, and comparability is weak. |
| Cost-based estimates | Resources spent creating, collecting or maintaining data | Cost reflects production effort, not necessarily usefulness or future returns. |
| Income or outcome estimates | Revenue, savings or productivity associated with a defined use | Attribution is difficult when many assets and decisions contribute. |
| Asset and intangible-capital frameworks | Conceptual estimates of data’s contribution to organizational capital | Methods and accounting treatment are not universally accepted. |
Potential value, recorded value and realized value should be kept separate. A large repository may have strategic potential without producing a documented financial return. Conversely, a small, well-governed dataset can produce a measurable service improvement.
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How to build a defensible data-value case
Rather than assigning a speculative price to all of an organization’s data, evaluate a specific dataset, use and outcome.
- Define the decision or service. State what will change—for example, a forecast, workflow, eligibility decision or customer feature.
- Identify the data contribution. Record source, coverage, freshness, quality, metadata, dependencies and the people or systems that can access it.
- Specify permitted uses. Check purpose limitations, consent or other applicable rights, contracts, intellectual-property constraints, retention rules and cross-organizational permissions.
- Estimate the counterfactual. Compare the proposed use with the current process or the best realistic alternative, not with a zero-cost baseline.
- Measure an outcome. Use an observed change such as processing time, error rate, avoided cost, adoption, revenue or service reach. Label modeled or proxy measures as estimates.
- Subtract the full cost. Include collection, cleaning, integration, infrastructure, security, audit, staff time, licensing and the cost of meeting rights and governance obligations.
- Assess distribution and incentives. Identify who gains, who bears risk, whether a partner has reason to contribute, and whether access arrangements create unfair dependence or concentration.
- Review and update. Reassess quality, relevance, permissions and outcomes as the data and use change.
Sharing is a governance decision, not a default setting
Responsible sharing matches controls to the data and the proposed use. OECD guidance highlights privacy and data-protection rights, intellectual property, security and ethics as considerations that can be affected by access and reuse. The appropriate arrangement may be internal access, a restricted partner interface, a de-identified release, a licensed product, a secure research environment or no sharing at all.
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Questions to resolve before access
- What exact purpose is authorized, and can the recipient use the data for anything else?
- Which fields are necessary, and can collection or disclosure be minimized?
- How will identity, confidentiality, security incidents and onward sharing be controlled?
- Who is responsible for quality, documentation, corrections, retention and deletion?
- What rights do contributors, customers or affected communities have?
- How will the arrangement be audited, paused or terminated?
These are policy considerations rather than jurisdiction-specific legal advice. Applicable law and contractual duties vary by location, sector and data type.
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Compare a defensive and an abundance-oriented strategy
| Decision axis | One-off, tightly held use | Multiple permitted uses |
|---|---|---|
| Reuse and reach | Usually limited to one team or workflow. | Designed for additional internal, partner or public uses subject to controls. |
| Value distribution | Focuses on benefits captured by the holder. | Considers benefits to users, suppliers and society, as well as the holder’s incentive. |
| Measurement quality | May rely on a single internal proxy. | Can test several outcomes, but attribution and spillovers remain uncertain. |
| Governance and rights | Fewer access pathways, but hidden reuse may be missed. | Requires explicit purpose, access, security, rights and stewardship rules. |
| Competition and incentives | Protects control and may preserve producer surplus. | Can stimulate innovation and fair access, while potentially increasing dependency or market-power concerns. |
The abundance approach is preferable when additional authorized uses create more value than the cost and risk of enabling them. The defensive approach may be appropriate where rights, safety, confidentiality or strategic exposure make reuse unacceptable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common mistakes that destroy the expected value
Assuming more collection means more returns
Volume does not substitute for relevance, quality or a defined use. Collect only what a legitimate purpose can justify and maintain.
Calling potential value an asset-balance value
OECD research proposes ways to measure data as an asset, but it does not establish a universally accepted price or automatic balance-sheet recognition. Internal strategy metrics should not be presented as standardized accounting values.
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Ignoring the holder’s economics
Open or partner access can create wider benefits while reducing exclusive revenue or bargaining power. Model both the total surplus and the portion the organization can realistically capture.
Treating governance as overhead
Weak permissions, documentation or security can invalidate a use, impose remediation costs and harm affected people. Governance is part of the production cost of usable data.
Confusing correlation with impact
An improvement after a data project may also reflect staffing, market conditions or another system change. State the counterfactual and confidence in attribution.
A practical operating model
Organizations can turn the mentality into routine practice without opening every repository. Establish a catalog that records owners, sensitivity, quality, lineage, permitted purposes and retention. Create an access process that routes requests through purpose and risk checks. Offer reusable interfaces or controlled environments instead of uncontrolled copies. Track both direct outcomes and approved downstream uses, and review whether contributors and users receive a fair share of benefits.
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At portfolio level, prioritize datasets with a clear decision or service, credible quality, reusable structure and a governance path that can scale. Stop or redesign initiatives whose measurable benefit does not cover their operating, rights and risk costs.
Bottom line for leaders
Abundance mentality is an economic lens: because data can often be reused without being consumed, carefully governed access can create value for holders, users and the wider economy. The opportunity is real but conditional. A sound decision names the use, measures the outcome, accounts for every cost and permission, and makes explicit who captures the gains and who bears the risks.
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