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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Marginal Propensity to Reuse (MPR) is Bill Schmarzo’s proposed framework for understanding how a curated digital asset—such as a dataset, model, or analytical insight—may create value when it is applied to multiple use cases. The idea is that later uses can take less incremental effort than creating the asset from scratch. That is a useful way to think about data economics, but MPR is not a standard financial statistic, and reuse alone does not guarantee savings or returns.
What does marginal propensity to reuse mean?
Schmarzo introduced MPR in a June 2021 article as the idea that reusing the same curated dataset across multiple use cases can increase the value attributable to that dataset while adding comparatively little cost. He later broadened the framing to include data assets, models, and analytical insights used across an enterprise. These are the author’s conceptual descriptions, not a formal accounting measure or an independently validated economic law.
The phrase borrows the structure of marginal propensity to consume (MPC), a separate economic concept. Schmarzo uses MPC as an analogy for a multiplier effect; MPR refers instead to how widely a digital asset is reused. It does not mean that an organization can calculate a recognized MPR percentage using a standard formula.
Sources: Schmarzo’s original MPR explanation and his July 31, 2026 TechTarget opinion article.
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How can reusing data create value?
A digital asset can often be copied and applied to another analysis without being consumed in the way a physical asset is. If a team has already collected, organized, documented, and made a dataset usable, another team may be able to build on that work rather than repeat it. In Schmarzo’s framework, each additional use could therefore add attributable value while requiring less incremental effort.
For example, a company that has curated customer transaction data for budgeting analysis might also use it to identify spending trends or improve internal forecasts, provided those uses are permitted and the data is appropriate. The possible advantage is reuse of preparation and infrastructure—not an automatic benefit from simply counting more use cases.
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- Potentially less duplicated work: teams may avoid repeatedly sourcing, cleaning, and reconciling the same information.
- Potentially faster follow-on work: a later use can begin from an asset that is already discoverable and understood.
- Value can flow across uses: if an underlying dataset is improved, dependent analyses may benefit as well, subject to how those systems are maintained.
These are the framework’s expected mechanisms, not guaranteed outcomes. The sources reviewed do not establish an independently measured MPR impact figure or a controlled estimate of savings or returns.
Why reuse is not cost-free
Digital assets may be reused without being physically depleted, but making reuse practical still takes work. A dataset that cannot be found, whose fields are unclear, or whose quality is unreliable may be of little use to another team. Access restrictions, integration work, governance, and the need to keep information current can all add cost.
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Reuse can also be inappropriate. Data must be suitable for the intended use, and the organization needs to respect applicable permissions and governance requirements. Reusing an asset does not make its contents accurate, relevant, or authorized for every purpose.
Schmarzo’s original explanation points to discoverability and access, cataloging and metadata, integration, quality, and user confidence as practical enablers. These are recommendations in his framework, not a ranked or experimentally validated list of causal drivers.
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- 【Ultra-Fast Data Transfers】 - Pairing this external hard drive with JMicron 578 solution USB 3.0 and USB 2.0 interfaces enables blazing-fast data transfer. It boasts theoretical read speeds of up to 125MB/s and write speeds of up to 103MB/s.
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How to assess whether an asset is reusable
Instead of assuming that more reuse is always better, assess whether a particular asset can support another use at a worthwhile incremental cost:
- Can the intended users find and access it? A catalog or clear ownership information can help teams discover what exists and how access is requested.
- Is its meaning clear? Metadata, definitions, and documentation should explain what the data represents, its limitations, and any permitted uses.
- Can it be integrated reliably? Check whether its structure and identifiers work with the systems or data needed for the new use.
- Is it trustworthy and current enough? Consider quality, freshness, and whether users can understand the source and limitations.
- Does the next use justify its added work? Compare the expected business value with the incremental cost of adaptation, validation, access, and ongoing maintenance.
This is a practical decision aid based on the framework’s stated enablers and economic logic, not a standardized MPR scoring method.
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- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
What MPR does—and does not—establish
MPR offers a lens for asking whether a data asset’s value can extend beyond its first application. It can help direct attention to the work that makes assets reusable, rather than treating data preparation as a one-off expense. But the concept should not be treated as proof that reuse will improve financial results: outcomes depend on the asset, the follow-on use, implementation costs, and whether the resulting work produces value.
The reviewed sources do not establish a standard MPR formula, independent empirical consensus, or a direct measured effect. Schmarzo’s July 2026 TechTarget opinion article mentions figures attributed to McKinsey, but those underlying publications are not verified here; they should not be read as MPR results.
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