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A chief data officer (CDO) builds the organization’s ability to manage and use data; a chief economist develops ways to understand how value is created and distributed. In Bill Schmarzo’s framework, their partnership connects data assets and analytics to better business decisions—not simply to selling data.
What do a chief data officer and a chief economist do?
Schmarzo’s December 5, 2025 article, originally published in 2022, presents these roles as complementary. The CDO coordinates the systems, policies, skills, and safeguards that let an organization use data. The chief economist develops microeconomic capabilities and uses economic analysis to model how the organization produces and distributes value. This is Schmarzo’s framework, not a universal definition of either job title.
| Dimension | Chief data officer | Chief economist |
|---|---|---|
| Primary remit | Align data and analytics systems, governance, skills, and value-oriented use across the organization. | Develop and coordinate microeconomic capabilities for understanding value creation and distribution. |
| Methods | Data stewardship and advanced analytics. | Econometrics and economic modeling. |
| Typical focus | Organizational data assets and the use cases built from them. | Individual behavior, market mechanisms, and the effects of decisions. |
| Intended contribution | Enable responsible, useful data-driven operations and capabilities. | Explain or inform how value is produced and shared. |
Why connect data leadership with economics?
Analytics can reveal patterns, but a pattern alone does not establish what a business should do. An economic lens asks how a decision changes incentives, behavior, costs, and value—and who gains or bears the cost. A CDO can help ensure the underlying data and systems are fit for that analysis; an economist can help frame the decision and interpret its likely effects.
In Schmarzo’s view, the collaboration should support both current operations and the development of data and analytic assets that may help a business model succeed in the future. In practical terms, that means agreeing on a decision to improve, identifying the data and analytical work it requires, and evaluating whether the resulting value justifies the costs and constraints.
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What does “nanoeconomics” mean in this framework?
Schmarzo uses nanoeconomics for applying economic reasoning to predicted behavior or performance at the level of an individual person or device. For example, an aggregate hypothesis might be that higher income is associated with more spending. A nanoeconomic approach asks how the expected spending response differs from person to person, rather than assuming one average effect applies to everyone.
Those individual estimates can then be grouped into market segments to help analyze broader patterns. The value of the method depends on whether the predictions are useful and responsibly applied; the term and example here are Schmarzo’s, not a claim that the method is a settled industry standard.
Five data-economics concepts Schmarzo proposes
Schmarzo’s framework gives names to ways of thinking about the value of data and analytics. They are concepts he advances in the article, not independently established formulas or guaranteed business outcomes.
- Nanoeconomics: Apply economic reasoning to predicted behavior or performance for an individual entity, then use those estimates to examine segments and trends.
- Data Economic Multiplier Effect: Account for the accumulated attributable value when a dataset is reused across multiple use cases.
- Marginal Propensity to Reuse (MPR): Schmarzo’s claim that increased reuse can increase attributable value without adding marginal cost. Whether reuse is actually cost-free depends on the work and constraints involved in a particular organization.
- Economies of Learning: Measure value created as data and analytic assets are used to learn and adapt over time.
- Schmarzo’s Economic Digital Asset Valuation Theorem: Emphasize how sharing, reuse, and refinement can affect the value of digital assets.
Does data monetization mean selling data?
Not necessarily. Schmarzo argues that monetization can mean turning insights into data products and applications, rather than selling raw data. That distinction matters: an organization may use analytics to improve a service, target a business decision, or build a product without transferring raw records to a buyer.
Calling an initiative “monetization” does not demonstrate that it creates value. The organization still needs to identify a specific use case, assess the value it produces, and account for implementation costs and relevant constraints, including governance and ethical use.
What do the Google, Amazon, and Apple examples show?
Schmarzo’s article uses company examples to illustrate the kinds of economic work that may complement data and analytics leadership. The page was published on December 5, 2025 and says it was originally published in 2022. Its examples should therefore be read as historical context, not verification of current appointments, job listings, or company practices.
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The article names Hal Varian and describes work involving auctions, revenue forecasting, advertiser behavior, and ad effectiveness. It also describes a feedback loop in which ad sales and user-response data can inform product improvements and future revenue. These are Schmarzo’s account of the example, not an independently verified description of Google’s current organization.
Amazon
The article names Pat Bajari and reproduces a job-posting description of work in market design, pricing, forecasting, program evaluation, online advertising, econometric models, and economic theory. The posting is not established as currently available or as evidence of today’s role structure. The article attributes this sentence to Amazon’s job-posting site: “Economists at Amazon are solving some of the most challenging applied economics questions in the tech sector.”
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Schmarzo says he could not find definitive evidence of an Apple Chief Economist. Instead, he points to an Economist/Core Data Scientist job description that lists statistical and econometric skills. That is not evidence that Apple currently has—or lacks—a chief economist.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to put the collaboration into practice
- Start with a decision. Specify the business or operational choice the organization needs to make, rather than beginning with a dataset or a broad promise to monetize data.
- Define the economic question. Identify whose behavior may change, what value is expected, and how costs, incentives, or market conditions could affect the result.
- Map the data and analytical requirements. The CDO’s remit, in Schmarzo’s framework, includes aligning systems, governance, skills, and data assets with the use case. The economist contributes economic modeling and interpretation.
- Evaluate the outcome and constraints. Compare the observed or estimated value with the costs and consider whether the data use is appropriate. Reuse or learning may create additional value, but neither should be assumed to be free or automatic.
- Decide whether to adapt, scale, or stop. Use what the organization learns to refine the data asset, analytical approach, or use case—and continue only when the evidence supports doing so.
Source and scope
This explanation draws on Bill Schmarzo’s article, dated December 5, 2025, which says it was originally published in 2022 and reshared after the former repository was deleted. The role descriptions, named examples, and five concepts above are attributed to that article; they should not be read as confirmation of current company appointments or practices.
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