Data valuation

• Data is non-rival. Multiple people can use data without it being depleted or used up.
• Data varies in whether it is excludable. Data can be a public good or a club good, depending on what type of information it contains. Some data can reasonably be shared with anyone who desires to access it (e.g. weather data). Other data is limited to particular users and contexts (e.g. administrative data).
• Data involves externalities. In economics, an externality is the cost or benefit that affects a third party who did not choose to incur that cost or benefit. Data can create positive externalities, as when new data is produced that combines with already existing data to produce new insights, increasing the value of both, and negative externalities, when data may be leaked, breached or otherwise misused.
• Data may have increasing or decreasing returns. Sometimes collecting more data increases insight or value, though at other times it can simply lead to hoarding.
• Data has a large option value. Due to the perpetual development of new technologies and datasets, it is hard to predict how the value of a particular data asset might change. Organizations may store data, anticipating possible future value, rather than actual present value.
• Data collection often has high up front cost and low marginal cost. Collecting data often requires significant investment in technologies and digitization. Once these are established, further data collection may cost much less. High entry barriers may prevent smaller organizations from collecting data.
• Data use requires complementary investment. Organizations may need to invest in software, hardware and personnel in order to realize value from data.

• Subject matter. Encompasses what the data describes, and what can it help with.
• Generality. Some data is useful across a range of analyses; other data is useful only in particular cases.
• Temporal coverage, Data can be forecast, real-time, historic or backcast. These are used differently, for planning, operational and historical analysis purposes.
• Quality. Higher quality data is generally more valuable as it reduces uncertainty and risk, though the required quality varies from use to use. Greater automation in data collection tends to lead to higher quality.
• Sensitivity. Sensitive data is data that could be used in damaging ways (e.g. personal data, commercial data, national security data). Costs and risks are incurred keeping sensitive data safe.
• Interoperability and linkability - Interoperability relates to the use of data standards when representing data, which means that data relating to the same things can be easily brought together. Linkability relates to the use of standard identifiers within the dataset that enables a record in one dataset to be connected to additional data in another dataset.

Doug Laney identifies six approaches for valuing data, dividing these into two categories: foundational models and financial models. Foundational models assign a relative, informational value to data, where financial models assign an absolute, economic value.[11]

• Intrinsic Value of Information (IVI) measures data value drivers including correctness, completeness and exclusivity of data and assigns a value accordingly.
• Business Value of Information (BVI) measures how fit the data is for specific business purposes (e.g. initiative X requires 80% accurate data that is updated weekly – how closely does the data match this requirement?).
• Performance Value of Information (PVI) measures how the usage of the data effects key business drivers and KPIs, often using a control group study.

• Cost Value of Information (CVI) measures the cost to produce and store the data, the cost to replace it, or the impact on cash flows if it was lost.
• Market Value of Information (MVI) measures the actual or estimated value the data would be traded for in the data marketplace.
• Economic Value of Information (EVI) measures the expected cash flows, returns or savings from the usage of the data.

Research by the Bennett Institute divides approaches for estimating the value of data into market-based valuations and non market-based valuations.[9]

• Stock market valuations measure the advantage gained by organizations that invest in data and data capability.
• Income based valuations seek to measure the current and future income derived from data. This approach has limitations, due to its inability to measure value realized in a wider business or societal ecosystem, or beyond financial transactions involving data. Where income from data is realized through trading data in a marketplace, there are further limitations, as markets fail to describe the full option value of data, and usually lack enough buyers and sellers for the market to settle on a price that truly reflects the economic value of the data.
• Cost based valuations measure the cost to create and maintain data. This can look at the actual cost incurred, or projected costs if the data needed to be replaced.

• Economic value of open data examines how open or free data creates value for: organizations that host or steward the data; intermediary organizations or individuals that reuse the data to create products and services; organizations and individuals that use these products and services.
• Value of personal data can be estimated by asking consumers questions such as how much they would be willing to pay to access a data-privacy service, or would charge for access to their personal data. Values can also be estimated by examining the profits of companies that rely on personal data (In 2018 Facebook generated $10 for every active user), and by examining fines handed out to organizations that breach data privacy or other regulations.

• A modified cost value approach suggests refinements to a cost based valuation approach. It proposes the following modifications: data collected redundantly should be considered to have zero value to avoid double counting; unused data should be considered to have zero value (this can be identified via data usage statistics); the number of users and number of accesses to the data should be used to multiply the value of the data, allowing the historical cost of the information to be modified in the light of its use in practice; the value should be depreciated based on a calculated "shelf life" of the information; the value should be modified by its accuracy relative to what is considered an acceptable degree of accuracy.[12]
• A consumption based approach builds on the principles in the modified cost value approach by assigning data users different weightings based on the relative value they contribute to the organization. These weightings are including in the modelling of data usage statistics and further modify the measured value of data.[13]
• Data hub valuation uses a cost based approach that measures the cost of data hubs where large repositories of data are stored, rather than measuring the cost of separate datasets. The data hub cost can then be modified, as in the consumption based and modified cost value approaches.[14] Another hub valuation approach uses a modified market value approach, by measuring savings to users from accessing data via hubs versus individually accessing data from producers, and user willingness-to-pay for access to data hubs.[15]
• A stakeholder approach engages key stakeholders to value data, examining how data supports activities which external stakeholders identify as creating value for them. It uses a model that combines the total value created by the organization, a weighted list of value creating initiatives (as defined by external stakeholders) and an inventory of data assets. This approach was developed in a collaboration between Anmut, a consultancy firm, and Highways England, a public sector agency for which data valuations based on market value, income gains or economic performance are less meaningful. The approach can also be applied in the private sector.[16][17]