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Aug 18, 2023 · Ker and Mazzini use four different methods to calculate the value of data using currently available information on data value generation. These are a cost-based approach, an income-based approach, an approach based on market capitalization, and one based on the link between trade flows and data flows.
- Abstract
- Media Summary
- Introduction
- Data valuation Framework
- Building and Scoring A Dimensional Data valuation Model
- Conclusions and Future Work
- Acknowledgments
- Disclosure Statement
- References
- Appendices
Data valuation has been given increasing thought for the past 20 years. The importance of data as an asset in both the private and public sectors has systematically increased, and organizations are striving to treat it as such. However, this remains a challenge, as data is an intangible asset. Today, there is no standard to measure the value of dat...
We often hear that data is becoming the new currency across our economy (e.g., Keller, 2020). It is a clear indication that we, as a society, want a way to value data in concrete terms. We are not there yet. Today, business gambles on the future value of data by acquiring competitors for huge amounts of money based on things like “eyeballs.” Govern...
As longtime practitioners and teachers of data management, we are struck by the many references to ‘data as an asset.’ The implication is that data should be valued similarly to traditional assets. When a market exists, discounted value of future utility can be measured in monetary terms. However, when no market exists, the value of data must be ca...
2.1. Models for Data Valuation
Our environmental scan reviewed many examples of data valuation spanning from more than 40 years ago to today. Through our research of prior approaches, we arrived at a data valuation framework that groups data valuation approaches into three models. We define these as follows: 1. The market-based model values data based on income (e.g., selling data), cost (e.g., buying data), and/or stock value (e.g., value of data-intensive organizations). Organizations routinely buy and sell data and data...
2.2. Model Overlap
While the three models are different and rooted in specific use cases, there is overlap among them. For example, governmental policy and legal regulation (e.g., privacy) affect all models. Similarly, survey questions can be constructed to accommodate any model. Finally, we saw that cost and utility (sometimes expressed in financial terms) was used as a valuation method across all models. This overlap highlights the underlying similarity of the three models as well as their unique focus. Figur...
2.3. Market-Based Model Examples
Market-based approaches to data valuation are an extension of physical asset valuation. Just like physical assets, data can be valued based on its cost, its sale value, or its income potential (Internal Revenue Service [IRS], 2020). In addition to these approaches to data valuation, companies are also using at least two different forms of cost, besides purchase cost. The first is data valuation in terms of insurance cost—what would the compromise or loss of data cost? The second is estimating...
For the second part of our research, we focused on building a dimensional data valuation model that expands on prior models.6 We designed a survey of about 30 questions around an extended set of dimensions, both intrinsic to data (e.g., data quality) and contextual (e.g., data usage). For data, we leveraged three types of data sets: COVID-19 data, ...
The first part of this article examines research into data valuation. We found many examples and were able to construct a framework that grouped the three approaches into the following models: 1. market-based models, which calculate data’s value in terms of cost and revenue/profit 2. economic models, which estimate data’s value in terms of economic...
We thank Dr. Nitin Naik and Dr. Kris Rosjford for useful insights and discussions. We thank the MITRE Corporation Innovation Program (MIP) for funding this research.
The views, opinions, and/or findings contained in this report are those of The MITRE Corporation and should not be construed as an official government position, policy, or decision, unless designated by other documentation. Approved for Public Release. Distribution Unlimited. Public Release Case Number: 21-3464.
Acil Allen Consulting. (2015, December). The value of earth observations from space to Australia. Spatial Information Systems Research Ltd. https://www.crcsi.com.au/assets/Program-2/The-Value-of-Earth-Observations-from-Space-to-Australia-ACIL-Allen-FINAL-20151207.pdf Adams, E., & Gounardes, A. (2020, June 1). A tax on data could fix New York’s budg...
Appendix A. Summary of Model Strengths and Weaknesses
Table A1 summarizes the strengths and challenges of each model.
Appendix B. Key Differences and Similarities Between Data and Traditional Assets
One of the things that makes data valuation particularly difficult is that data is, in some ways, different from physical assets. For example: 1. Data is nonrivalrous, as it can be consumed simultaneously by multiple parties. However, this must be seen in context, as others argue data value can be diminished through broad consumption (Nash, 2014). 2. Data is an intermediate good. It reveals ways in which to derive value from other assets. 3. Data is freely generated and traded. Personal data...
Aug 2, 2024 · Frequency Distribution: This shows how often each value in a dataset occurs. 2. Inferential Analysis. Inferential analysis allows researchers to make predictions or inferences about a population based on a sample of data. It is used to test hypotheses and determine the relationships between variables.
data availability (Slotin, 2018), or simulating counterfactual outcomes (Arrieta-Ibarra et al., 2020). Slotin (2018) reviews five types of data valuation methodologies, concluding that impact-based approaches are preferable as they are generally easier to understand and to communicate.
Statistical methods for data analysis are the tools and techniques used to collect, analyze, interpret, and present data in a meaningful way. From businesses optimizing operations to researchers uncovering new discoveries, these methods are foundational to making informed decisions based on data.
Mar 25, 2024 · Data analysis is the systematic process of inspecting, cleaning, transforming, and modeling data to uncover meaningful insights, support decision-making, and solve specific problems. In today’s data-driven world, data analysis is crucial for businesses, researchers, and policymakers to interpret trends, predict outcomes, and make informed decisions.
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Aug 18, 2023 · With the growing use of digital technologies, data have become core to many organizations’ decisions, with its value widely acknowledged across public and private sectors. Yet few comprehensive empirical approaches to establishing the value of data exist, and there is no consensus about which methods should be applied to specific data types or purposes.
