Comment: A valuation approach guided by application of professional best judgment, guided by the absence of a repeatable, scalable standard to measure the value of data.
Fleckenstein, M., Obaidi, A., & Tryfona, N. A Review of Data Valuation Approaches and Building and Scoring a Data Valuation Model, Harvard Data Science Review, 5(1). https://doi.org/10.1162/99608f92.c18db966 & https://hdsr.mitpress.mit.edu/pub/1qxkrnig/release/1 MITRE Corporation: Approved for Public Release. Distribution Unlimited. Public Release Case Number: 21-3464.
The authors report that there is increasing desire to treat data as an
asset "in both the private and public sectors...However, this remains a
challenge, as data is an intangible asset. Today, there is no standard
to measure the value of data." Much like Azcoitia, it is this team's view that there is no: "repeatable approach to data valuation:" The use case will define selection of the methods that are used to determine value.
- Part (1) Introduces current practice
- Part (2) Reports classification of data valuation models
- Part (3) Reports an assessment of the model classes
- Part (4) Reports the results of test case analysis
- Part (5) presents Conclusions and References.
Part (I): Discusses three overlapping approaches to valuation: Business (P&L), Public Goods (Government/Non-profit), Dimensional (Attributes of Value).
Part (2): Data Valuation Framework & Part 3: Model Details: The authors studied different methods, spanning more than 40years; thence grouping the methods into three classes:
1) Market-based models (estimates of cost and revenue): "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-intensive companies."
2) Economic models (estimates of economic and public benefit): "The economic model values data in terms of its economic impact. This model is frequently used by governments to assess the value of publicizing data. For example, governments share weather data, which helps sustain an ecosystem of weather forecasting."
3) Dimensional models (using categories or dimensions): "The dimensional model values data by assessing attributes inherent to a data set (e.g., data volume, variety, and quality) as well as the context in which data is used (e.g., how the data will be used and integrated with other data). For example, organizations inherently decide to acquire, keep, or prioritize one of several similar but different data sets. To date, this is an informal process."
The researchers note that the models are not fit-for-purpose for all use
cases, are speculative, can overlap, and can be influenced by factors
other than the data itself. Figure 1 is a Venn Diagram that helpfully diagrams the overlap of the data classes that are included in the taxonomy.
Part (3): The authors review the strengths and weaknesses of each class of model.
1) Market-based: Sec.2.3 & 3.1
2) Economic: Sec.2.4 & 3.2
3) Dimensional: Sec.2.5 & 3.3
The authors note there is no single method to deliver a standard valuation. The type must be selected to fit the use case. The value-add of the approach is that many methods may have to be used in a "framework, with each use case leveraging one or more models" to build a multi-dimensional estimate.
Part (4): Building and Scoring a Dimensional Data Valuation Model: Here is detailed the building and scoring of a dimensional data valuation model. The model was used to define dimensions to assess the value of two use cases. The goal "was to design an easy-to-use, customizable approach that helps organizations assess the value of specific data sets for specific use cases using a small, consistent set of dimensions." This method uses "professional data management experience;" and "in the use case of "flight scheduling and navigation data, we vetted the results with the data set owners."
Part (5): Conclusions and References: The authors' report developing "an easy-to-use, repeatable model to value data for two use-cases;", where the model combines (a) dimensional analysis; (b) "professional data management experience;" and (c) for one of the two use cases, review of results by the data owners.
The authors conclude that the dimensional approach "can be used effectively to compare two similar data sets or to evaluate the addition of a data set to an existing data pool...(but) falls short of being able to value data in monetary terms;" and will likely require use of the other model types to fully develop a valuation.
References included.
Notes and analysis by blogger. Image: Pxhere. CC0.
