Data Asset Valuation
Data asset valuation is the process of understanding/calculating the economic value of data.
Chapter 3 treats data as an intangible, non-fungible asset whose value is contextual and use-dependent. It does not provide one universal price formula.
Source-supported valuation perspectives
Organizations may reason about value using approaches such as:
- replacement/recovery cost if data is lost;
- market value in merger/acquisition contexts;
- revenue or identified business opportunities enabled by the data;
- value from packaging/selling data or insights where appropriate;
- risk-related cost — loss, retention of data that should not exist, inaccurate data causing financial/customer/legal/reputational harm.
The point is to create a consistent organizational method, not pretend that all datasets share a universal market price.
Governance role
The DGC can organize the effort and establish standards/methods for reasoning about information value so investment, risk and quality decisions can be compared consistently.
Finance and business stakeholders may supply economic assumptions; Governance standardizes the decision method.
Information gaps
The absence of needed information can itself represent liability or opportunity cost. Governance can use information-gap/loss/risk reasoning to justify Data Management improvements.
Exam trap
“DAMA prescribes one exact monetary formula for every data asset” → False.
Scenario
Executives ask the DGC for one exact universal price formula.
Better response: define a repeatable valuation approach appropriate to the organization and use multiple relevant source-supported perspectives. Do not claim Chapter 3 provides one universal formula.
Source anchors: Chapter 3 pp. 79–81 and p. 93.