Investing in an organization’s data liquidity—the ease of data asset reuse and recombination—should unleash faster and more data monetization activities. At the same time, building data liquidity is costly, with challenges and risks. For example, it is unclear how companies can make sure that value created from greater liquidity is captured by the organization. To date, we have identified two approaches that large organizations are using to increase the data liquidity of strategic data assets: (1) structuring data as digital data assets (DDAs), and (2) enhancing the company’s data platform capability with advanced cloud-based technologies and techniques.
Continuing our 2021 investigation, this study explores how companies evaluate, monitor, and actively capture value produced as a result of data liquidity strategies. Research questions include:
- How do companies operationalize and measure data liquidity at both the organizational and data asset level?
- How do companies identify, form, and execute an effective data liquidity strategy?
- Does the nature of the company’s strategic data assets influence its data liquidity strategy? If so, why and how?
- What organizational capabilities influence how companies identify, form, and execute an effective data liquidity strategy?
Methods: This study will draw on interviews (Phase 1) and surveys (Phase 2) with executives in organizations who are actively engaged in formulating and executing data liquidity strategies. The team will also produce at least one vignette or case study on a company that has increased its data monetization returns as a result of investments in its data liquidity strategy.
Seeking: Members of the MIT CISR Data Advisory Board and other Heads of Data who are interested in or actively trying to understand data liquidit
Contact: Joaquin Rodriguez
