Big organizations, especially those with big aspirations for leveraging AI technology, increasingly need data assets with high data liquidity (i.e., ease of data asset reuse and recombination) to achieve their aims. However, developing and fully exploiting liquid data assets requires significant changes to data architecture, development processes, operations, and organizational design. Further, effectively managing liquid data assets requires that organizations learn how to measure and realize value from data assets and establish adaptable data asset management practices.
Research questions we will pursue include:
- How can organizational design enable the establishment of data liquidity management practices and processes?
- What are the current states of data liquidity investments and outcomes in organizations?
- What management practices help organizations effectively build liquid data assets?
This study will rely on a large-scale case study of a global manufacturing organization that has learned over time how to successfully build and monetize liquid data assets as a part of the organization’s standard operations. Additionally, the study will analyze data from a 2024 MIT CISR survey of 349 executives who understand their organization’s data monetization investments and outcomes. The analysis will identify the current states in organizations of data asset experience, digital data assets, data liquidity investments, and data asset reuse and recombination.
SEEKING: We are seeking executives who will react and contribute to insights during the analysis phase of the project.
CONTACT: Barb Wixom