As organizations become more AI-fueled and pursue complex digital business opportunities, they must have sufficient data and AI capabilities—and guardrails that keep them on course. They must invest in practices that ensure compliance with an evolving regulatory landscape (e.g., AI regulations), address increased scrutiny regarding sustainability impact (e.g., the carbon footprint of AI models), and manage new (especially generative AI) and old vendor dependencies. They must manage data assets throughout their lifecycle and oversee masses of data users.
Research questions we will pursue include:
- What do leaders consider “acceptable data use” in their organization, and what practices do they use to establish sufficient data and AI guardrails?
- What practices help leaders manage the data asset lifecycle, specifically the deletion of data amidst growing regulatory and legal constraints and monetization opportunities?
- How should leaders incorporate the sustainability impact of data management and AI model processing when formulating the profit formula (costs, benefits, and risks) of their data assets?
- How do vendor involvement, cross-organizational data sharing, and decentralized users influence the dynamics of acceptable data use oversight?
This exploratory study will draw on interviews with members of the MIT CISR Data Advisory Board and select other data leaders and CxOs around the globe. The interviews will explore contemporary acceptable data use management challenges—and what responses leaders are finding to be effective.
SEEKING: We are seeking to interview data and analytics executives who are successfully navigating contemporary acceptable data use challenges such as AI model sustainability and data deletion.
CONTACT: Barb Wixom
