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Data Environments Play a Critical Role in Modern Business
Data Quality Forms the Foundation of Data Readiness
Data Governance and Compliance Are Crucial to Data Readiness
Organizations Focus on Data Integration and Interoperability
Data Quality and Integrity Are Critical Requirements
Data Security and Ethical Considerations Present Ongoing Challenges
Conclusion
Research Methodology
Respondent Demographics
Research Report: Data Readiness for Impactful Generative AI
Research Report
Apr 02, 2025
by
Stephen Catanzano, Emily Marsh, Enterprise Strategy Group Research
As organizations race to develop generative AI solutions to enable data-driven decision-making, create unique customer experiences, improve efficiencies, and build competitive advantages, the importance of data readiness has become increasingly evident. The need to prepare and manage enterprise data effectively for generative AI has placed a sharp focus on data quality, governance, and bias. These factors are now shaping how and when organizations bring their generative AI solutions to life.
This research highlights a clear demand for generative AI driven by the fear of missing out. This is set against the backdrop of data readiness influencing the pace of adoption.
To gain further insight into these trends, Enterprise Strategy Group surveyed 385 IT and data professionals at organizations in North America (U.S. and Canada) involved with or responsible for the data governance and AI technologies, processes, and programs used to manage their organization’s data.
Page Count: 25
Table of Contents
Executive Summary
Report Conclusions
Introduction
Research Objectives
Research Findings
Data Environments Play a Critical Role in Modern Business
Data Quality Forms the Foundation of Data Readiness
Data Governance and Compliance Are Crucial to Data Readiness
Organizations Focus on Data Integration and Interoperability
Data Quality and Integrity Are Critical Requirements
Data Security and Ethical Considerations Present Ongoing Challenges
