RESEARCH@DBTA: Data Issues Complicate Generative AI Initiatives, Survey Shows

Both operational and generative AI depend on distilling reliable data sources. A new survey, however, finds they are being overwhelmed by data sources and providing the data needed to deliver insights. In a survey of 600 chief data officers and data professionals released by Informatica, 41% admit they’re juggling 1,000 or more data sources. A number, 79%, expect to increase in 2024.

Generative AI adoption is already well underway at half of the sites covered in the survey. Investment in generative AI is driving mutual investment in data management, the report’s authors find. Data managers cite the ability to deliver reliable and consistent data fit for generative AI (39%) and improving data-driven culture and data literacy (39%) as the top data strategy priorities in 2024.


The ability to deliver data fit for analytics and AI rose from seventh place to first place this year. This represents a shift from last year’s report in which the ability to improve governance over data and data processes was the top data strategy priority (now cited by 38%). This category dropped to third place in the current survey.

AI requires more comprehensive data management to function properly, but also can play a role in improving data management. Among survey respondents implementing or planning to implement generative AI, 73% use or plan to use this tech to improve time-to-value with faster insights from data. Another 66% are looking to drive more productivity through automation and augmentation, while three in five (60%) use or plan to use generative AI to enable more self-service and data democratization.


At the same time, nearly all (99%) generative AI adopters have encountered roadblocks. More than two in five, 42%, of data leaders cited data quality as the main obstacle, followed by data privacy and governance (40%), and then AI ethics (38%).

Accordingly, to overcome these roadblocks, more than three in four (78%) data chiefs predict their data investments will increase in 2024, including 33% who foresee significant increases.

Another challenge is the increasing technical burdens associated with supporting data-driven enterprises. At least 39% of data leaders reported the increasing number of data consumers is the top technical obstacle to realizing their data strategy, while 38% said it was the increasing volume and variety of data.

In a similar survey conducted a year ago, the main obstacle was a lack of a complete view and understanding of their data estates (the fourth-highest obstacle in 2024).

Internal organizational resistance also threatens to derail data strategies and priorities, the report warned. Almost all survey respondents, 98%, admit organizational obstacles hold back their data strategies, including a lack of leadership support (45%), inability to justify ROI for budget (45%), and lack of cooperation/alignment across business units (44%).


Data privacy and protection (45%), data quality and observability (41%), and data integration and engineering (37%) remain the top data management capabilities to invest in to support these priorities.

Data managers are rethinking the metrics employed to measure the success of their data management efforts.

A total of 43% cite “improving readiness of data for AI and analytics initiatives” as their preferred metric for measuring data strategy effectiveness. Another 42% look to measure data literacy across their organizations.

Data managers and professionals are tasked with a variety of responsibilities, the survey showed. Data analytics and insights (30%) is the most often-cited responsibility, followed by data privacy, protection, and compliance (29%), and data strategy and governance (29%).


Additional responsibilities cited include improving data literacy and data culture (28%), enabling stakeholder collaboration (27%), and enabling data sharing and democratization (26%). Other top responsibilities include delivering usable, trusted data for decision making (25%), and defining and measuring data-related performance metrics (25%), as well as enabling AI initiatives (24%) and mitigating data-related risks (24%).

The report’s authors point to the growing role data managers and professionals have in driving new growth for their organizations in the 2020s. “With AI and data management, data leaders can recognize that it is not one driving the other, but rather that the two go hand in hand—and making the most of both means transformative change for these technologies, leaders’ strategies, and the future of their organizations,” they stated.