AI—both generative and machine learning/statistical—is essentially dead in the water without well-vetted, timely, quality data. This is holding back AI efforts more than anticipated, a recent survey finds.
Data issues hampering AI were explored in a recent survey released by Precisely and Drexel University, which exposed widespread data trust issues and its impact on data and AI initiatives. Data quality and governance have hampered organizations for decades, and such deeply rooted distrust in the data is now being reflected in their embrace of AI output.
AI success is clearly impacted—and stalled—by lack of data readiness, the survey of 565 data and analytics professionals worldwide finds. Six out of 10 professionals, 60%, see AI as a “key influence on data programs”—a 46% increase from the previous survey in 2023. However, only 12% report that their data is of sufficient quality and accessibility for effective AI implementation.
While 76% of respondents say data-driven decision making is a top goal for their data programs, 67% still don’t completely trust the data they rely on for these decisions, a concern that has risen from 55% in 2023.
A lack of data governance, cited by 62% of organizations, is the primary data challenge inhibiting AI initiatives. “This is likely due to the role that data governance programs play in managing an organization’s data usage—including where it’s stored, its lineage, who has access to it, whether it has personally identifiable information (PII) attributes, and more,” the study’s authors report.
A yawning skills gap further impedes AI adoption. The shortage of skills and resources needed for data management, analytics, and AI has also grown this year. At least 60% of respondents cite a lack of AI skills and training as a significant challenge in launching AI initiatives. In addition, 42% say a shortage of skills and resources continues to be one of their biggest challenges to data programs overall, which is up from 37% in 2023.
Close to two-thirds, 64%, identify data quality as their top data integrity challenge, which is up from 50% in last year’s survey. In addition, the overall perceptions of data quality have declined, with 77% of respondents rating the quality of their data as average or worse, compared to 66% in the previous year. “This can be blamed on the acute need for AI-ready data,” according to the survey’s authors.
The costs of managing and preparing data for next-generation initiatives are a top concern for enterprises, cited by 50% of respondents. Lack of adequate tools for automating data quality processes is cited by 49% of respondents. Inconsistent data definitions and formats (45%), and data volume (43%) are also top concerns.
The research also shows that poor data quality continues to be pervasive across enterprises, with 50% of respondents reporting that data quality is the number-one issue impacting their organization’s data integration initiatives.
The main goal of data initiatives continues to be data-driven decision making, cited by 77% and in line with the previous year’s results. Operational efficiency ranks second, at 73%, which is down slightly from last year.
There has been a greater focus on regulatory compliance and customer retention during the past year. Data governance adoption has risen dramatically. To combat challenges with data trust, quality, and AI success, organizations are increasingly realizing the importance of robust data governance programs. This year, 41% of organizations identified data governance as a top challenge to data integrity, second only to data quality, and up from 27% last year. In line with this, adoption has increased, with 71% reporting that their organization has a data governance program, compared to 60% in 2023.
Organizations that have invested in data governance programs report benefiting from improved data quality (58%), improved quality of data analytics and insights (58%), increased collaboration (57%), increased regulatory compliance (50%), and faster access to relevant data (36%).
Data enrichment and location intelligence have also emerged as key data initiatives. A total of 28% report data enrichment as a priority for data integrity, which is up from 23% in 2023. Organizations are now seeking to reveal maximum context from their data for enhanced innovation, operational efficiencies, and competitive advantage.