Data Integrity Trends

Key study findings from the 2025 study "Data Integrity Trends and Insights" shed light on the most pressing challenges organizations face as they prepare for AI and other data initiatives; as well as how they prioritize investments in data integrity to address them, as AI success is hampered by a lack of data readiness. The report shows that although 60 percent of organizations say AI is having an important impact on data programs (an increase of 46 percent from 2023), only 12 percent say their data is of sufficient quality and accessibility for effective AI implementation.

For years, organizations have struggled with low-quality data, leading to a deep-rooted distrust of the data used for analytics and AI. While 76 percent of companies say that data-driven decision making is one of the main goals of their data programs, 67 percent do not fully trust the data they rely on to make these decisions. Lack of data governance is the biggest data problem holding back AI initiatives.

As more organizations prioritize data-driven decision making, the lack of skills and resources needed for data management, analytics and AI has also intensified this year. 42 percent say the lack of skills and resources continues to be one of their biggest challenges for data programs.

"While companies want to harness the power of AI, a lack of talent is hindering AI integration," said Murugan Anandarajan, Professor and Academic Director at the Center for Applied AI and Business Analytics at Drexel University's LeBow College of Business. "Our research findings underscore this gap, with 60 percent of respondents citing a lack of AI skills and training as a key challenge in adopting AI initiatives."

AI and data quality

Given the results relating to AI, it is not surprising that data quality is cited as a key focus for companies worldwide. 77 percent of respondents rate the quality of their data as average or worse. The biggest obstacle to achieving high quality data is the lack of suitable tools to automate data quality processes; inconsistent data definitions and formats, as well as data volume, are also major issues. The study also shows that poor data quality affects all aspects of data integrity.

To address the challenges of data trust, data quality and AI success, organizations are increasingly recognizing the importance of robust data governance programs. This year, 51 percent of organizations indicated that data governance is the biggest challenge to data integrity after data quality, a dramatic 89 percent increase from the previous year. Companies that have invested in data governance programs report that they are benefiting from improved data quality, data analytics and insights, collaboration, regulatory compliance, and faster access to relevant data.

The 2023 report predicted the emergence of data augmentation and spatial analytics as business-critical technologies, and this year's report shows a significant leap in adoption. Organizations are now seeking to extract maximum context from their data to drive innovation, operational efficiency and competitive advantage. Similarly, 21 percent of organizations say spatial analytics is a priority for data integrity initiatives.

"Our joint study with Drexel LeBow shows that despite the increasing importance of data-driven decision making, organizations' confidence in their data readiness has dropped significantly," says Josh Rogers, CEO of Precisely. "To fully realize the business benefits of analytics and AI, organizations must invest in data integrity. Building a foundation of accurate, consistent and contextual data can help them make informed decisions and truly realize the value of their AI initiatives."

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