- For companies to remain competitive and efficient, they have to make sure their data is accessible and ready for AI.
- New research from Cloudera points to the current gaps in data readiness across industries.
- Fragmented governance and data silos are among the top factors holding companies back.
The race to capitalize on AI is changing. Success is no longer determined by who adopts and deploys AI the fastest, but by whether your data infrastructure is ready to support the technology at scale.
A new survey conducted by hybrid AI and data platform Cloudera, of more than 1,200 IT leaders across industries and regions, found that while respondents are confident in their data strategies, reality suggests that gaps in data readiness are holding ROI back.
Seventy-nine percent of respondents said their initiatives are hindered by limited access to data across environments. A separate global study conducted by Harvard Business Review Analytic Services with Cloudera found that only 7% of enterprises said their data is completely ready for AI. Meanwhile, Gartner predicts organizations will abandon 60% of AI projects this year because they lack AI-ready data.
"Taken together, these findings expose a critical disconnect between where organizations want to be with AI and where they actually stand," said Sergio Gago, chief technology officer at Cloudera.
"By investing in data readiness as a strategic initiative, organizations can close that gap and better position themselves to scale efficiently and derive sustained business value from AI."
Cloudera's research highlights several key challenges that persist, even as organizations push ahead with current data and AI strategies. Here's what they are and what your company can do to address them.
Fix data quality by tightening governance
AI can only be as good as the data it's trained on. Becoming more competitive means moving beyond general-purpose LLMs trained on public data and toward models grounded in your own. Next, and even more critical, is making sure that data is high quality.
Cloudera's survey shows that IT leaders have misplaced confidence in the quality of their data. While 84% of respondents believed in the accuracy, completeness, and alignment of their organization's data, 30% cited data quality as the main reason AI projects fail to deliver ROI.
Part of this is a governance issue. Less than one in five respondents said their data is fully governed. And while 71% said their data is mostly governed, any gaps can lead to weaker AI outputs. Modern AI tools pull from everything — not just clean databases, but documents, emails, meeting recordings, and more. That means all data (structured, unstructured data, and anything in between) must be governed.
A less platform-dependent approach to governance is a strong place to start. Ideally, you define a governance policy once and enforce it wherever the data lives, without requiring separate systems or manual oversight.
Make data easy for teams and tools to access
Well-governed, high-quality data is useless if people and AI tools can't access it. Yet Cloudera's survey found that access and integration remain major bottlenecks.
Even among technology organizations that have made significant investments in cloud and modern data platforms, 56% of respondents said that they lack full access to their data. In fact, the rapid adoption of new technologies may be part of the problem.
Each new platform added without a unified strategy creates another silo, fragmenting data across systems — a top issue preventing teams from using data effectively, according to 34% of IT leaders. Integration remains a concern as well, with just 30% of IT leaders saying their data sources are fully integrated across systems.
This leaves teams and tools working from different, incomplete versions of the same data, making accurate results harder to achieve. The organizations pulling ahead are those that have inverted that model — building a more mature architecture where data is the foundation everything else sits on top of, rather than a byproduct of whichever tool generated it.
Understand your organization's unique barrier to data readiness
These are strong best practices, but there's also no universal playbook for data readiness. Cloudera's research underscores that different industries face different barriers.
For instance, while tech companies, the public sector, and telecommunications organizations agree that data quality is their top challenge, energy and utility companies are more worried about cost overruns. Meanwhile, healthcare, manufacturing, and financial services struggle most with weak integration into workflows.
Leadership-level challenges may also exist. Other common barriers to effective data use include complicated access requirements and processes, insufficient training and data literacy, and cultural resistance to data sharing. Preparing for the future starts with an honest look at where your data is falling short today.
"Our research makes clear that most leaders recognize the importance of data readiness, yet significant structural, cultural, and governance challenges continue to hold them back," said Gago.
"The companies that actually pull ahead will be the ones who aren't afraid to admit their own weaknesses, and treat improving them as a strategic priority rather than an IT to-do list."
This post was created by Insider Studios with Cloudera.
The post Data readiness will be a core indicator of success in the AI era. Here's how to make sure your company is ready. appeared first on Business Insider
