Cloudera Announces Latest Research Report 'Data Readiness Index': Approximately 80% of Companies Report 'AI Utilization Constrained by Data Access Challenges'
Cloudera has released the 'Data Readiness Index', revealing that while 96% of companies have adopted AI, about 80% face difficulties in data access across environments, limiting their AI initiatives and highlighting an 'illusion of AI readiness'.
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- 📰 Published: April 23, 2026 at 02:00
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Cloudera K.K. (Location: Chuo-ku, Tokyo; President and Executive Officer: Yuji Yamaga), the only company providing AI for data anywhere, announced the release of its latest global research report, the 'Data Readiness Index: Decoding a Successful AI Foundation,' which analyzes companies' readiness for large-scale AI utilization. Conducted among approximately 1,300 IT leaders worldwide, the survey revealed that while AI adoption is advancing, many companies have not sufficiently established the data foundation necessary for its success. Of particular note is the striking paradox shown in the survey results. Furthermore, a clear contradiction between corporate processes and strategies versus the actual state of data access and utilization has been brought to light. While 96% of companies report integrating AI into their core business processes and 85% state they have a clear data strategy, about four out of five companies (approximately 80%) recognize that their AI and data utilization efforts are stalling due to hindered data access across multiple environments.
This gap highlights a new challenge: the 'illusion of AI readiness.' That is, a situation where companies perceive themselves as fully prepared to scale AI utilization despite critical data issues remaining unresolved.
Cloudera's Chief Technology Officer (CTO), Sergio Gago, stated: "Companies are not struggling with the implementation of AI itself, but rather with the challenge of moving it beyond the experimental stage and embedding it into daily operations. The effectiveness of AI depends on the quality of the data that supports it. Without seamless access to all data, the accuracy, reliability, and business value that AI brings will be severely limited. AI cannot exist without data."
While AI adoption progresses, achieving ROI remains a challenge
Although AI has permeated widely across enterprises, realizing a stable Return on Investment (ROI) remains difficult. As reasons why AI initiatives fail to deliver expected outcomes, respondents primarily cited data quality (22%), cost overruns (16%), and a lack of integration into existing operations (15%). These figures illustrate the difficulty of translating AI investments into actual business outcomes.
Furthermore, infrastructure constraints are exacerbating the challenges. Nearly three-quarters (73%) of respondents answered that processing performance constraints are hindering the advancement of their operations, highlighting the difficulty of scaling AI utilization in fragmented environments.
Data Gap: Access, Governance, and Visibility Challenges
At the root of these challenges is a lack of comprehensive data access and management.
While 84% of respondents stated they are confident in the accuracy, completeness, and consistency of their company's data, such perceptions often conceal deeper, underlying issues like data silos, inconsistent quality, and access restrictions. Data that appears reliable in isolation often fails to function properly when utilized across the organization, between systems, or within AI applications, ultimately revealing a lack of governance and consistency.
Fewer than one in five respondents (18%) stated that their company's data is fully governed, highlighting the gap between perception and reality. Although 71% answered that 'most of our company's data is governed,' a truly data-driven decision-making process requires a consistent and reliable data foundation across the entire organization.
Without comprehensive governance that integrates data and applies clear standards, companies face risks such as missed opportunities, flawed decision-making, and outputs that fall short of expected results.
Differences in Data Readiness by Industry
The state of data readiness varies significantly by industry. For example, while 54% in the telecommunications industry answered that they 'fully grasp the location of their data,' this figure stood at only 30% in financial services and 31% in the public sector. Regarding data access, 51% in the telecom industry stated they 'can access all data at any time,' whereas only 24% in financial services and 16% in the public sector reported the same.
However, this high level of data readiness does not necessarily translate directly into business outcomes. 60% of those in the telecom industry responded that infrastructure performance continuously hinders operational advancement, which is the highest percentage across all industries.
These challenges also impact AI utilization. The barriers to AI ROI vary by industry; while data quality is cited as the primary challenge overall, cost overruns (25%) constitute the biggest hurdle in the energy and utilities sector. On the other hand, in healthcare, manufacturing, and financial services, lack of integration into operations remains a major issue.
This gap highlights a new challenge: the 'illusion of AI readiness.' That is, a situation where companies perceive themselves as fully prepared to scale AI utilization despite critical data issues remaining unresolved.
Cloudera's Chief Technology Officer (CTO), Sergio Gago, stated: "Companies are not struggling with the implementation of AI itself, but rather with the challenge of moving it beyond the experimental stage and embedding it into daily operations. The effectiveness of AI depends on the quality of the data that supports it. Without seamless access to all data, the accuracy, reliability, and business value that AI brings will be severely limited. AI cannot exist without data."
While AI adoption progresses, achieving ROI remains a challenge
Although AI has permeated widely across enterprises, realizing a stable Return on Investment (ROI) remains difficult. As reasons why AI initiatives fail to deliver expected outcomes, respondents primarily cited data quality (22%), cost overruns (16%), and a lack of integration into existing operations (15%). These figures illustrate the difficulty of translating AI investments into actual business outcomes.
Furthermore, infrastructure constraints are exacerbating the challenges. Nearly three-quarters (73%) of respondents answered that processing performance constraints are hindering the advancement of their operations, highlighting the difficulty of scaling AI utilization in fragmented environments.
Data Gap: Access, Governance, and Visibility Challenges
At the root of these challenges is a lack of comprehensive data access and management.
While 84% of respondents stated they are confident in the accuracy, completeness, and consistency of their company's data, such perceptions often conceal deeper, underlying issues like data silos, inconsistent quality, and access restrictions. Data that appears reliable in isolation often fails to function properly when utilized across the organization, between systems, or within AI applications, ultimately revealing a lack of governance and consistency.
Fewer than one in five respondents (18%) stated that their company's data is fully governed, highlighting the gap between perception and reality. Although 71% answered that 'most of our company's data is governed,' a truly data-driven decision-making process requires a consistent and reliable data foundation across the entire organization.
Without comprehensive governance that integrates data and applies clear standards, companies face risks such as missed opportunities, flawed decision-making, and outputs that fall short of expected results.
Differences in Data Readiness by Industry
The state of data readiness varies significantly by industry. For example, while 54% in the telecommunications industry answered that they 'fully grasp the location of their data,' this figure stood at only 30% in financial services and 31% in the public sector. Regarding data access, 51% in the telecom industry stated they 'can access all data at any time,' whereas only 24% in financial services and 16% in the public sector reported the same.
However, this high level of data readiness does not necessarily translate directly into business outcomes. 60% of those in the telecom industry responded that infrastructure performance continuously hinders operational advancement, which is the highest percentage across all industries.
These challenges also impact AI utilization. The barriers to AI ROI vary by industry; while data quality is cited as the primary challenge overall, cost overruns (25%) constitute the biggest hurdle in the energy and utilities sector. On the other hand, in healthcare, manufacturing, and financial services, lack of integration into operations remains a major issue.