Data readiness is no longer just a technical ambition; it’s an operational requirement. Still, execution across industries is lagging. Data foundations weren’t built for the demands of the AI era, and while these challenges manifest differently across sectors, the mandate is consistent: organizations must rethink how they unify, govern, and access their data to bring AI to their data, wherever it lives.
Cloudera’s recent Data Readiness Index digs into what organizations need to build a solid foundation that can fuel AI at scale. The survey results show that enterprises remain constrained by structural, cultural, and governance obstacles; however, these challenges manifest differently across industries. These insights can help leaders foretell the strategic changes to bridge the gap between ambition and execution.
Technology: Scaling AI Meets Data Fragmentation
Technology companies have long been some of the most AI-forward organizations, but the survey reveals that even in advanced settings, scale is exposing structural weaknesses. More than half (56%) of technology organizations report they lack full access to their data, despite significant investment in cloud and modern data platforms.
The shift to production-scale AI requires technology organizations to rethink their infrastructure. The fragmented, unreliable data systems that hinder scaling AI result from the difficulty in operationalizing it across products and teams. This is reflected by 30% of leaders citing data quality as the main reason AI projects fail to deliver ROI, and 39% noting infrastructure issues always hinder operations.
In the technology sector, closing the data readiness gap involves enabling AI to run where data already lives—without requiring costly data movement. This starts with creating a unified, governed data and AI foundation across clouds, data centers, and edge environments, delivering a consistent experience while maintaining full control over distributed data.
Manufacturing: Legacy Systems Collide with Real-Time Demands
Manufacturing companies are always pushing to streamline operations across the product lifecycle, but fragmented data prohibits full optimization of these efforts. 42% of manufacturing organizations cited siloed data as preventing teams from using their data effectively, and over half (52%) still lack full access to their data. Clearly, access is a central barrier to achieving data readiness, and operational complexity is compounded by isolated and unreachable data. The operational task of closing the gap between data ambition and execution requires making sure teams can access 100% of their data across environments, not just isolated subsets.
For manufacturers, production uptime, predictive maintenance, and supply chain continuity all depend on timely and reliable data. Equally important is investment in data integration and standardization layers, addressing the 20% of manufacturers who cite weak workflow integration as the primary reason data initiatives fail to deliver ROI. By focusing on scalable data pipelines and industrial platforms that operate across facilities, a unified, real-time infrastructure embedding data into core workflows can become a reality.
Energy & Utilities: Governance Becomes the Gatekeeper of Scale
Highly regulated environments, like those faced by IT leaders in the energy and utilities industry, require a careful balance between innovation and control. Regulatory compliance and grid reliability are both at stake, as energy and utilities organizations must ensure that data is not only accurate and secure, but also consistently governed across highly distributed environments. Energy and utilities organizations show relatively strong governance maturity, with 65% reporting that all or almost all their data is governed.
On the other hand, 25% cite cost overruns as the main reason data initiatives fall short of ROI, pointing to the financial and operational challenges of modernizing data infrastructure in highly regulated and distributed settings. Strict regulatory requirements need complete visibility and control over data, while real-time grid operations rely on timely, reliable data to balance supply and demand, prevent outages, and handle disruptions. Any gaps in accessibility can lead to security and compliance threats.
Energy and utilities operate in environments where every decision carries regulatory, financial, and public safety implications. That means data must be accessible, auditable, and secure across every system it touches.
Telecommunications: Complexity at Scale
Massive, distributed telecom environments create complex data and high stakes. Maintaining performance is one of those stakes, requiring real-time monitoring and quick adjustments, which can impact the customer experience. Issues like dropped calls, slow data speeds, and service interruptions quickly translate into customer dissatisfaction and churn. Telecom environments generate massive volumes of streaming data, and without the ability to process and act on data in real time, both network performance and customer experience suffer.
Telecommunications organizations lead in several areas of data readiness, with 54% reporting full visibility into their data and 51% able to access it across environments. They also report the highest level of fully governed data, with one-third (33%) of respondents reporting fully governed data environments. Yet despite this maturity, 60% say infrastructure performance consistently hinders operations—by far the highest of any industry surveyed. Scale and complexity, not access, are now the primary barriers, and data latency is an operational risk.
To overcome the gap between data readiness and operational performance, telecommunications organizations should invest in infrastructure built for speed, scale, and continuous processing. When latency directly impacts service quality, the solution is to enable telecom providers to automate network operations, enabling experts to deliver consistent, high-quality customer experiences.
The Bottom Line
Across various sectors, a common theme appears: organizations need to implement data effectively at scale. Data readiness enables organizations to bring AI to their data wherever it lives, unlocking the full value of 100% of their data across clouds, data centers, and edge environments. Cloudera’s Data Readiness Index demonstrates the opportunity for organizations to invest in data readiness now, ensuring they are well-positioned to lead in an AI-driven future.
How confident are you in your data readiness? Read the full report to gain deeper insights into how global organizations are approaching the data foundations that enable AI at scale.
