A new report from the data analytics firm Ocient presents a sobering picture for enterprise leaders, revealing that the transformative promise of artificial intelligence has yet to materialize into significant financial gains for nearly half of all businesses. The company’s annual “Beyond Big Data” report, titled “From Roadmap to Reality,” identifies poor data quality and overwhelmed technical infrastructures as the primary culprits stalling AI-driven revenue growth. At the same time, the research indicates that mounting pressures from security and regulatory compliance are fundamentally reshaping how enterprises manage their data, prompting a noticeable shift away from cloud-exclusive strategies in favor of more flexible and cost-effective deployments.
Based on a comprehensive survey of over 500 senior IT and data executives, each managing workloads exceeding 150 terabytes, the report illustrates an industry at a critical inflection point. The relentless and rapid expansion of data, compounded by the sophisticated demands of modern AI workloads and increasingly stringent compliance mandates, has pushed existing data infrastructures to their breaking point. In response, leaders are undertaking major overhauls of their data architectures. They are increasingly adopting hybrid deployment models and making substantial investments in systems designed to bolster security, improve data governance, and ensure their platforms can sustainably handle the performance demands of future AI initiatives.
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“Today’s enterprise IT and data leaders face a critical challenge: ensuring data remains a source of opportunity rather than a vulnerability,” said John Morris, CEO of Ocient. “The latest Beyond Big Data report makes it clear that without efficient, secure, and flexible data foundations, enterprises limit their ability to turn data into AI-driven business results.”
The report delves into the specific performance gaps hindering AI adoption. The primary obstacles were identified as poor data quality, cited by 55% of respondents, and the inability of existing solutions to scale effectively, mentioned by 48%. A staggering 84% of leaders confirmed that their AI initiatives have significantly magnified the complexity of their data processing requirements. Furthermore, a persistent talent shortage remains a significant bottleneck, with 46% reporting difficulty in finding staff with the necessary skills to manage advanced AI and machine learning workloads.
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Security concerns continue to dominate the executive agenda, having been ranked as the top pain point by leaders in every edition of the report since its inception in 2022. This focus is translating into direct investment, with the percentage of leaders prioritizing security spending projected to jump from 55% in 2023 to 75% in 2025. When implementing AI, over 60% of executives named data security and privacy as their single biggest worry. This heightened sense of risk is directly influencing deployment strategies, as 53% of leaders stated they would change their deployment model for enhanced security, and 51% would do so to achieve better data sovereignty and compliance.
This security-first mindset is contributing to a decline in the popularity of cloud-only data strategies. The report found that the top barriers to unlocking the full value of data are its rapid growth, cited by 49% of leaders, and associated costs, noted by 46%. Consequently, only 23% of respondents now prioritize a cloud-only approach, a significant 13% decrease from 2024. This trend is coupled with a rapid acceleration in the modernization of databases and data warehouses, a priority that has climbed from 58% in 2023 to an anticipated 75% in 2025 as companies prepare for hybrid and on-premises workloads. Looking ahead, over half of all leaders expect that at least 50% of their AI and machine learning workloads will be deployed in a hybrid cloud environment within the next three years.
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