Today’s C-suite technology leaders are driving enterprise-wide innovation. As architects of data strategy, they play a central role in how organizations harness information to fuel growth, enable AI, and deliver smarter, faster decisions across the business.
To succeed, leaders must design long-term strategies that unify and govern enterprise data at scale. Once overlooked, a strong data foundation is now seen as a prerequisite for building real-time analytics, deploying artificial intelligence, and ensuring the entire organization can act on insight, not instinct.
To better understand how data is shaping enterprise priorities, Fivetran partnered with Forbes Insights to survey 500 U.S.-based C-suite technology leaders. The results offer a window into the current state of enterprise data strategy — and the roadblocks standing in the way. Key challenges include managing the growing complexity of diverse data formats and structures. Leaders cite integration challenges (36%), scalability limitations (34%), and security and compliance risks (33%) as their top hurdles. As most organizations now rely on more than 50 data sources to inform decision-making, the urgency to simplify and streamline data integration has never been clearer.
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Empowering the business with modern data tools
As enterprises pursue AI-driven transformation, executives recognize that scalable data operations begin with better data management. To meet business goals and support innovation, leaders are prioritizing the centralization of data (56%) and distributed access across teams (51%). These strategies create the foundation for timely insights and advanced AI workflows, but building this foundation remains a challenge.
Fragmented systems, federated data sources, and legacy infrastructure continue to limit enterprise scalability. Respondents cite a range of barriers to streamlining their data integration stack, including limited automation and lack of real-time capabilities.
Modern data integration platforms are now seen as ways to combat these and other challenges and as critical enablers of operational efficiency, real-time analytics, and AI scalability. Nearly half of C-suite leaders plan to invest $500,000 or more in integration infrastructure over the next year to support long-term AI success.
Across industries, organizations that invest in modern data movement tools are unlocking new value, reducing technical bottlenecks, improving decision-making, and building the scalable foundation needed to power AI and innovation.
Powering AI with integrated data
AI is only as effective as the data that fuels it. As large language models (LLMs) continue to redefine enterprise strategy, the pressure to harness high-quality, proprietary data is mounting — with nearly 90% of surveyed technology leaders planing to use proprietary data to train LLMs in 2025, which is a clear sign competitive advantage hinges not on access to public models, but on how organizations manage and mobilize their unique data assets.
But that vision remains elusive for many. Fragmented systems, limited observability, and slow implementation cycles continue to hold enterprises back. Skills gaps, change management challenges, and security concerns rank among the top barriers to deploying new data technologies. Without an integrated foundation, organizations struggle to scale LLMs and deliver AI that is relevant, compliant, and production-ready.
To close this gap, 44% of respondents plan to adopt a modern integration platform to unify structured and unstructured data. They note that a truly modern integration platform must be holistic with enforceable governance, scalability, and observability, or else their goals can't be met. Regardless, the takeaway is clear: in the era of AI, data integration isn't just a technical function — it's a strategic imperative.
Redefining the core role of governance and security
As AI initiatives scale and data becomes increasingly distributed, technology leaders are facing a critical inflection point: innovation must go hand-in-hand with trust. The need to safeguard data through security, compliance, and governance is not an afterthought. It’s a core requirement of any modern data strategy.
Survey data underscores this shift. Nearly three-quarters of CIOs say they are prioritizing security investments ahead of innovation, and 64% report delaying innovation altogether to address compliance concerns. As enterprises expand their data pipelines and introduce AI into more workflows, risk compounds — making precision in access management, auditability, and data lineage non-negotiable.
A centralized data foundation supported by modern integration platforms is emerging as the solution. These systems provide the visibility, control, and automation necessary to govern data at scale — replacing outdated models of static access with dynamic, revocable permissions and real-time monitoring.
This level of control is not just about risk mitigation — it’s about creating the operational clarity and confidence needed to scale securely.
Conclusion
Looking ahead, the C-suite is evolving. Nearly half of tech leaders expect expanded responsibility for data privacy and compliance, and 45% anticipate playing a larger role in shaping enterprise-wide data strategy.
As analytics, infrastructure, and AI converge, the next generation of technology leadership will be defined by those who can fuse innovation with accountability — and steer the business forward with trust.
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