Unlocking AI Potential: What the 2025 State of the Data Lakehouse Survey Taught Us
Artificial intelligence is a key component of creating and maintaining competitive advantage, but AI’s success isn’t just about algorithms or models; it’s about the data that powers them. We recently released our annual Dremio State of the Data Lakehouse in the AI Era Survey, a comprehensive research study that was conducted with over 560 IT decision-makers. The goal of the report is to provide a clear picture of how organizations are transforming their data strategies to unlock AI’s full potential and the findings were enlightening. Here’s what the survey revealed about the evolving role of data in the AI era.
The AI-Ready Data Imperative
At the heart of AI success lies one critical element: data. But not just any data, AI-ready data. According to the survey, 85% of organizations are already leveraging their data lakehouses to support AI model development, and another 11% plan to follow suit. These numbers underscore the importance unified, high-quality, and accessible data has in fueling AI innovation.
What sets AI-ready data apart is that it’s clean, governed, and seamlessly integrated into workflows that empower AI models to deliver actionable insights. For many businesses, this is a game-changer enabling faster innovation and better decision-making. Yet, the road to AI readiness is not without its obstacles. Governance and security remain the top challenges and were the main obstacles, with 36% of respondents flagging them as key concerns. The cost and complexity of data preparation were the other popular concerns as it continues to hinder progress for one third of all organizations.
Aside from empowering models, AI-ready data fosters greater collaboration across teams. By providing a unified source of truth, it ensures that data scientists, analysts, and business stakeholders can work together more effectively. This alignment drives better outcomes, from more accurate predictions to faster decision-making cycles. Organizations that prioritize AI-ready data are building a strong foundation for sustained advantage.
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Data Products: The New Frontier
AI-ready data products are emerging as the next big thing in how businesses operationalize their data strategies. The survey found that 65% of organizations now have formalized processes for creating and managing data products. This shift represents a move toward greater reliability and scalability in how businesses handle their most valuable asset—data.
What makes these products so impactful is their ability to break down traditional silos. They bring together technical teams and business stakeholders, ensuring that everyone works from the same playbook.
Moreover, data products are becoming integral to AI workflows.
By packaging data into reusable and governable units, businesses can accelerate the development of AI models, agents and applications. This approach reduces redundancy and ensures consistency, allowing teams to innovate faster while maintaining control over data quality and governance.
The Power of Open Standards
If AI-ready data is the fuel, open standards are the engine that makes the system run smoothly. Open technologies like Apache Iceberg and Polaris are redefining how businesses approach scalability, interoperability, and vendor independence. By adopting these standards, organizations can avoid the pitfalls of vendor lock-in while ensuring their architectures are future-proof.
The survey highlights the growing adoption of open standards, with Iceberg and Polaris leading the way. These technologies offer the flexibility to integrate with diverse tools and platforms, making them invaluable for organizations managing complex AI workloads. In a world where agility is king, open standards provide the foundation for innovation.
Open standards also drive competition in the marketplace, empowering businesses to choose the best solutions for their needs. This fosters an ecosystem of innovation, where vendors must continually improve their offerings to stay relevant. For businesses, this translates to better performance, lower costs, and a broader range of options.
Breaking Free from Data Silos
Another important takeaway from the survey is the shift toward data unification. Nearly 90% of IT decision-makers aim to consolidate their analytics data into a single platform. This trend speaks to the growing recognition that fragmented data systems hinder efficiency, scalability, and collaboration.
Unified platforms not only streamline operations but also enable the democratization of data. By balancing decentralized access with centralized governance, businesses can empower teams across the organization while maintaining control over security and compliance. It’s a delicate balance, but one that’s essential for enabling enterprise-wide AI adoption.
Additionally, consolidation supports more efficient resource utilization. By reducing duplication and fragmentation, organizations can lower costs and improve performance. This approach ensures that every team has access to the data they need while maintaining the integrity and security of the overall system.
Automation to the Rescue
For data professionals, the day-to-day grind often includes repetitive tasks like cleaning raw data and managing manual processes. These frustrations were echoed in the survey, with manual processes cited as a top pain point by 28% of respondents. Yet, these challenges also highlight a massive opportunity: automation.
Tools that streamline workflows and automate repetitive tasks are more than just time-savers; they’re productivity boosters. They free up data teams to focus on strategic initiatives. Automation doesn’t just improve efficiency—it improves job satisfaction, turning data professionals into strategic drivers of business value.
Automation also plays a crucial role in scaling AI initiatives. By automating data preparation and transformation, businesses can accelerate the deployment of AI models and reduce time-to-value. This not only enhances operational efficiency but also ensures that AI projects deliver measurable impact.
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So, What Does This Mean for the Future of AI?
The 2025 State of the Data Lakehouse Survey paints a compelling picture of where the industry is headed. AI-ready data, open standards, and automation aren’t just trends, they’re building blocks for the AI-era. Realizing the full potential of these innovations requires discipline and focus. Based on the challenges outlined in the study, businesses need to:
- Invest in unified platforms that prioritize accessibility, quality, and governance.
- Embrace open technologies to future-proof their architectures and stay agile.
- Leverage automation to tackle inefficiencies and empower their teams.
As organizations navigate this landscape, the lakehouse is poised to be a cornerstone of success. By combining the best aspects of data lakes and data warehouses, it offers the scalability, cost-efficiency, and flexibility that today’s AI-driven businesses demand. The future is bright for those who can align their data strategies with the opportunities presented by AI.
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