AiThority Interview with Justin Borgman, Co-founder and CEO, Starburst

Justin Borgman, Co-founder and CEO of Starburst, talks about AI-driven Data ecosystem trends, strategies to deliver speed and accuracy across hybrid and multi-cloud environments, impact of Generative AI on data management, and more in this quick chat: 

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Hi Justin, please start with what inspired you to build Starburst and the problem you aimed to solve.

When I founded Starburst, I was driven by the need for a better way to analyze data spread across multiple systems without relying on traditional data warehouses. Businesses were facing an explosion of data, fueled by the rise of cloud computing, but the existing solutions were slow, expensive, and restrictive. Organizations were spending massive amounts of time and money just moving and duplicating data before they could even begin analyzing it. It was clear that a new approach was needed—one that would allow companies to harness the full power of their data without being held back by outdated infrastructure.

Starburst was built to solve this challenge by enabling businesses to query data where it lives, eliminating the need for costly migrations and storage limitations. Instead of forcing data into a single, centralized system, we allow organizations to access insights in real-time, across any platform, cloud, or database. This shift reduces costs and dramatically speeds up analytics decision-making and time to market, empowering teams to be more agile and data-driven.

This transformation is especially critical in the AI era. Machine learning, advanced analytics, and real-time decision-making depend on fast, efficient access to data. Starburst accelerates AI-driven innovation by removing bottlenecks and giving businesses the freedom to analyze all their data seamlessly. Whether it’s fine-tuning better models, optimizing analytics workflows, or uncovering new data-driven opportunities, our platform ensures that companies can fully leverage their data to drive smarter, faster outcomes.

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Security and governance are critical concerns in decentralized data environments. How does Starburst ensure secure and compliant data access across diverse sources?

Security and governance are at the core of Starburst, especially as businesses navigate decentralized data environments, analytics and traditional predictive AI/ML, and emerging AI use cases like agentic AI. Organizations need to ensure that their data remains secure and compliant while enabling seamless access across cloud, on-prem, and hybrid environments. Our platform is designed to provide that balance—giving enterprises the flexibility to analyze data anywhere while maintaining strict governance and control.

Starburst enforces robust security policies through fine-grained, role-based access controls, ensuring that only authorized users can interact with critical datasets. We integrate with enterprise identity management systems and provide full auditing and logging capabilities, offering complete visibility into data access and usage. By maintaining compliance with industry regulations like GDPR and HIPAA, we help organizations protect sensitive information without hindering innovation.

As AI adoption accelerates, secure and governed data access is more important than ever. Starburst ensures that AI models interact only with approved data, reducing risk while maximizing the value of enterprise data. Our platform empowers businesses to drive AI and analytics initiatives with confidence, knowing their data is protected, compliant, and under control.

Talk about the major trends you think are shaping the AI-driven data ecosystem in the coming years.

Data strategy is inseparable from AI strategy. The AI-driven data ecosystem is evolving at an unprecedented pace, and I believe we are just at the beginning of this transformation. Every day, hundreds of companies use Starburst to unlock value from their data. Tomorrow, that number will be in the thousands, spanning industries, sectors, and geographies. Whether in the cloud, on-premises, or through hybrid lakehouses, businesses are increasingly realizing that their data strategy isn’t just about analytics—it’s about AI.

AI has reached a critical inflection point where it’s no longer an experiment or a luxury; it’s a necessity. Every company I talk to is either leveraging AI today or actively planning to. And with that shift comes a new understanding of data’s value. It’s no longer just about reporting and dashboards—organizations are thinking about how their data fuels AI-driven innovation, and the data lakehouse is quickly being cemented as the future-proof data architecture choice for the AI workloads of the future. 

This shift isn’t happening in the distant future—it’s happening now. AI and analytics are converging, and businesses need data strategies that support both. An era of human-led data consumption through standard BI practices will eventually give way to an era where machines and AI agents consume more and more data. AI is only as powerful as the data it can access, but reaching, sharing, and governing that data is a challenge. The companies that win in this era will be the ones that break down data silos, embrace data collaboration, and effectively secure and govern that data in a way that scalably protects critical customer and business information. 

As enterprises scale their AI-driven analytics, maintaining high performance without skyrocketing costs is a challenge. What strategies does Starburst employ to deliver speed and efficiency across hybrid and multi-cloud environments?

Starburst delivers the data access, collaboration, and control needed to scale analytics and also turn AI from an experiment into a competitive advantage. Our platform allows businesses to query data where it lives—across hybrid and multi-cloud environments—eliminating the need for expensive duplication and storage. 

We optimize performance through intelligent query acceleration, dynamic workload management, and cost-aware compute scaling, ensuring fast insights while controlling infrastructure costs. By leveraging our federated query engine, companies can unify data access across warehouses, lakes, and lakehouses, maximizing efficiency. This approach ensures that as AI workloads grow, enterprises can maintain speed, agility, and cost-effectiveness without compromising performance.

How do you view the impact of Generative AI on data management and analytics platforms across industries?

Generative AI is transforming industries, and at the core of that transformation is data. There is no AI without data—every model depends on high-quality, well-governed data to train, improve, and generate meaningful insights. That’s why data isn’t just the foundation of analytics; it’s the foundation of AI as well. The businesses that succeed in this new era will be the ones that build that foundation in a secure, reliable, and scalable way.

At Starburst, we’ve always been the engine powering data analytics, and now that role is expanding to AI. The same principles that drive a successful analytics data stack—accessibility, organization, and governance—are just as critical for AI and machine learning. Whether a company is using Starburst to power a BI dashboard, a data application, or an AI model, our platform ensures they have the flexibility and efficiency to scale.

As Generative AI adoption accelerates, organizations must rethink how they manage and access data. Starburst enables businesses to break down data silos, query data in real time, and ensure their AI models are fueled by trusted, high-quality information. Whether they’re leveraging AI today or planning for the future, companies need a strong data foundation, and that’s exactly what we provide.

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If you could fast-forward five years into the future, what’s one breakthrough in data analytics or AI that you hope Starburst will have pioneered—and how do you see it reshaping the industry?

We see the data-driven workflows first pioneered in the Big Data era for managing petabyte scale data as being a quotidian necessity for every enterprise in the next 5 years. That’s because data will only accelerate in velocity, as more and more machine-led data creation and data consumption drives productivity and value from data on a scale we have not yet realized coming off the age of Big Data. This means that data architecture decisions are even more critical – and to accelerate the development of AI organizations need to invest today in future-proof architectures.

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