In an exclusive interview with Onkar Sharma, Cetin Ozbutun, Executive Vice President of Autonomous Database and Data Warehouse Technologies at Oracle, discusses the evolution of Oracle's Autonomous Database, its role in modern enterprise data management, and how emerging technologies like AI and machine learning are helping businesses drive innovation and achieve operational efficiency.
Onkar: Autonomous Database has been a significant leap for Oracle, and it has now been in production for nearly six years. What inspired Oracle to develop this solution, and what were the primary challenges it sought to address?
Cetin: Autonomous Database is something we started building many years ago, and it came into production about six years ago. When I first joined Oracle 36 years ago, fresh out of college, we were focused on building cool features for our database products. Customers loved many of these features, but over time, the sheer number of functionalities became overwhelming for them.
They weren't asking for more features—they were asking for solutions to make database management easier. Specifically, they struggled with hardware provisioning, software configuration, and maintaining their Oracle databases, especially when they had tens of thousands of databases running. Their number one request was, "Can you fix all the lifecycle issues around the database?" With the advancements in cloud technology, we saw an opportunity to provide a fully managed database as a service. That’s what the Autonomous Database does—it allows customers to simply use the database without worrying about the hardware or maintenance. It’s always available, upgraded, secure, and easy to use.
Onkar: With AI and machine learning reshaping the landscape, how does Oracle's Autonomous Database differentiate itself? How does it assist CIOs in navigating the challenges of modern data management?
Cetin: AI and machine learning are integral to how we manage the Autonomous Database. We operate a large fleet of databases, and AI helps us automate tasks like patching, security updates, and ensuring high availability—tasks that would otherwise require thousands of human resources. Think of it as having AI-powered robots that continuously monitor and maintain the database infrastructure.
For our customers, we’ve integrated machine learning techniques into the database itself. For example, when customers load data into the Autonomous Database, it's automatically profiled, allowing us to run their queries efficiently. We've also integrated large language models (LLMs) like GPT-3.5, GPT-4, and others, which enable customers to interact with the database in natural language. A feature called Select AI allows users to ask questions in their native language, and the system translates that into a SQL query, making the interaction more intuitive.
Onkar: Could you elaborate on how Select AI and other LLM capabilities are helping clients develop applications, especially in today’s AI-driven world?
Cetin: Select AI is just one example of how we're enabling innovation. Traditionally, interacting with databases required knowledge of SQL, but now, with natural language processing, users can query their data in their native language—whether it's English, Turkish, or German—and the system translates that into a SQL query for them.
We’ve seen customers use this feature to build support applications where feedback is provided in multiple languages. The Autonomous Database can take those comments, run them through an LLM for translation, and analyze the sentiment and intent behind them, even if the original feedback was in languages like Mandarin or Portuguese.
Another key feature is vector search. For instance, if you’re building a house-hunting application, you could upload a picture of a house and search for similar-looking homes in the database. The Autonomous Database can combine semantic search with traditional relational filters, allowing for more dynamic and complex search capabilities.
Onkar: Given the growing focus on AI regulations globally, how does Oracle ensure compliance with these evolving frameworks, and how should CIOs approach responsible AI practices when using autonomous technologies?
Cetin: Compliance with global regulations is critical, especially as countries implement data sovereignty laws and AI regulations. Oracle has built cloud regions across the world to comply with local regulations, including data residency requirements. For example, in regions like Saudi Arabia or India, customer data remains within the country’s borders, ensuring compliance with local laws.
When it comes to AI, we give our customers full control over the large language models (LLMs) they use with our Autonomous Database. We don’t impose our own LLMs; instead, we allow customers to choose from over 35 LLMs across seven vendors. This flexibility ensures that customers can comply with the regulations and privacy standards that best suit their needs, whether they use public or private models.
Onkar: How is Oracle enabling organizations to innovate faster and respond to market changes with its Autonomous Database? Can you share examples of how this has impacted clients’ data strategies?
Cetin: One of the most common challenges CIOs face is delivering new applications and features quickly enough to meet business demands. The Autonomous Database addresses this by automating many of the time-consuming tasks related to data infrastructure. In just two minutes, CIOs can provision databases with hundreds of CPUs and terabytes of storage. This speed and flexibility allow businesses to scale up during peak times and scale down when they don’t need the capacity, optimizing both performance and cost.
We’re seeing hundreds of thousands of databases benefiting from this elasticity and automation. CIOs no longer have to worry about manual scaling or managing databases during workload spikes. This, coupled with the ability to integrate with tools like Slack, Microsoft Teams, and MongoDB APIs, gives companies the agility they need to innovate faster.
Onkar: Lastly, with all the buzz around Generative AI, how do you see its role evolving in business applications, and what should CIOs focus on to harness its potential effectively?
Cetin: Generative AI has certainly captured the imagination of many industries, and we’re seeing companies rushing to develop AI-driven applications. But I’ve been through several technology cycles—whether it was the rise of the internet or the cloud—and what I’ve learned is that real value emerges when hype turns into practical use cases.
AI is changing outcomes across industries, and while there’s fierce competition to build the largest LLMs, not every company needs to build one. What they do need, however, is to leverage AI to analyze and extract insights from their own enterprise data. That’s where Oracle excels. Our database holds some of the world’s most critical data, and when combined with AI, it drives significant value for businesses. The key for CIOs is to focus on applying AI to their existing data to enhance decision-making and operational efficiency.
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