How operational databases are fueling the AI revolution

As generative AI continues its meteoric rise, the operational database has become the must-have tool in the enterprise toolkit. It’s the connecting link between all the possibilities of today’s multimodal foundation models, and the ability to deliver accurate, up-to-date enterprise gen AI apps.

With an AI-enabled operational database in play, organizations can realize huge gains, fast. Just like Regnology has, with its regulatory reporting chatbot that expedites the process of obtaining accurate answers to regulatory inquiries. Regnology’s CIO Antoine Moreau says, “Compliance analysts and reporting specialists interact with the chatbot in a conversational manner, saving time and addressing diverse regulatory reporting questions.”

Another company, Linear , is keeping pace with its expanding customer base by scaling up into the tens of terabytes of data, without increasing engineering effort.?

Companies like Regnology and Linear are bang on trend, according to Google Cloud’s 2024 Data and AI Trends Report . Two of the five predictions in the report revolve around operational data and data platform modernization, which are both closely tied to operational databases.

Why the operational database is tech’s rising star

Just to be clear, I have always been a huge fan of operational databases. They are the engine powering everything from customer transactions to business operations — without them, much of what we do in business today would grind to a halt.?

Recently, I’ve become an even bigger fan. Particularly of operational databases that integrate gen AI capabilities like vector support.?

These databases empower organizations to forge ahead with AI-powered innovation, using techniques like Retrieval Augmented Generation (RAG) to provide accurate, domain-specific knowledge to enterprise gen AI applications. Armed with this up-to-date data, the gen AI app in turn can deliver the most accurate answers —?no hallucinations in sight.

What’s more, with their exceptional vector capabilities, these databases make it easy for developers to tap into a whole new treasure trove of data in their gen AI apps. Vector embeddings can encode the semantic meaning in unstructured data like product descriptions, images, helpdesk tickets, and conversation history to provide even more helpful and relevant responses.

And the best part? You can do all this where your data already lives, in your trusted data store. These databases are already supported by a robust ecosystem, delivering business-critical reliability, data protection, and performance.?

Letting go of legacy databases

To start realizing all the incredible business outcomes that gen AI promises to deliver, many organizations need to migrate legacy databases to modern, open data platforms. Yet migration is hard, for a number of reasons:

• Many databases don’t offer the performance, scale, and manageability for the highest end workloads
• Manually migrating databases and code takes a lot of effort and historically there haven’t been great tools
• Migration can be costly, particularly when you need to double-up on resources or require additional development support

The good news? Gen AI and modern migration tooling can both pitch in to help overcome challenges like these. You just need the right tools for the job.?

What to look for in an operational database

To hit the ground running with AI and start delivering those high-quality, AI-assisted user experiences everyone’s talking about, your operational database needs to tick a number of boxes. Ideally, it should offer:

• Embedded vector search with good performance and high recall?
• Integration with open source orchestration frameworks and AI service endpoints
• A proven track record of durability, reliability, and efficiency

With characteristics like these in play, your database is ready to work together with a foundation model to start delivering highly personalized and impactful results for your gen AI apps.

There is no doubt that databases are fueling the AI revolution. And that operational databases, which sit at the heart of all application data, will play a critical role in how developers build tomorrow’s AI-assisted experiences. The question is, are your databases ready for it, or is it time to transform?

For more trends and predictions in the data and AI space, read the 2024 Data and AI Trends Report .