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Oracle Brings Artificial Intelligence Into Their Data Management Toolchain

Databases are an obvious foundation for artificial intelligence (AI) development pipelines. Now it would appear that modern database architectures are tapping into that intelligence in order to tune their 24×7 operation.

Database vendors have been touting the “self-healing” capabilities of their offerings for many years, with the larger buzzword of “autonomic” often used to describe these and other IT platforms’  self-managing, self-securing, self-repairing, and self-optimizing features.

In Oracle’s product nomenclature, it has recently introduced the word “autonomous” to describe much the same promise. The recent launch of  Oracle Autonomous Data Warehouse Cloud was the most noteworthy database launch this year in the enterprise arena. Oracle seems to be pinning its first-to-market claims for its cloud data warehouse (DW)’s “autonomous” features on machine learning (ML) algorithms embedded in the underlying Oracle Database 18c.

It’s not clear, apart from marketing considerations, why the use of ML merits a word different from “autonomic,” which describes features that Oracle and its database rivals have been offering for quite some time. Contrary to its news release headlines, it certainly hasn’t released a “revolutionary” database or “redefined” the cloud database category.

Regardless, Oracle’s new ML-driven cloud DW management functionality is worth calling out. It boasts the cloud DW’s ML-driven improvements in provisioning, scaling, performance, security, backup, and availability, as well as its ability to patch, tune, update, manage, troubleshoot, and repair itself, even as warehousing workloads and data volumes change. It promises all of this with no human intervention or operational administration.

Be that as it may, it would be even more impressive if Oracle were to back up these claims with customer case studies of automated database administration that resulted in far fewer human administrators. Besides, Wikibon won’t be convinced that Oracle’s putting some seriously new self-management functionality into its data platforms until it rolls out the promised next ML-driven management tools for its OLTP, NoSQL/IoT, and graph databases, as well as its next-generation analytics, mobility, application development, and integration solutions in the coming year.

Even then, it’s not clear that Oracle’s leading the show with these supposedly “autonomous” IT management features. As Wikibon’s been discussing in our research for the past year, AI-powered solutions are already well-established in the IT operations management (ITOM) market.

Are Oracle’s branding, messaging, and marketing professionals even aware of that broader industry landscape?

Here are me and my colleagues discussing the theme of AI for ITOM on theCube last fall.

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