AI and the machine learning that underpins it are surging as top technology initiatives. Yet, the question is this: Are data enterprises ready for the changes it will bring?
For data managers, AI and machine learning not only offer new ways of delivering rapid insights to business users but also the promise of improving and adding intelligence to their own operations. While many AI and machine learning efforts are still works in progress, the technologies hold the potential to deliver more enhanced analytic capabilities throughout enterprises.
For starters, the emergence of AI and machine learning is bringing greater autonomy to databases—but industry experts caution that more complete autonomy is still a distance away. This is “an exciting emerging area,” said Gerrit Kazmaier, executive vice president of SAP HANA and Analytics. “But trusting AI and machine learning solutions to take full responsibility for the management of database systems across all profiles—from low-risk to enterprise-critical applications—will take time.”
Most AI-driven database advances, at least for the near-term, will be seen at the periphery of data environments and will take time to move to the center due to the complexity of enterprise data environments. “The focus to date has been on adding more complex functionality that requires constant configuration and tuning, particularly where schemas do not match or data is semi-or fully unstructured,” said Lewis Carr, senior director of product management at Actian. At the same time, the use of AI and machine learning inference in an unsupervised mode is now being successfully applied in edge environments—“where there isn’t any skilled technical support, let alone data engineers or scientists,” he added.
The various requirements seen in the core of enterprises span many infrastructure concerns, including “the installation, upgrades, administration, monitoring, and tuning of the database platform,” said Anthony Roach, senior product manager for MarkLogic. He predicts, however, that such oversight will be improved as software becomes more “introspective,” and predictive algorithms are employed to forecast and act on impending faults.
Add to these the virtually unlimited capabilities of the cloud, “which means you can react and re-route around those faults seamlessly,” Roach added. “The cloud will make database management a solved problem and the enterprise will take on the more critical task of data management—including security, privacy, lifecycle management, and more.” At this time, however, these requirements are “beyond the capabilities of current or proposed AI and machine learning systems.”
In the meantime, AI and machine learning solutions are increasingly playing roles in managing key aspects of data management. “Pointing AI at the databases themselves to auto-tune and optimize their behavior is becoming critical to successful data management as SaaS, hybrid, and edge-to-cloud databases become reality,” said Richard Beeson, CTO for OSIsoft. “To optimize the deployment and usage of massively distributed and dynamically available resources—compute, storage, networking—that underlie the management of data is becoming (or has become) untenable for any world-class system.” Databases will become autonomous, but only incrementally in 2020, Beeson explained. “For the average company—not SaaS
While completely AI-driven autonomous databases are still a ways off, there are a number of groundbreaking applications emerging. For example, the very idea of what constitutes a data model is changing. “Forget human schema concepts like star and snowflake,” said Paige Roberts, open source relations manager at Micro Focus. “Internal AI can design data models a lot better than people. The database is far more in touch with how it stores and retrieves data, and can measure to the microsecond.”