Clouds and Autonomous Databases are Poised for Growth

Clouds, autonomous databases, and fast-growing data environments dominated the results of surveys by Unisphere Research, a division of Information Today, Inc., throughout 2019. Data keeps expanding beyond the bounds of traditional corporate on-premise systems, and the demands on data managers are growing. The following are highlights from our research over the past 12 months.


A survey of 202 data managers and professionals conducted among members of the Independent Oracle Users Group (IOUG) in partnership with Amazon Web Services (AWS, “2019 IOUG Databases in the Cloud Survey”) found that there are no longer enough on-premise resources to keep up with the growth of data management requirements. Already, one-fourth of corporate data is being maintained by cloud providers, and data managers intend to move as much of their data environments into the cloud as possible, as soon as possible.

When asked about the details of their latest database projects, organizations are close to evenly split between deploying in the cloud and on-premise, with a tilt toward on-premise. A total of 44% deployed their projects in the public cloud or as part of a hybrid architecture spanning cloud and on-premise deployment, while 52% indicated their most recent database project involved an on-premise implementation. When it came to cloud, 14% of respondents opted to host their entire database in the cloud, while 12% turned to cloud SaaS offerings that included back-end data functionality.

The rise of data in the cloud was captured in 2019 in an additional survey of 203 data managers affiliated with the Quest-IOUG Database & Technology Community. Close to half of respondents, 49%, have moved some or all of their Oracle-based applications to a public cloud provider, such as AWS, Microsoft Azure, Oracle Cloud, VMware Cloud on AWS, Google Cloud Platform, or IBM Cloud. Another 24% are considering such a move.

One in five enterprises indicated that a majority of its Oracle-based applications have been moved to the public cloud. When including those sites with more than 26% of their Oracle-based applications in the cloud, the number with a significant share rises to more than one-third.


Along with the challenges of bringing data to emerging enterprise initiatives is the ability to deliver the data in real time. Many enterprises now regard real-time data as critical to their ongoing operations—and are actively preparing to introduce real-time data capabilities into their infrastructures, according to a survey of 241 DBTA readers that was sponsored by Pythian. To get there, they are increasing their adoption of emerging technologies such as machine learning and data lakes (“Profiling the Data-Driven Business, 2019”).

Notably, there is a strong shift underway to real-time delivery of data and insights to enhance intelligent enterprise strategies. Close to half of the enterprises in the survey indicated they are aggressively preparing for real-time data capabilities to further improve their data platforms. Forty-nine percent see real-time (sub-second) analytics, not just real-time ingestion, as a vital piece of their data platform planning. The biggest use cases for real-time data include the more timely delivery of reports or dashboards, as well as ensuring real-time data feeds to decision engines.

There is quite a bit of enthusiasm for higher levels of automation to manage these capabilities. Currently, 48% are using the technology—up from 25% in a similar survey conducted a year ago. Machine learning has become valuable as companies are dealing with vast and rapidly growing volumes of data and the associated challenges of finding value and drawing insights from that data. The appeal with machine learning is that the algorithms do the heavy lifting of figuring out what data matters.


Tellingly, data managers are turning to automation and autonomous databases. Database functions such as backup and recovery are already highly automated, and there are plans in place to automate such day-to-day functions as monitoring, provisioning, and maintenance, a separate survey of 217 data managers found. Just about all data managers, 95%, have seen increases in data volume over the last 3 years and expect this trend to continue. More than nine in 10 respondents are finding it difficult to keep up with this growth. Three out of every four DBAs manage more than 10 databases, with some handling hundreds of databases (“2019 Quest IOUG Autonomous Database Adoption Survey”).

By far, backup and recovery functions are the areas that have seen the greatest levels of automation so far, with close to half of respondents, 48%, reporting high levels of automation. Database monitoring is also a highly automated area, cited by 43%. Enterprises intend to step up their automation of database backup and monitoring processes. Database security will also increasingly be automated as database managers continue to brace for the onslaught of threats that accompany the move to digital.

Close to seven in 10 respondents, 68%, indicated that autonomous databases—constructed as cloud databases that use machine learning to eliminate the human labor associated with database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs—as ready to assume basic database tasks. A majority—more than nine out of 10—indicated that at least some of their autonomous database capabilities will be delivered via cloud services.