Three Data Management Technologies in the Innovation Trigger Phase

DataOps, private cloud database platform as a service (dbPaaS) and machine learning (ML)-enabled data management are making their debut in the 2018 Gartner Hype Cycle for Data Management.

"We are witnessing only three technologies entering the Innovation Trigger phase because, in line with what we see in the industry, there is less focus on innovation and more on execution at scale in the data management space,“ said Donald Feinberg, vice president and distinguished analyst at Gartner.“ The Innovation Trigger is the first phase of the Hype Cycle where a breakthrough, public demonstration, product launch or other event generates significant press and industry interest.

In addition, more and more vendors are switching to a cloud-first delivery model, which rapidly accelerates several technologies, such as dbPaaS and integration platform as a service (iPaaS). In fact, dbPaaS is less than two years away from mainstream business adoption. In-memory functionality is also becoming more widely available and pervasive throughout all data management technologies. “Those are more delivery platforms than technologies, they can move rapidly to the plateau of productivity,“ added Feinberg.

DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization. Much like DevOps, DataOps is not a rigid dogma, but a principles-based practice influencing how data can be provided and updated to meet the need of the organization’s data consumers.

Private cloud dbPaaS offerings merge the isolation of private cloud database platforms with the self-service and scalability of the public cloud. They recently started to appear in vendors’ portfolios and provide a cloud experience in an on-premises data center. Gartner analysts said private cloud dbPaaS can play the role of a transition technology as organizations develop their long-term cloud strategy.

Rudimentary machine learning ML has been used in data management products since the 1970s. Today, with the increased availability of ML and artificial intelligence (AI) libraries, vendors use modern varieties of ML for many self-management operations within data management software. These solutions not only tune and optimize the use of the products themselves, but suggest new designs, schemes and queries.