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18 September 2018 | ITSM
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Hype Cycle for Data Management Positions Three Technologies in the Innovation Trigger Phase in 2018
DataOps, private cloud database platform as a service and machine learning enabled data management are making their debut in the 2018 Gartner Hype Cycle for Data Management...

The Gartner Hype Cycle for Data Management helps CIOs, chief data officers(CDOs) and other data and analytics leaders understand the maturity of data management technologies they are evaluating, in order to provide a cohesive data management ecosystem in their organizations (see Figure 1).

"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 private cloud database platform as a service (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 Mr. Feinberg.

Figure 1. Hype Cycle for Data Management, 2018  

https://emtemp.gcom.cloud/ngw/globalassets/en/newsroom/images/graphs/hypecycle-sept-11.png  

Innovation Triggers in 2018

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.

“DataOps is a new practice without any standards or frameworks,” said Nick Heudecker, research vice president at Gartner. “Currently, a growing number of technology providers have started using the term when talking about their offerings and we are also seeing data and analytics teams asking about the concept. The hype is present and DataOps will quickly move up on the Hype Cycle.”

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.

“Private cloud dbPaaS is an option for organizations that are unable or not ready to move to public cloud offerings, due to security, regulatory or other concerns,” said Adam Ronthal, research director at Gartner. “Often, these organizations use existing on-premises infrastructure for dbPaaS, which as a result will shorten mainstream business adoption.”

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.

“We placed ML-enabled data management in the pre-peak section of the Hype Cycle because many of the modern use cases are in infancy,” Mr. Feinberg explained. “However, the technology will move fast. Many of the products using ML in data management are today only available on cloud platforms and likely to be trained with massive amounts of data. The improvements resulting from these training sessions will spread to on-premises software and there will be a surge in ML-enabled data management adoption during the next few years.”


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