Source: Gartner (September 2018)
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.”
More detailed analysis is available to Gartner clients in “Hype Cycle for Data Management, 2018”. This research is part of the Gartner Trend Insight Report “2018 Hype Cycles, Riding the Innovation Wave.” With profiles of technologies, services and disciplines spanning over 100 Hype Cycles, this Trend Insight Report is designed to help CIOs and IT leaders respond to the opportunities and threats affecting their businesses, take the lead in technology-enabled business innovations and help their organizations define an effective digital business strategy.
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