If asked to list the top trends that are shaping the enterprise data center today, most technologists and tech investors would likely agree on a core set. The list would include technologies like such as cloud computing, containers and virtualization, microservices, machine learning and data science, flash memory, edge computing, NVMe and GPUs. These technologies are all important for organizations pushing digital transformation.

The harder question: What’s coming next? Which emerging technologies or paradigm shifts are poised to be the next big thing? And what effects will they have on the hardware and software markets?

One new trend that has started to gain traction within large enterprises is a practice known as DataOps. The name is a play on the better-known DevOps paradigm, a practice that was codified about a decade ago with the aim of integrating software development (“dev”) and operations (“ops”). While sharing some of the goals of DevOps, DataOps is distinct and indicative of some of the major shifts we see today.

DevOps Defined

Let’s start with DevOps. DevOps, first described in 2008, is an IT practice that aims to maximize automation and repeatability in the process of building and deploying applications. The thesis was that if software developers and operations professionals could collaborate tightly, building and deploying applications would be faster and cost less. The goals of the practice include agility, faster time to market and continuous application delivery.

Companies like VMware, Docker, Puppet and Chef have all ridden the DevOps wave.

The DevOps Disillusionment

Despite the early frenzy and excitement by the software cognoscenti, DevOps has plateaued. A 2017 study reveals that DevOps has not totally delivered on its promise. Of the 2,197 IT executives interviewed in the study, only 17% listed DevOps as having had a strategic impact on their organizations — much lower than, for example, big data (41%) and public cloud infrastructure as a service (39%). One explanation — DevOps methodologies did not consider data-intensive applications.