The DataOps Landscape

Data has emerged as an imperative foundational asset for all organizations. Data fuels significant initiatives such as digital transformation and the adoption of analytics, machine learning, and AI. Organizations that are able to tame, manage, and unlock their data assets stand to benefit in myriad ways, including improvements to decision-making and operational efficiency, better fraud prediction and prevention, better risk management and control, and more. In addition, data products and services can often lead to new or additional revenue.
As companies increasingly depend on data to power essential products and services, they are investing in tools and processes to manage essential operations and services. In this post, we describe these tools as well as the community of practitioners using them. One sign of the growing maturity of these tools and practices is that a community of engineers and developers are beginning to coalesce around the term “DataOps” (data operations).
Our conversations with members of this nascent community revealed a few key activities associated with DataOps: automation, monitoring, and incident response. In brief, DataOps is composed of tools and processes for monitoring and automating tasks and software that raise the efficiency of operations in support of all data products and services. DataOps tools and processes allow organizations to deliver data products and services quickly, reliably, and efficiently.

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