DataOps (data operations) is a concept and method of introducing continuous data analytics into an organization's business processes. In much the same way that DevOps integrates continuous development and improvement into the software development process, DataOps relies on cooperation and communication between everyone involved to ensure that an organization's data workflow is managed as efficiently — and made available to end users as easily — as possible. Automation is a key element of the DataOps process as well.
At its core, DataOps relies on statistical process control (SPC) to monitor and control data flows and the data analytics pipeline, which continuously monitors and verifies data flowing through an operational system. If an anomaly occurs, the data analytics team is notified through an automated alert.
However, DataOps is still evolving, with principles that can be traced back through DevOps and Agile methodology and development, as well as various aspects of data science. Just like the development of the manufacturing process, data analytics and orchestration systems need to validate their processes along the way to make sure that mistakes distort the rest of the analysis. DataOps is intended to provide this data quality control.
In the following article, we’ll explore the problems DataOps solves, the benefits, business value and future of DataOps, and how you can get started with DataOps to create a data-driven enterprise.