Do not get confused DataOps with DevOps

DataOps and DevOps are two distinctly different pursuits. Both are based on agile frameworks that are designed to accelerate working cycles. But where DevOps focuses on product development, DataOps aims to reduce the time from data need to data success. At its best, DataOps shortens the cycle time for analytics and aligns with business goals.

When DataOps is successful, organizations can realize immense improvements in how they find, use, and extract value from their data.

Data Operations, or DataOps, is like DevOps in that both are based in agile, continuous improvement thinking. And while DataOps has a similar methodology to DevOps, its goals are distinct. DataOps is designed to build high quality data and analytics solutions at an increasingly accelerated pace, and with higher reliability, as time goes on.

As organizations have struggled beneath a deluge of data, their data teams faced growing expectations that the business put that data to work. Data teams were inspired by the DevOps methodology to create DataOps.

DataOps was created to leverage the underlying manufacturing methodologies of lean manufacturing, statistical process control, and, of course, agile development.

DataOps seeks to quickly find the right data for the right application. It brings together business users, data Scientist, data analysts, IT, and application developers to fulfill the business need for insights. DataOps then works to continuously improve and adjust data models, visualizations, reports, and dashboards to achieve business goals.

DataOps fosters cross-functional collaboration and automation to build fast, trustworthy data pipelines so your business can wring the most value from your data.

The difference between DataOps vs DevOps use of agile comes back to the product delivered:

• The DevOps methodology begins with a (relatively) static product and delivers an improved version of that (relatively) static product and user base.
• The DataOps methodology, conversely, begins with a fluid and constantly changing set of data and data sources, and seeks to address a fluid and constantly changing set of business needs, stakeholders, users, and goals.