If you find yourself hearing a lot about DataOps and then subsequently asking, “What is DataOps? ” you’re not alone.
The concept of DataOps has become prevalent in recent years as a way to ensure teams effectively manage data and maintain efficient access to high quality, timely data.
DataOps is a process-oriented approach to help manage data that creates, shortens, and amplifies feedback loops and allows for continued experimentation to learn from mistakes and achieve mastery. This guide details everything you need to know about DataOps, including:
How do you measure the value of DataOps?
What is DataOps?
DataOps is an automated, process-oriented methodology used by analytics and data teams to improve quality and reduce the cycle times of data and analytics. Or, as Gartner defines it :
“DataOps is a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts.”
Specifically, DataOps focuses on providing agile data onboarding, enhanced data quality , trusted information, and governance and security policies natively woven into the fabric of key workflows – all in an iterative process that’s constantly refined and allows teams to surface new insights effectively and efficiently.
DataOps is also about more than just data flowing, it’s about the context behind the data. For example, when there is a change in a data artifact, DataOps looks at elements like why the change occurred, who was responsible, and how users can fix downstream reports.
Importantly, DataOps doesn’t operate in isolation; it must operate in unison alongside DevOps and data science practices like MLOps.
What is DevOps?
DevOps is a set of practices that combines software development and information technology operations to shorten the system development lifecycle and provide continuous delivery with high software quality. Critically, teams can’t achieve these goals if they don’t have the right data that’s well governed and trusted. Therefore, DataOps is critical to powering DevOps.
What is MLOps?
MLOps was born out of the need for data science teams to more effectively operationalize machine learning models. The rise of machine learning models has compressed the need of data scientists to get access to information, operationalize that data, and surface it in production in a manner that allows DevOps teams to use it efficiently. Once again, this all starts with having regular access to real-time and trusted data, therefore relying on DataOps.
A Note on DataOps vs. Data Operations
It’s also important to note that DataOps is not necessarily the same as data operations. Data operations is much broader than DataOps and the two have different goals.
In terms of reach, data operations extend to data potential as well as quality and accessibility of data. To that end, data operations have a goal of ensuring data reaches its full potential and provides maximum value, whereas the goal of DataOps focuses more on ensuring quick and easy access to quality, real-time data.
What’s the Difference Between DataOps and DevOps?
Although DataOps and DevOps are related and have many similarities, there are also many differences that separate the two practices.
On the similarity front, both DataOps and DevOps help drive collaboration, focus on Agile methodology practices, use automation, solicit user feedback, and rely on quick iterations to deliver value faster and offer continuous delivery.
That said, DataOps is more complex than DevOps and uses (but adds onto) many DevOps principles.
