DataOps has become a trending buzzword in the IT industry. But what is DataOps? Is it worth incorporating DataOps in
your organisational data operations?
Let’s find out!
What Is DataOps?
DataOps compilation of technical processes, organisational workflows, cultural approaches, and data architectural styles
Innovations and research, delivering advanced insights to clients with rapidly
Impeccable data quality and minimal error rates
● Successful collaboration and cooperation between intricate arrays of teams, technological resources, and IT
● Efficient measurement, robust monitoring, and transparency in results.
In practical terms, DataOps integrates Agile methodology, lean manufacturing, and DevOps culture to the process of data
Therefore for a precise understanding of DataOps, we’ll need to explore the terminologies: Agile process, lean
manufacturing, and DevOps.
The Three Pillars Of DataOps Management
For effective DataOps management, both collaboration and innovation are necessary. Therefore, DataOps implements
Agile Development into data analytics for enabling data teams and users to function collaboratively in a more productive
and effective way.
Using the Agile environment, the data team publishes new or modified analytics in short increments or slots called
With rapid advancements, the data teams can consistently reassess and streamline their priorities and efficiently comply
with the evolving innovation requirements from the ongoing user feedback loop.
This level of responsiveness is impractical to achieve through a Waterfall project management style since it blocks a team
into a long development cycle without collaborating with the users until the one “star-studded” deliverable at the end.
In a DataOps culture, Agile approaches empower enterprises to respond quickly to evolving client/user demands and
accelerate time to value.
The process or method of “lean manufacturing” was initially conceptualise