What is DataOps?
During the DataOps Virtual Event I spoke about Zaloni’s DataOps Cycle (see image below) and defined DataOps. We define DataOps as more than technology, it is a discipline that aligns platforms, processes, and people within the arenas of data governance, consumption, and collaboration in order to optimize every data journey.
To see how DataOps cycles are transforming enterprises, let’s start with the “why” of DataOps (thanks Simon Sinek!). Why do enterprises need a DataOps approach? Because of their uncontrolled data sprawl and a lack of a standardized approach to data governance. And how do they fix it? That’s where DataOps comes in.
Control Without Friction
The concept of DataOps is not new, although it may seem like a recent buzzword. Think about DevOps or RevOps methodologies which have been proven successful for decades. The overarching premise for an xOps approach is control without friction. DataOps is:
- Agile and extensible, bringing together 1st and 3rd party data pipelines
- A cycle that scales as data transformations occur
- A “single pane of glass” that connects people, processes, and platforms through one collaborative view
Your DataOps strategy is as important as the products and services your company delivers. Optimizing the DataOps cycle results in cost savings, accelerated analytics, faster time to new product value, and modern ML/AI enablement – benefits shared across IT, Engineering, Analytics, and Marketing departments.
At Zaloni, the DataOps cycle is a complete feedback loop aligning data stewards, engineers, analysts, and scientists. While enterprises often begin projects focusing on an area or two represented in the diagram, one of the benefits of an end-to-end platform is that it facilitates organic expansion to a fully coordinated data ecosystem. This holistic approach to DataOps strategy ensures that today’s use cases, which often bridge multiple data disciplines, are achieved with faster, better results.
DataOps Learning Resources
If you’d like to take a deeper dive into the DataOps Cycle and learn more about the ideal “day in the life of your data” from a process and technical perspective, I invite you to listen to the recording of the DataOps Virtual Event.
If you’d like to learn more about DataOps, Gartner defines it here, and, shameless plug, I’ve also written about it here:
- Defining DataOps, Data Pipelines and Their Impact on Analytics Success
- TechTarget: Using DataOps to Create Business Value from Big Data
- Tech Native: The 7 Benefits of DataOps You’ve Never Heard
- vm blog: The secret COVID competitive advantage no one predicted – DataOps
What DataOps is Not
Now for the flip side. There are still many misconceptions about DataOps, so allow me to articulate what DataOps is not. First, it’s not a tool or technology. DataOps is a data management methodology that takes into consideration people, processes, and platforms to improve data supply chain efficiency.
DataOps is more than just data prep, ETL or a data catalog. DataOps is the end-to-end management and governance of every step in the data supply chain from a data source to the end-user, or data consumer. Through an integrated supply chain, process improvements are made and productivity is improved through user collaboration to reduce costs while accelerating time to analytics.
To learn more about DataOps: