How DataOps Amplifies Data and Analytics Business Value

DataOps techniques can provide a more agile and collaborative approach to building and managing data pipelines.

The pandemic has accelerated the need for data and analytics leaders to deliver data and analytics insight faster, with higher quality and resiliency in the face of constant change. Organizations need to make better-informed and faster decisions with a focus on automation, real-time risk assessment and mitigation, continuous value delivery and agility.

The point of DataOps is to change how people collaborate around data and how it is used in the organization 

As a result, data and analytics leaders are increasingly applying DataOps techniques that provide a more agile and collaborative approach to building and managing data pipelines.

What is DataOps?

Gartner defines DataOps as 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 point of DataOps is to change how people collaborate around data and how it is used in the organization,” says Ted Friedman, Distinguished VP Analyst, Gartner.

 Use DataOps to link “Can we do this?” to “How do we provide an optimized, governed data-driven product 

“Rather than simply throwing data over the virtual wall, where it becomes someone else’s problem, the development of data pipelines and products becomes a collaborative exercise with a shared understanding of the value proposition.”

Successful DataOps practices

To implement DataOps successfully, data and analytics leaders must align DataOps with how data is consumed, rather than how it is created in their organization.

If those leaders adapt DataOps to three core value propositions, they will derive maximum value from data.

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