Data Operations and DataOps: Not One and the Same

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Click to learn more about author John Pocknell.

In today’s competitive enterprise landscape, having a proper DataOps strategy in place correlates with better data intelligence and optimization within an organization – breaking down silos and enabling data democratization and better business agility at scale. But DataOps as a methodology is not to be confused with data operations, which enables organizations to maximize the business value of the data they own and the infrastructure that supports it to accelerate their journey to achieving fully realized data empowerment. That is, the power to leverage data to transform everything.

But in order to fully understand the opportunity presented by data operations, organizations must first recognize where DataOps – and all Ops – can bolster critical business outcomes, and understand how those key Ops slot into the adoption of data operations practices accordingly. 

DataOps vs. Data Operations – What’s the Difference?

According to Gartner, “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. DataOps uses technology to automate the design, deployment and management of data delivery with appropriate levels of governance, and it uses metadata to improve the usability and value of data in a dynamic environment.” 

DataOps, which has been around for several years now, is still an evolving concept. A recent survey found that, while 42% of organizations have some mix of manual and automated processes, 93% say there’s room to incorporate more automation into their data operations practices. When done right, DataOps can pinpoint concerns for data analytics teams and improve overall collaboration. That’s DataOps in a nutshell, but data operations is much more than that. 

What data operations does differently is take into account the broader view of the data pipeline, which must include the hybrid infrastructure where data resides and the operational needs of data availability, integrity, security (both in regards to endpoint security and regulatory compliance), and performance to maximize its potential. If the comprehension of this isn’t there, then truly beneficial data operations can’t be realized. In essence, it’s only through the integration of a fully optimized DataOps pipeline, with sound data operations practices, that continuous data intelligence can be achieved. 

Data Operations Is a Critical Pillar to Realizing Data Empowerment

Together with Data Governance and data protection, data operations plays an essential role in democratizing organizational data and enabling companies to realize the full effects of data empowerment; the end goal for organizations on the road to data maturity. Organizations that are data empowered are better able to leverage data assets to drive operational and strategic business decisions, achieve regulatory compliance, and propel revenue growth, market share, and overall competitiveness.

For example, look at organizations like Cigna – one of the largest health services providers in the world. They deal with massive amounts of customer and patient personal data on a daily basis, all within a highly regulated environment. They’re an organization that has been prioritizing proper data management tactics and data operations for years, though particularly within the last decade as federal regulations like HIPAA and GDPR and CCPA have gone into effect. 

In fact, Cigna recently created a new subsidiary called Evermore, specifically committed to creating a more formal integration of the company’s acquisitions from a data process and policy standpoint. In today’s modern IT world, democratizing data is one thing – but achieving data empowerment is quite another. Organizations that can do both, like Cigna, are at the forefront of the data maturity curve.  

Uncovering the Next Ops

As organizations increasingly heighten their awareness and adoption of Ops practices, it will be interesting to see which Ops emerge next as a focal point for IT teams. 

In fact, you may already be aware of a new concept called XOps. The goal of XOps, which includes DataOps, MLOps, ModelOps, and PlatformOps, is to “create an enterprise technology stack that enables automation and reduces the duplication of technology and processes.” In simple terms, XOps is the next natural evolution of Ops in the workplace and across the enterprise, aimed at making data and analytics deployments work better and in tandem with other software disciplines.

As the role of the IT practitioner becomes increasingly complex, think about where you can leverage DataOps toolkits and data operations best practices to enhance your business outcomes. Because at the end of the day, your data should be actionable and empowering – and with some time and effort, every organization can be a data-driven organization. 

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