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Reducing Data Friction Just One of Many DataOps Benefits

Making data accessible for different usage scenarios and types of users amid complex data regulations is just one of the benefits of adopting a DataOps approach to provisioning.

• By James E. Powell
• November 29, 2018

Looking for faster, automated delivery of data? It’s time to consider DataOps, the emerging practice that aligns people, processes, and technology.

For Further Reading: DataOps: An Interview with Tamr CEO Andy Palmer Data Ops: What Is It? Do We Need It? Data Fabrics for Big Data

In the recent TDWI Checklist Report: Seven Ways to Liberate Enterprise Data with a Platform for Data, David Loshin, TDWI instructor and industry thought leader, explains that DataOps, “which embraces data management processes and procedures to shorten the development cycle and increase the dependability of releases,” is taking off as organizations recognize how much they depend on the free flow of data.

With use cases including sharing operational databases and enabling backup that supports forensics by restoring data from specific points in time, the need for DataOps is on the rise. Loshin also points to enterprises migrating data from on-premises systems to the cloud (or between cloud environments) as another popular use for a modern DataOps platform.

Loshin offers recommendations in seven distinct areas to help enterprises understand how DataOps can be put to use and the steps an organization needs to take to reap its benefits.

Take data privacy, for example. IT departments can’t use production data in test mode because of privacy and security concerns, but creating original test data for a development environment can be a burden. Loshin advocates integrating tools that “can automate the rapid provisioning of different types of test data based on developer needs while observing imposed data obligations for protection” such as masking data to keep it private.

Another security concern, the author explains, is how to reduce “data friction” by overcoming internal barriers to accessing the right data. Consider an organization transitioning to cloud computing platforms that store data sets in hybrid environments, making data provisioning more complicated. Likewise, developers often have to work with isolated data silos, which may run afoul of data privacy regulations.

To overcome these challenges, Loshin suggests enterprises use a data platform that can “finesse data access and compliance challenges by masking sensitive data, and overcome data access issues by ... creating virtual replicated copies [of source data].”

Other areas Loshin tackles include eliminating manual resource requirements through automation and simplifying data collaboration with data protection. He explains how an enterprise can provide self-service to different types of data customers in order to remove the bottleneck that often forms at the IT department. Loshin also offers suggestions for managing heterogeneity by using a data platform as a single point of control and how to simplify data migration (to the cloud or between clouds) by leveraging a data platform.

You can read the full report here. Visitors new to TDWI must complete a short, one-time registration for access.