A 7-Step Checklist to Bring DataOps to Life
As companies make significant headway with DevOps to meet the needs of modern app development and deployment, DataOps is an emerging practice that can further enhance individual and team outcomes by bringing people, technology and process together to create an iterative agile flow.
Recognized for the second year in a row in three separate Hype Cycle reports for 2019, DataOps is “a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization,” according to Gartner.
With the exponential growth of data, companies are demanding it be made more accessible across the organization and freed from department- or technology-based silos. As more organizations take on initiatives that depend on the free flow of data, DataOps has become increasingly appealing. But how can organizations actually apply it? Executing DataOps effectively requires a platform-based approach that breaks down unnecessary data barriers, speeds up processing times without jeopardizing security and generally simplifies processes that were once complex.
Not all platform-based solutions that make DataOps possible are created equal, however. According to the latest report from the TDWI, there are seven key attributes to look for to ensure that the solution you choose enables the flow of secure, high quality data across your enterprise.
1. Accelerated Data Delivery
DevOps promises users higher quality and faster releases when it comes to software development, but without DataOps, legacy approaches to test data management can be a huge obstacle that prevents DevOps from functioning as it should.
These traditional processes typically involve developer requests being pushed to the end of the queue due to slow moving request-fulfill processes and long refresh times, all of which hurt quality and turnaround times.
Instead of relying on dated data practices, look for DataOps solutions that can automate the rapid provisioning of different test data based on developer needs, while observing modern data security practices, such as masking non-production data, to accelerate data delivery and reduce delays in the DevOps lifecycle.
2. Reduced Data Friction
Data managers that don’t leverage DataOps are increasingly unable to meet data users’ growing demand for greater access and flexibility—this causes friction within the organization.
With the right platform-based technology in place, however, DataOps can become a key enabler of data flow, giving data users access to and control over the data they need while providing data managers with the efficiency, oversight and confidence to support the business at scale, reducing or eliminating friction.
How does DataOps make such a difference? By automating the provisioning of high-quality data sets, which can help overcome inherent bottlenecks by compressing source data, creating virtual replicated copies and rapidly transmitting them to development, QA and data analysis teams.
3. Less Manual Work
Delivering data to enterprise data users involves multiple manual processes, from arranging access to source data sets, to validating metadata and ensuring synchronization of replicated copies. As data volume grows, the challenge of keeping up these tasks without DataOps becomes greater, and—because every manual process is prone to error—riskier.
But, automating much of the data provisioning process through a platform designed for DataOps enables teams to identify where the bottlenecks are and reduce manual tasks, thereby lowering the threat of making mistakes as well.
4. Simpler Data Collaboration With Data Protection
Designing and establishing key security practices is integral in today’s data sharing economy. From a DataOps standpoint, a platform that delivers a comprehensive approach to data security must do all of the following: allow teams to identify sensitive data, continuously mask it by replacing confidential information with fictitious yet realistic values, apply governance measures to control data access and finally, provision secure data copies to any target environment while maintaining compliance with privacy regulations.
By using data masking as part of data sharing procedures, data managers will not only simplify the process of collaborating across teams, but also make it more secure.
5. Greater Self-Service Capabilities
At a large enterprise that hasn’t yet made the leap to DataOps, the scale of managing data can be overwhelming, especially when different parties including DBAs, developers, data analysts and other business decision makers have unique needs that arise at different times.
But what if data users no longer had to wait for database administrators to complete their requests? A self-service DataOps platform gives data managers the capability to identify data sources, configure a self-contained environment designed for a specific data user and manage those data containers to ensure synchronization and consistency with the source. All that amounts to greater access, transparency and flexibility for users.
6. A Single Point of Control
The modern enterprise depends on a heterogenous set of sources rather than a single source, but provisioning heterogenous data for all use cases, including development, testing and reporting, often involves a complex process that allows information to flow where it’s needed. But it doesn’t have to be this way, thanks to DataOps.
A data platform can smooth out the differences among data sources and consolidate the data connectors by acting as a proxy that connects different types of data sources and serving as a central hub for provisioning virtual data copies. And, better organized data is more accessible data.
7. More Seamless Migration to the Cloud
Organizations today recognize that the cloud is a key enabler of digital transformation, but migration to the cloud requires a clear, DataOps-driven strategy. Whether a company aims to accelerate software delivery, provide self-service models to developers, automate workflows or enhance IT productivity, data must flow— securely and rapidly—to make the transformation possible.
Because a data platform enables broader access to data, replicates it in a way that ensures nothing is lost during a migration and masks it, it’s simply a safer way to migrate. Plus, because it automates data synchronization, there’s no need to transmit physical media, which also makes the process more seamless for data managers.
Download “Seven Ways to Liberate Data with a Platform for DataOps” for a deep-dive into these seven principles. “Seven Ways to Liberate Enterprise Data with a Platform for DataOps” copyright © 2018 by TDWI, a division of 1105 Media, Inc. Excerpt reprinted by permission of TDWI. Visit TDWI.org for more information.