DataOps can best be described as a philosophy and set of practices intended for data-driven businesses to receive the maximum value out of their data. And in the day and age we live in, essentially every business is data-driven.
This is because the ability to use data and subsequently drive data management is critical for continuous innovation, gaining new insights, developing software, and improving operational efficiencies. DataOps doesn’t just make it easier for actually storing data, it makes it easier for successfully utilizing that data as well.
Here’s why and how.
Why is DataOps necessary?
DataOps is an approach for analytics teams to attain real-time insights from their data storage, based on the core principles of DevOps. DevOps is likewise a philosophy and set of practices, intended to shorten the developmental life cycle of software as much as possible in order to provide continuous delivery. It’s designed to complement Agile software development, to the point that many DevOps practices actually originated from Agile development practices.
In other words, you can think of DataOps as the same principles of DevOps applied to data storage and analytics. When successfully implemented, DataOps provides an Agile and collaborative approach to data storage, analytics, and governance. The result is that software development teams, IT teams, and data analytics teams can also work together on data management.
Implementing DataOps for superior data storage management
Here are three principles to keep in mind when implementing DataOps for your data storage management:
Follow the core principles of Agile
The entire philosophy of DataOps is already based on Agile methodology, which focuses on breaking up projects into smaller and more easily digestible phases. In the case of data storage and management, Agile data processes focus on beginning with individual data subsets and incremental value delivery for each subset.
For this to work, the process needs to be collaborative across relevant cross-functional teams and with as much of the overall process automated as possible. In addition, the purpose of your data analytics should be moved in such a way that they closely match with your business goals and objectives.
Speaking of automation …
Detect changes to source code with automation
One of the biggest problems that data management and IT teams run into is how when the format of a data source has changed, it affects all other applications and software that likewise rely on that data. When those applications are not yet ready for the changes, it results in downtime, which can affect multiple teams and other applications as well.
The best way for DataOps teams to avoid this is to utilize applications that work with updating and changing data sources. When changes occur, the chances should be automatically detected. Mechanisms should then be put into place that spread the new information to the affected applications. By automating the process of detecting changes to an application’s underlying source code as part of the overall approach to data storage, downtime can be minimized.
Another way automation can be used in DataOps is to automate masked data delivery to ensure it’s transferred in a more secure manner. Leaving production data unmasked in a storage database can be a severe security risk (such as if contractors need to access your storage database for evaluation but then are exposed to unmasked customer or business financial data).
Actually use data for development and not just analytics
Actually use data for active development versus just leaving it in storage to be viewed and studied for analytical purposes when necessary. That’s because in the environment today where virtually all online businesses are data-driven, it’s crucial for organizations to gain new insights from the data they have stored.
Application development in particular is driven based on its requirements for new data. Data storage and development teams utilizing DataOps principles would be wise to provision data in its original form (instead of transforming it), setting self-service workflows and semantics that are developer-friendly, and integrating the data for application development.
As with DevOps, DataOps is slowly being adopted by businesses all over the world as the gold standard approach for achieving the most value out of their data storage. DataOps doesn’t just make it easier for teams to store and analyze their data, but also to make active use out of it for software development.