DataOps is enterprise data management for the AI era. Now you can seamlessly connect your data consumers and creators to rapidly find and use the value in all your data. Data operations is not a product, service or solution. It's a methodology: a technological and cultural change to improve your organization's use of data through better collaboration and automation. That means improved data trust and protection, shorter cycle time for your insights delivery, and more cost-effective data management.

More than a discrete, technology platform, DataOps is an approach or a methodology. It means assembling many data technologies and practices into an integrated environment. Data flows easily through this system from your data sources through a data refinery and a data repository to data consumption, which then helps you make a positive impact on your business. Along the way, your technologies, processes and people are vital to its effective conclusion.

The fifth element of a DataOps framework is the most important and most difficult: culture and people. To fulfill the potential of DataOps, you must have or build a culture of collaboration among your IT and cloud operations, data architecture and engineering, and data consumers such as data analysts and data scientists. Only then can DataOps put the right data in the right place at the right time to foster real business value. Successful DataOps architecture supports and requires collaboration across your company. As your data consumers pull data and insights for their business initiatives, they must be able to quickly build and shape their data and the data pipelines it comes through. And the architecture must make these data operations as easy and convenient as possible to foster adoption and smart business.



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