A wide suite of enabling technologies and processes make DataOps possible in your enterprise, including data management technology (data catalogs, data virtualization, data pipelines, AI model management) as well as technology for versioning, test automation, deployment automation and release management, and runtime orchestration, or even collaboration. Automation for testing and deployment use AI and ML to support processes and workflows, helping you avoid manual configuration. You'll want to rely on technology to lower barriers to interoperability. Whether you integrate your technologies into a single foundation or form a collection of interoperable technologies, you want those technologies to work across all of our current and anticipated data environments: on-premises, cloud, multicloud and hybrid.
Smart metadata is vital. Use smart technologies with extensive AI and ML, which your intelligent metadata will use to improve its inferences. When you create metadata automatically at ingestion, automatically detect it at runtime, and tag data objects accordingly, you significantly reduce your team's manual effort. As a result, you speed development of your data pipelines and accelerate adoption and effective analysis by your teams.