Where Can DataOps Be Applied?

DataOps can be applied anywhere you find data friction. Below are some use cases by category

Machine Learning and Artificial Intelligence

Your machine learning (ML) initiative is only as good as the data you feed it. ML algorithms require high-quality representational data for training, and reliable data streams for execution.

Software Development Lifecycle

Developers and testers require access to test data at multiple stages. DataOps can be applied to the software development lifecycle (SDLC) to increase release speed and improve application quality.

Cloud Migration

While spinning up cloud infrastructure is easier than ever, moving data from an on-prem environment to the public cloud (or from one cloud to another) can be slow, manual, and risky. DataOps can simplify and speed the process migrating data to the cloud.

Data Analytics

Access to real-time, actionable insights can mean the difference between success and failure in business. Failure to get clean, relevant data to the right systems and teams can result in poor decisions or missed market opportunities.

Data Protection, Privacy, and Governance

With data breach incidents regularly making the news and increasing pressure from regulatory bodies and consumers alike, organizations must protect sensitive data across the enterprise. DataOps helps establish consistent governance policies and controls to enable data to flow freely.