The DataOps methodology offers a new way to improve both the quality and speed of data analytics.

The proliferation of data and data systems — spurred by an increasing number of use cases for advanced data analytics — has catapulted DataOps into the mainstream for modern organizations. The DataOps methodology has been growing in popularity among data teams, offering a new way to improve both the quality and speed of data analytics.

Traditionally, data pipelines relied on very little automation and required intensive coding. As organizations modernized and began focusing on self-service analytics and machine learning, companies latched onto DataOps, which brings a software engineering perspective and approach to managing data pipelines — similar to the DevOps trend.

The DataOps methodology matches the mantra of agile software development: change is inevitable. One must architect processes and technology to embrace change. And change isn’t limited to schema changes either — it includes shifting business requirements, delivering data and reports to new stakeholders, integrating new data sources, and more. By focusing on automated tooling that supports quick change management and iterative processes, DataOps delivers on organizational goals like increasing the data team’s output to the business while decreasing overhead.

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