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What is Dataops (Data Operations)? Difference between DataOps and DevOps

  • 2 mths ago

What is Dataops (Data Operations)?

During an analytics project, companies spend 80% of their time on tasks like data preparation rather than data analysis. Businesses, therefore, focus on gaining the agility to improve the data processing speed and increase the data quality to derive key insights. This focus requires an agile data management approach like DataOps.

DataOps is a process-oriented data management practice focused on improving communication, integration and automation of the data flowing between data managers and consumers within an organization. DataOps combines DevOps, agile management, personnel, and data management technology, providing a flexible data framework that delivers the right data to stakeholders at the right time.

DataOps uses technology to automate the design, delivery, and management of data delivery with the right level of governance and metadata to improve data value in today’s dynamic environment. It creates predictable delivery and changes the management of data, data models, etc., to deliver value faster.

Why do you need DataOps?


  • DataOps promotes agile development, without which data projects may take years, rendering any collected insights useless. Multiple levels of management cause delays and create bad data. DataOps ensures that the code gets into production quickly, delivering value continuously. Agile methodology promotes short, sharp sprints, resulting in faster business insights.
  • In this complex data landscape, understanding the data can be tough. DataOps unlocks value from the data by integrating testing into the data analytics pipeline and providing quality control. It enables clear measurement and transparency of results to help make competitive business decisions.
  • Numerous building blocks are involved in the data lifecycle, and automation can cut down on manual, time-consuming tasks like data reporting and quality checks. DataOps is the science of automating the data analytics lifecycle to minimize errors, improve data quality and promote agility.
  • A Properly designed DataOps process streamlines the data process and creates harmony between the different pockets of innovation. It makes the process adaptable and easy to maintain.


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