DataOps is an emerging set of practices, processes, and technologies for building and automating data pipelines to meet business needs quickly. As these pipelines become more complex and development teams grow in size, organizations need better collaboration and development processes to govern the flow of data and code from one step of the data lifecycle to the next – from data ingestion and transformation to analysis and reporting.

The goal is to increase agility and cycle times, while reducing data defects, giving developers and business users greater confidence in data analytic output. DataOps is not something that can be implemented all at once or in a short period of time. DataOps is a journey that requires a cultural shift. DataOps teams continuously search for new ways to cut waste, streamline steps, automate processes, increase output, and get it right the first time. For large organizations with big development teams, DataOps is an antidote to many of the woes that beset IT and development organizations.

Our initial 2018 report on the topic, titled “DataOps: Industrializing Data and Analytics”, defined DataOps and its components, explored business and technical drivers, and examined challenges. This report takes the next step and examines how organizations are adopting DataOps practices in the field. Using a survey and interviews with data analytics practitioners, the report will examine the rate and scope of DataOps adoption. It will catalog perceptions of DataOps and which components organizations have adopted, and which they haven’t, along with key challenges and benefits they’ve experienced.

Readers will learn:

• User perceptions of DataOps
• The rate of DataOps adoption by industry, company size, and location
• DataOps adoption by technique and component (i.e., agile, test automation, orchestration, continuous development/continuous integration)
• Key challenges organizations face with DataOps
• Key benefits organizations experience with DataOps
• Best practices in doing DataOps
• Case studies and anecdotes of DataOps at companies
• 
  

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