Seven steps to high-velocity data analytics with DataOps

Data analysts, data scientists, and data engineers are already working on teams delivering insight and analysis, but how do you get the team to support experimentation and insight delivery without ending up in an IT versus data engineer versus data scientist war? Christopher Bergh and Gil Benghiat present the seven shocking steps to get these groups of people working together.

To paraphrase an old saying: “It takes a village to get insights from data.” Data analysts, data scientists, and data engineers are already working on teams delivering insight and analysis, but how do you get the team to support experimentation and insight delivery without ending up in an IT versus data engineer versus data scientist war? Christopher Bergh and Gil Benghiat present the seven shocking steps to get these groups of people working together, helping you achieve data agility. Christopher and Gil show you how to deliver business value quickly and with high quality and how to create a technical environment to truly enable speed and quality by supporting Agile analytic operations. After looking at trends in analytics and offering a brief review of Agile, Christopher and Gil explain how to apply DevOps techniques to create an Agile analytics operations environment, including how to add tests, modularize and containerize, do branching amd merging, use multiple environments, parameterize your process, use simple storage, and use multiple workflows deploy to production with W. Edwards Demming efficiency. They also explain why “don’t be a hero” should be the motto of analytic teams—emphasizing that while being hero can feel good, it is not the path to success for individuals in analytic teams.