How do you build an architecture to support a diverse set of teams, roles, and data in a modern organization? Data analysts, data scientists, and data engineers are already working in teams delivering insight and analysis, but how do you get the team to support experimentation and insight delivery without ending up failing? Eric Estabrooks presents the seven steps to get these people and their architecture moving. These seven steps are practical, doable steps that can help you achieve DataOps. DataOps is a collaborative Data Management practice put in mid-2018 on the Gartner 2018 Hype Cycle and is emerging as an area of interest with other analyst groups who recognize the upcoming shift in the industry.

In order to dive into DataOps, Eric outlines the steps to apply DevOps techniques from software development to create an Agile analytics operations environment, including how to add tests, modularize and containerize, do branching and merging, use multiple environments, parameterize your process, use simple storage, and use multiple workflows to deploy to production with W. Edwards Deming efficiency. They explain why “don’t be a hero” should be the motto of analytic teams – emphasizing that while being a hero can feel good, it is not the path to success for individuals in analytic teams.

Eric's goal is to teach analytic teams how to deliver business value quickly and with high quality. They illustrate how to apply Agile processes to your department. However, a process is not enough. Walking through the seven shocking steps will demonstrate how to create a technical environment that truly enables speed and quality by supporting DataOps.