September 10, 2019 – The DataOps Transformation

Phoenix DAMA & Century Link Presents Gil Benghiat

SESSION SUMMARY

The list of failed big data analytics projects is long. They leave end-users, data analysts and data scientists frustrated with long lead times for changes. This presentation will illustrate how to make changes to big data, models, and visualizations quickly, with high quality, using the tools teams love. We synthesize techniques from DevOps, Deming, and direct experience.
To paraphrase an old saying: “It takes a village to get insights from data.” 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 in an IT versus data engineer versus data scientist war? Gil Benghiat present the seven steps to get these groups of people working together. These seven steps contain practical, doable steps that can help you achieve data agility through DataOps.
After looking at trends in analytics and a brief review of Agile, Gil outlines the steps to apply DevOps techniques from software development to create an DataOps data platform, 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 deploy to production with 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.
You can view DataOps in the context of a century-long evolution of ideas that improve how people manage complex systems. It started with pioneers like W. Edwards Deming and statistical process control – gradually these ideas crossed into the technology space in the form of Agile, DevOps and now, DataOps. Organizations eager to adopt AI and machine learning (ML) are up against significant challenges.  Analysts like Gartner, Forrester, and others have been writing and talking extensively in the past year about DataOps.
Gil’s goal is to teach analytic teams how to deliver business value quickly and with high quality. We will illustrate how to apply Agile and lean process to your department. However, the process is not enough. Walking through the seven shocking steps will demonstrate how to create a technical environment to truly enable speed and quality by supporting DataOps.

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