September 10, 2019 – The DataOps Transformation

Phoenix DAMA Presents Gil Benghiat

Click Here to Register

LOCATION:
Blue Cross Blue Shield2525 W Townley AvenuePhoenix, Az 85021

DATE:
Tuesday September 10, 2019
8:00 – 8:30 AM: Light Breakfast
8:30 – 12:00 Noon: The DataOps Transformation
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.

 

SPEAKER BIO

Gil has spoken on DataOps, Data Engineering, and Data Science at dozens of conferences including ODSC, Strata Data, Enterprise Data World, Data Architecture Summit, and multiple CDO/CAO events including the MIT CDO conference.  Gil is one of the founders of DataKitchen, a company on a mission to enable analytic teams to deliver value quickly and with high quality.  Gil’s career has always been data-oriented and has included positions collecting and displaying network data at AT&T Bell Laboratories (now Alcatel-Lucent), managing data at Sybase (purchased by SAP), collecting and cleaning clinical trial data at PhaseForward (IPO then purchased by Oracle), integrating pharmaceutical sales data at LeapFrogRx (purchased by Model N), and liberating data at Solid Oak Consulting. Gil holds an MS in computer science from Stanford University and a BS in applied mathematics and biology from Brown University.