Name
And that’s Agile, Jack! How Jack in the Box Transformed our Enterprise Data Architecture
Megan Jackson Stowe
Description

Ever had two tacos at Jack in the Box? Ever wonder what goes in to every decision of how to source, price, prepare, and deliver that taco to you? There’s a lot of data that feeds those questions. Jack in the Box sells a million+ tacos per day, so we’re also talking BIG transactional data (terabytes). In late 2017, executives, marketers, and sales analysts at this quick-serve giant struggled to make these decisions, because their outdated enterprise data architecture simply couldn’t support the volumes of transactions flowing through the system, nor keep up with the rapidly changing industry environment as trends like mobile app and delivery service provider channels surged. In this talk, we’ll take you through the journey of how the first agile team at Jack in the Box transformed business intelligence with a move to AWS Redshift + Tableau in the cloud – and how we leveraged an Agile approach to tackle data engineering, data visualization, product adoption, and legacy retirement through careful parallel coordination of 20+ sprints, lots of releases, and many lessons learned along the way.

This session is for anyone who has a passion for data, and is looking for Agile ways to transform your enterprise business intelligence. Let’s taco ‘bout tacos, baby! -- Tacos not included

Learning Objectives
• We learned to curate buckets of capacity in three major workstreams that can deliver measurable value in parallel: product, data, views (and organize your roadmap and sprints this way)
• We learned to minimize delays in user story execution through careful sprint orchestration of dependencies (if new data is needed for a view, ensure that’s available at least 1-2 sprints ahead of the dashboard story)
• We learned we had to deliver something immediately to drive adoption, the perfect back-end tech can come later (adoption is the long pole, tech is often easy, product needs to start IMMEDIATELY garnering buy-in, even when you have very little built in the new environment)
• We learned that we had many unique groups of users in our target population, and it helps to use dimensions like organization size, technical expertise, and level of interest to cohort users then deploy a unique adoption strategy for each
Learning Level
Learning
Track
Process
Session Type
Experience Report
Keywords
Enterprise Transformation, Tableau, Data Architecture, Data Visualization, Solution Modernization, Data & Analytics, Cloud, Redshift, XR