GE Aviation has more than 200,000 tables at its disposal and has made it a mission to implement self-service data, a program to enable anybody to build a data analytics product. The data engineering and analytics teams understood that successful self-service is a continual journey that starts before self-service technologies are deployed and extends well beyond. As an organization-wide initiative, buy-in from the whole company, and continual engagement were needed to reach the 1,300 users and counting.

Jonathan Tudor and Ross Schalmo take a deep dive into how each part of the business became invested in successful self-service and how the engagement continued well beyond deployment. From creating an ongoing partnership with the business to the gamification of tagging data in the data catalog to forming a published dataset council, which selects the best datasets to leverage for each business domain. They take into account the importance of early and continued branding of self-service initiatives, including GE Aviation’s “5 Ss of Self-Service” approach: search—make finding data easy; stitch—focus on data engineering and transformation; science—facilitate predictive modeling and machine learning; show—visualize data and socialize outcomes to get people to take action; and share—ensure that good data is used and reused across the organization.

The results of this initiative have been so strong, and the engagement efforts so successful, that groups outside of the data and analytics team have taken up championing self-service. For example, the aviation engineering team managed to reduce the time it takes to produce 250 regular reports from four weeks of effort to four hours. This led to a joint partnership between the engineering and the digital league (a cross-functional team focused on empowering the business through data products) teams to create a business-wide training. This training, offered every two weeks and spread across eight global sites, explores how to leverage tools like Alation, Dataiku, and Spotfire; how to think about the data lake; write SQL; use statistics and create business impactful data products.

Prerequisite knowledge

• A basic understanding of self-service, self-service tooling, and data and analytics organization structure

What you'll learn

• Discover how to achieve buy-in and ensure continued engagement in self-service initiatives, including principles and initiatives

This session is from the 2019 O'Reilly Strata Conference in New York, NY.