Data Transformation
at GE Aviation

From Silos to Self-Service: A White Paper on How They Did It


To date, despite the hype around AI in the media, very few businesses have managed to execute on incorporating the fundamental processes that enable these data insights at scale, much less automating them to enable AI services.

This white paper tells the story of GE Aviation, a company that bucks this trend and that has been able to empower the organization - not just at a high-level, but down to the individual level - to use data for day-to-day processes.

Inside, discover the history of data teams at GE Aviation, the technological and organizational setup that enabled their transformation, use cases, how they handle data education, and much more.

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About the Authors

Somesh Saxena & Jon Tudor
Product Owner of Dataiku and Alation & Sr. Manager Self-Service Data and Analytics

Somesh Saxena manages a team of full-stack data engineers and helps lead the Self-Service Data Program. Somesh supports a community of over 1,800 self-service developers building digital products to make data-driven decisions, and he has trained over 700 employees through the Digital Data Analyst training, which teaches digital tools, data science, and process excellence. Somesh is front and center of the digital cultural transformation at General Electric Aviation.


Jon Tudor has been at General Electric Aviation for 10 years, including 13 different roles spanning networking, project management, compliance, cloud automation, data architecture, ingestion, engineering, and self-service. Starting in data and analytics in 2014 at the inception of GE Aviation’s data lake, he founded the Self-Service Data Program in late 2016 with the implementation of Dataiku and Alation and has since established a team and continued to grow the program, including global training, a support network across all business divisions, and six innovative products that enable over 1,800 users to create their own data and analytics solutions.

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What's Inside the White Paper

GE Aviation has implemented their own version of a self-service system that serves their specific needs and requirements and that allow them to use real-time data at scale to make better and faster decisions throughout the organization. This white paper tells the story of how they developed a program that now has more than 1,800 users throughout the company. 


Specifically, it covers:


  • How GE Aviation developed a Self-Service Data program using Dataiku and a suite of tools that unlocks employees’ ability to use data to get insights quickly.
  • The technological stack and organizational setup at GE Aviation that enable these systems.
  • The lifecycle of a data product at GE Aviation.
  • How GE Aviation handles data governance and data education (including suggestions for employee onboarding material for a self-service data system).
  • The return on investment (ROI) GE Aviation has seen from their data initiatives.



GE Aviation: Organizational Setup for Self-Serve Data Success

Self-service initiatives often fail in large enterprises for a variety of reasons (ongoing issues with data access, insufficient tooling - or tooling that doesn’t meet users’ daily needs, lack of data accuracy or data confidence, data security problems, a complete lack of connection between self-service and operationalization, etc.). All of these issues boil down to a larger problem: self-service gets treated a one-time project that gets launched, then forgotten.

At GE Aviation, this is far from the reality. The Self-Service Data (SSD) Team supports over 1,800 users today. They are a centralized group based at the company’s office in Cincinnati, but they work to support users around the globe. This team ensures that nothing is blocking people from using SSD, whether that be an initial knowledge gap or technical issues along the way. That means teaching users to do things for themselves instead of simply acting as a more traditional help team who takes tickets and solves issues on behalf of the users.

Importantly, they also ensure that the initiative keeps its momentum - that is, that it doesn’t become outdated or stale and continues to evolve with the needs of the business and the users - by introducing new automations and process improvements to reduce repetitive work across the board.

For example, the team recently turned the rather arduous process of triggering data product deployment (which involved manually switching the product’s environment, making sure any scenarios were turned on and running manual checks on those scenarios, etc.) into an automated process so that macros do all of these checks behind the scenes and users can simply automate their data product with a click of a button in Dataiku. The SSD Team can now spend more time on other priorities, like supporting any issues in production and improving education around the tools and processes.

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