I will show the journey that Tires took from its first attempts to extend their BI services for analytics to operate a mission critical industrialization environment which runs several AI projects. Beneath the problems and obstacles that were taken I will also explain the decisions that were taken and show the industrialization environment which was created. I also will explain why it was necessary to have such an environment instead of making use of classical BI tools.

Target Audience: Project leader, decision makers, CIO, Software engineers
Prerequisites: None
Level: Basic

Extended Abstract:

When Continental Tires started to establish Data Science as a function it decided to grow this out of BI but with an external employee. The journey was a long one but due to the given experience Continental was able to leave out some of the painful experiences that were made in other companies. So they decided to build up an infrastructure for industrialization of Use Cases from the first minute. The example of Tires is a good one to understand the roadmap from BI to Data Science industrialization. While in the beginning there was the hope that a simple extension of BI and BI tools would deliver an answer today the organization is a complete different one. Also Tires created an own landscape for industrialization of AI. Why this is done so and why this might be useful will be shown in the talk.