Patrick Ketterl works on vehicle-based software development. In the interview, he talks about what interests him about Big Data and what skills are beneficial to work in his unit.
Hello Mr. Ketterl, please introduce yourself briefly to our readers.
My name is Patrick Ketterl and I have been working for Daimler for eight years. I studied industrial engineering at Pforzheim University of Applied Sciences, majoring in IT. Following practical experience at Daimler, BMW and Agilent Technologies as well as several positions in Daimler's IT development function, today I am responsible for vehicle-based software development.
Why did you choose Daimler as your employer?
I quickly realized that I was drawn to the automotive industry. I have always had an interest in cars. Emotional products, a wide range of opportunities for development and the possibility of international assignments were some of the factors that decided it for me.
What was your previous experience at Daimler – starting from your first day in the company until today?
I finished my degree in 2009, in the middle of the financial crisis. That made me all the more happy that I was able to get a job with my employer of choice. I spent 15 months getting to know various units at Daimler as a trainee: I worked in IT development, test planning at Daimler Trucks and overall passenger car testing. After completing the traineeprogram I took on various tasks as part of a major software development project. These included supplier management and overall project management. After almost five years I then decided to start a new chapter and assume responsibility for test and release management. The challenge was to update 30 interconnected systems to a new version, twice a year. In early 2016 I then had the great opportunity to work on a major Big Data project relating to the development of autonomous driving. This is a very interesting field that I am still working in today!
Can you briefly explain what Big Data is? What interests you about it?
We talk about a Big Data scenario when the limits of traditional data processing are met. These limits may relate to the volume of data, the speed at which data is created, or the variety of data. My current project meets all three of these criteria: the measurement data generated during the development of autonomous driving is generated so quickly and with too much volume to be processed using normal tools and methods. Depending on the sensors and use cases, we also use different data formats that need to be reduced to a common denominator before they can be analyzed. What I find fascinating is that the huge volume of data allows us to spot patterns. This enables us to identify defects and potential for improvement faster for the development of sensors, control devices and algorithms in self-driving vehicles.
Please tell us a bit more about your team and your work. What challenges are you facing at the moment?
The biggest challenge is the fact that we are working on a field of the future. We want to arrive at an overall solution for the huge volume of data in development for which there are currently few examples, never mind established standards. A new technology can quickly become obsolete, and new methods can turn out to be dead ends. We work flexibly in scrums in order to be able to respond to developments in the Big Data environment, and always have our eyes on the "big picture". In addition to my team, our project group comprises other specialist units, development partners and the participating IT units. The fact that they are spread between multiple continents and time zones makes the work even more interesting.
What skills are beneficial to work in your unit?
In order to be faster than the competition there are times when we have to creatively skip traditional processes or work in parallel on multiple alternatives. The pressure to implement is high, which also has an impact on our project. We are looking for flexible thinkers who can see the big picture and work pragmatically on solutions. All of the people working on my project are very interested in new technologies and methods, and have the desire to pursue a vision through to implementation.