Hello, Tristan. You are a Senior Digital Consultant at MBMC and are currently assisting Procurement with the digitalisation of their processes. What do you do specifically?
In our current project, we are working on creating more transparency in our supply chain, which means gathering data where no underlying data existed beforehand. We then interpret these data in a cloud solution, data compliant, and provide it as dashboards to management as well as many other internal stakeholders in Procurement. This helps them make even better decisions. Projects of this kind typically last four months. Generally speaking, there are three types of applications in the field of data analysis: descriptive analysis, such as the current project, which interprets information to yield more knowledge and a better basis for decision-making; predictive analysis; and prescriptive analysis with specific problem-solving measures. For the most part, these applications build on the understanding of the data that was previously acquired.
Could you use an example to explain how beneficial the usage of data analysis can be?
We have a great deal of internal data that are generated or gathered and stored throughout the supply chain. In our project, we merge these data sources to allow us to interpret details about production needs even better, for instance. This enables individual units to tap into a much broader dataset and to ultimately make more informed decisions. For example, if the supply manager knows of production needs at the Sindelfingen plant early on, he can better anticipate which vehicles should have delivery priority there.
You are responsible for issues involving digitalisation in the Digital Accelerators unit. Are you part of a set team?
Only for the current project – not generally. We form project teams three times each year. That enables my colleagues and me to take turns working with each other. As part of MBMC, Digital Accelerators focus on data-based problem solving. As such, teams are most often made up of a data scientist, a digital consultant and a project manager. A strategy consultant is also included for comprehensive projects as needed. As a digital consultant, I bridge the gap between what the internal customer wants and what is feasible with the underlying data. In turn, the data scientist considers how the data architecture needs to be set up for it to be useful and develops complex models based on this. My former mentor said my job title actually is “data translator”. I show our customers how data can assist their work and I listen to their requests. Because even the coolest tool doesn't help if no one uses it.
Do you think agility and diversity are crucial criteria for successful collaboration?
Absolutely. The dynamic projects alone make us agile. Achieving success in short timeframes only happens with very agile team structure. We work very efficiently, especially in sprints with the developer teams. Diversity is definitely a success factor. At MBMC, we have colleagues with diverse profiles. For instance, we are very international and career changers like me are quite common. Since the team setup can change every time, it never gets boring, either. The different personalities, perspectives and challenges create a great work environment!
You joined Mercedes-Benz as a dual student in business studies with a concentration in international business right after your school graduation. What brought you to data analysis?
After my degree, I started to work in the Employer Branding team, where I frequently got to talk with data scientists, data analysts and developers during marketing initiatives. I was also involved with our internal competence centre for advanced analytics. In doing so, I was excited by what I learned about the opportunities that data offer. After seven years of employment, I took a year of leave to obtain my master's degree in Business Analytics in Dublin. I kept in touch with the company, found a mentor and then applied to work at Mercedes-Benz Management Consulting.