10 June 2025

KLU Talks Business

With Holger Bürger and Tobias Kassner, GARBE Industrial Real Estate

How do you remain a pioneer in a market full of uncertainty? How do you succeed in developing concrete strategies from data? And what role does cooperation with universities play in this?

GARBE Industrial Real Estate develops and manages logistics properties across Europe. Technology, sustainability, and data-driven thinking are key levers in this process—as are fresh ideas from outside the company. In conversation with Holger Bürger (Head of Digitalization & IT) and Tobias Kassner (Head of Research & ESG), it becomes clear that innovation arises when practice and science work together on an equal footing. One example is the cooperation with the Master's program in Business Analytics & Data Science (BADS) at KLU.

What role do data science and technologies such as AI play in your work with logistics, real estate, and data?

Tobias Kassner: Our market is full of uncertainties – space availability, types of use, vacancies. Data science helps us to create clarity and derive decisions. For example, we use AI-supported image recognition to identify potential properties – based on aerial photographs, access roads, number of gates, or vehicle movements. We then analyze ownership structures, usage, and investment potential. It's almost like detective work – and it only works with technology plus expertise.

Holger Bürger: We always think in terms of potential. Employees, space, technology. AI is finally able to keep up with our ideas. We want more than PDF reports – we need forecasts, scenarios, and sound bases for decision-making. This requires clean data and people who can link it intelligently. Data science opens up new perspectives for us – and evaluates information from a wide variety of sources to do so.

How do you stay up to date – and promote new ideas within the company?

Holger Bürger: One key measure is that we focus specifically on collaboration with universities. Students and researchers often bring fresh ways of thinking – they don't immediately think “that won't work.” This is extremely valuable for new approaches to solutions. That's why we support master's students in the field of data science, for example, currently in the BADS program at KLU. And, of course, the logistical background of a data specialist is particularly interesting to us.

Tobias Kassner: University collaborations are not a nice-to-have for us, but a strategic lever. Especially in projects related to market transparency and sustainability strategies, the exchange with students gives us new perspectives. And: Those who work with us not only bring ideas to the table – they also get to know our reality with all its challenges. That is genuine exchange on an equal footing.

KLU and GARBE Industrial are working together as part of a corporate sponsorship program in the Master's program in Business Analytics & Data Science (BADS). GARBE Industrial provides scholarships to selected students, who benefit from close contact with GARBE Industrial.

Technological progress, rapid change – how do you remain a pioneer?

Tobias Kassner: Future viability means understanding the past. Much of our data is stored in PDFs, scanned construction plans, or unstructured documents – some of which date back to the last century. Our goal is to make it systematically usable. To this end, we are building a data warehouse that is more than just a storage facility: it should think for itself, provide context, and reveal connections. Only when we can reliably analyze what has happened can we make informed decisions about what is to come. Sustainability is a key area of focus here. We are working on decarbonization roadmaps, analyzing energy consumption, and digitizing consumption data. Our managing partner Christopher Garbe coined the term “sustainomics” to describe the consistent integration of economics and ecology.

Holger Bürger: We also notice this in recruiting. Sustainability is a criterion for many young talents – and if we are not credible, our chances of winning them over decrease. The topic is a competitive advantage if you take it seriously.

Technologically, a lot is possible – but not always reliable. Many tools deliver 80 percent accuracy, but we need 99 percent and more. That's why we often still work pragmatically: we still extract some data manually because it's faster and more reliable than error-prone AI. Nevertheless, we consistently invest in structure, processes, and automation. And we promote a culture in which our data teams don't just process requests, but also think for themselves, network, and contribute new perspectives. That's the difference.