"You start out wanting to save the world, and by the end, all you want is to turn it in." That’s how Dr. Christian Hitz describes his own final thesis – a turning point many students can probably relate to. Today, Hitz heads the Information Systems – Data Science unit at the Institute of Business Information Technology at the School of Management and Law at ZHAW.
Every day, he encounters a new academic reality: the expectations for final theses have increased, while digitalization and AI create both new opportunities – and misunderstandings.
“Many companies think data science is just a more advanced form of data analysis. But data science is applied science, involving multiple disciplines of processing, refining, and communicating data.”
How can universities, companies, and students work together to create strong thesis projects with substance, relevance, and insights for everyone involved? Storytelling is one of the core disciplines of data science: explaining the unfamiliar through what is already known. Hitz demonstrates this in real time, using three metaphors to give a clear and tangible answer to this question.
The Bakery Metaphor: How Data Scientists Bake Answers from Data
What does a data scientist do? For Hitz, the answer is simple – at least if you picture a bakery: “The data scientist doesn’t bake bread rolls – they bake answers.” Just as a baker transforms raw ingredients into nourishing products, a data scientist works with data, tools, and methods to produce reliable, actionable results.
In this analogy, the “oven” is high-performance computing infrastructure, the ingredients are data points, and the recipe is the scientific process. “If the result doesn’t satisfy – if it’s not usable or valid – then you used the wrong recipe or asked the wrong question.” That’s the key difference, according to Hitz, between superficial data analysis and real scientific work. And: “If you focus only on the tools without knowing what kind of roll you want to bake, you’re not going to get fed.”
The Ski Metaphor: Why AI Is Just a Tool
“I would never forbid students from using AI tools. But they are just that – tools.” Hitz is clear: AI can speed up research but not replace it. Much like in ski racing, it’s not the skis that win the race – it’s the athlete.
“No one says the ski won. It’s always the skier who decides.” The metaphor highlights a key message: strong tools can support, but never replace, the scientific thinking process. Tools like LLMs or model context protocols dramatically change the pace of academic work. “What used to take 45 minutes, students now do in five.”
The real question becomes: “What do we do with the time we’ve gained?” For Hitz, the answer is depth. “Our challenge is to use that time for deeper thinking, better questions, and stronger links to practice. In technical terms, this means investing more time in what is known as problem framing.”
The Silverback Metaphor: Why Dialogue with Industry Inspires
“The best theses emerge from conversations with the silverbacks of the industry,”
says Hitz with a smile. By “silverbacks,” he refers to experienced practitioners who challenge, inspire, and sometimes deeply impress students – and, of course, the exchange works both ways.
The problem? These encounters often happen by chance – at a conference buffet, in a continuing education course, or via personal networks. “Collaboration between universities and companies often lacks orchestration.” There’s no structured, visible bridge connecting thesis topics with real business challenges. Many company issues go unnoticed, while students struggle to find meaningful questions. “What’s missing is matching.”
Yet for Hitz, the thesis has huge potential: “It’s a mini research project – and it needs a serious counterpart willing to engage in dialogue.” It’s not about free consulting, he emphasizes, but about research-based insight. And when this kind of dialogue works?
“Many students tell me their thesis was a life-changing moment.”
Not because it was easy, but because they were challenged in a real-world setting, took responsibility, and grew. And companies benefit too: “They often gain some of the most committed employees they’ve ever had.”
Outlook: Time for Orchestrated Dialogue
The future of student research lies at the intersection: between tools and methods, academia and industry, ambition and application. AI won’t solve everything. But it can free up space. “The question is: what do we do with the time we’ve gained?”
There are plenty of answers – you just have to bake the right ones.