Reflections from Former Data Schoolers: How Data School’s Ethos, Praxis, and Values ​​Branch, Ripple, and Resonate Outwards

As I started to map out the various paths taken by former Data School employees, I was initially struck by the remarkable range of brilliant minds who had once entered Data School’s orbit, and have since dispersed across a wide constellation of academic, governmental, consulting, and public sector organisations. The table below shows that from the 44 former employees I successfully identified, many have advanced into roles in higher education and research, including positions as PhD candidates, PostDocs, lecturers, or specialists in the field of higher education; numerous others have found employment in the public sector and government as policy advisors, data management analysts, or digitalisation consultants; finally, a substantial number have taken on roles as data analysts, while others are engaged as consultants and advisors or work in areas related to marketing, social media, and communications. In addition, five individuals pursued alternative paths outside the realms of data and AI, taking up roles such as project managers in music and design-related fields, running a café, or becoming self-employed in other areas, while two others are still completing their Master’s degrees.

Alumni Count	Industry Sector	Employing Organisations
13	Higher Education & Research	Utrecht University, HU University of Applied Sciences Utrecht, SMU Centre for Digital Law, Radboud University Nijmegen, University of Kassel, Linköping University, University of Groningen, University of Zurich, Eberhard Karl University of Tübingen, University of Siegen
12	Public Sector & Government	Stec Groep, VNG, Municipality of Veenendaal, Municipality of Zutphen, Municipality of Rotterdam, Ministry of Housing and Spatial Planning, Materiel and IT Command (Ministry of Defence), Eneco, National Police, NCOD, DHD
10	Data & Analytics	DANS, DataX, DPG Media, NOS, IKEA, Oxari, Tax Administration, VodafoneZiggo, Huisman
6	Consulting & Advisory	Dialogic, Parell, The Green Land, Berenschot, Derksen & Drolsbach
3	Marketing, Social Media & Communications	EuroParcs, Into Europe, Freelance
44	Total Alumni Placed

In mapping these trajectories and visualizing how once-intersecting points have since moved outwards in varied directions, I began thinking about the act of tracing itself: about how patterns emerge, how movement is rarely linear, how processes loop and return. These reflections, in turn, brought to mind adrienne maree brown’s book Emergent Strategy (2017), a work that invites us, through the lens of fractals, to reconsider how we build organisations, how we act for change, and how we collaborate with, relate to, and learn from one another. Suddenly, a question began to take shape: What if we reimagined our collaborative work as an ongoing mode of fractal practice – a kind of living geometry that emerges through our smallest gestures, both individual and collective, shaping the ground for something larger to take form? What if the most meaningful impact is not something we scale vertically or extend outwards in linear, unidirectional lines, but something mutually co-created that reverberates in slow, iterative, accumulative, sometimes unexpected but often generative ways? As brown reminds us, fractal thinking, especially within the context of the institutional ecologies we are entangled in, brings forward a crucial insight:

What we practice at the small scale sets the patterns for the whole system.

adrienne maree brown (2017, p. 53)

This is, in many ways, what I have come to perceive Data School as embodying: a praxis of enacting, at the small scale, the very horizons it hopes to see take shape at the systemic level. A rhythm where the smallest acts, repeated continuously, even in the face of uncertainty, resistance, or the inertia of traditional institutional hierarchies, can ripple across contexts, from the micro to the macro, composing patterns that carry forward. This praxis is alive across many dimensions, from the textures of team dynamics, to an ethos of collaborative research not simply for but fundamentally with society, and a deep commitment to developing tangible compasses in the form of tools that support stakeholders in orienting themselves and moving meaningfully forward through the intricacies of our data-driven society.

In this blog post, I want to trace how this ethos continues to resonate beyond the organisation, positioning it as a kind of generative seedbed from which distinct-yet-connected configurations continue to unfold. I spoke to five former team members who were part of Data School at different times and in different roles: as interns, student assistants, junior researchers, or combinations of all three. Their reflections offer a glimpse into how the values, commitments, and practices of Data School have not only travelled with them, but taken root elsewhere, echoing familiar patterns while forming something entirely their own.

The Seed Pattern: During Data School

Throughout our conversations, each former team member reflected in distinct yet resonant ways on their time at Data School, sharing how it had shaped them both professionally and personally. One of the most prominent threads across the five interviews was the experience of “being thrown in at the deep end” – encouraged to step outside their comfort zones, stretch beyond familiar edges, and take on responsibility from the get-go. Yet this was never described as a sink-or-swim scenario. Rather, it unfolded within a space full of care, where a foundation of trust, autonomy, and mentorship made it feel as though you were standing on someone’s shoulders, able to reach what once felt out of reach, while still knowing that if you stumbled, someone would be there to catch you. Lisa de Graaf, now working as a consultant in digital transformation at Berenschot, recalled:

I started working at Data School without really knowing what I was going to do. I had to figure it out as I went, which meant trying a lot of things on my own and taking on real responsibility. I definitely stepped out of my comfort zone. At the same time, team leads like Mirko and Iris played a formative role, as they guided us and gave us the chance to learn by doing, to see how things are done in practice.

Lisa de Graaf

Several also reflected on the value of having the freedom to experiment, to try things out, to follow hunches, and to pursue lines of inquiry and threads of curiosity without needing to know in advance where they might lead. That kind of openness, defined by the possibility to learn by doing and the space to figure things out along the way, brought to my mind Samuel Beckett’s famous line: “Ever tried. Ever failed. No matter. Try again. Fail again. Fail better.” For instance, Sofie de Wilde de Ligny, currently a PhD candidate at Utrecht University, shared with me how that openness oriented her towards discovering her own rhythm as a researcher:

One of the most important parts of my internship was that I got to know myself as a researcher and I became more confident in that role. What I really appreciated was how they gave us responsibility, but also the trust to handle things on our own. After being thrown in at the deep end, you build confidence in approaching people and conducting practical research. That’s what I value most now, a grounded confidence in using action research methods and everything that comes with that. It was a really meaningful learning experience.

Sofie de Wilde de Ligny

Several former team members also reflected on what it meant to be welcomed in, not because they had already arrived at some supposedly expected destination in their professional journeys, but because Data School recognised potential in the places they were just departing from. Their reflections, in turn, reminded me of Bill Watterson’s brilliant commencement address at Kenyon College, which speaks to life, creative integrity, and the courage it takes to stay playful, curious, and open to not knowing. In Watterson’s words, “A playful mind is inquisitive, and learning is fun. If you indulge your natural curiosity and retain a sense of fun in new experience, I think you’ll find it functions as a sort of shock absorber for the bumpy road ahead. […] The truth is, most of us discover where we are headed when we arrive. At that time, we turn around and say, yes, this is obviously where I was going all along. It’s a good idea to try to enjoy the scenery on the detours, because you’ll probably take a few.” As Tim de Winkel, now a senior researcher at HU University of Applied Sciences Utrecht, put it:

Data School hires people with little experience because they see potential in them. That’s why so many young scholars are there – it’s one of the few places where this is possible. In that office at Drift 13, ambition thrives, and certain aspects of a researcher naturally develop. Looking back, what I appreciate most is that, at least for a time, it provided plenty of room to grow and valuable opportunities to be part of. You weren’t under the wing of a senior researcher in a way that kept you small.

Tim de Winkel

The Echoes: After Data School

Some of the former team members I spoke with also reflected on the echoes that continue to shape their current praxis, grounded in the ways of thinking, doing, and learning that took form during their time at Data School. For instance, Luana Sousa, now a lecturer at HU University of Applied Sciences Utrecht, shared:

Data School shaped pretty much everything. I think the job I have today is largely because of the work I did and what I learned there, for example, working with Python, R, Tableau, and also developing a way of thinking about data privacy and research. It’s not just about whether you can do something, but whether you should. That critical perspective – thinking about privacy, algorithms, and data collection – really shaped how I see things. It’s something I now try to bring into my teaching and pass on to my students.

Luana Sousa

Lea Stöter, now a PhD candidate at the University of Kassel, spoke about the lasting impact of Data School’s collaborative research practices and how they shaped their expectations for academic work:

It was great to see how Data School collaborated with people, companies, and communities, and really shared knowledge, rather than staying in an ivory tower of research, which I really appreciated. I think this also shaped my expectations for how research should be, in the sense of it being collaborative and crossing disciplinary borders. The collaborative process of figuring something out together, going back and forth, looking things up, trying to make sense of them, and then coming together to shape concrete thoughts and ideas, was really impactful for me. It’s something I try to bring into my teaching, and I hope I can carry it forward in my own work someday.

Lea Stöter

In Their Words: Data School’s Impact

Finally, former team members also shared their views on the broader impact of Data School’s work. Many described it as a vital bridge, linking academia with society in ways that felt tangible, relevant, and urgently needed. For Sofie, for example, the value lies in how Data School brings academic thinking directly into contact with societal conversations and challenges:

I would say that Data School bridges the gap between academia and huge societal challenges. For society, it’s so valuable to have such a research platform that focuses on creating real impact. For instance, I only learned about things like high-impact journals and citation counts later, when I started my PhD. At Data School, those things never came up. We were more focused on what we could do in the world. That’s what I see as its value, bridging that gap in a fun, valuable, and effective way.

Sofie de Wilde de Ligny

Lea emphasized a similar idea, focusing on how Data School opens up academic knowledge and makes it practical.

What Data School does so well, and where I see real impact, is in breaking down the ivory tower of academia. Not doing research just for research’s sake but constantly working with the insights and tools that come from it. And then sharing, adapting, and translating those tools. That’s so important, for building bridges, for closing gaps. Not just between universities and society, but also across disciplines and among people with different backgrounds. For me, that’s where Data School’s impact lies – in the practice of sharing knowledge that supports all kinds of work and people.

Lea Stöter

Luana echoed this idea, reflecting both on the societal relevance of Data School’s work and her personal experience as an international student navigating a new environment:

This connection between academia and what we might call the real world is so important. Often, we hear about research that doesn’t lead to real outcomes. But Data School is about action, about using academic knowledge to actually do something in society. That’s what stands out. And as a student, especially an international student, it meant a lot to be part of an internship where so many stakeholders cared about the results. That’s the key, creating something in academia that’s useful for the collective.

Luana Sousa

On the other hand, Lisa highlighted that Data School’s most significant impact lies in encouraging stakeholders to care about the complexities of our datafied society – to reflect, question, and engage in dialogue around how emerging disruptive technologies can be implemented in ethical, responsible, and fair ways within their organizations. As she put it:

I think Data School has been really influential in making data ethics a serious field within public institutions. That, to me, is one of its biggest contributions. Encouraging critical thinking around how we use technology, especially now, is incredibly important.

Lisa de Graaf

Finally, reflecting on the idea that some forms of impact resist being captured by institutionally entrenched, linear narratives of quantifiable, visible, and neatly measurable change, Tim concluded that the difference Data School makes speaks for itself:

I would argue that Data School’s office became a phenomenon over the decade. I still see a lot of my students who worked here, many of them now doing PhDs. A lot of people had similar experiences, sometimes frustrating ones, like when we didn’t know what to do. But how do you measure the impact or the value? I don’t know. I think it’s fairly obvious. Even the biggest critic knows the office, knows a lot of the people who came out of it. I’d say the impact of Data School doesn’t need to be measured, because it’s right there, in plain sight.

Tim de Winkel

Conclusion: Ripple Effect

What emerges from these reflections is a vibrant constellation of experiences whose connections form a resonant whole. Former Data School employees have been “thrown in at the deep end,” entrusted with real responsibilities early on, and encouraged to take that crucial first leap of faith. They have consistently been nudged beyond their comfort zones while also given the freedom to explore, experiment, and learn through doing, even if that meant experiencing a few inevitable stumbles along the way.

What made this learning experience singularly productive was its proximity to practice. Working directly with public sector organizations meant engaging with institutional systems, constraints, and routines – not merely observing, analysing, or writing about them, but actively working with, within and alongside them. Through this involvement, former employees began to understand how research functions in applied contexts, how institutions operate from within, and what kinds of environments they may or may not aspire to be part of going forward. Along the way, they also gained clarity on the contributions they would like to make, now better aware of what they can bring to the table.

Though positioned within academia, the Data School thus creates a space where learning unfolds through practical experience, in real settings, alongside others. Whether through collaborative projects, team-based inquiry dynamics, or the development of practical tools for navigating the intricacies of our data-driven society, Data School’s work exemplifies a form of research, collaboration, and learning that is situated, relational, and ethically engaged.

This approach is part of Data School’s broader praxis: the idea that change begins not with scale, but with practices that are lived, repeated, and reshaped in context. In this sense, Data School is not only collaborating to create change but also building the capacity of those they work with – from public sector stakeholders involved in joint projects and fellow colleagues in academia to junior scholars who join the team while still finding their footing – to become agents of change themselves, carrying forward this sense of possibility and the spark of making a difference, however small, in their corner of the world and into whatever futures lie ahead.