Marcel van Oosterwijk

Blog

  • Practice Note: Learning to See Stories

    I want to become better at videography, and the obvious way to approach that would be to start learning video: camera settings, frame rates, codecs, Log profiles, color grading, stabilization, camera movement and editing. There is certainly no shortage of technical subjects to explore.

    The more I think about it, however, the more I suspect that starting there would teach me the wrong thing first. Before learning how to make better video, I need to learn how to see a story.

    Modern cameras already make technically good images remarkably easy. My Canon EOS R can produce beautiful photographs and perfectly respectable video. My iPhone can record excellent footage with almost no preparation, while a GoPro can capture perspectives that would have required specialised equipment not very long ago. I therefore don’t think the technology is currently my biggest limitation. The more difficult question is much simpler: what should I point the camera at, and why?

    That question is equally relevant to photography. It is quite easy to return from a trip with dozens of attractive photographs of buildings, landscapes, food and interesting objects. Each image may be perfectly good in isolation, yet the collection does not necessarily communicate what happened, what we experienced or why a particular moment mattered. A beautiful photograph records something worth seeing. A visual story has to do more: it needs to help someone understand an experience.

    For now, I have reduced that problem to three simple questions.

    Where are we? What are we doing? What makes this moment special?

    The first question establishes the environment. A wider image gives the viewer enough context to understand where the story takes place. The second moves closer and shows the activity or interaction that gives the scene its purpose. The third searches for the detail that gives the moment its character: an expression, a gesture, a texture, steam rising from a cup, a hand adjusting a camera, or something else that might easily have gone unnoticed.

    Conveniently, these three questions also correspond reasonably well with the way I already think about focal lengths. A wide-angle view can establish the context, a normal focal length can show the activity, and a telephoto or macro view can isolate the meaningful detail. It is certainly not a complete theory of visual storytelling, nor is it intended to be. Its value is precisely that it is simple enough to remember while actually taking photographs.

    Imagine, for example, that my partner and I arrive somewhere with a beautiful landscape. My natural photographic instinct might be to find the strongest composition and make the best landscape photograph I can. There is nothing wrong with that, but if I want to communicate the experience of being there, the landscape itself is only the beginning. One photograph could establish the vista. Another could show Elena enjoying the view. A third might show her photographing it, perhaps followed by a detail of the resulting photograph on the back of her camera. None of those additional photographs needs to be as visually impressive as the landscape itself. Together, however, they communicate something the landscape photograph cannot: the experience of being there.

    The same idea works on a much smaller scale. In a café, I might begin with an image that establishes the place, then photograph the preparation of the coffee, and finally notice the steam rising from the cup or the texture of the foam. Instead of collecting several unrelated attractive images, I am deliberately looking for images that have a relationship with one another.

    This led me to a slightly unusual conclusion. Perhaps the best way for me to begin learning videography is not with video at all, but with photography.

    Photography slows the problem down. I can stop and think about what establishes the scene, what activity matters and which detail completes the sequence. I do not yet have to worry about camera movement, clip duration, stabilisation, sound or editing rhythm. I can concentrate entirely on recognising the story and expressing it through a small sequence of images. My initial exercise can therefore be very simple: whenever something seems worth recording, try to tell its story in at least three photographs—introduction, activity and detail.

    Once that way of seeing becomes natural, the transition to video should be much less dramatic. Instead of taking the photograph that establishes the location, I can record a short establishing clip. Instead of photographing an activity, I can allow it to unfold for several seconds. Instead of isolating a detail in a still photograph, I can give the viewer time to observe it. The medium changes, and with it many technical possibilities, but the basic storytelling structure remains remarkably similar.

    Thinking about videography this way also changes how I look at my equipment. My EOS R is clearly the more capable photographic tool when I want maximum image quality, control over depth of field, different lenses or simply the deliberate experience of using a dedicated camera. That does not automatically make it the best tool for learning video. Using it for video introduces additional decisions about lenses, the 4K crop, resolution, support and eventually perhaps a larger rig. None of those questions is unreasonable, but none of them helps me recognise a story either.

    For the foreseeable future, the iPhone therefore makes more sense as my primary video camera. It removes much of that friction, is always available and quick to deploy, and its different focal lengths already provide something close to the wide, normal and telephoto perspectives I use when thinking about photographic stories. The GoPro adds another useful capability: viewpoints. It can go on a bicycle, be attached to something, sit very low to the ground or capture immersive walking and driving sequences without requiring a large support system.

    This means I no longer need to decide which of these cameras is somehow the best. They can simply have different roles. The Canon remains my deliberate photographic camera, particularly when image quality, lenses, portraits or the pleasure of photography itself matters. The iPhone becomes the everyday storytelling camera and, at least initially, my primary video camera. The GoPro provides movement, immersion and viewpoints that would otherwise be inconvenient or impossible. They complement one another rather than compete.

    There is also a broader lesson in this that I recognise from other areas. When learning something new, it is tempting to begin with the most visible tools and techniques of the discipline. Videography appears to be about cameras, codecs, camera movement and editing because those are the things we can observe videographers using. Yet those techniques are supporting a more fundamental capability. In my case, that is the ability to recognise a story and decide what someone needs to see in order to experience it.

    That is the capability I want to develop first. I want to become better at recognising the context that establishes a story, the activity that moves it forward and the detail that gives it meaning. Photography gives me a deliberately simpler environment in which to practise that skill. Once I can reliably tell a small story with three photographs, I can start replacing those photographs with moving images and gradually introduce everything else that videography makes possible.

    The technology can become more sophisticated as my needs become more sophisticated. The EOS R can always return to the video workflow if it eventually provides a creative advantage that I actually understand and need. There is no reason to introduce that complexity merely because the camera is capable of it.

    For now, I would much rather make a technically ordinary video that tells an engaging story than a technically beautiful video that says very little.

    Perhaps learning videography does not begin with learning video after all. Perhaps it begins with learning to see.

  • Practice Note: A Second Life For The Notes?

    When I first started writing Practice Notes, I mainly saw them as a way to preserve observations.

    An idea emerges during a conversation, a project, an experience or simply a moment of reflection. If I capture it, it remains available. If I do not, there is a good chance that it will eventually disappear. This became part of what I now think of as continuity of understanding: preserving enough of what we learn that someone, somewhere, can make use of it later.

    Recently, however, I realized that this may only be the first part of the story. The individual Practice Notes are probably not the end product. They are source material.

    As the collection grows, connections begin to appear between notes that were written at different times and about very different subjects. An observation about assessment turns out to have something in common with an idea about product development. A family story raises the same question about lost context that appears in system documentation. Even something as ordinary as choosing a watch can become an exploration of judgment, context and suitability.

    I did not set out to connect all of these subjects. In many cases, the connections only became visible afterwards.

    That suggests that a sufficiently large collection of observations can contain something that none of its individual pieces contains on its own: recurring patterns in the way its author understands the world.

    This gives the Practice Notes a second possible purpose.

    The first is preservation. Capture observations, experiences and ideas before they disappear.

    The second is discovery. Once enough material has accumulated, look across it and ask what keeps returning. Which assumptions shape apparently unrelated conclusions? Which mental models appear in different contexts? Which ideas have gradually evolved over time without that development ever being planned?

    Until recently, doing this with decades of material would have been difficult. Someone would have to read everything, categorize it, compare ideas across hundreds of fragments and somehow reconstruct the relationships between them.

    AI changes what is practical.

    It can help examine a large body of material and identify recurring concepts, relationships, tensions and patterns. Not to invent a philosophy on behalf of its author, but to help reveal patterns that may already be present.

    That makes me wonder what could eventually emerge from my own collection of Practice Notes.

    A working title might be:

    Method in the Madness

    It would not be a philosophy book that I deliberately sat down to write. Instead, it would be a reconstruction of the recurring ways I appear to think, derived from observations accumulated over many years and across many different subjects.

    And once that possibility exists, another one follows.

    Every person carries a different body of understanding. Our experiences differ, as do our professions, cultures, histories and assumptions. If more people preserved fragments of that understanding over time, each collection might reveal its own patterns.

    But there may also be patterns between those collections.

    Ideas might emerge independently in different professions or different parts of the world. Similar mental models might appear under completely different names. Experiences that initially seem unrelated might reveal common structures when viewed together.

    AI could potentially help us discover those patterns as well.

    A working name for that idea is:

    Pluribus

    From many individual bodies of understanding, something larger may emerge — not because everyone thinks alike, but because the differences and similarities between their experiences become visible.

    This suggests a progression.

    Continuity preserves understanding so that it does not simply disappear.

    Method in the Madness looks across one person’s accumulated understanding and discovers the patterns within it.

    Pluribus looks across many such bodies of understanding and searches for patterns that transcend the individual.

    Perhaps that is where the Practice Notes eventually lead.

    Not towards one enormous collection of notes, and not towards a book I already know how to write.

    Towards enough preserved understanding that we can eventually discover what the collection has been telling us all along.

    This version also makes Pluribus feel less like a sudden sci-fi leap: it follows naturally from the same mechanism applied first to one body of knowledge and then to many.

  • Practice Note: Building on Established Reference Models

    The term reference model is already well established within systems engineering, enterprise architecture and other engineering disciplines. It generally refers to an abstract representation of a domain that defines concepts, their relationships and a common frame of reference while remaining independent of a particular implementation.

    For readers unfamiliar with the concept, the Wikipedia article provides a concise overview of the commonly accepted definition:

    https://en.wikipedia.org/wiki/Reference_model

    This Practice Note does not attempt to redefine what a reference model is. Instead, it establishes the perspective adopted throughout this body of knowledge.

    Many reference models successfully describe what exists. They identify concepts, components and relationships. They provide a shared vocabulary and a structural understanding of a domain.

    In practice, however, organizations face a different challenge. Over time, the structure often survives while the understanding behind it gradually disappears. Documentation remains available, yet the reasoning that shaped it becomes difficult to reconstruct. New team members can read the model without fully understanding why it exists, what assumptions influenced it or under which circumstances it should evolve.

    From the perspective of these Practice Notes, a reference model is therefore valuable not only because it represents a domain, but because it enables the continuity of understanding of that domain.

    Three questions become particularly important.

    • Does the model preserve the intent behind its concepts and relationships?
    • Can its meaning be reconstructed when context has been lost?
    • Can the model evolve without losing the reasoning that originally made it useful?

    These questions extend beyond documentation. They concern the preservation of organizational knowledge and the ability to make informed decisions as organizations, products and capabilities evolve.

    The Practice Notes therefore build upon the established concept of a reference model rather than replacing it. Their contribution is to explore how reference models can support continuity of understanding, deliberate development and defensible assessment throughout the lifecycle of organizations, capabilities and products.

    Future reference documents in this body of knowledge adopt this perspective as their foundation.

  • Practice Note: AI as an Enabler for Knowledge Synthesis

    While continuing to add new Practice Notes to the growing body of knowledge on this blog, I found myself wondering how each new insight would affect the publications we were in the process of creating.

    A single new observation might strengthen an argument in a derived theoretical paper. Another might influence the capability methodology. A third might require changes to the product guidance, consulting offering or training material.

    Each new Practice Note had the potential to affect several different publications.

    Initially, this seemed perfectly reasonable. Then another thought occurred to me.

    What if I had to perform all of those updates manually?

    Every new insight would require reviewing multiple publications to determine whether they should be updated. Every update would require checking whether the same insight should also appear elsewhere. Every publication would gradually become another artifact requiring maintenance. As the body of knowledge continued to grow, so would the effort required to keep all of those representations aligned.

    At that moment I realized something important.

    The challenge was not maintaining the knowledge. The challenge was maintaining all of its representations.

    The previous Practice Note proposed maintaining a single canonical body of knowledge and generating all other knowledge assets from periodic Knowledge Snapshots.

    Conceptually, this governance model is straightforward: practice Notes become the authoritative source, Knowledge Snapshots provide stable baselines. Publications, methodologies, training material and consulting offerings become derived representations of that knowledge.

    While this may work in theory, the question is whether it is practical. Historically, the answer would often have been no. The bottleneck wasn’t the creation of knowledge, but the repeated synthesis of that knowledge into many different representations.

    Every publication required manual interpretation. Every revision required manual consistency checking. Every new insight increased the cost of keeping the knowledge assets aligned. The governance model was sound, but the economics were not.

    Recent advances in AI fundamentally change the economics of knowledge synthesis. AI is exceptionally well suited to transforming an existing body of knowledge into multiple coherent representations for different purposes and audiences.

    This is a fundamentally different role from creating knowledge. Knowledge continues to originate through experience, observation, experimentation, discussion and reflection.

    AI enters the process only after that knowledge exists. Its contribution is synthesis.

    This distinction is important.

    Organizations learn through experience. People make observations. Teams discover better ways of working. Experts refine concepts and challenge assumptions. Those activities create knowledge.

    Once that knowledge has been captured, however, another activity begins. The knowledge must be organized. Synthesized. Presented. Explained. Adapted for different audiences. Connected to existing concepts. Translated into methodologies, publications, training material and consulting guidance. This is knowledge synthesis.

    For many organizations, synthesis has traditionally been the most expensive part of knowledge management.

    The governance model described in the accompanying Practice Note becomes practical because the economics of synthesis have changed.

    Rather than manually maintaining many independent artifacts, organizations can maintain one canonical body of knowledge and periodically regenerate derivative assets from approved Knowledge Snapshots.

    Those assets may include:

    • conceptual publications;
    • capability descriptions;
    • product guidance;
    • assessment methodologies;
    • consulting playbooks;
    • training material;
    • onboarding guides;
    • indexes;
    • traceability matrices;
    • AI assistants.

    Human effort shifts accordingly. Instead of maintaining documents, people improve understanding. Instead of synchronizing publications, they enrich the canonical body of knowledge. The cost of regeneration decreases dramatically.

    The governance model itself remains technology independent.

    Organizations still require:

    • stewardship;
    • critical thinking;
    • peer review;
    • traceability;
    • professional judgement;
    • continuous learning.

    AI does not determine what becomes organizational knowledge. It does not decide which observations are significant. Those remain human responsibilities.

    AI helps synthesize the resulting knowledge into coherent and consistent representations.

    Without governance, AI simply produces more content. With governance, AI enables organizations to preserve continuity while continuously communicating an evolving understanding.

    This observation extends beyond the framework described in these Practice Notes.

    Any organization maintaining professional disciplines, methodologies, capability frameworks, operating models or enterprise knowledge faces the same synthesis challenge.

    As AI reduces the cost of synthesis, organizations gain an opportunity to redesign how they govern knowledge.

    Rather than maintaining many independently evolving artifacts, they can maintain one authoritative body of knowledge and regenerate the representations required for different purposes.

    This improves consistency. It improves traceability. Most importantly, it improves continuity of understanding.

  • Practice Note: The New Bottleneck

    As a consequence of using AI in my writing, the cost of writing has decreased dramatically. An idea that once required several evenings of outlining, drafting, rewriting and polishing can now become a coherent paper much faster. Arguments can be tested in different forms. Weak transitions can be repaired. Examples can be added. Alternative structures can be explored without starting again from scratch. For that, I am incredibly grateful.

    But one important cost has not decreased nearly as much: the cost of serious review. A reviewer still has to read the paper carefully. They still have to understand the argument rather than merely recognize the words. They may also use AI, but that’s not enough.

    They still have to compare the claims with their own experience, identify hidden assumptions, notice contradictions, challenge weak reasoning and decide whether the model is actually useful. That takes time. More importantly, it takes qualified attention.

    AI can help produce more documents, or more incremental versions of the same documents. It does not automatically create more people willing and able to judge whether those documents deserve to be trusted.This changes the bottleneck.

    Previously, writing itself limited how much material could be produced. If creating a paper took months, review requests were naturally infrequent. Now drafting is cheaper. A paper can be improved repeatedly, and new versions can appear almost whenever a new insight emerges. That sounds positive. Until every small improvement is sent to the same colleagues for another serious review.

    The cost of producing the new version may have become low. The cost imposed on the reviewer has not. That creates a new responsibility for the writer. Cheap drafting should not lead to expensive review churn.

    I may revise a paper privately many times. I may collect new examples, conceptual distinctions, field observations and corrections in a backlog. But a new version should only be published when those changes together create a significant improvement.

    Not merely better wording. Not one extra paragraph. Not version 1.04 because I had another thought on Tuesday. A new review should be worth the attention it asks for.

    This suggests a simple structure. Field notes can remain the exploratory layer. They capture observations while they are fresh. Several notes may begin pointing in the same direction. The stable insight then enters the backlog for the relevant paper. Only when enough important changes have accumulated does the formal paper absorb them and become a genuinely new version.

    The production cycle becomes faster. The release discipline should become stricter. This applies far beyond my own papers.

    Organizations can now produce reports, proposals, strategies, requirements, policies and presentations at unprecedented speed. Much of it will be polished, plausible and professionally written. But polished language is not the same as sound reasoning. And increased output does not create increased capacity for careful judgment.

    The danger is not only that AI will produce poor documents. It is that it will produce more documents than anyone can seriously assess. The scarce resource is shifting from creation to judgment.

    The cost of producing documents has collapsed. The cost of deciding whether they deserve to be trusted has not.