When a complex technical domain isn’t easily captured in a model, look for metaphors that bring clarity.
One of us (Mathias) consulted for a client that acted as a broker for paying copyright holders for the use of their content. To do this, they figured out who the copyright holders of a work were. Then they tracked usage claims, calculated the amounts owed, collected the money, and made the payments. Understanding who owned what was one of the trickier parts of their business.
-“It’s just a technical problem.”
-“But nobody really understands how it works!”
-“Some of us understand most of it. It just happens to be a complicated problem.”
-“Let’s do a little bit of modeling anyway.”
Determining ownership was a complicated data matching process which pulled data from a number of data sources:
- Research done by the company itself
- Offshore data cleaning
- Publicly available data from a wiki-style source
- Publicly available, curated data
- Private sources, for which the company paid a licence fee
- Direct submissions from individuals
- Agencies representing copyright holders
The company had a data quality problem. Because of the variety of data sources, there wasn’t a single source of truth for any claim. The data was often incomplete and inconsistent. On top of that, there was a possibility for fraud: bad actors claimed ownership of authors’ work. Most people acted in good faith. Even then, the data was always going to be messy, and it took considerable effort to sort things out. The data was in constant flux: even though the ownership of a work rarely changes, the data did.
The engineers were always improving the “data matching”. That’s what they called the process of reconciling the inconsistencies, and providing a clear view on who owned what and who had to pay whom. They used EventSourcing, and they could easily replay new matching algorithms on historic data. The data matching algorithms matched similar claims on the same works in the different data sources. When multiple data sources concurred, the match succeeded.
Initially, when most sources concurred on a claim, the algorithm ignored a lone exception. When there was more contention about a claim, it was less obvious what to do. The code reflected this lack of clarity. Later the team realised that a conflicting claim could tell them more: It was an indicator of the messiness of the data. If they used their records of noise in the data, they could learn about how often different data sources, parties, and individuals agreed on successful claims, and improve their algorithm.
For example, say a match was poor: 50% of sources point to one owner and 50% point to another owner. Based on that information alone, it’s impossible to decide who the owner is. But by using historical data, the algorithm could figure out which sources had been part of successful matches more often. They could give more weight to these sources, and tip the scales in one direction or the other. This way, even if 50% of sources claim A as the owner and 50% claim B, an answer can be found.
The code mixed responsibilities: pulling data, filtering, reformatting, interpreting, and applying matching rules. All the cases and rules made the data matching very complicated. Only a few engineers knew how it worked. Mathias noticed that the engineers couldn’t explain how it worked very well. And the business people he talked to were unable to explain anything at all about how the system worked. They simply referred to it as the “data matching.” The team wasn’t concerned about this. In their eyes, the complexity was just something they had to deal with.
Mathias proposed a whiteboard modeling session. Initially, the engineers resisted. After all, they didn’t feel this was a business domain, just a purely technical problem. However, Mathias argued, the quality of the results determined who got paid what, and mistakes meant customers would eventually move to a competitor. So even if the data matching was technical, it performed an essential function in the Core Domain. The knowledge about it was sketchy, engineering couldn’t explain it, business didn’t understand it. Because of that, they rarely discussed it, and when they did, it was in purely technical terms. If communication is hard, if conversations are cumbersome, you lack a good shared model.
Through modeling, the matching process became less opaque to the engineers. We made clearer distinctions between different steps to pull data, process it, identifying a match, and coming to a decision. The model included sources, claims, reconciliations, exceptions. We drew the matching rules on the whiteboard as well, making those rules explicit first class concepts in the model. As the matching process became clearer, the underlying ideas that led to the system design started surfacing. From the “what,” we moved to the “why.” This put us in a good position to start discovering abstractions.
Gradually, the assumptions that they built the algorithm on, surfaced in the conversations. We stated those assumptions, wrote them on stickies and put them on the whiteboard. One accepted assumption was that when a data source is frequently in agreement with other sources, it is less likely to be wrong in the future. If a source is more reliable, it should be trusted more, therefore claims from that source pulled more weight in the decision of who has a claim to what. When doing domain discovery and modeling, it’s good to be observant, and listen to subtleties in the language. Words like “reliable,” “trust,” “pull more weight,” and “decision” were being used informally in these conversations. What works in these situations, is to have a healthy obsession with language. Add this language to the whiteboard. Ask questions: what does this word mean, in what context do you use it?
Through these discussions, the concept of “trust” grew in importance. It became explicit in the whiteboard models. It was tangible: you could see it, point to it, move it around. You could start telling stories about trust. Why would one source be more trusted? What would damage that trust? What edge cases could we find that would affect trust in different ways?
Trust as an Object
During the next modelling session, we talked about trust a lot. From a random word that people threw into the conversation, it had morphed into a meaningful term. Mathias suggested a little thought experiment: What if _Trust_ was an actual object in the code? What would that look like? Quickly, a simple model of Trust emerged. Trust is a Value Object, and its value represents the “amount” of trust we have in a data source, or the trust we have in a claim on a work or usage, or the trust we have in the person making the claim. Trust is measured on a scale of -5 to 5. That number determines whether a claim is granted or not, whether it needs additional sources to confirm it, or whether the company needs to do further research.
It was a major mindshift.
The old code dynamically computed similar values to determine “matches.” These computations were spread and duplicated across the code, hiding in many branches. The team didn’t see that all these values and computations were really aspects of the same underlying concept. They didn’t see that the computations could be shared, whether you’re matching sources, people, or claims. There was no shared abstraction.
But now, in the new code, those values are encapsulated in a first class concept called Trust objects. This is where the magic happens: we move from a whiteboard concept, to making Trust an essential element in the design. The team cleaned up the ad hoc logic spread across the data matching code and replaced it with a single Trust concept.
Trust entered the Ubiquitous Language. The idea that degrees of Trust are ranked on a scale from -5 to 5, also became part of the language. And it gave us a new way to think about our Core Domain: We pay owners based on who earns our Trust.
Trust as a Process
The team was designing an EventSourced system, so naturally, the conversation moved to what events could affect Trust. How does Trust evolve over time? What used to be matching claims in the old model, now became events that positively or negatively affected our Trust in a claim. Earning Trust (or losing it) was now thought of as a process. A new claim was an event in that process. Trust was now seen as a snapshot of the Trust earning process. If a claim was denied, but new evidence emerged, Trust increased and the claim was granted. Certain sources, like the private databases that the company bought a license for, were highly trusted and stable. For others, like the wiki-style sources where people could submit claims, Trust was more volatile.
During the discussions about the new Trust and Trust-building concepts, the team went back to the business regularly to make sure the concepts worked. They asked for their insights into how they should assign Trust, and what criteria they should use. We saw an interesting effect: people in the business became invested in these conversations and joined in modeling sessions. Data matching faded from the conversations, and Trust took over. There was a general excitement about being able to assign and evolve Trust. The engineers’ new model became a shared model within the business.
Trust as an Arithmetic
The copyright brokerage domain experts started throwing scenarios at the team: What if a Source A with a Trust of 0 made a claim that was corroborated by a Source B with a Trust of 5? The claim itself was now highly trusted, but what was the impact on Source A? One swallow doesn’t make Spring, so surely Source A shouldn’t be granted the same level of Trust as Source B. A repeated pattern of corroborated Trust on the other hand, should reflect in higher Trust for Source A.
During these continued explorations, people from the business and engineering listed the rules for how different events impacted Trust, and coding them. By seeing the rules in code, a new idea emerged. Trust could have its own arithmetic: a set of rules that defined how Trust was accumulated. For example, a claim with a Trust of 3, that was corroborated by a claim with a Trust of 5, would now be assigned a new Trust of 4. The larger set of arithmetics addressed various permutations of claims corroborating claims, sources corroborating sources, and patterns of corroboration over time. The Trust object encapsulated this arithmetic, and managed the properties and behaviors for it.
From an anemic Trust object, we had now arrived at a richer model of Trust that was responsible for all these operations. The team came up with polymorphic Strategy objects. These allowed them to swap out different mechanisms for assigning and evolving Trust. The old data matching code had mixed fetching and storing information with the sprawling logic. Now, the team found it easy to separate it into a layer that dealt with the plumbing separate from the clean Trust model.
The Evolution of the Model
In summary, this was the evolution:
- Ad hoc code that computes values for matches.
- Using Trust in conversations that explained how the current system worked.
- Trust as a Value Object in the code.
- Evolving Trust as a process, with events (such as finding a matching claim) that assigned new values of Trust.
- Trust as a shared term between business and engineering, that replaced the old language of technical data matching.
- Exploring how to assign Trust using more real-world scenarios.
- Building an arithmetic that controls the computation of Trust.
- Polymorphic Strategies for assigning Trust.
When you find a better, more meaningful abstraction, it becomes a catalyst: it enables other modeling constructs, allowing other ideas to form around that concept. It takes exploration, coding, conversations, trying scenarios, … There’s no golden recipe for making this happen. You need to be open to possibility, and take the time for it.
The engineers originally introduced the concept of “matching,” but that was an anemic description of the algorithm itself, not the purpose. “If this value equals that value, do this.” Data matching was devoid of meaning. That’s what Trust introduces: conceptual scaffolding for the meaning of the system. Trust is a magnet, an attractor for a way of thinking about and organizing the design.
Initially, the technical details of the problem were so complicated, and provided such interesting challenges to the engineers, that that was all they talked about with the business stakeholders. Those details got in the way of designing a useful Ubiquitous Language. The engineers had assumed that their code looked the way it needed to look. In their eyes, the code was complex because the problem of matching was complex. The code simply manifested that complexity. They didn’t see the complexity of that code as a problem in its own right. The belief that there wasn’t a better model to be found, obscured the Core Domain for both business and engineering.
The domain experts were indeed experts in the copyrights domain, and had crisp concepts for ownership, claims, intellectual property, the laws, and the industry practices. But that was not their Core Domain. The real Core was the efficient, automated business they’re trying to build out of it. That was their new domain. That explains why knowledge of copyright concepts alone wasn’t sufficient to make a great model.
Before they developed an understanding of Trust, business stakeholders could tell you detailed stories about how the system should behave in specific situations. But they had lacked the language to talk about these stories in terms of the bigger idea that governs them. They were missing crisp concepts for them.
We moved from raw code, to a model based on the new concept of Trust. But what kind of thing is this Trust concept? Trust is a metaphor.1 Actual trust is a human emotion, and partly irrational. You trust someone instinctively, and for entirely subjective reasons that might change. Machines don’t have these emotions. We have an artificial metric in our system, with algorithms to manipulate it, and we named it Trust. It’s a proxy term.
This metaphor enables a more compact conversation, as evidenced by the fact that engineers and domain experts alike can discuss Trust without losing each other in technical details. A sentence like “The claims from this source were repeatedly confirmed by other sources,” was replaced by “This source has built up trust,” and all knew what that entailed.
The metaphor allows us to handle the same degree of complexity, but we can reason about determining Trust without having to understand every detail at the point where it’s used. For those of us without Einstein brains, it’s now a lot easier to work on the code, it lowers the cognitive load.
A good metaphor in the right context, such as Trust, enables us to achieve things we couldn’t easily do before. The team reconsidered a feature that would allow them to swap out different strategies for matching claims. Originally they had dismissed the idea, because, in the old code, it would have been prohibitively expensive to build. It would have resulted in huge condition trees and sprawling dependencies on shared state. They’d have to be very careful, and it would be difficult to test that logic. With the new model, swapping out polymorphic Strategy objects is trivial. The new model allows testing low level units like the Trust object, higher level logic like the Trust-building process, and individual Claims Strategies, with each test remaining at a single level of abstraction.
Our Trust model not only organizes the details better, but it is also concise. We can go to a single point in the code and know how something is determined. A Trust object computes its own value, in a single place in the code. We don’t have to look at twenty different conditionals across the code to understand the behavior; instead we can look at a single strategy. It’s much easier to spot bugs, which in turn helps us make the code more correct.
A good model helps you reason about the behavior of a system. A good metaphor helps you reason about the desired behavior of a system.
The Trust metaphor unlocked a path to tackle complexity. We discovered it by listening closely to the language used to describe the solution, using that language in examples, and trying thought experiments. We’re not matching data anymore, we’re determining Trust and using it to resolve claims. Instead of coding the rules, we’re now encoding them. We’re better copyright brokers because of this.
Be wary of bad, ill-fitting metaphors. Imagine the team had come with Star Ratings as the metaphor. Sure, it also works as a quantification, but it’s based on popularity, and calculates the average. We could still have built all the same behavior of the Trust model, but with a lot of bizarre rules, like “Our own sources get 20 five-star ratings.” When you notice that you have to force-fit elements of your problem space into a metaphor, and there’s friction between what you want to say and what that metaphor allows you to say, you need to get rid of it. No metaphor will make a perfect fit, but a bad metaphor leads you into awkward conversations without buying you clarity.
To make things trickier, whenever you introduce a new metaphor, it can be awkward at first. In our case study, Trust didn’t instantly become a fully explored and accepted metaphor. There’s a delicate line between the early struggles of adopting a new good metaphor, and one that is simply bad. Keep trying, work on using your new metaphor, see if it buys you explanatory power, and don’t be afraid to drop it if it does not.
And sometimes, there simply isn’t any good metaphor, or even a simpler model to be found. In those cases, you just have to crunch it. There’s no simplification to be found. You just have to work out all the rules, list all the cases, and deal with the complexity as is.
To find good metaphors, put yourself in a position where you’ll notice them in conversation. Invite diverse roles into your design discussions. Have a healthy obsession with language: What does this mean? Is this the best way to say it? Be observant about this language, listen for terms that people say off the cuff. Capture any metaphors that people use. Reinforce them in conversations, but be ready to drop them if you feel you have to force-fit them. Is a metaphor bringing clarity? Does it help you express the problem better? Try scenarios and edge cases, even if they’re highly unlikely. They’ll teach you about the limits of your metaphor. Then distill the metaphor, agree on a precise meaning. Use it in your model, and then translate it to your code and tests. Metaphors are how language works, how our brains attach meaning, and we’re using that to our advantage.