Using fewer parts
The best-performing firms make a narrow range of products very well. The best firms’ products also use up to 50 percent fewer parts than those made by their less successful rivals. Fewer parts means a faster, simpler (and usually cheaper) manufacturing process. Fewer parts means less to go wrong; quality comes built in. And although the best companies need fewer workers to look after quality control, they also have fewer defects and generate less waste.
— Yvon Chouinard, Let my people go surfing
Chouinard’s observation applies to software products almost verbatim. Using fewer parts makes for better software: Easier to maintain, easier to extend, better margins. But what does “fewer parts” mean? And how do you know which ones to remove?
Fewer parts means making parts reusable. A good design minimizes number of components at constant functionality. That means avoiding duplication and making things reusable. If you can reimplement a system with a smaller number of components (functions, classes, services, etc.), that’s a sign that the original solution was either over- or under-engineered. Over-engineered because it introduced abstractions that weren’t necessary; under-engineered because it failed to identify reusable parts. It can be tempting to make fewer but larger components but those almost always end up being less re-usable. You might have fewer functions in such a design but you don’t have fewer parts.
Fewer parts means fewer representations of the data. All else equal, the amount of logic required to support n representations of the same data scales like n². It’s not uncommon for teams to maintain protobuf models, SQL schemas, Open API specs, GraphQL schemas, etc. all to support a single product. They might have a source of truth that defines the “core” data models (e.g. in protobuf), but still end up spending a ton of bandwidth on maintaining model converters and crafting migrations. Most people intuitively prefer to have fewer data representations, but the challenge is that different applications typically need different views or different derived properties of the data. That can lead to a proliferation of derived models which may not have strict one-to-one relationships with the original models.
Fewer parts means fewer languages and fewer tools. There is almost never a good enough reason to add another language to your stack. The increase in complexity and maintenance burden is consistently underestimated vs. the benefits. The same goes for databases. Performance reasons are often not strong enough to justify adding a new type of DB to cater to your latest special use case.
Fewer parts means smaller teams. Smaller teams spend less time coordinating and more time building and owning things. In most start-ups, a small number of engineers (3-4) build the first iteration of the product, which ends up generating 80% of the lifetime value of the product. It’s clearly possible to build complex things with a small, focused team. But as more money is raised, engineering teams balloon because they lose focus and add components that are not directly aligned with creating customer value. It’s Parkinson’s law at work. Companies perceive things to be mission-critical for the product, then craft a budget based on that, which must then be used once allocated, so more people are hired who then produce yet more parts, and so on.
Fewer parts means fewer counterparties. Most things break at the boundaries (especially if they’re external). The greater the surface area, the riskier and the harder to maintain a system becomes. Prefer to deal with a small number of high-quality vendors, and be prepared to pay a premium. The obvious interjection here is concentration risk: If a key vendor goes into administration or decides to drop the product you rely on, that might pose an existential risk to you. Such counterparty risk can indeed matter greatly and needs to be considered, but I’ve found in practice it’s often more manageable than people think. There are SLAs and contractual notice periods, and the majority of counterparties will honor them, giving you time to adjust. If you do need to replace a vendor, you start out with a much clearer picture of the requirements and the scope of the integration, which cuts down on time-to-market.
If using fewer parts is a good idea, how come modern software production appears to be so bloated? Dozens of vendors, a stack that’s 7 layers deep and includes 4 languages, teams of 60+ developers, etc. feel like the norm. Clearly, companies believe they need this many parts to deliver value to customers. Few people are deliberately trying to waste resources after all. But the problem is that people lose sight of what activities actually create value. As a company grows, a disconnect starts to develop between the activities performed by its employees and the value that is delivered to customers. In a 10 person firm, everyone speaks to customers, everyone knows the value chain and everyone uses the product. In a 1000 person firm, by definition most employees have never spoken to customers and may work on parts of the system that are increasingly far removed from what the customer sees. This is one instance where great management can make a huge difference. In well-managed firms, management goes to great lengths to communicate the link between firm activities and value creation. The focus is on customers and the problems they face, rather than process and efficiency gains. If you focus on serving your customers better, efficiency will take care of itself.
A few principles I follow to keep the number of parts small:
Hire fewer but better people and pay them more.
Work with fewer but better vendors and be willing to pay a premium. Be systematic about selecting them and understand the risks.
Each project you decide to allocate resources to must have a 3-4 sentence description of how it creates value for customers. People often struggle with this if the work is abstract or far removed from what the customer sees (say, work on infrastructure) but I’ve found it’s always possible if the work is worth pursuing.
