The Elegance Question: What Makes Some Systems Just Work?
Some systems just work. They fit naturally into how teams operate, handle growth without drama, and adapt to change without requiring heroic efforts. Others fight you at every turn – simple changes become complex projects, and seemingly routine tasks require 3 senior engineers and a prayer.
After more than twenty years in technology systems integration and consultancy, I’ve seen plenty of both. I’ve helped design systems that still run smoothly years later, and I’ve also contributed and rescued some tangles. Working on those difficult systems taught me just as much, perhaps more, than the successful ones. The difference between them isn’t always about the technology stack or the budget. It’s something more fundamental: elegance in design.
What Does Elegance Mean in Technology?
When we talk about elegant systems in technology engineering, we’re not purely judging aesthetic beauty. An elegant system is one that scores well across multiple criteria, performance, maintainability, user satisfaction, business alignment but without making painful trade-offs.
Here’s what elegance looks like in practice:
- It solves the actual problem (not every possible problem)
- It grows naturally within expected boundaries
- New features slot in without major surgery
- New team members become productive quickly
Imagine a payment processing system in a retail bank. An elegant design might separate validation, processing, and settlement into clear services. When the bank needs to add an additional payment scheme, it’s a matter of implementing established interfaces, not rewriting core logic across multiple components.
The elegant solution isn’t always the simplest – banking is genuinely complex. But it’s the one where complexity serves a clear purpose.
How Systems Lose Their Way
Starting on the Wrong Foot
Sometimes systems never achieve elegance because they’re over-architected from day one. Often the allure of cutting-edge technology or new approaches can lead to choices that create more problems than they solve.
When there’s too much distance between architecture and engineering teams, crucial details often get lost in translation. Whilst a system might be designed to achieve theoretical excellence engineers may struggle with practical implementation. I’ve seen architecture teams that catalogue patterns like event sourcing, CQRS as well as saga patterns for good measure. Sometimes it can feel like solutions looking for problems rather than the other way around.
Imagine a straightforward inventory management system built with a complex event-sourcing architecture, GraphQL subscriptions, and a Kubernetes cluster – when a traditional database-backed application would have served perfectly well. The technology might be impressive, but if specialised developers are required to add a new product field, it’s probably missed the mark. Over-ambitious technology selection often solves problems the business doesn’t have while making the real requirements harder to implement.
The Gradual Drift
Most systems start well. Clear objectives, sensible architecture, good intentions all round. The drift towards complexity happens slowly, through many small decisions, all of which made sense at the time.
Consider an e-commerce platform that begins with a straightforward model: products, orders, customers. Then comes marketplace functionality, so seller accounts are added. Then multi-currency support. Then regional tax variations. Each addition is necessary, but without careful design, you may end up with a system where calculating shipping costs requires understanding 47 different components.
When Things Start to Wobble
Scaling surprises. A retail system designed for processing thousands of daily orders suddenly faces Black Friday volumes. Instead of revisiting core assumptions, the team adds simple caching. Then cache warming. Then cache invalidation strategies. Before long, managing the solution becomes harder than the original problem.
Team transitions. When the architects and engineers who knew why certain decisions were made invariably move on, their context goes with them. New engineers, trying to make sense of things, add their own patterns. The system becomes a patchwork of different patterns and philosophies.
Governance gone wrong. Too much and people find creative workarounds. Too little and you get duplication and overlaps. Both extremes lead away from elegance.
Vendor constraints driving architecture. Sometimes systems lose elegance by contorting themselves around vendor limitations. Imagine an analytics platform that produces an event stream, but because the cloud provider charges per API call, the team implements complex batching logic, buffering, and retry mechanisms. What should be a simple publish-subscribe pattern becomes a system of timers, queues, and error handling optimised for a pricing model rather than business need. The vendor constraints end up defining your architecture more than your requirements do.
The permanent temporary fix. We’ve all been there, to meet a deadline… “Just this once, we’ll put the business logic in the user interface.” Three years later, critical rules are scattered across the frontend, backend services and database. Think of “broken windows” – once you allow one quality compromise, others quickly follow.
Warning Signs
People Problems
The hero dependency. “We need Sarah for any payment system changes.” This isn’t about Sarah being clever – it’s about a system that’s become so complex or poorly documented that the wider team has lost the confidence to work with it. When components are too intricate or convoluted, most engineers step back, leaving one or two people who’ve built up the mental model and stamina to navigate it.
Deployment dread. If releases require weekend scheduling, war rooms, and detailed rollback plans for routine updates, something’s amiss. Deployments should be boring.
User frustration. Imagine a banking app where balance checks are instant for most customers but are slow or error for others. These inconsistencies often point to architectural issues rather than technical constraints alone.
Technical Smells
Data model confusion. The data layer is often a good place to start. Imagine a system storing highly relational data – customers, orders, products with complex relationships using a NoSQL document store which then requires complex application-level joins, consistency checks and transaction logic. There’s also the reverse – using a relational database to store large semi-structured data or even JSON blobs that would be better suited to NoSQL document store approach.
Model drift can also be a problem – tables with 200+ columns perhaps with different groups of columns applied based on scenarios, also the “God Table” problem where one table handles multiple entities in one place.
Pattern soup. Consider an enterprise system using REST APIs here, message queues there, and direct database calls for “performance.” Each pattern might be perfectly valid, but together they make it difficult for engineers to reason about and select an appropriate path.
Pretend microservices. You’ll have come across them, distributed monoliths where services must be deployed together, share databases, and topple over when one has an issue. Remember you need certain prerequisites for microservices to actually help rather than hinder.
Reinventing the wheel. Custom authentication systems, home-grown message queues, hand-rolled monitoring tools. Unless these are your core business, you’re probably not improving on established solutions.
Operational Headaches
Guess fix troubleshooting. When fixing issues involves more guesswork than science, “try bouncing the API, that’ll help”, you’ve got a problem. Well executed systems tell you what’s wrong.
Surprise bills. Both over-optimisation, so tight that tiny changes need architectural rework and under-optimisation, throwing money at problems suggest architectural issues.
Finding a Better Way
Start with the Right Questions
What problem are we actually solving? Sounds obvious, but it’s amazing how often we solve imagined problems or plan for an assumed future. A financial services firm might assume they need real-time everything, when actually most users are happy with updates within a few minutes.
What can we learn from others? Focus innovation where it adds unique value and aligns to the business problem.
How will this look in three years? Technology choices that seem clever today might seem errm… less so tomorrow. A “boring” technology approach often ages better than the cutting edge. Validate that new technologies and frameworks align with the core problem, think carefully about how they might mature. Take the opportunity to use a tech spike or PoC but remember to appraise against some objective criteria before adoption.
Principles That Help
Keep boundaries clear. Whether using Domain-Driven Design or just common sense, clear component boundaries enable evolution without revolution. It seems obvious but a payment service should handle payments only, not customer profiles or account balances.
Document the why, not just the what. Architectural Decision Records and lightweight RFCs capture context and reasoning. Future team members, including yourself, will thank you.
Listen to pain. If every change is difficult, that’s the system telling you something. Regular architecture and tech debt reviews will help catch drift before it becomes a serious problem.
Have courage. Sometimes the elegant solution means admitting the approach isn’t working. It takes courage to simplify, to remove features or to admit “we overcomplicated this.”
A Quick Elegance Check
Ask yourself these questions about your system:
- Could you explain the architecture to a new starter over lunch?
- Do routine changes feel routine?
- Where do you spend most time – solving business or technical problems?
- Would you want to inherit this system?
If you’re uncomfortable with the answers don’t worry you’re not alone. The question is: what are you going to do about it?
The Ongoing Journey
Elegance in system design isn’t a destination, it’s a direction. Technology evolves, requirements change, teams transform. What matters is consciously choosing simplicity over cleverness, clarity over sophistication, and purpose over possibilities.
At Enablis, we help organisations navigate these challenges. Not because we have all the answers, but because we’ve asked these questions enough times to help others find their own path to more elegant solutions.
Consider this: What would our systems look like if we optimised for the engineers of 2030? Would they admire our cleverness, or appreciate our restraint? Sometimes the most elegant solution is the one that doesn’t try to be clever at all.
