The Problem With Over-Engineering

Imagine you come up with a brilliant idea for a solution that solves the problems of thousands of customers. You initiate a project, gather a team of experts, design a rich interface that provides great user experience, build the product to provide all envisioned functionality, and, finally, release it to the public.

And then… you realize hardly anyone uses it. Chances are, you’ve over-engineered your solution.

Our latest guide, The Problem With Over-Engineering, outlines everything you need to know about this potential hazard — from causes and costs, to how to avoid it. Here’s a preview of what you can expect to learn.

What is Over-Engineering?

Over-engineering is like overthinking — you’re trying to find solutions to problems that either don’t exist yet or are too early to tackle. In the end, you’ve designed and implemented your system with too much logic, too many abstractions, and an excess number of flows. But, unless absolutely imperative, this will create needless complexity, costing you significantly more than the final value delivered.

The Causes of Over-Engineering

There are many causes of over-engineering, but in the end, it all boils down to two common reasons:
We don’t give ourselves enough time to think.
We give ourselves too much time to think.

It’s easy to spot a pattern here — younger companies (and teams) tend to jump to development too fast while older, wiser ones tend to delay the process.

Not enough time means:

1. Analysis Is Cut Short

Essential heuristic data that would otherwise give a better context to the business problem and its history is often overlooked — or worse, skipped entirely. Such data is usually (if not always) available in the form of performance statistics, customer experience analytics, different use cases, and existing codebase. Overlooking such integral resources is the by-product of unrealistic team progress and questionable expectations.

2. Roadmap and Strategy Get Omitted

Without a careful analysis of the available heuristic data, it’s harder to create a sufficient product roadmap. This eventually leads to increased project ambiguity and, more importantly, a misunderstood delivery strategy. In turn, that distorts the actual shipping value.

3. Problem Boundaries Expand

The lack of clear problem definition, execution strategy, and comprehensive roadmap will undoubtedly affect the development process in a variety of negative ways (oftentimes in combinations):

• A lost perception of urgency
• A wrong sense of priority
• An assumed technology stack and architecture, instead of more suitable options

As long as a strong project management team is in place, such risks could be mitigated rather seamlessly. However, without enough knowledge of the problem and its history, the team will start expanding their approach towards it over time. Inevitably, they’ll get to a much more challenging and complex solution than what was actually sufficient all along.

To illustrate that, let’s look at a fairly common use case — the proof of concept (PoC). Imagine you’re building a PoC to test an idea. That PoC is intended to answer a number of questions about the viability of the product, including:

• What flows will be necessary?
• How well will this idea be perceived?
• Will it provide good user experience?
• Which features will be used and which will be completely ignored?
• How fast will you achieve your ROI?

You might even consider conducting usability testing with a target group to validate your direction of choice for your product or service.

Now that you’ve invested so much in proving the market potential of your original concept, you are ready to begin building your real solution. The first step is to identify the key features you want to release initially. To speed up development, engage in building around the existing prototype, reusing its codebase and structure. It seems logical, right? After all, it already has the necessary flows, interfaces, and UX implemented.

But, that’s a costly mistake. Here’s why:

The main purpose behind building a prototype is to test ideas fast so that, if they don’t turn out to be feasible, you can quickly come up with better ones at a minimum cost. What happens when you turn your prototype into a minimum viable product (MVP) is that, instead of building fast (which often means ugly code, quick hacks, abandoning programming principles like DRY, SOLID and TDD, and little to no abstraction), your development team starts building smart. If your senior programmer knows (or suspects) that whatever code they write for the prototype will later be reused in the MVP, they’ll write it in a way to avoid refactoring later on. They’ll write beautiful code, abstracted, with unit testing and all but over-engineer it in the process.

Not giving enough time to analyze the results and behavior of the prototype, plan next steps, create a roadmap for the MVP, and make sure everyone understands it the same way will inevitably shift the focus in the wrong direction. In the end, you might still release a good MVP, but at a significantly higher cost (increased budget, timeline, and scope).

What happens on the other extreme, though?

Too much time means:

1. The What-If Mentality

• What if in two years the solution needs to work on a Microsoft database, instead of MySQL?
  • Is it wise to implement object-related mapping (ORM)?
• What if this third-party tool I’m using drops their REST API someday?
  • Do I plan for simple object access protocol (SOAP) support?
• What if we need to change the selected payment provider in five years?
  • Do I opt to add an abstraction and implement a backup option?

When we spend too much time architecting and planning the perfect solution, we start capturing every edge case. We run endless different scenarios and try to foresee future user flows. We overthink. And then, inevitably, we overengineer.

2. The Just-In-Case Mentality

A direct consequence of over-analysing a problem is attempting to tie up all of its loose ends — even when some of them are irrelevant.

One example of this is adding authentication via social media in the scope, just in case the user might want to have it in the future. Another is adding more complex access control logic, just in case more roles get introduced later on.

Unless you’re building a life-saving tool that must account for every conceivable scenario no matter how improbable — the just-in-case mentality almost always leads to expanding the scope with features of little immediate value. Moreover, it adds needless complexity to the end solution.

The Costs of Over-Engineering

An over-engineered solution will hurt your business. It might not be immediate or direct, but it will inevitably have a negative impact on your product’s life cycle for multiple reasons.

Delayed Time to Market

By default, the term ‘over-engineering’ means building more than necessary. It should come as no surprise that an over-engineered solution takes longer to ship than originally planned. The sooner a product is released, the faster and better user feedback can be gathered to remain competitive on the market. This also helps us stay on track with user demands for their preferred set of features and their input in the planning cycle.

Cost of Inheritance

Similarly, an over-engineered solution is more expensive. It’s not just the cost of building additional features that expands the budget. Its broader set of characteristics also results in altering the test strategy, maintaining related features, validating parent flows, and updating documentation. Simply put, adding new features to a project’s scope increases the work for its existing features.

Technology Commitment

The choice of technology and project decisions can get clouded when the problem is approached with little, wrong, or assumed knowledge. While this is one of the less obvious costs of over-engineering, it still creates a huge impact on future enhancements. Supporting the wrong technology could become increasingly expensive, leaving developers stuck with an over-engineered product indefinitely. On the other hand, building from scratch sometimes costs even more.

To continue reading and learn how to avoid over-engineering your solution, check out our guide The Problem With Over-Engineering.