How Decision Matrices Create Better Engineering Decisions
Most design or decision documents include a pros and cons list for different approaches, but this rarely gives you an accurate comparison between two or more options.
If you have more than two approaches, how can you accurately compare them based on pros and cons alone? The lists often contradict each other, don’t overlap enough, or emphasize different things. It becomes too easy to focus on certain points and miss others. And because each reviewer weighs bullets differently, two people looking at the same list can come to completely different conclusions.
This is especially true across companies. Startups may prioritize velocity and simplicity, while more established companies optimize for scaling and robustness. Engineers from different backgrounds naturally emphasize different criteria.
This is where decision matrices come into play.
Instead of just a list of pros and cons, decision matrices require a predefined set of criteria that each approach is weighed against. This gives you a more concrete comparison and ensures that each reviewer keeps the same criteria in mind. Not just their own bias. If a specific criterion should matter more, you can weigh it accordingly.
My First Encounter With Decision Matrices
At a previous company, I was leading the design for a project that overlapped heavily with two or three other projects happening at the same time. Our design had downstream impacts, and the other projects depended on it. Because of this, we needed alignment on a design that worked for all systems so we could build in parallel.
After the first design iteration, it became clear that we had conflicting priorities. Each group cared about different aspects, and this led to debates about what the system should look like and who should own which pieces.
An engineering manager stepped in to help mediate and set direction. This is when I was introduced to my first decision matrix.
They took our competing approaches (and a few they suggested themselves), listed out the shared criteria, and created a matrix comparing each approach against each criterion. While it didn’t resolve everything immediately, it reframed the conversation. We could now align on:
- the criteria that mattered most
- the leading design contenders
- how each design ranked across those criteria
The actual decision wasn’t the hard part, agreeing on the criteria and the tradeoffs was. The decision matrix made that alignment possible.
How Decision Matrices Work
At a high level, decision matrices follow a simple process:
- Align on a set of criteria. (e.g., extensibility, effort, reliability)
- Define the grading or weighting scale (1–N, or low/medium/high). I prefer numbers.
- Create the table: alternatives on one axis, criteria on the other.
- Rank each alternative across each criterion, including notes on why.
- Compare alternatives directly based on how well they meet the shared criteria
You can weight criteria, but I recommend weighting at most one. Otherwise you lose clarity on priorities.
What a Decision Matrix Looks Like in Practice
Recently, I had to document my reasoning for a technical decision after I had already made it. While slightly contrived, it shows what a realistic matrix might look like.
The criteria included:
- Extensibility – how easily we can iterate and change over time
- Production Readiness – durability and reliability in real-world use
- Time to Implement – how quickly we can ship with existing infra
- User Experience – the best possible UX given our time constraints
We ranked three approaches from 1–3 for each criterion (3 being best):
| Approach | Extensibility | Production Readiness | Time to Implement | User Experience | Total Score |
|---|---|---|---|---|---|
| Approach A | 3 | 1 | 2 | 3 | 9 |
| Approach B | 1 | 3 | 1 | 1 | 6 |
| Approach C | 3 | 3 | 3 | 2 | 11 |
This made the decision much clearer not because one approach dominated in every category, but because it balanced the criteria best given our constraints.
The approach chosen:
- fit our existing architecture well, giving us production-ready background workflows
- was easy to implement using existing examples
- was extensible for future changes
- provided a decent UI (not the best, but good enough, with a path to improve)
Why Decision Matrices Work
Decision matrices work because they:
- Force explicit criteria (no hidden priorities)
- Turn subjective debate into structured reasoning
- Document why something was chosen, not just what was chosen
- Make future revisits easier (“here’s what we optimized for back then”)
That said, they aren’t a silver bullet for every decision but they shine when multiple alternatives and competing priorities exist.
Where Else Can You Apply Decision Matrices
They aren’t just for engineering. You can use them for:
- Feature prioritization (impact vs. effort is technically a decision matrix)
- Vendor or tool selection
- Build vs. buy decisions
- Career paths or role comparisons (with caution, not every personal decision fits this model)
Any time you have multiple options and can agree on shared criteria, a decision matrix helps you reason systematically with the information you have at that moment.
Conclusion
Decision matrices aren’t about “scoring” your choices. They make your reasoning transparent. Alignment comes from shared criteria and shared context.
It’s easy to get overruled by a more senior or louder voice in the room. But if you aren’t aligned on the decision, using this process can help create that shared alignment.