Agile is FrAgile. Waterfall is WaterFail. When projects go sideways and some inevitably do, senior leadership’s first question often becomes: “Are our engineering teams productive?” Cue the cycle of internal finger-pointing, dubious charts in slide decks, and expensive consultants armed with generalizations.
The Elusive Metric of Engineering Productivity
Objectively quantifying software engineering productivity has always been a challenge. Unlike a manufacturing line that produces identical outputs on a schedule, software engineering is inherently variable and creative. Measuring raw code output is meaningless. Ticket completion? Also problematic. Engineering work is not the same as IT support or customer service.
This is why DevOps-centric metrics, like deployment frequency, lead time for changes, and incident response (think Google’s DORA metrics or Microsoft’s SPACE framework) have emerged as more relevant tools. They abstract the problem in a way that reflects operational maturity rather than just output.
But here’s the catch: these metrics still don’t directly measure business value. And that’s the metric the C-suite actually cares about.
What the Business Really Wants
Business leaders aren’t interested in how many times you deploy. They want to know: Did this project deliver value? Did it move the needle?
To meet that need, many executives default to measuring engineering productivity by project success and completion. It’s practical, even if it’s not fully accurate. But this approach blurs the line between engineering productivity and broader project efficiency which includes marketing, design, compliance, and other external dependencies.
Still, it’s the metric that gets used. So where does that leave engineering teams?
The Red Herring of Productivity
Let’s be honest: “engineering productivity” only enters the conversation when a major project goes off the rails. And when that happens, engineering is often blamed: for not coding fast enough, for misinterpreting vague requirements, or for missing impossible deadlines.
The real issue? We’re conflating outcome with output.
Yes, outcome matters. But engineering output is just one component. When other departments operate with different expectations or frameworks, collaboration breaks down. Misalignment, not incompetence, is usually the culprit.
Agile vs Waterfall Isn’t Just an Engineering Debate
There’s a long history here. Waterfall dominated for decades: define everything upfront, then build. Agile flipped that model: build fast, fail fast, adapt.
The problem? Non-engineering teams often don’t understand these frameworks, let alone adopt them. Imagine marketing launching a feature-heavy campaign with a hard deadline, driven by “use-it-or-lose-it” budgeting model, while engineering is trying to iterate and learn. It’s a clash of philosophies that tend to lead to disastrous outcomes.
In contrast, for projects with predictable outcomes (say, a platform migration), Waterfall might be exactly what’s needed. But it must be agreed upon in advance.
The Real Answer Is Alignment
Forget trying to create the perfect engineering productivity metric for all contexts. Internally, DevOps metrics like DORA and SPACE are valuable. But externally, what matters is engineering’s ability to successfully deliver outcomes that support business goals.
Here’s the takeaway:
- Agile offers flexibility, experimentation, and continuous learning. But it accepts uncertainty and does not guarantee outcomes.
- Waterfall promises predictability, but risks delivering something obsolete with the time it takes or if the business context changes by then.
Neither is right or wrong, what matters is alignment. Both engineering and business stakeholders must understand and agree on the project’s execution model from the outset.
When the methodology aligns with the project’s nature and constraints, that’s when productivity and success truly follow.
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