To Each Its Own Meaning

Why Your One-Size-Fits-All Approach Keeps Failing

I spent about three years trying to apply the same scaling strategy to every project I worked on. It didn't matter if it was a small internal dashboard or a customer-facing product—the workflow, the timeline, the team size, the testing approach. Everything got the same template. It burned out my team and delayed half the releases by at least six weeks. The problem wasn't the template. The problem was that the template assumed all work was interchangeable. To Each Its Own Meaning is one of those phrases that sounds like something your grandmother would say at a dinner table, but it's actually a rigorous framework for making decisions when you have multiple competing priorities and limited resources. It means exactly what it says: treat each item according to its own specific characteristics rather than forcing it into a category it doesn't fit.

The Core Principle of To Each Its Own Meaning

The phrase comes from an older observation about classification and valuation. In practice, it shows up in fields ranging from software architecture to inventory management to legal interpretation. The common thread is simple: two things that look similar on the surface often have fundamentally different requirements underneath. If you handle them the same way, you introduce waste, risk, or failure. Here's how I actually apply it in my day-to-day work. I start by identifying the attribute that actually matters for a given decision. Most people default to the most visible attribute—revenue, size, timeline, team count. That's usually wrong. The attribute that matters is the one that determines failure mode. What happens if you get this wrong? Who gets hurt? What's the cost of correction? Take a recent example. We had two services that looked identical on paper. Both were Node.js backends, both hit roughly the same traffic volume, both had similar database query patterns. A template-driven approach would have put them on the same infrastructure, same alerting thresholds, same deployment pipeline. They weren't the same service. One handled payment processing with a three-second timeout requirement and a hard compliance audit trail. The other was an internal logging aggregator with no user-facing consequences if it went down for an hour. I split them. Completely different infra, different SLAs, different on-call rotations. The payment service cost more to run but the risk was contained. The logging service ran cheaply on a single instance because downtime was acceptable.

If I had applied the same template to both, we'd either be overspending on the logging service by a factor of four or exposing the payment service to unacceptable risk.

How to Apply This When You're Under Pressure

The lazy version of this principle is just saying "it depends." That's not useful. Here's the actual process I go through when I need to decide whether two things should be treated the same or differently. First, list every distinguishing factor between the items you're comparing. Not the obvious ones. The operational ones. Data sensitivity, regulatory constraints, dependency depth, blast radius, recovery time objective, cost structure, failure frequency, user impact distribution. Write them down. Don't think them. Thinking lies to you under time pressure. Second, assign a weight to each factor based on consequence severity, not likelihood. A rare catastrophic failure is worth more attention than a frequent minor inconvenience. I use a simple scale: catastrophic (business-ending or regulatory penalty), severe (significant revenue loss or customer churn), moderate (annoyance or short-term cost), negligible (no real impact).

Third, make the call. If the weighted factors diverge significantly between the two items, treat them differently. If they converge, treating them the same is fine. Most people skip the weighting step and go straight to a gut feeling, which is just unexamined bias at that point. This usually takes about twenty minutes for a pair of items. Doing it properly saves somewhere between six hours and three days of rework later, depending on how wrong the initial decision was.

When To Each Its Own Meaning Actually Breaks Down

I want to be clear about where this approach fails because nobody talks about that. It doesn't work when you lack sufficient information to identify the distinguishing factors. If you're making a decision about a vendor, a technology choice, or a partner without understanding their actual operational constraints, you're just guessing with extra steps. The framework amplifies uncertainty when data is thin, and it gives you a false sense of confidence. It also breaks down in organizational settings where consistency is valued over optimization. Large companies often deliberately apply uniform policies even when they're suboptimal because the alternative—having every team make their own call—creates fragmentation, compliance gaps, and support nightmares. If you're in a place like that, this principle will put you at odds with leadership. That's not a flaw in the principle. It's a reflection of the tradeoff between control and efficiency that every organization has to make. There's a third edge case that caught me recently. I was working on a migration where two datasets looked identical in schema but had completely different data quality profiles. One had clean, validated entries. The other was mostly user-generated content with inconsistent formatting and missing fields. I tried to apply the same migration script to both, assuming the schema was the truth. It broke on the second dataset in production because the schema didn't account for the actual data variance. The workaround was straightforward once I saw it: run a profiling pass on each dataset before any migration, check the actual value distributions, and only then decide on the approach. That profiling step added about four hours to the project but prevented what would have been a two-day rollback.

A Few Things Beginners Get Wrong

The most common mistake I see is treating this principle as an excuse to over-differentiate. Just because two things aren't identical doesn't mean you need a completely separate process for each. Differentiation has a cost. Documentation, training, maintenance, support—each unique path you create adds operational overhead. The question isn't "are they different?" It's "is the difference large enough to justify the cost of treating them differently?" Usually the answer is no for superficial differences and yes for consequential ones. Another mistake is assuming that to each its own meaning means you can never use a standard. Standards are valuable. The principle just says that standards should be applied deliberately, not automatically. A standard is a shortcut for cases where differentiation isn't warranted. Recognizing when a standard applies is part of the same skill. The counter-intuitive part that most people miss is that this principle often leads to more standardization, not less. When you actually examine your items carefully, you tend to find that most of them share the same relevant characteristics and can safely use the same approach. The few that are genuinely different get the specialized treatment. The net result is usually a cleaner, simpler system than the one you'd build by starting with a single template and tweaking it repeatedly.

There's also a measurement trap. People who adopt this approach sometimes stop tracking aggregate metrics because they're focused on individual cases. You still need to monitor the overall picture. If you find yourself spending 80 percent of your time making individualized decisions, you probably aren't identifying the right distinguishing factors or you're over-differentiating. Revisit your weighting system.

What to Do When You Don't Have Time for This

Not every decision gets a twenty-minute analysis. Sometimes you need to ship. In those cases, use a simplified heuristic: default to the standard approach unless you can name a specific, concrete reason why this instance is different. "It feels different" doesn't count. "It handles a transaction type we haven't seen before" counts. "The data came from a source we know has quality issues" counts. Being able to articulate the reason forces you to actually think about it rather than just having a vague unease. This heuristic also creates a useful record. If someone pushes back on your decision, you can point to the specific reason. If the reason turns out to be wrong, you can correct it. If you just had a feeling, you have nothing to defend. The principle itself isn't complicated. The hard part is the discipline to actually examine each case instead of reaching for the nearest template. Most people don't lack the framework. They lack the willingness to slow down long enough to apply it. That's the real bottleneck, and there's no shortcut around it other than making it a non-negotiable step in your process.