The Field Wasn’t Empty

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Intro

Some of the worst data failures don’t come from bad actors—they come from creative problem solvers. People trying to get things done—often by bypassing governance processes they see as (and sometimes are) too slow, too rigid, or simply invisible in the moment.

During global workflow rollouts, teams often make assumptions. Dangerous ones. Like assuming a field is unused because it’s blank in their region. Or that they can safely reuse a field because no one they know is touching it. These decisions don’t feel like sabotage—they feel like clever hacks until they break everything.

Lessons

Here are some of the lessons that I have witnessed either first-hand or second-hand. The details have been changed to protect my (our) egos.

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Lesson One: You Only See Your Slice

In one global rollout, a team assumed that a certain metadata field was unused. It was blank across their data, and the documentation was… let’s say aspirational. So, they repurposed it for internal tracking.

Meanwhile, another region was using that same field for its original purpose. Quietly. Reliably. Consistently.

When the global reports were aggregated, the data looked schizophrenic. One country’s values looked like metrics. Another’s looked like timestamps. No one could trust anything, and the post-mortem was ugly. The fix required re-segregating years of data—an expensive and time-consuming effort that pulled teams off other priorities, delayed reporting cycles, and created ripple effects across downstream systems.

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Lesson Two: The Automation That Erased 100,000 Comments

In another case, a regional lead assumed a comment field wasn’t used. It had no visible content in his dataset, and no rules appeared to be enforcing its population.

So he built a clever automation: every time someone touched that field, it triggered a workflow. Nice idea—except as the global rollout went live, that field was used by other teams. The automation interpreted those legitimate values as triggers… and wiped them.

Over 100,000 valid comments are gone. Many included crucial status updates, context for decisions, or explanations for exceptions—context that teams would later scramble to recover, often unsuccessfully.

The team thought it was catching up with a backlog of legacy records. It wasn’t. It was erasing institutional knowledge.

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Lesson Three: When Presence Equals Intent (Until It Doesn’t)

Rather than add a field to indicate whether a record should sync to an external system, a clever dev said: “Let’s just use the presence of a Display Name. If there’s a name, we push it.”

It worked until someone else built a tool to populate missing display names.

That tool, unaware of the coupling, triggered a flood of unintended data syncs. Not once. Not twice. But multiple times—because the original hack was undocumented, fragile, and invisible to new team members.

The idea was clever. The consequences were costly—a repeating pattern fueled by undocumented hacks, assumptions based on local visibility, and shortcuts that skip design reviews.

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The Real Risk Isn’t AI—It’s Us

We like to blame machines. But the truth is, we were breaking things long before AI showed up—we just didn’t do it at scale, or with the illusion of precision that makes those mistakes harder to detect and undo. We just didn’t do it at scale, or with the illusion of precision.

Repurposing fields isn’t clever. It’s a time bomb. And if we let AI optimize on top of a landmine field, we won’t just have bad decisions—we’ll have faster, more confident, harder-to-undo ones.

If a field feels unused, confirm it. Globally. If you’re going to trigger workflows from content changes, treat those fields as contractually governed. And never, ever treat presence as intent.

Because in data, what you don’t know is hurting you. You just haven’t aggregated the damage yet.