Let me describe a moment most data teams know too well.
It’s late afternoon. You’re about to shut your laptop when that dreaded Slack message lands:
“THE DASHBOARD IS BROKEN.”
Revenue’s suddenly at zero. Active users doubled overnight for no clear reason. Something’s off, and you’re the one who has to figure it out—fast.
You start digging: logs, event names, last week’s code changes, maybe even the source database. You’re patching leaks, but the real problem is upstream, somewhere in the shadowy maze of data production that happened long before this dashboard ever lit up.
If this feels familiar, you’re not alone.
And the problem isn’t just a bug or a missing join. It’s deeper. It’s structural.
We often design systems where data quality is treated like an afterthought—something we clean up at the end instead of planning for at the beginning. We’ve built pipelines like we’re building houses with no plumbing diagrams, only to discover too late that something critical is missing.
Now imagine it’s not just a dashboard.
It’s an AI model.
And it’s feeding live decisions to customers, operations, or regulators 24/7.
Suddenly, the cost of bad data isn’t just inconvenient. It’s risky.
That’s why the idea of “shifting left” is gaining so much traction.
It’s not just a buzzword. It’s a mindset—and a much-needed wake-up call for how we think about building data systems that actually work.
A Quick Origin Story: From DevOps to Data
So where did this idea of “shifting left” even come from?
It started in the software world. Back in the early 2000s, dev teams realized they were catching bugs way too late. Code was written, passed to QA, and only after weeks of testing would major issues surface—leading to last-minute scrambles and release delays.
The solution? Shift those checks earlier in the cycle.
Test as you build. Shorten the feedback loop. Automate it.
That approach became known as “shifting left”—because on a project timeline, you’re moving testing (and thinking) closer to the left edge: the beginning.
This shift powered the rise of DevOps, CI/CD pipelines, and ultimately faster, safer software releases.
Now, that same logic is reaching data teams.
Instead of catching issues where data ends up—in dashboards, models, or reports—what if we caught them at the start?
When the data is first created, transformed, or logged?
What if we treated data quality the way engineers treat code quality?
The answer isn’t just better data. It’s fewer surprises, faster decisions, and smarter AI.
Enter the Rule of Ten
Here’s a timeless idea from systems engineering that perfectly explains why this shift matters:
The Rule of Ten says that every time you delay fixing a problem, the cost multiplies by 10.
This principle, introduced by Barry Boehm in his foundational book Software Engineering Economics, has since been echoed in manufacturing, product design, and—now—data.
Let’s break it down.
- A missing field caught during planning? Easy fix. Maybe $10.
- The same issue found during QA? Now it’s $100.
- Found in production, affecting reports or AI decisions? Could be $10,000+ in lost trust and productivity.
Apply that to data:
Say a product team silently renames an event without telling anyone. A week later, your conversion dashboard flatlines. Marketing panics. Data engineers scramble to investigate. And leadership starts making bad decisions based on flawed reports.
That five-second rename? It cost the product team nothing.
But it triggered hours of lost work, confusion, and reputational damage.
That’s the Rule of Ten, playing out in real time.
And that’s why shifting left in data isn’t just a best practice—it’s a cost-saving, risk-reducing strategy.
The AI Factor: Why This Matters Right Now
Five years ago, if your data was off—if someone mistagged a campaign or logged the wrong event—it might’ve messed up a report. You’d fix it. Annoying, but manageable.
Today? That same bad data might be training a machine learning model.
And models don’t shrug and say, “Hmm, that doesn’t look right.”
They just learn.
They amplify whatever they’re given—right or wrong—and do it at scale.
Here’s a simple example:
Let’s say your customer service AI learns from historical ticket logs. If those logs are full of vague categories, inconsistent tags, or mislabeled priorities, guess what?
The model will learn to send high-priority issues to the wrong queue. You won’t catch it until customers are angry, churn increases, or worse—compliance flags are raised.
That’s the danger of data debt in an AI world.
Garbage in, automation out.
That’s also why companies are finally waking up to the data quality stack—not just for governance, but for survival.
The Trust Stack: A 3-Layer Mental Model
To work reliably, AI needs trust.
But trust isn’t just an output—it’s a structure, built from the ground up.
Here’s a simple way to think about it:
- Raw Data Quality: The foundation. If your data is missing, messy, or duplicated, everything above it is unstable. You can’t automate garbage.
- Context & Understanding: Clean data still needs meaning. What does “churn” actually mean in your business? What’s a “qualified lead”? Documentation, shared definitions, and clear metadata live here.
- Model Behavior: At the top sits your model. But if it behaves strangely, can you trace the inputs? Can you explain the decision? Can you reproduce it? Without good layers below, you’re guessing.
We call this the Trust Stack, and here’s what it teaches us:
If any layer is weak, the whole system suffers.
Shift-left makes sure the bottom two layers are strong—before we ever hit “run” on the model.
TL;DR — What You Should Take From Part 1
Shifting left means building better data earlier in the process—because late fixes are expensive, risky, and unreliable.
It’s not about adding more tools. It’s about changing when and how we care about quality.
Here’s what to remember:
- Every data issue you ignore early becomes a bigger one later.
- AI makes bad data more dangerous—not less.
- Trust in data isn’t a given. It’s built, layer by layer.
- And the only sustainable way forward is to stop reacting—and start designing for trust from the start.
