Data observability: catching bad data before it reaches decisions

August 28, 2026 · 5 min · Free, with full transcript

Bad data doesn't announce itself. This playbook shows CDOs how to build detection mechanisms that intercept data quality failures before they corrupt reports, models, and the decisions that follow.

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Welcome to the Leaders Insights Podcast.

Today's episode, Data Observability, catching bad data before it reaches decisions.

Every CDO I talk to swears their dashboards are clean.

Then a number blows up in a board meeting.

So which is it?

Are they lying or do they just not know?

They don't know.

And that's the whole point.

Bad data is polite.

It doesn't crash anything.

Your pipeline runs green, the report loads, the chart renders.

It's just quietly wrong and nobody notices until finance asks why revenue jumped 40% overnight.

So the durable lesson here isn't buy a data observability tool.

What is it?

The lesson is, assume your data is broken until something proves it isn't.

Observability, instrumenting your pipelines so they tell you when they misbehave.

The way engineers monitor servers is just the mechanism.

The mindset is treating data quality as a runtime problem, not a cleanup project you

do once a quarter.

That sounds like a rebrand of the data quality tools people already bought and ignored.

Fair hit.

The old tools check data at rest, côse gant.

Someone wrote rules, ran a batch job, filed a report nobody read.

The shift is checking data in motion continuously and alerting a human before the bad number

reaches a decision.

Monte Carlo, the vendor that basically coined data observability, puts the average time to

detect a data incident at four days without it.

Four days.

Your quarterly forecast is already out the door.

Give me a real one.

Where did silent bad data actually cost someone?

Unity, the game engine company.

Back in 2022, they told investors a bad data ingestion problem in their ad targeting model

cost them roughly $110 million in a single year and knocked a chunk off the stock.

The model wasn't broken.

It was fed garbage from a large customer and kept optimizing confidently against it.

Nobody was watching the input.

So the model did exactly what it was told, with poison.

Precisely.

And this is why I'm allergic to teams that obsess over model accuracy and ignore data

freshness, whether the data actually arrived when it should.

A 99% accurate model on stale data is a very precise liar.

Okay, I'm a CDO with a modest budget and 40 pipelines.

Where do I actually start?

Not with all 40.

Start with the five that feed decisions someone would get fired over.

Board reporting, regulatory filing, the churn model, the share of customers who walk, anything

where a wrong number has a name attached to the consequence.

Instrument those first.

And instrument how?

Be concrete.

Board checks boring on purpose.

Freshness.

Did the data land on time?

Volume.

Did roughly the expected number of rows show up?

Not 10 million yesterday and 8,000 today.

Schema.

Did someone rename a column and silently break everything downstream?

And distribution?

Does the shape of the values look like it did last week?

That's 80% of real incidents right there.

Distribution checks sound like where you drown in false alarms.

Three marketers urge an alert nobody reads.

You're right, and that's how observability dies.

Alert fatigue kills more of these programs than bad tech.

The fix is ownership.

Every alert routes to one named person who can actually fix it.

Not a channel with 300 people muting it.

If an alert has no owner, delete the check.

A test nobody acts on is just anxiety with a dashboard.

What's the number that tells a CDO this is working versus theater?

Time to detection and time to resolution.

How fast you catch it.

How fast you fix it.

If you can't say both in minutes and hours, you don't have observability.

You have hope.

Good teams catch it before the business does.

That's the entire game.

You find out before your CEO does.

And if they can't afford the fancy platforms?

Then write the freshness and volume checks yourself in whatever you already run.

DPT, the transformation tool half the industry uses.

Ships basic tests free.

This is not a licensing problem.

It's a nobody decided to own it problem.

One thing they can do Monday morning?

Pick your single most important report.

Trace it back to the source table.

Add one freshness check and one volume check on that table.

Routed to one person.

If it breaks this week, you just caught something that would have reached the board.

Start there.

That's your five.

The full CDO track, structured no fluff, is at MBA-training.com.

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