Found a wrong record in your data? Here's what it actually means

"If one record is wrong, how can I trust the rest?"

We sometimes hear this from clients, and we get why. You open a freshly enriched customer file, spot something you know is wrong — your own name at an address you left five years ago, a coworker tagged with an income that's clearly outdated — and suddenly the whole file feels suspect.

But that instinct — to judge the whole file by one bad record — is exactly the wrong way to evaluate consumer data. And it leads to worse decisions, not better ones.

Spot-checking is the wrong test for data quality

First things first: spot checking for 100% accuracy is not a valid way to review data quality in a customer file.

Whether you have spotted an incorrect record in a 100k person audience for direct mail or you've identified an incorrect attribute from your customer file, the key is to go back to first principles: your ultimate goal is likely to drive an increase in conversion rates or reach more people. One bad row isn't going to spoil the bunch.No dataset at scale is 100% accurate, however, our mission is to have the best data set on the market. We rely on many upstream vendors and getting feedback on their data quality only helps us get better at our mission.

What to do when you spot a bad record

When you find one bad record, here’s what to do:

• First, consider your use for the data. If the file is 95% accurate instead of 100% accurate, can you still achieve your goal?
• Second, report it to us via faraday.ai/data-corrections. When a client flags something, the issue almost always originates with one of our upstream vendors — but because it ended up in your file, it's our job to act on it. Our team reviews every submission, escalates with the upstream vendor when needed, and applies corrections back into the pipeline. It's not a quality guarantee — it's a correction loop, and it's how the dataset stays trustworthy over time.

Our number one goal is to provide data that drives meaningful lift on your campaigns, and ultimately delivers significant ROI for your company. (Side note: we strive to be assessed by the ROI we deliver, check out some of our case studies to see just how much lift we generate).

The reality is that chasing 100% accuracy at this scale would mean slower files, longer QA cycles, higher prices, and less freshness — all in service of a number that doesn't change the campaign outcome. So the right question isn't "is every record correct?" — it's "does this file help me reach more of my best customers than a random list would?"

How to actually evaluate consumer data

If you want to know whether your file is doing its job:

• Run a holdout test. Pick a random subset of your enriched audience and a comparable subset without enrichment. Run the same campaign to both. The lift between them is your real answer.
• Look at downstream metrics. Response rate, conversion rate, CPA, ROAS. These are the only metrics that reflect whether the file is working.
• Evaluate match rates in context. Match rates depend on what identifiers you bring to the table (check out our thoughts on this). Compare against realistic benchmarks, not vanity numbers.

None of these require inspecting individual rows. All of them tell you something a spot-check cannot: whether the file is actually delivering value.

The bottom line

It’s just like life, don’t let one data point throw you off your game! Stay focused on measuring what matters: response rate, CPA, lift versus a random list. If the numbers clear your benchmarks, the file did its job. If they don't, that's the signal worth investigating — not the one weird row that caught your eye.

When you do find a record that's off, flag it. We want to know. And if your campaign results aren't where they should be, talk to us. That's the conversation we want to be having.