Every company thinks it’s sitting on a goldmine of data. They’ve got customer information, sales numbers, and website analytics. But when they actually try to use it, nothing adds up. The reports contradict each other. The numbers don’t make sense. And everyone starts pointing fingers at the analytics team.

Here’s the thing, though: the analytics team isn’t the problem. The data is bad, and nobody wants to admit it.

What Data Cleansing Actually Involves

Data cleansing sounds simple enough, right? Just clean the data. Except it’s like cleaning a house where people keep tracking mud through while you’re mopping.

First, you’ve got to play detective. Hunt through thousands of records looking for stuff that doesn’t belong. Duplicate entries where someone got added twice. Missing phone numbers. Email addresses, birth data that are obviously invalid.

But then there are the subtle problems. A customer with an order history that doesn’t match their profile. Sales numbers that are technically possible but seem way off compared to everything else. Addresses that exist but are formatted in seventeen different ways.

What most people end up doing is building a bunch of rules. If a phone number doesn’t have ten digits, flag it. If an email doesn’t have an @ symbol, flag it. If a sale amount is 500% higher than average, flag it for review. It’s not perfect, but it beats manually checking every single entry.

The actual cleaning part is where you question your life choices. Delete this duplicate. Merge those two records that are obviously the same person. Standardize these addresses so the system stops thinking “Street” and “St.” are different places. Fill in missing data from other sources when you can find them. Hour after hour of this.

Why This Actually Matters for Analytics

Bad data isn’t just annoying. It actively sabotages your business decisions.

Let’s paint a picture. You’re trying to figure out why customers leave. Except 40% of your churned customers have no exit survey data. Another 30% have the reason listed as “other” because the dropdown menu didn’t have the right options. So you’re making retention strategies based on the 30% of customers who actually gave you useful information. How do you think that’s going to work out?

Or try this one: you’re analyzing which products sell best in which regions. Sounds straightforward. Except nobody updated the territory assignments when sales reps moved around. So you’ve got sales from Texas showing up in California’s numbers. California looks amazing. Texas looks terrible. You shift inventory based on this. Texas runs out of stock while California sits on excess inventory. Everyone’s confused why the strategy failed.

The scariest part is when bad data affects big decisions. We’ve all seen companies launch products nobody wanted because the survey data was entered incorrectly. Watched them kill profitable marketing campaigns because the attribution was broken. Saw them give bonuses to the wrong sales team because territories were messed up in the system.

What Actually Works for Keeping Data Clean

First thing, make it impossible to enter bad data. Literally impossible. If someone tries to submit a form without a valid email format, it won’t let them. Phone number missing digits? Computer says no. It’s saving everyone hours of cleanup later.

What else works is having someone actually own data quality. Not as a side project. Not as a “when you have time” thing. Someone whose actual job is making sure the data doesn’t turn into garbage. Because if it’s everyone’s responsibility, it’s nobody’s responsibility, and we all know how that ends.

Regular audits help too. Once a month, grab a random sample of data and really look at it. Check if those customers are real. Verify those addresses actually exist. Make sure those sales numbers add up. You’ll always find something wrong. Fix it before it spreads.

And here’s one nobody likes to hear: sometimes you need to throw data away. For example, customer’s record from 2010 with three missing fields and an email has no important information. Delete them. They’re just making your averages weird and your database slow.