Bad Data Quality Is Costing Your Business More Than You Realise

Nobody sits down and decides to fill their business systems with bad data. It accumulates. One record at a time, over months and years, until one day you pull a customer list and find three entries for the same company — one spelled correctly, one abbreviated, one with an old address. Your CRM says you have 2,400 contacts. Realistically, you have about 1,800 unique ones and 600 duplicates with conflicting information.

This isn’t a tidy-up job for a slow Friday. Bad data quality is actively costing you money, time, and the ability to make sound decisions. Here’s how to quantify it, find it, and fix it.

The Four Types of Bad Data

Data quality problems generally fall into four categories. Most businesses have all four happening simultaneously.

1. Duplicate Records

The same customer, supplier, or job entered more than once. Sometimes the duplicates are obvious — “Smith Electrical” and “Smith Electrical Pty Ltd.” Sometimes they’re subtle — same company, different contact person, entered separately by two different staff members.

Duplicates cause downstream chaos. You send the same quote twice. You undercount your actual customer base. Your revenue-per-customer metric is artificially low because revenue is spread across two records. Your marketing emails go out twice to the same person, which looks unprofessional at best and gets you flagged as spam at worst.

2. Inconsistent Formats

Phone numbers stored as “0413346978”, “(08) 6444 6308”, “08-6444-6308”, and “+61 8 6444 6308” — all valid, all different, all impossible to search consistently. Dates entered as “22/02/2026”, “Feb 22, 2026”, and “2026-02-22”. State fields containing “WA”, “Western Australia”, “W.A.”, and “West Aus.”

Inconsistent formatting makes it impossible to filter, sort, search, or report accurately. When you run a report on all WA customers and only search for “WA”, you miss everyone entered as “Western Australia.” The data is there. You just can’t find it.

3. Missing Fields

A customer record with no email address. A job record with no margin data. A supplier entry with no payment terms. The record exists, but it’s incomplete — and the missing information is often the piece you need most.

Missing fields are usually a symptom of a process problem. The person entering the data either didn’t have the information at the time, didn’t think it was important, or the system didn’t require it. The gap goes unnoticed until someone pulls a report and finds that 30% of the records are missing the field they’re trying to analyse.

4. Stale Data

Information that was correct when it was entered but hasn’t been updated since. A customer who moved premises two years ago. A supplier whose pricing changed last quarter. A contact person who left the company six months ago.

Stale data is the hardest to detect because it doesn’t look wrong. The record is complete, consistently formatted, and not duplicated. It’s just outdated. You only discover it when a quote goes to the wrong address, a phone call reaches someone who no longer works there, or your costing is based on supplier rates from eighteen months ago.

Quantifying the Cost

Bad data costs are real but rarely appear as a line item. They hide inside other problems.

Wasted labour. Your team spends time searching for information that should be findable in seconds. They make phone calls to verify details that should be in the system. They manually deduplicate records before running reports. Research from IBM estimated that knowledge workers spend roughly 50% of their time dealing with mundane data quality issues — finding, correcting, and verifying information that should be reliable.

Bad decisions. Your dashboard says average job margin is 32%. But the margin field is missing on 25% of your jobs — and those happen to be the lower-margin ones where nobody bothered to complete the costing. Your actual average margin is closer to 26%. You’ve been pricing based on a number that was never real.

Lost revenue. A lead comes in and your sales team can’t find whether you’ve quoted this customer before. They treat them as new. Meanwhile, the customer is frustrated because they spoke to someone six months ago and expected continuity. Or worse — you have a duplicate record showing no recent activity, so the customer falls off your follow-up list entirely.

Compliance risk. If you’re in an industry with record-keeping obligations — construction, healthcare, financial services — incomplete or inaccurate records aren’t just inefficient. They’re a liability. An audit that reveals inconsistent job records or missing safety documentation creates problems that extend well beyond inconvenience.

Data Quality Issue	Business Impact	Typical Cost
Duplicate customer records	Double-handling, inaccurate reporting, wasted marketing spend	5-10% of marketing budget wasted
Inconsistent formatting	Failed searches, incomplete reports, unreliable filtering	2-5 hours/week in workarounds
Missing fields	Incomplete analysis, decisions based on partial data	Unquantifiable — you don’t know what you’re missing
Stale records	Wrong addresses, outdated pricing, dead contacts	Variable — from embarrassment to material financial loss

Finding the Problems

Before you can fix bad data, you need to know where it is. Here are practical ways to audit your current data quality.

Run a duplicate check. Export your customer or contact list to a spreadsheet. Sort by company name, then by email, then by phone number. You’ll find duplicates within minutes. Pay attention to near-matches — “JB Hi-Fi” and “JB HiFi” and “JB Hi Fi” are the same company entered three different ways.

Check field completion rates. For your most important data — customers, jobs, invoices — look at what percentage of records have every key field filled in. If your job records have a “margin” field but only 60% of jobs have it populated, your margin reports are based on a biased sample.

Sample for staleness. Pick 20 random customer records and verify the contact details are current. If more than a quarter are outdated, your dataset has a staleness problem that’s probably worse than the sample suggests.

Cross-reference between systems. If the same data exists in multiple systems (CRM and accounting, for example), compare them. Do the customer counts match? Do the revenue figures align? Discrepancies point to data quality issues in one or both systems.

Fixing It: Prevention Over Cleanup

Cleaning up existing bad data is necessary, but it’s a one-time fix for an ongoing problem. The real solution is preventing bad data from entering your systems in the first place.

Validation at Point of Entry

The single most effective data quality measure: don’t let bad data in. Required fields that actually enforce completion. Format validation that standardises phone numbers, postcodes, and email addresses as they’re entered. Dropdown menus instead of free-text fields for anything with a known set of values (states, job types, service categories).

This doesn’t require sophisticated technology. Even a well-structured form with basic validation rules catches the majority of data quality issues before they become problems.

Automated Deduplication

Rather than manually hunting for duplicates, set up rules that flag potential matches when a new record is created. “A customer with this ABN already exists — did you mean Smith Electrical?” This catches duplicates at the point of creation, when the person entering the data has the context to make the right decision.

Standardisation Rules

Automatically format data as it enters the system. Phone numbers always stored in the same format. Addresses standardised against a postal database. Company names matched against existing records. These rules run silently in the background and eliminate the inconsistency problem entirely.

Scheduled Data Reviews

Set a recurring process — quarterly is usually enough — to review data completeness and accuracy. Not a full audit, but a targeted check of key fields on key record types. Catch staleness and drift before it compounds.

The Compounding Effect

Bad data quality gets worse over time, not better. Every day that passes without fixing it means more duplicates, more inconsistencies, more missing fields, and more stale records. The longer you wait, the bigger and more expensive the cleanup becomes.

But the reverse is also true. Once you implement prevention measures — validation, deduplication, standardisation — data quality improves automatically with every new record. The cleanup effort is finite. The prevention effort is ongoing but nearly invisible once it’s built into your systems.

Start with the data that drives your most important decisions. Clean it. Then make sure it stays clean. Everything else — better reports, more accurate dashboards, smarter decision-making — follows from there.