Data Quality
Data & TrackingAlso: Data Integrity · Data Hygiene
Quick definition
Data quality is how accurate, complete, consistent and up to date your marketing data actually is. It covers everything from correctly formatted email addresses to duplicate customer records to conversion events firing the right number of times. Poor data quality quietly corrupts every report built on top of it.
How it varies across Australia
Data quality problems compound rather than announce themselves. Across the Australian businesses we review, the pattern is consistent. Customer relationship management (CRM) systems accumulate duplicate contacts, analytics tools double count events, and nobody notices until a board report gets challenged. The gap between businesses with clean data and messy data shows up less in the numbers themselves and more in how confidently a team can act on them.
See data and tracking scores across Australian industries →What good data quality actually covers
The data reflects what actually happened, not an inflated or duplicated version of it.
Required fields are filled in, not left blank or defaulted.
The same customer or event is recorded the same way across every system.
Records reflect the current state, not a stale snapshot from months ago.
What it actually means
Data quality is the plumbing nobody thinks about until it leaks. Every dashboard, every attribution model, every conversion rate report sits on top of raw data. If that data is duplicated, mislabelled, incomplete or stale, everything built on it inherits the problem.
The common failure modes are boring and unglamorous. Duplicate contacts in a customer relationship management (CRM) system inflate list sizes and skew retention rate calculations. A tracking pixel that fires twice doubles your reported conversion rate. A form field that allows free text instead of a dropdown means your segmentation is a mess of typos.
The reason data quality rarely gets fixed proactively is that no single broken record looks like an emergency. It's death by a thousand small errors. A churn number that's off by a few percentage points doesn't trigger alarm bells, it just quietly steers decisions in the wrong direction for months.
Good data quality isn't a project you finish. It's closer to a maintenance habit, similar to how you'd treat physical inventory. Something you check on a schedule, not something you fix once and forget.
Bad data doesn't announce itself. It just quietly makes every decision built on top of it slightly wrong.
How it shows up
Data quality problems show up as numbers that don't reconcile. Your CRM says one customer count, your email platform says another. Your conversion rate looks suspiciously high compared to industry norms because a tag is firing on page load instead of on actual submission. A cohort analysis for lifetime value produces wildly inconsistent results month to month because customer records are being merged and split inconsistently.
It also shows up in trust. Once a team catches one obviously wrong number in a report, they start second-guessing every other number in it, even the correct ones.
The Australian context
Australia's Privacy Act amendments and the Spam Act enforced by the Australian Communications and Media Authority (ACMA) make data quality a compliance issue, not just an operational one. A CRM full of stale consent records or duplicated contacts makes it harder to prove you're only emailing people who actually opted in. Getting data quality right isn't just about better reporting, it reduces genuine regulatory exposure.
Where people get this wrong
Related terms
Common questions
What is data quality in marketing?
Data quality is how accurate, complete, consistent and current your marketing data is across systems like your CRM, analytics tools and ad platforms. It determines whether the reports and decisions built on that data can actually be trusted.
How do I know if I have a data quality problem?
Numbers that don't reconcile between systems are the clearest sign. If your CRM, email platform and analytics tool report different customer counts for the same period, or your conversion rate looks implausibly high or low, start there.
How often should data quality be checked?
Treat it as ongoing maintenance rather than a one-off project. A monthly check on duplicate records, tracking events and consent status catches problems early, before they compound into a reporting crisis.
Who is responsible for data quality?
It's usually shared. Marketing owns the workflows that create most bad data, such as forms and campaign tagging. Data or engineering teams own the systems that store and merge it. Neither can fix it alone.
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About New Rebellion
New Rebellion is a marketing intelligence consultancy. We build tools, score Australian businesses on how their marketing actually performs, and publish Debrief every day. This dictionary is part of how we work in the open.
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