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What is Data Quality?
The degree to which your data is accurate, complete, consistent, and timely – in short, whether you can actually trust the numbers your team relies on.
What it is
Data quality is exactly what it sounds like – can you trust your data? It covers accuracy (are the numbers right?), completeness (is anything missing?), consistency (do the same things have the same names everywhere?), and timeliness (are you looking at last week's numbers or this morning's?). Think of it like the quality of ingredients in a kitchen. You can have the best chef and the best equipment, but if the ingredients are off, the meal is off. Your dashboards, reports, and AI models are only as good as the data feeding them.
Why it matters for your business
Gartner estimates that poor data quality costs organisations an average of $12.9 million per year. For mid-market companies, the impact is usually more subtle – it is the CFO who does not trust the monthly report and rebuilds it manually, the sales team working off a CRM full of duplicates, or the marketing team targeting people who already churned. One Australian logistics company we worked with discovered that 23% of their customer records were duplicates with conflicting addresses. They were sending invoices to the wrong locations and losing $400,000 annually in collections delays.
How we approach it
We profile your data first – running automated checks across completeness, uniqueness, format consistency, and referential integrity. Then we build quality rules directly into your data pipelines so problems are caught at ingestion, not discovered in a board meeting three months later. We set up monitoring dashboards that flag issues in real time, so your team can fix problems while they are small.
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Key Takeaways
- •Poor data quality costs organisations an average of $12.9 million per year according to Gartner – and the real cost is decisions made on wrong numbers.
- •Quality problems compound: one bad record in your CRM becomes wrong invoices, wrong targeting, and wrong forecasts.
- •The fix is not a one-time cleanup – it is quality rules built into your pipelines that catch issues at the source.
- •Start by profiling what you have. Most companies are surprised by what they find.