In a world built on data, it is not enough to simply have it – it needs to be accurate, complete, and fit for purpose. Among all the traits that define high-quality data, accuracy remains the most visible, most critical, and most costly when ignored.
Yet many organisations conflate accuracy with overall quality. They assume that if a data point looks correct, the dataset must be reliable. The reality is more complex – and more strategically important.
Defining the Distinction
Data accuracy refers to whether data reflects the truth – the correct name, the actual transaction, the verified measurement. It is about precision.
Data quality, on the other hand, is broader. It includes completeness, consistency, timeliness, and other traits that determine whether data is useful and trustworthy.
Think of accuracy as the integrity of each brick. Data quality is the strength of the entire wall.
Both matter. But today, we focus on accuracy – because when it fails, trust fails with it.
Why Data Accuracy Is a Strategic Issue
Inaccurate data is not just inconvenient – it is expensive, risky, and reputation-damaging. Across industries, businesses lose millions in missed decisions, regulatory penalties, and preventable errors.
Accuracy problems manifest in subtle ways:
- Incorrect customer addresses that block deliveries
- Mismatched records that cause delays in financial reporting
- Faulty clinical data that undermines patient care
- Supply chain errors that throw off inventory forecasts
Each issue might seem minor. But across a system, they accumulate into real business drag – friction, cost, risk.
What Happens When Accuracy Fails
Finance:A Breakdown of Trust
In financial institutions, data integrity is non-negotiable. Inaccurate reporting can lead to regulatory fines, investor distrust, and internal delays. Trade discrepancies and exception-handling costs escalate quickly when the underlying data is unreliable.
Firms that invest in clear governance and rigorous data stewardship report fewer errors – and faster operations.
Healthcare:Precision When It Matters Most
Healthcare providers rely on accurate data not just for reporting, but for life-critical decisions. Yet the complexity of medical data makes accuracy hard to maintain. Stakeholders often trust financial metrics more than clinical ones – not because of relevance, but because of structure.
Accurate patient records, diagnostic codes, and treatment histories are essential – not just for compliance, but for care.
Supply Chain: One Error, Multiple Disruptions
When systems lack accurate stock, demand, or shipment data, the entire chain suffers. In disconnected or partner-managed systems, accuracy degrades quickly. The result? Overordering, stockouts, inefficiencies – and hours lost reconciling avoidable discrepancies.
Insurance: Risk Without Visibility
In insurance, accuracy drives everything from risk pricing to claims handling. Inaccurate input leads to incorrect premiums, delayed payouts, and rising fraud exposure. Beyond loss, poor accuracy undermines regulatory compliance – and that threatens solvency.
The Business Case for Accuracy
Organisations that prioritise accuracy consistently report:
- Reduced operational cost
- Faster time to insight
- Better audit and compliance posture
- Higher customer satisfaction
They also unlock greater confidence in their data – confidence that allows them to move faster, partner more boldly, and take informed risks.
Accuracy Without Quality Still Falls Short
Accurate data points can still mislead if the dataset is incomplete, inconsistent, or out of date. A correct number in the wrong context still drives the wrong decision.
This is why accuracy is necessary – but not sufficient.
In the coming blogs, we will explore the other traits that define data quality. For now, accuracy stands as the cornerstone – the first signal that data is trustworthy, and the first indicator when something is wrong.
From Awareness to Action
Improving data accuracy starts with visibility:
- Where are the most business-critical errors occurring?
- What decisions rely on data that may be flawed?
- Who owns the accuracy of your key datasets?
- What assumptions are left unchecked?
Most inaccuracy is not malicious – it is cultural, procedural, and inherited. But it is also correctable.
At VisioValor, we work with organisations to improve the value and performance of their data assets – including identifying where accuracy breaks down, and how it can be improved.
Inaccuracy may seem like a local issue. But left unresolved, it creates global drag. Let us help you shift from correction to confidence.
Your data might be accurate in places. But if it is not trusted, it is not working.
Article by: Dr Sophia Fourie