Making Data Quality Measurable: The Numbers That Matter

Data quality is not a feeling, it is a number

Most organisations already know their data is not perfect. Products show up twice in the system, prices are occasionally wrong, orders get stuck because a required field is empty. Yet for many leadership teams, data quality stays frustratingly abstract. Something you notice when things go wrong, not something you track every day. That is exactly the problem. What you do not measure, you cannot improve. And what you do not improve keeps costing you money.

In this article we walk through the numbers that genuinely matter when you want to take data quality seriously. No theoretical frameworks, just concrete metrics you can bring into conversations with your team, your suppliers and your systems.

The five dimensions of data quality

Data quality is not a single thing. It breaks down into several distinct dimensions, each of which needs its own measurement. The five most important ones are:

• Completeness: are required fields missing? Think of products without a description, customers without a postal code, or orders without a delivery address.
• Accuracy: does the data reflect reality? A price that does not match your ERP, or a stock level that disagrees with what is actually on the shelf.
• Consistency: do your systems tell the same story? If your PIM uses a different unit of measure than your webshop, you have a consistency problem.
• Timeliness: how current is your data? A product change that takes three days to appear in your webshop is a timing problem, plain and simple.
• Uniqueness: how many duplicates are hiding in your systems? Duplicate customer numbers or product codes are a classic headache in environments where multiple systems are connected.

Every one of these dimensions can be expressed as a percentage. That is the starting point for an honest conversation about where you actually stand.

The KPIs worth tracking

If you are responsible for operations or sit on the leadership team, these are the figures you want to see on your dashboard:

• Error rate per data flow: what share of messages or records in an integration contain an error? Anything above two percent is a signal to act.
• Time to fix: how long does it take to resolve a data error after it has been detected? The lower this number, the better your processes are set up.
• Percentage of records processed automatically: how much data moves from A to B without a human touching it? A high percentage means less manual work and fewer mistakes.
• Validation score per source system: which system is producing the most errors? This tells you where the root cause lives, not just where the damage lands.
• Duplicate rate: what percentage of your customer or product records exist more than once? Anything above one percent deserves attention.

None of these figures are difficult to calculate, but you do need a central point where all your data flows come together. Without that overview, you are measuring per system at best, and missing the full picture entirely.

What AI adds to your data quality measurement

Measuring is one thing, but the real power comes from recognising patterns before they turn into errors. That is where AI earns its place. In the integration platform we use AI to flag anomalies in data flows early, not to report after the fact on what went wrong, but to surface the warning signs while there is still time to act.

A practical example: if a supplier normally sends a product feed every morning at seven and that pattern suddenly changes, that is a signal worth investigating. Not necessarily an error, but something to look at. The same logic applies to unusual field values, unexpected volumes, or prices that fall outside a normal range. AI turns data quality from a reactive exercise into a proactive one.

How to get started without drowning in complexity

The biggest trap organisations fall into with data quality is starting too big. An elaborate framework, dozens of KPIs, a lengthy implementation project. And then nothing happens, because nobody knows where to begin.

Our advice is straightforward: pick three numbers you can measure right now and start there. Look at your error rate on your busiest data flow, measure your duplicate rate in your customer database, and check how long it typically takes to resolve a data error once it has been spotted. Those three figures already tell you a great deal about the health of your data landscape.

From there you can expand. More dimensions, more systems, more automation. But start concrete, start small, and make it visible to everyone involved.

Visibility is where improvement begins

Improving data quality does not start with technology. It starts with visibility. If you do not know what is broken, you cannot fix it. And if you cannot show what has improved, you will not get the buy-in you need for the next step.

The organisations that consistently perform well on data quality are not necessarily the ones with the most advanced systems. They are the ones that measure consistently, report honestly, and make decisions based on those numbers. That is a mindset, not a tool.

Want to know which data quality metrics are most relevant for your organisation and how to make them measurable within your current landscape? Reach out via our contact form and we will be happy to think it through with you.