Data quality is the difference between having data and being able to use it. It determines whether teams can trust their numbers, act with confidence, and build insights without constantly second-guessing the underlying information.
No organisation struggles with a lack of data. We record, log, analyse in many ways: Excel lists with assets, procurement software, an ERP system, financial reporting, a client sending over a list with a quote request.
Organisations struggle with data that is inconsistent, fragmented, and difficult to work with across teams and systems.
Data quality is not about perfect datasets or theoretical models. It’s about whether people can answer basic but critical questions without manual workarounds.
Questions like:
In practice, data quality shows up in everyday data objects such as procurement data, asset data, supplier and product information, invoices, contracts, and operational records. When these are inconsistent or poorly structured, every downstream use becomes slower, riskier, and more expensive.
Poor data quality rarely causes immediate failure. Instead, it creates friction everywhere.
Good data quality does the opposite. It removes friction, reduces risk, and gives teams confidence in their work.
Platforms like Microsoft Fabric and modern AI tools assume structured, consistent data. Without a clean foundation, these tools amplify confusion rather than insight – they make very high confidence claims without having a correct source.
With good data quality, organisations can move faster, experiment safely, and avoid vendor lock-in.
Analysts should spend their time creating insights, not fixing column names, formats, or mismatched IDs. Clean, structured data allows analysts to focus on understanding drivers, risks, and opportunities instead of data hygiene.
Imagine – procurement has a structured way that allows them to see what’s happening in spend in multiple ways. They see how the cost of the same product varies across suppliers. They see which products they are buying in which quantities and where. They match risks.
What happens in this world? Suddenly cost inefficiencies become clear. Rather than many one-offs, it’s possible to negotiate improved, structured contracts.
Whether it’s manufacturer performance or asset information, data quality makes the difference between reactive reporting and proactive control. Clean asset lists are a foundation to predictive maintenance, life cycle management and smart quotations.
New systems, dashboards, and AI tools often assume the data problem has already been solved. In reality, they inherit the same inconsistencies that existed before.
Data quality is not fixed by buying another platform. It is fixed by deliberately structuring, validating, and maintaining the data that flows through those platforms – including the implementation of (automatic) quality checks.
We start with existing data from spreadsheets, exports, and operational systems. We focus on the data that actually drives decisions, such as procurement, asset, and cost data. Automation accelerates the work, while human validation ensures accountability and trust.
The result is a clean, reusable data foundation that supports analysis, operations, and future automation without forcing organisations into rigid systems or one-off clean-ups.
Teams gain confidence in their numbers. Analysts move faster. Procurement and operational decisions become more grounded. Digital and AI initiatives become feasible instead of frustrating. Most importantly, the organisation regains control over its data instead of working around it.
This demo shows how Pearstop is able to automatically handle working with an internal classification system – saving 1.3 FTE and allowing procurement to make better buys through data-informed decisions.