Data quality

What it is

Data quality measures how well a dataset serves the purpose it is used for. It is not an abstract ideal but a judgment relative to a specific use: data that is good enough for a marketing segmentation may be unfit for financial reporting. Quality is usually broken down into six widely used dimensions:

• Accuracy: values correctly describe the real world (a customer's address is their actual address).
• Completeness: required fields and records are present (no missing revenue lines).
• Consistency: values agree across systems (the same customer has the same status in CRM and billing).
• Timeliness: data is current enough for the decision (yesterday's inventory, not last month's).
• Validity: values conform to defined rules and formats (a date is a real date, a country code exists).
• Uniqueness: no unintended duplicates (one record per customer, not three).

Why it matters

Every downstream use inherits the quality of its inputs. Dashboards, regulatory filings, machine learning models and customer communications all degrade when the underlying data is wrong, stale or duplicated. The cost is rarely visible as a single line item; it shows up as rework, wrong decisions, compliance risk and eroded trust in data.

How it is used in practice

Organizations operationalize data quality through:

• Profiling: scanning data to discover patterns, ranges and anomalies.
• Rules and validation: encoding expectations (for example, `age between 0 and 120`).
• Scorecards and thresholds: tracking dimension scores over time and alerting when they drop.
• Remediation: fixing at the source, deduplicating, and enriching from trusted references.
• Ownership: assigning data stewards accountable for specific domains.

Quality is measured continuously, not once. A common practice is to attach a data quality score (percentage of records passing rules) to each critical dataset.

Worked example

A company has 100,000 customer records. Checks reveal:

• 4,000 duplicate records (uniqueness fails).
• 7,000 missing email addresses (completeness fails).
• 2,000 invalid postal codes (validity fails).

If a marketing campaign targets by email, roughly 7 percent of customers are unreachable and duplicates inflate reported reach. Deduplicating and validating first raises campaign accuracy and lowers wasted spend. The same cleaned dataset then feeds finance and AI teams, so one fix compounds across functions.