Data Quality

Data quality measures how reliable your data is for making decisions. High-quality data is accurate, complete, timely, consistent, valid, and free of duplication.

In depth

Data quality is fitness for use. The same dataset may be fine for trend-spotting but unsuitable for board reporting.

Here are six commonly used measures for evaluating data quality:

• Accuracy: Values reflect reality. For example, a paid invoice shows the right amount and date.

• Completeness: All required fields are present. For example, there are no missing customer IDs or product SKUs.

• Timeliness: You have data when you need it. For example, the sales team gets access to daily sales targets before the morning stand-up.

• Consistency: Data aligns across systems. For example, revenue values in your data warehouse dashboard match revenue values in your finance report.

• Validity: Values conform to rules and standards. For example, dates parse, currencies use standard formats, and email addresses pass format checks.

• Uniqueness: No duplicated data. For example, one order, one row.

Pro tip

Treat your most important fields like a contract. Define the expected type, allowed values, and update cadence. Add tests for grain, time zones, and late backfills. Log rule failures so you can spot drift early.

Keep an eye on the areas where quality can break: manual entry, schema shift, late-arriving events, joins at the wrong grain, and quiet pipeline failures. Small gaps can stack up into larger problems. A single missing dimension can skew cohort charts. A stale table can trigger the wrong alert.

Measure data quality directly. Track data freshness per table, null and outlier detection per column, join coverage across keys, and pass rates for validation rules. Review trends weekly, then fix the issues that move business outcomes.

Why Data Quality matters

Good data quality is the foundation of confident decisions. Without it, teams spend meetings arguing over numbers instead of acting on them.

Here is why data quality matters across the business:

• Trusted decisions: No more meetings where different teams bring different numbers.

• Faster delivery: Clean, up-to-date data enables reliable, fast dashboard and report building.

• Lower risk: Accurate, complete data reduces non-compliance and generates fewer customer issues.

• Better self-serve: Business users can make confident, informed decisions based on consistently defined data.

Data Quality - in practice

These examples show where data quality breaks down and how to catch it early.

• Ecommerce orders: A duplicate data load inflates "Gross Sales" by 3 percent. A uniqueness rule on order_id catches this issue before it reaches executive dashboards.

• Marketing attribution: Incomplete UTM parameters leave gaps in campaign data. A completeness rule flags missing medium and campaign parameters so the team can correct or drop those rows.

• Revenue reporting: A warehouse data refresh runs late. A latency monitor warns the finance department before monthly close so they can pause exports.

• Customer success: A join on account_id changes grain after a schema update. A grain test fails, blocking the deployment and preventing mismatched data.

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Data Quality and PowerMetrics

PowerMetrics helps you manage data quality starting at the semantic layer. You define metric definitions once, certify trusted versions, and tag them for discovery. Access controls for users, groups, and roles keep editing rights clear. The PowerMetrics Query Language (PMQL) applies time logic, filters, and groupings consistently, reducing calculation drift between tools. Error propagation surfaces issues when inputs are null or out of bounds. Goals and notifications alert you when a metric misses its target or steps outside an expected range.