What is Data Quality?

Data Quality Explained

Data quality isn't about perfection—it's about fitness for purpose. Data that's "good enough" for one use case might be completely inadequate for another. A customer email that's missing for marketing campaigns is a quality issue; the same missing email in a data warehouse used for revenue analysis might not matter.

The key question is: Does the data allow you to achieve your intended outcome reliably?

The Six Dimensions of Data Quality

Data quality is typically measured across six key dimensions:

1. Accuracy

Does the data correctly reflect reality? An inaccurate customer address leads to undelivered packages. An inaccurate revenue figure leads to wrong business decisions. Accuracy is often the most critical dimension but also the hardest to verify—you need a "source of truth" to compare against.

2. Completeness

Is all required data present? Completeness issues manifest as null values, missing rows, or incomplete records. A customer record missing a phone number might be acceptable; missing the customer ID is not. Data profiling helps identify completeness issues automatically.

3. Consistency

Does data match across systems and time? If your CRM says a customer is "Active" but your billing system says "Churned," that's a consistency problem. Inconsistent data creates confusion and undermines trust in analytics.

4. Timeliness

Is data available when needed? A real-time fraud detection system needs data in milliseconds. A monthly financial report can tolerate day-old data. What matters is whether data is fresh enough for its use case.

5. Validity

Does data conform to expected formats and rules? An email address should contain an "@" symbol. A state code should be from a valid list. An order amount should be positive. Validity rules catch obvious errors before they propagate.

6. Uniqueness

Are there duplicate records? Duplicates inflate counts, skew averages, and create confusion. A customer appearing twice means their lifetime value is overstated. Anomaly detection can catch sudden spikes in unique counts that indicate duplicate problems.

Why Data Quality Matters

Poor data quality is expensive. Studies estimate it costs organizations 15-25% of revenue through:

• Wrong decisions: Analytics built on bad data lead to bad strategies
• Wasted resources: Marketing to invalid emails, shipping to wrong addresses
• Compliance risk: Regulatory reporting with inaccurate data creates legal exposure
• Lost productivity: Time spent investigating and fixing data issues
• Eroded trust: Stakeholders stop believing reports when errors are found

Use our cost of bad data calculator to estimate your organization's annual impact.

Data Quality vs Data Observability

Data observability and data quality are complementary concepts:

• Data quality is the goal—achieving accurate, complete, consistent data
• Data observability is the capability—monitoring and visibility that helps you maintain quality

You need observability to achieve and maintain quality. Without monitoring, quality issues go undetected until someone notices wrong numbers in a dashboard.

How to Improve Data Quality

1. Define Quality Standards

Start by defining what "good" looks like for your data. Which fields must be non-null? What are valid value ranges? What's the acceptable freshness threshold?

2. Measure Current State

Use data profiling to understand your current quality levels. How many nulls are there? What's the duplicate rate? What's the distribution of values?

3. Implement Validation Rules

Add checks that validate data against your quality standards. Run these checks during ingestion, transformation, and before serving data to consumers.

4. Monitor Continuously

Data quality isn't a one-time fix. Set up continuous monitoring with anomaly detection to catch quality degradation as it happens.

5. Fix Issues at the Source

When you find quality issues, trace them back to their source. Is it a bug in the ETL? A problem in the source system? A user entering bad data? Fixing root causes prevents recurrence.

Frequently Asked Questions

What is data quality?

Data quality is a measure of how well data serves its intended purpose. High-quality data is accurate, complete, consistent, timely, valid, and unique. Poor data quality leads to wrong decisions, wasted resources, and lost trust.

What are the dimensions of data quality?

The six key dimensions of data quality are: Accuracy (does data reflect reality?), Completeness (is all required data present?), Consistency (does data match across systems?), Timeliness (is data available when needed?), Validity (does data follow rules and formats?), and Uniqueness (are there no duplicate records?).

Why is data quality important?

Data quality is important because organizations rely on data for decision-making. Poor data quality leads to incorrect analytics, failed business processes, compliance risks, and eroded stakeholder trust. Studies estimate poor data quality costs organizations 15-25% of revenue.