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Data Quality Measures: What Are They, Why They Matter & How To Implement Them In 2025

Updated June 12th, 2025

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Quick Answer: What are data quality measures? #


Data quality measures are quantitative observations that reflect the current state of data against predefined quality dimensions—like completeness, accuracy, or timeliness. These measures help teams identify specific quality issues, such as how many rows are missing values or contain invalid entries.

Key metrics include:

  • Accuracy, which checks for errors;
  • Completeness, ensuring all required data is present; and
  • Consistency, verifying uniformity across systems.

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Up next, we’ll explore the most common data quality measures, understand how they’re observed, and how to implement them in practice.

Table of contents #

  1. Data quality measures explained
  2. What are the 9 key data quality measures?
  3. What are the benefits of tracking data quality measures?
  4. What are the challenges in implementing data quality measures?
  5. How does a unified data quality studio help with data quality measures?
  6. 9 best practices to implement data quality measures
  7. Summing up: Why data quality measures matter more than ever
  8. Data quality measures: Frequently asked questions (FAQs)
  9. Data quality measures: Related reads

Data quality measures explained #

Data quality measures are raw counts or percentages that answer: What does the data look like right now? They’re essential for spotting quality issues early and guiding remediation and form the foundation for diagnostics, reporting, and improving trust in data-driven decisions.

Think of measures as the diagnostic layer in your data quality strategy. They tell you what’s broken, where it’s broken, and how severe the issue is. For example:

  • If 200 out of 1,000 customer records are missing phone numbers, that’s a measure of completeness.
  • If 45% of product IDs fail a validation rule, that’s a measure of validity.
  • If 300 duplicate records exist in a CRM system, that’s a measure of uniqueness.

Why do data quality measures matter? #


Data quality measures matter because they bring visibility and specificity to your data quality initiatives. They make quality issues concrete by providing actual numbers that teams can act on, rather than vague notions of “bad data.”

They help you focus on the most impactful data quality problems, thereby driving prioritization.

Lastly, data quality measures are the raw inputs that feed into performance metrics, KPIs, and trust indicators used across the business.

How do data quality measures differ from dimensions and metrics? #


Data quality dimensions, measures, and metrics work together to define and track data quality, but they serve different roles:

  • Dimensions set the standards (e.g., completeness, accuracy). They define what “good” data looks like.
  • Measures are the raw counts or observations (e.g., 200 missing phone numbers). They tell you what’s wrong and where.
  • Metrics track performance over time (e.g., 80% completeness rate). They help you monitor trends and improvements.

Think of it this way – dimensions describe, measures detect, and metrics monitor.

Also, read → Do data quality dimensions matter?

What are the 9 key data quality measures? #

Here’s how key measures map to the most common data quality dimensions:

  1. Completeness
  2. Consistency
  3. Validity
  4. Availability
  5. Non-duplication (Uniqueness)
  6. Accuracy
  7. Timeliness
  8. Precision
  9. Usability

Let’s explore each data quality measure in detail.

1. Completeness #


Completeness helps in determining if any essential data is missing from the collection.

For completeness, the measure is the count of missing values in required fields. For example, 200 rows missing phone numbers out of 1,000 total records.

2. Consistency #


Consistency evaluates whether data is consistent across different data sources or systems.

For consistency, the measure is the number of conflicting values for the same entity across systems. For example, customer addresses differ in CRM vs. billing systems in 75 cases.

3. Validity #


Validity involves data being in the proper format, within acceptable ranges, and aligned with predetermined standards. A date field containing “32/01/2023” would be invalid since there’s no 32nd day in a month.

For validity, the measure is the percentage of values that violate expected formats, types, or allowed values. For example, 5% of email addresses fail the required format validation.

4. Availability #


Availability ensures that data can be easily accessed and is available to those who need it.

For availability, the measure is the percentage of time data is accessible to users or systems.

For example, data was accessible 99.5% of the time over the past month.

5. Non-duplication (Uniqueness) #


Uniqueness refers to the elimination or reduction of duplicate records. For uniqueness, the measure is the number of duplicate records found within a dataset.

For example, 120 duplicate customer IDs were detected in the CRM.

6. Accuracy #


Accuracy refers to the extent to which data is correct, reliable, and free from errors. For accuracy, the measure is the percentage of values that correctly reflect real-world data.

For example, 92% of product SKUs match the official master inventory list.

7. Timeliness #


Timeliness assesses if data is available when it’s needed and whether it’s up-to-date. For timeliness, the measure is the percentage of records updated within an acceptable time window.

For example, 85% of transaction records were refreshed within 24 hours of the event.

8. Precision #


For precision, the measure is the count of generalized or overly broad values that reduce usability.

For example, 300 entries list “APAC” instead of specifying a country like “Singapore” or “India.”

9. Usability #


Data should be presented in a way that makes it easy to understand and apply. For usability, the measure is the percentage of fields that are readable, well-labeled, and documented.

For example, 65% of columns in the dataset include standardized names and clear descriptions.

What are the benefits of tracking data quality measures? #

Tracking data quality measures translates abstract concepts into actionable insights. Key benefits include:

  • Improved decision-making: Quantitative data quality measures help teams identify exactly where quality breaks down, so decisions aren’t based on guesswork.
  • Faster root cause analysis: Measures help pinpoint specific tables, fields, or processes causing downstream issues.
  • Regulatory compliance: Measurable data quality supports audit readiness and adherence to standards like GDPR, HIPAA, or BCBS 239.
  • Operational efficiency: Detecting issues early through measures reduces time spent on manual cleanup and rework.
  • Continuous improvement: Teams can set thresholds, track improvements, and hold stakeholders accountable for measurable progress.

What are the challenges in implementing data quality measures? #

Despite their value, implementing and maintaining quality measures comes with obstacles:

  • Resistance to change: Teams may be hesitant to adopt new quality checks that expose flaws in their data processes.
  • Inadequate resources: Data quality often lacks dedicated personnel, tools, or budget.
  • Lack of clear ownership: Without defined accountability, quality issues go unresolved.
  • Data silos: Quality measures lose value if they’re not consistently applied across fragmented systems.
  • Inconsistent data standards: Varying rules across departments make it difficult to enforce uniform measurement.
  • Legacy systems and outdated technology: Older systems often lack the flexibility or integration support to support automated measures.
  • Data privacy and security concerns: Sensitive data can’t always be accessed freely for validation and measurement.
  • Complexity of data: Nested structures, external data sources, and multi-domain models make measuring quality more difficult.
  • Monitoring and measuring data quality: Continuous tracking requires automation as manual reviews don’t scale.
  • Skills gap: Teams may lack the analytical or technical expertise needed to design, implement, and interpret quality measures.

Addressing these challenges requires a strategic approach that includes choosing the right data quality dimensions and ensuring that metadata is managed well.

Metadata is what gives data quality measures their meaning and context. When metadata is managed well, it turns raw measures into actionable insights for both technical and business teams.

Metadata helps by:

  • Defining field-level expectations (e.g., required fields, allowed values)
  • Tracking lineage to understand where quality breaks occur
  • Tagging data with ownership and business context
  • Identifying impacted downstream assets (like dashboards or ML models)
  • Supporting audits and compliance through traceability

Without metadata, a measure like “200 missing values” lacks clarity on what field it refers to, why it matters, or who should act.

That’s why a metadata-led data quality studio like Atlan is essential to ensuring effective data quality management in your organization.

How does a unified data quality studio help with data quality measures? #

A unified data quality studio like Atlan transforms data quality measures from isolated stats into a connected, operational system. Here’s how:

  • Connects measures to metadata: Atlan maps quality measures to specific fields and assets using tags, lineage, and glossary terms.
  • Centralizes monitoring: Integrates with tools like Monte Carlo, Soda, and Great Expectations to provide a single view of quality metrics across dimensions and systems.
  • Automates issue detection and routing: When a measure exceeds a threshold (e.g., >10% null values), Atlan alerts the right data owner via Slack, Jira, or email.
  • Visualizes impact: With column-to-dashboard lineage, you can instantly see what downstream assets are affected by quality issues.
  • Supports continuous improvement: Dashboards and reporting centers help track historical trends, coverage, and failures across teams.

By uniting metadata, automation, and collaboration, Atlan turns static data quality measures into a living system that enforces and improves data quality at scale.

9 best practices to implement data quality measures #

Implementing data quality measures can be a challenging task, but it’s crucial for organizations to ensure reliable and accurate data for making informed decisions.

In this section, we’ll take a closer look at the best practices to implement data quality measures and how they can help organizations establish a data-driven culture.

1. Data governance policy #


  • Form a Data Governance Committee with representatives from various departments to oversee the development and enforcement of the policy.
  • Define roles and responsibilities related to data management, such as data owners, data stewards, and data custodians.
  • Document the processes for data collection, storage, access, sharing, and security.
  • Establish guidelines for data privacy and compliance with relevant regulations.
  • Regularly review and update the data governance policy to ensure it remains relevant and effective.

2. Data quality framework #


  • Identify the key dimensions of data quality for your organization, such as accuracy, completeness, consistency, timeliness, and relevancy.
  • Develop a set of data quality metrics and indicators for each dimension.
  • Establish data quality goals and targets for each metric.
  • Create a process for assessing and monitoring data quality using the defined metrics and indicators.
  • Set up regular data quality audits to ensure adherence to the framework.

3. Data stewards #


  • Appoint data stewards from various departments or functional areas.
  • Define the responsibilities of data stewards, such as identifying data quality issues, resolving them, and ensuring data adheres to the organization’s data governance policy and quality standards.
  • Provide training and support to data stewards to help them effectively perform their roles.
  • Establish communication channels for data stewards to report on data quality issues and collaborate with other stakeholders.

4. Data quality tools #


  • Research and evaluate data quality tools that align with your organization’s needs.
  • Implement chosen tools to automate data validation, cleansing, profiling, and enrichment.
  • Train employees on the proper usage of these tools.
  • Continuously monitor the effectiveness of the tools and make adjustments as needed.

5. Data catalog #


  • Identify the key data sources, data sets, and data elements within your organization.
  • Collect and document metadata, including data definitions, data lineage, and data sources.
  • Develop a centralized, searchable data catalog to store and manage the metadata.
  • Regularly update the catalog to reflect any changes in data sources or data elements.

6. Data quality dashboards and reports #


  • Identify key data quality metrics to monitor.
  • Design and develop data quality dashboards and reports that display these metrics.
  • Set up automated processes to regularly update the dashboards and reports with the latest data.
  • Share the dashboards and reports with relevant stakeholders to promote transparency and accountability.

7. Training and education #


  • Develop training materials and resources on data quality, data governance principles, and best practices for working with data.
  • Offer regular training sessions or workshops for employees.
  • Encourage participation in external training events, conferences, or webinars to stay up-to-date with industry best practices.
  • Include data quality and governance topics in onboarding processes for new employees.

8. Continuous improvement #


  • Establish a process for gathering feedback from users, data stewards, and other stakeholders on data quality processes and tools.
  • Analyze feedback and identify areas for improvement.
  • Implement changes and improvements based on feedback.
  • Regularly review and update data quality processes, tools, and metrics to ensure their effectiveness.

9. Collaboration and communication #


  • Set up regular meetings or forums for the data curation and modification team to collaborate with other stakeholders.
  • Create channels for open communication, such as chat groups, email lists, or shared project spaces.
  • Encourage a culture of openness and transparency around data quality issues and initiatives.
  • Share data quality successes and challenges with the organization to foster a data-driven culture.

Also, read → Data Quality Best Practices for Accurate Insights

Summing up: Why data quality measures matter more than ever #

Data quality measures are the bridge between theory and action. They quantify how well your data meets key dimensions like completeness, accuracy, and consistency, making it possible to monitor, improve, and govern data with precision.

When paired with strong metadata and a centralized trust engine like Atlan, these measures not only flag issues but also help teams resolve them quickly and track progress over time.

Data quality measures: Frequently asked questions (FAQs) #

1. What are data quality measures? #


Data quality measures are quantitative observations that reflect how data performs under specific quality dimensions. For example, the number of missing values in a required field is a measure of completeness.

2. How are data quality measures different from metrics? #


Measures are raw counts or proportions (e.g., 200 missing values). Metrics are calculated performance indicators derived from measures (e.g., 80% completeness score). Metrics are used for trend tracking and decision-making.

3. How do data quality measures relate to dimensions? #


Dimensions are the qualitative standards (like accuracy or consistency). Measures capture how well your data conforms to these standards by counting issues or errors.

4. Why are data quality measures important? #


They allow teams to track data health, prioritize remediation, and monitor improvements. Without them, quality issues stay hidden and unaddressed.

5. What tools can help manage data quality measures? #


Modern tools like Atlan integrate quality measures with metadata, lineage, and workflows. They help automate monitoring, alert stakeholders, and surface issues in real time.

6. Who is responsible for tracking data quality measures? #


Data quality analysts, data stewards, and data engineers typically manage and monitor quality measures. But clear ownership and collaboration across teams are key to resolving issues.

7. Can data quality measures be automated? #


Yes. With the right tools, you can automate profiling, rule validation, anomaly detection, and alerts, reducing manual effort and catching issues early.

8. What role does metadata play in data quality measures? #


Metadata provides the context behind every measure—what the data field represents, where it came from, who owns it, and how it’s used.

Without metadata, it’s difficult to interpret quality issues or take corrective action. Tools like Atlan use metadata to connect quality measures to business impact, ownership, and lineage.

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