9 Key Data Quality Metrics You Need to Know in 2025

Quick Answer: What are data quality metrics? #

Data quality metrics are quantifiable indicators that track how well your data meets defined quality standards—such as completeness, accuracy, or timeliness—over time.

They are typically expressed as percentages, ratios, or scores and provide a measurable way to monitor, compare, and improve data quality across systems, domains, or teams. They are the backbone of data quality dashboards and continuous improvement efforts.

The 9 data quality metrics that are universally applicable include:

• Completeness to leave no room for blanks
• Consistency to ensure alignment and agreement
• Validity to warrant adherence to standards
• Availability to guarantee timely access
• Uniqueness to ascertain novelty and avoid duplicates
• Accuracy to mirror real-world value
• Timeliness to check preparedness and freshness
• Precision to deliver the right level of detail
• Usability to make data easy to understand and apply

Up next, we’ll explore the essential data quality metrics, understand how they’re measured, and how to implement them in practice.

---

Table of contents #

1. Data quality metrics explained
2. What are the 9 key data quality metrics?
3. Other data quality metrics to consider
4. What role does metadata play in tracking data quality metrics?
5. How does a data quality studio like Atlan make metrics actionable?
6. Data quality metrics: From observation to optimization
7. Data quality metrics: Frequently asked questions (FAQs)
8. Data quality metrics: Related reads

---

Data quality metrics explained #

Data quality metrics are standards used to evaluate the quality of a dataset. They’re like a health check for your data. These metrics help in tracking changes to data quality whenever it moves, gets cleaned, transforms, or gets stored in new systems.

Unlike raw observations (measures), metrics standardize and contextualize these observations to track performance over time.

For example, if 200 out of 1,000 records are missing phone numbers, the data completeness metric would be 80%. This percentage makes it easy to benchmark against goals, compare across datasets, or detect quality decay.

Why do data quality metrics matter? #

Data quality metrics matter because they translate abstract quality goals into measurable outcomes. They provide a clear, quantifiable way to track how well your data meets expectations and where it’s falling short.

Did you know that poor data quality can adversely impact your organization? 👀

You may witness:
- Decreased efficiency in operations.
- Lower accuracy of analytics and insights.
- Compromised decision-making.
- Reduced customer satisfaction.
- Increased costs.

— MarkovML (@markov_ml) May 16, 2023

Here’s why they’re essential:

• Preserve data integrity: Metrics help ensure your data remains trustworthy and untampered as it moves through pipelines.
• Ensure data consistency across systems: Measuring discrepancies between sources reveals integration issues before they create downstream confusion.
• Support regulatory compliance: Many frameworks (e.g., GDPR, HIPAA, BCBS 239) require you to document and monitor data quality. Metrics provide the evidence you need to prove compliance.
• Improve decision-making: When you know which data is reliable and which isn’t, you can route insights accordingly or exclude problematic sources.
• Support accountability: They help assign responsibility by showing which teams or systems are linked to recurring quality issues.
• Enable continuous improvement: Metrics let you track trends over time, so you can spot degradation early and measure the impact of improvements.
• Justify investments: Data leaders can use metrics to demonstrate how poor data quality affects revenue, compliance, or efficiency, building the case for better tooling or headcount.
• Power automation and alerts: Threshold-based metrics can trigger alerts or remediation workflows when quality dips below acceptable levels.

Without metrics, data quality is an aspiration. With them, it becomes a measurable, accountable business function.

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

Data quality metrics are calculated indicators that quantify data quality performance over time, typically expressed as percentages or rates (e.g., 80% completeness rate).

They are derived from data quality measures, which are raw, quantitative observations of data issues (e.g., 200 missing phone numbers).

Also, read → What are data quality measures?

Both measures and metrics relate back to data quality dimensions, which are the overarching qualitative categories defining what “good data” looks like (e.g., completeness, accuracy). In essence, dimensions define, measures count, and metrics track.

Also, read → Do data quality dimensions matter?

---

What are the 9 key data quality metrics? #

There’s no one-size-fits-all when it comes to data quality metrics. Different data types require different metrics to gauge their quality accurately.

For example, numerical data might need precision and outlier detection, while textual data might require spelling accuracy and readability scores.

Let’s explore the specific details of each of the nine metric types mentioned at the beginning of the article:

Here’s how key metrics 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 metric in detail.

1. Completeness to leave no room for blanks #

Completeness refers to the degree to which all required data is available in the data asset. This metric tells you how much essential data is absent. For instance, if 80 out of 1,000 records are missing email addresses, your completeness rate is 92%.

Here is a table listing methods that ensure completeness.

Method	Description
Null check	Find and fill empty or null data points in the dataset.
Coverage check	Make sure your data covers all necessary dimensions of the entity it represents.
Missing value analysis	Identify patterns in missing data to find systematic data collection issues.
Data imputation	Fill in missing data based on various strategies like mean, median, mode, or predictive modeling.
Cross-reference check	Compare your data with a trusted source to identify any missing elements.
Cardinality check	Assess if the number of unique values in a field matches expectations.
Data sufficiency verification	Ensure you have enough data to support your analysis and conclusions.
Business rule confirmation	Verify that all business rules or conditions are met in the data collection process.

Null/Not null check: A common method to measure completeness

The Null/Not Null check targets empty or null values in your dataset, identifying gaps that could compromise the validity of your analysis.

Here’s a step-by-step process to conduct a Null/Not Null check:

1. Identify your dataset: Choose the dataset you want to inspect.
2. Define your null hypothesis: Decide what it means for your data to be null.
3. Prepare your tools: Use a data analysis tool like Python, R, or Excel.
4. Scan each field: Check each field in your dataset to see if there are any null values.
5. Record null locations: Note the locations of any null values.
6. Analyze the pattern: Look for patterns in the occurrence of nulls and their causes.
7. Decide how to handle null: Choose how to handle null values by deciding whether to replace, eliminate, or keep them.
8. Take action: Put your choice for how to handle null values into practice.
9. Verify the changes: Make sure your action was appropriately implemented.
10. Document your process: List the steps you took so you may refer to them later or apply them to different datasets.

2. Consistency to ensure alignment and agreement #

Consistency is about making sure your data is standardized across different platforms, systems, and even within the same dataset.

This metric measures how often a single data point (like a customer ID) appears with conflicting values across sources (e.g., CRM vs. ERP). A lower percentage signals mismatches.

The following table covers the methods typically employed to ensure consistency.

Method	Description
Cross-system check	Compare data across different systems. They should match.
Standardization	Maintain uniform data formats. For instance, date fields should follow one format throughout.
Data deduplication	Remove duplicate data entries to avoid confusion and inconsistency.
Business rule check	Ensure data complies with the rules or constraints defined by your business requirements.
Harmonization	Align disparate data representations to achieve uniformity.
Entity resolution	Identify and link different representations of the same entity within or across datasets.
Temporal consistency check	Check if data maintains logical order and sequencing over time.

Cross-system check: A common method to measure consistency

A cross-system check involves comparing the same data points across different systems or platforms and examining them for discrepancies. It flags disparities and enables corrective action.

Here’s a step-by-step process to conduct a cross-system check:

1. Identify systems: Determine which systems hold the data you want to compare.
2. Choose data points: Pick key data points that are common to these systems.
3. Establish a baseline: Decide which system will serve as the standard or baseline for comparison.
4. Collect data: From each system, extract the chosen data points.
5. Compare: Match the same data points across systems. Look for discrepancies.
6. Record differences: If you spot differences, document them. This record helps pinpoint inconsistencies.
7. Analyze differences: Understand why the differences exist. This might involve checking data entry procedures or system updates.
8. Resolve differences: Plan how to align inconsistent data. This could mean changing data collection or updating processes.
9. Implement changes: Carry out the changes in each system or adjust the way data is handled.
10. Monitor consistency: After implementing changes, keep monitoring data consistency across systems over time.

3. Validity to warrant adherence to standards #

Validity checks if data follows set rules, like a specific format or range. This data quality metric assesses whether data entries conform to the expected type, range, or format. For example, all phone numbers must follow a specified pattern (e.g., +1-XXX-XXX-XXXX).

The methods listed in the table below help in measuring validity checks.

Method	Description
Format checks	Checks if the data matches the expected format.
Range checks	Confirms data falls within a specific range.
Existence checks	Makes sure data is present where required.
Consistency checks	Verifies data is uniform across all sources.
Cross-reference Checks	Compares data with another reliable source for confirmation.
Logical checks	Reviews data to see if it makes sense. For example, a customer’s age can’t be negative.

Format check: A common method to measure validity

A format check ensures that each data field adheres to the expected pattern or format (like YYYY-MM-DD for dates or a 10-digit numeric phone number).

Here’s a step-by-step guide to conduct a format check:

1. Define expected format: Specify what the correct format should be for each field (e.g., phone number = 10 digits).
2. Select target fields: Identify which fields need format validation.
3. Build or choose a regex rule: Use regular expressions to define the valid structure (e.g., ^d{10}$ for phone).
4. Scan data: Run the regex against each record to flag invalid entries.
5. Count and report: Calculate the number of valid vs. invalid entries.
6. Investigate outliers: Review the malformed entries for source issues.
7. Correct errors: Apply transformations or escalate remediation.
8. Monitor ongoing validity: Set up automated validation as part of your data pipeline.
9. Document rules: Ensure format checks are standardized across datasets and teams.

4. Availability to guarantee timely access #

Availability measures whether data is accessible when needed, especially for critical workflows and decision-making. This metric evaluates how often data is missing from required systems or is inaccessible due to outages, delays, or permission issues.

For instance, if a report fails 3 times out of 30 due to missing or unavailable data sources, the data availability rate is 90%.

Here is a table listing common methods to ensure availability:

Method	Description
Data pipeline monitoring	Tracks whether data pipelines complete successfully and on time.
Uptime/downtime tracking	Measures system or dataset availability using SLAs.
Access audit logs	Analyzes who accessed which data and whether access was blocked.
Latency measurement	Measures how long it takes for data to be ready after it's ingested.
Permissions check	Verifies if users have access to necessary datasets.
Data freshness monitoring	Ensures datasets are refreshed at expected intervals.
SLA compliance tracking	Tracks whether data meets defined service-level agreements.

Data pipeline monitoring: A common method to measure availability

Monitoring the success and timeliness of data pipeline runs helps ensure that data is consistently available when needed.

Step-by-step to monitor data pipeline availability:

1. Define critical pipelines: Identify pipelines whose output is essential for daily operations or reporting.
2. Set success criteria: Define what constitutes a successful run (e.g., no errors, completed within X minutes).
3. Track execution status: Log every pipeline run, including success/failure, time taken, and errors.
4. Alert on failures: Set up automatic notifications when a pipeline fails or exceeds latency thresholds.
5. Analyze failure patterns: Identify recurring issues—delays, dependencies, infrastructure problems.
6. Escalate to owners: Route alerts to responsible teams for investigation.
7. Document SLA thresholds: Clearly define acceptable limits for delays or failures.
8. Review regularly: Conduct post-mortems and tune systems for improved reliability.

5. Uniqueness to ascertain novelty and avoid duplicates #

Uniqueness in data points ensures they only exist once in the system. Here’s a table covering popular methods that ensure data uniqueness.

Method	Description
Deduplication	Removes identical entries from the dataset.
Key constraint	Enforces unique keys in a database to prevent duplicate entries.
Record matching	Finds and merges similar records based on set rules.
Data cleansing	Removes duplicates through a process of checking and correcting data.
Normalization	Minimizes data duplication by arranging data in tables.
Fuzzy matching	Uses logic that looks for patterns to detect non-identical duplicates.

Key constraint: A common method to measure uniqueness

The key constraint method is often used to avoid duplicates before they enter the system.

In databases, unique keys are defined to ensure that no two records or entries are the same. This means every entry must be unique, stopping duplicates right at the gate.

With key constraints, you can maintain the quality of your data and keep your system efficient.

Here’s a step-by-step process for the key constraint method:

1. Identify your data: Know the data you’re working with.
2. Choose your key: Select a unique field. This could be an ID, email, or something else unique.
3. Set key constraint: In your database, set this field as the unique key.
4. Verify the constraint: Make sure your database rejects duplicate entries for this field.
5. Input data: Start entering your data. The system should now prevent duplicates.
6. Monitor and test: Regularly try adding duplicates to make sure the constraint is still working.
7. Handle errors: If a duplicate slips through, have a plan. You could either delete it or update the original.
8. Review the constraint: Check if the field still serves as a good unique key over time. If not, you may need to adjust.
9. Document your process: Write down your steps, errors, and adjustments. This record can guide you in future data management tasks.

6. Accuracy to mirror real-world value #

Accuracy is a vital data quality metric that evaluates whether data is correct and free from error. There are several methods that can help you ensure accuracy, as mentioned in the table below.

Method	Description
Equality check	Compare the original and transformed data field by field. The values should match.
Validation rules	Set conditions that data must meet like an age field can’t exceed 120 or go negative.
Data profiling	Use statistical methods to find errors within the data.
Reference data check	Cross-check data values with a trusted external source to ensure data values are correct and consistent.
Completeness check	Verify that all expected data is present. The absence of data can lead to inaccurate results.
Consistency check	Ensure that data is consistent across all systems. Inconsistent data can lead to wrong conclusions.
Uniqueness check	Make sure there are no unnecessary data duplications in the dataset. Duplicate data can lead to misleading analytics.
Timeliness check	Make sure the data is relevant and up-to-date. Outdated data may not reflect current trends or situations.

Let’s explore one such method — equality check.

Equality check: A common method to measure accuracy

Equality check is a method where we compare the original data (source) with the transformed data (target) for each field. This helps us see if the values remain consistent and correct.

Here’s a step-by-step process to conduct an equality check:

1. Identify the source and target data: Determine which datasets you are comparing — a source (original data) and a target (data that’s moved or transformed).
2. Align data fields: Make sure you’re comparing the same data fields or elements in each data asset.
3. Formulate equality conditions: Define what constitutes “equal” data points. This may be an exact match or within a tolerance range, based on your data type.
4. Perform the check: Use tools or scripts to compare each data point in the source and target datasets.
5. Document any discrepancies: Make a record of any mismatches you discover. This is crucial for identifying patterns or recurring issues.
6. Analyze disparities: Examine the reasons behind any disparities. It can be the result of problems with data transformation, data entry mistakes, or technical difficulties.
7. Correct discrepancies: Make the appropriate adjustments to clear up the errors. This can entail updating data or modifying problematic procedures.
8. Revalidate data: After making corrections, perform the equality check again to ensure that the issues have been resolved.
9. Monitor over time: Regularly repeat this process as data changes over time.

7. Timeliness to check preparedness and freshness #

Timeliness checks if your data is up-to-date and ready when needed.

The following table lists the most popular methodologies for ensuring timeliness.

Methodology	Description
Real-time monitoring	Allows instant tracking of data as it moves through pipelines.
Automated alerts	Sends notifications when there are significant delays or failures.
Scheduled jobs	Runs data jobs at optimal times to avoid bottlenecks and improve flow.
Load balancing	Distributes data jobs across systems to prevent overload and ensure swift processing.
Parallel processing	Uses multiple cores or servers to process data simultaneously, improving speed.
Data partitioning	Divides data into smaller, more manageable parts, speeding up processing time.
Late arrival handling	Implements strategies to manage late-arriving data, such as using default placeholders.

Real-time monitoring: A common method to measure timeliness

Real-time monitoring tracks data movement through pipelines instantly so that you can visualize data flow. With real-time monitoring, you can spot delays or disruptions quickly. So, if a job fails or takes too long, you’ll know right away.

Here’s a step-by-step process for real-time monitoring:

1. Define objectives: Identity what data or processes you need to monitor. This could be a data flow, job completion, or error detection.
2. Choose tools: Pick a real-time monitoring tool that suits your needs. This could be an in-house tool or a third-party solution.
3. Configure the tool: Install and set up the monitoring tool in your environment. This configuration entails defining the data or procedures that the instrument should monitor.
4. Customize alerts: Specify what counts as a problem or a delay. Create alerts for these occurrences so that you are informed right away.
5. Test the system: Test the monitoring tool to see if it performs as planned. Make sure it can correctly identify and report problems.
6. Start monitoring: With your tool configured and tested, begin monitoring your data or processes in real time. Be alert to any issues or delays that surface.
7. Evaluate and modify: Periodically review the performance of your monitoring system. Simplify or refine configurations and alert settings as needed. This continual adjustment ensures that your tool remains effective and relevant.

8. Precision to deliver the right level of detail #

Precision refers to the level of detail and granularity in data values.

For example, a customer location listed as “Asia” is far less precise than “Mumbai, India.” If 60 out of 500 records use generalized values like region or continent instead of city-level detail, your precision rate is 88%.

Here’s a table listing methods that help ensure precision:

Method	Description
Granularity checks	Evaluate whether data is captured at the required level of detail.
Domain-specific pattern check	Verify values follow precise formats relevant to the business (e.g., GPS vs. city names).
Data normalization	Convert vague terms into standard, specific representations.
Controlled vocabulary enforcement	Restrict input to predefined values with sufficient precision.
Completeness of subfields	Check whether all parts of a multi-field element (e.g., address) are populated.
Audit of default values	Identify and reduce use of placeholder or generic entries (e.g., “Unknown”).

Granularity check: A common method to measure precision

A granularity check ensures that data is captured at the level of detail necessary for its intended purpose—such as ensuring “product category” isn’t stored instead of “product SKU.”

Here’s a step-by-step walkthrough to conduct a granularity check:

1. Define required precision: Clarify the expected detail (e.g., city vs. country, SKU vs. product category).
2. Identify key fields: Focus on attributes where loss of precision affects business outcomes.
3. Scan for vague values: Use pattern matching to flag generic entries (e.g., “APAC”, “Unknown”, “Default”).
4. Quantify impact: Count how many records fall below the desired precision level.
5. Analyze context: Understand if precision is missing due to data entry, system limits, or legacy issues.
6. Set correction rules: Where feasible, enrich or replace vague data using authoritative sources.
7. Monitor ongoing quality: Build alerts for future vague values and track improvements over time.

9. Usability to make data easy to understand and apply #

Usability refers to how easily data can be accessed, interpreted, and applied by its intended users. Even if data is accurate and complete, it’s not truly valuable unless it’s understandable and usable in real-world decision-making.

For example, 40 out of 500 product entries contain non-standard codes or unclear field names that confuse users. That brings the usability rate to 92%.

Here’s a table of methods commonly used to measure and improve usability:

Method	Description
Naming convention checks	Ensure column and table names follow clear, human-readable standards.
Label audits	Validate that fields have business-friendly labels (e.g., "Date of Purchase" vs. "dop").
Field clarity reviews	Identify fields with ambiguous meanings and clarify them via glossary or tooltips.
Data formatting validation	Check for consistent units, date formats, currencies, etc. across records.
Business glossary mapping	Map technical terms to business terms using a centralized glossary.
Redundancy detection	Identify overlapping or unused fields that reduce data clarity.
Metadata enrichment	Add contextual metadata (description, owner, usage notes) to make data self-explanatory.

Naming convention check: A common method to measure usability

A naming convention check ensures that data assets (tables, columns, fields) are labeled in a way that’s meaningful to both technical and business users.

Here’s a step-by-step guide to conduct a naming convention check:

1. Define standards: Establish a naming convention guide (e.g., use camelCase, include units, avoid abbreviations).
2. Extract metadata: Use metadata tools to list all field names across critical datasets.
3. Compare against rules: Flag names that violate your standard (e.g., cryptic names like “fld_1”).
4. Engage domain owners: Collaborate with data stewards and domain experts to suggest replacements.
5. Implement updates: Rename columns and update documentation or glossaries where applicable.
6. Educate teams: Communicate updated standards across teams to promote adoption.
7. Automate future checks: Use a metadata control plane or linter to catch future violations automatically.

---

Other data quality metrics to consider #

Domain-specific data quality metrics consider unique characteristics, requirements, and challenges of different domains and provide a targeted assessment of data quality.

Let’s look at a few domain-specific data quality metrics relevant to:

• Specific industries, such as high-frequency trading and telecommunications
• Specific data types, such as geospatial, time-series, and graph data

High-frequency trading #

• Latency: Determines how long it takes to analyze data after it is generated in order to allow for timely execution.
• Data integrity: Assesses the accuracy and dependability of the trading data.
• Order synchronization: Examines the accuracy of order data when used with various trading platforms.

Telecommunications #

• Call drop rate: Measures the percentage of dropped calls to assess network reliability.
• Voice clarity: Evaluates the clarity and quality of voice communication.
• Signal strength: Assesses the strength and coverage of the network signal.

Geospatial data #

• Spatial accuracy: Determines how well borders and locations are located.
• Attribute consistency: Measures the degree to which attribute data from various sources is consistent.
• Topology validation: Determines whether the geometric connections between spatial entities are accurate.

Time-series data #

• Data completeness: Determines whether all necessary data points exist within a certain time frame.
• Temporal consistency: Determines how consistently data values change over time.
• Data granularity: Assesses the level of detail and precision of time-series data.

Graph data #

• Connectivity: Measures the presence and accuracy of relationships between entities in the graph.
• Graph integrity: Evaluates the correctness and validity of the graph structure.
• Centrality measures: Assesses the importance and influence of nodes within the graph.

Also, read → An implementation guide for data quality measures

---

What role does metadata play in tracking data quality metrics? #

Metadata is the connective tissue that gives your data quality metrics meaning and context.

Without it, even the most precise metric—say, “completeness score = 92%”—doesn’t tell you what’s missing, where it’s missing, or who’s responsible for fixing it.

Here’s how metadata strengthens data quality metrics:

• Contextual clarity: Metadata tells you what each field represents, its expected format, and business meaning. This is crucial for interpreting whether a low validity score is a real issue or just noise.
• Lineage visibility: You can trace where the metric applies across upstream and downstream systems. This helps locate the exact pipeline, job, or transformation that introduced a data issue.
• Ownership assignment: Good metadata systems link data assets to responsible individuals or teams, enabling clear accountability for metrics that fall below thresholds.
• Business impact mapping: With metadata, you can see which reports, dashboards, or regulatory filings depend on assets with poor quality. This helps you prioritize what to fix first.
• Glossary alignment: Metrics can be tagged and tracked against consistent business definitions, ensuring everyone evaluates data quality the same way.

In short, metadata turns raw metric numbers into actionable, business-relevant signals.

---

How does a data quality studio like Atlan make metrics actionable? #

A metadata-led data quality studio like Atlan embeds data quality metrics across your actual workflows. Here’s how:

• Connects metrics to metadata: Atlan maps quality metrics directly to data assets, glossary terms, and lineage graphs, so that every score comes with its context, owner, and downstream impact.
• Lineage overlays: You can view rule status inline across lineage and search, so impact is obvious at a glance.
• Smart scheduling: Run checks on a CRON, on-demand, or whenever fresh data lands
• Centralized quality monitoring: Atlan integrates with tools like Anomalo, Great Expectations, and Soda, unifying metric signals in a single pane of glass with real-time visibility.
• 360o quality: Atlan pulls upstream quality signals from Monte Carlo, Soda, Anomalo, and more for a 360° quality view.
• Routes issues to owners: When a metric fails, Atlan sends alerts via Slack, Jira, or email to the relevant steward. So, you get instant notifications that pinpoint what broke, why, and who’s affected.
• Tracks and reports trust: Atlan’s Reporting Center helps you visualize data quality trends over time, tie them to business KPIs, and report progress to leadership.

By combining metadata, automation, and collaboration, Atlan turns your data quality metrics into a living part of how your teams work every day.

---

Data quality metrics: From observation to optimization #

Data quality metrics transform vague concerns about “bad data” into concrete, measurable indicators you can monitor and improve over time.

By linking each metric to a specific dimension (completeness, accuracy, or timeliness), you give teams a shared language for identifying issues, prioritizing fixes, and tracking progress.

Paired with metadata and automated data quality platforms like Atlan, these metrics help you understand where, why, and how to take action. As a result, you get cleaner pipelines, better decisions, and a more trustworthy data ecosystem.

---

Data quality metrics: Frequently asked questions (FAQs) #

1. What is a data quality metric? #

A data quality metric is a calculated value that quantifies the performance of data against a specific quality dimension, such as completeness or accuracy. It typically appears as a percentage, rate, or score.

2. How do metrics differ from measures and dimensions? #

Dimensions define what good data looks like (e.g., accuracy), measures count data issues (e.g., 80 missing records), and metrics track performance over time (e.g., 92% completeness rate).

3. Why are data quality metrics important? #

Data quality metrics are essential for ensuring reliable and actionable insights. They help organizations avoid costly errors, improve decision-making, and maintain compliance with regulatory standards.

4. What are the most common metrics for data quality? #

The most common metrics include accuracy, completeness, consistency, timeliness, validity, availability, uniqueness, precision, and usability.

5. How do data quality metrics improve business performance? #

By tracking data quality metrics, businesses can reduce errors, enhance operational efficiency, and make informed decisions. High-quality data leads to better customer experiences, optimized processes, and increased profitability.

6. How can data quality metrics help in compliance auditing? #

Data quality metrics ensure that data complies with industry regulations and standards. Metrics like validity and accuracy play a crucial role in preparing for compliance audits by highlighting areas that require improvement.

7. What role does metadata play in tracking data quality metrics? #

Metadata provides essential context—like lineage, ownership, and usage—that helps you understand where metrics apply, trace the root cause of quality issues, and assign accountability for remediation.

---

Data quality metrics: Related reads #

• Data Quality Explained: Causes, Detection, and Fixes
• Data Quality Framework: 9 Key Components & Best Practices for 2025
• Data Quality Measures: Best Practices to Implement
• Data Quality Dimensions: Do They Matter?
• Resolving Data Quality Issues in the Biggest Markets
• Data Quality Problems? 5 Ways to Fix Them
• Data Quality Metrics: Understand How to Monitor the Health of Your Data Estate
• 9 Components to Build the Best Data Quality Framework
• How To Improve Data Quality In 12 Actionable Steps
• Data Integrity vs Data Quality: Nah, They Aren’t Same!
• Gartner Magic Quadrant for Data Quality: Overview, Capabilities, Criteria
• Data Management 101: Four Things Every Human of Data Should Know
• Data Quality Testing: Examples, Techniques & Best Practices in 2025
• Atlan Launches Data Quality Studio for Snowflake, Becoming the Unified Trust Engine for AI

---