Key Data Quality Metrics to Track in 2026 (and How to Use Them)

High-quality data is the foundation of every modern enterprise — fueling analytics, powering AI models, and driving smarter decisions. But poor-quality data remains one of the biggest barriers to success.

According to Forrester’s Data Culture & Literacy Survey, 2023, more than one-quarter of global analytics and data professionals say their organizations lose over $5 million annually due to poor data quality, with 7% estimating losses of $25 million or more.

From incomplete customer records and inconsistent KPIs to missing data values in dashboards, even minor inconsistencies can cascade into lost revenue, compliance risks, and eroded trust. The good news? Businesses can measure, monitor, and continuously improve their data quality with the right data quality metrics in place.

Key Takeaways

• Data quality metrics are quantifiable measures that evaluate how well data meets business and technical requirements.

• These metrics ensure that data is accurate, complete, consistent, valid, timely, and unique—key dimensions of data quality.

• Tracking metrics allows organizations to monitor data quality proactively, supporting AI readiness, compliance, and reliable decision-making.

• Implementing metrics requires a structured, governance-led approach supported by automation and AI.

• Continuous improvement is critical—data quality should evolve as types of data, workflows, and business goals change.

What are data quality metrics?

Data quality metrics are specific, measurable indicators used to evaluate the condition of your data against defined standards. They provide visibility into how well your organization’s data meets expectations for usability, reliability, and business impact.

For example, data engineers might measure data accuracy by comparing data warehouse records to verified sources or assess timeliness by calculating the lag between data creation and usage in analytics dashboards. Metrics like these quantify—and ultimately optimize—the fitness of data for its intended purpose.

What’s the difference between data quality metrics and data quality dimensions?

It’s easy to confuse the two. Data quality dimensions are the qualitative categories that describe overall aspects of data integrity—such as accuracy, completeness, or validity. Data quality metrics, by contrast, are the quantitative measures used to evaluate those dimensions.

Think of it this way:

• Accuracy is a dimension.

• Percentage of records matching the source of truth is a metric within that dimension.

Tracking metrics across multiple dimensions helps organizations spot low-quality data, assess data lineage, and prioritize the areas that most influence analytics and AI outcomes.

Why should you track data quality metrics?

In 2026, data is not just an operational resource — it’s a strategic differentiator. But without metrics, data quality issues remain subjective and reactive. By quantifying data performance, organizations can:

• Measure reliability across systems and departments.

• Identify and prioritize gaps that most affect profitability and KPIs.

• Build trust in analytics and AI models, ensuring business decisions rely on high-integrity data.

• Comply with regulatory standards, such as BCBS 239, GDPR, and emerging AI governance standards.

• Create accountability through transparent dashboards and consistent measurement.

As Gartner highlights, AI-ready data drives success across every enterprise. For AI systems to deliver accurate, explainable outcomes, they must learn from validated, trustworthy inputs. Tracking data quality metrics for datasets that feed AI models is therefore essential.

Metrics such as accuracy, completeness, and validity help data engineers confirm that data values meet defined standards before entering an AI pipeline. A single case of low-quality data—like mislabeled images or inconsistent pricing information—can degrade model performance. By embedding data validation and quality measurement early in the workflow, teams can optimize AI training sets, protect data integrity, and improve downstream predictions.

Simply put, if you can’t measure your data quality, you can’t improve it.

Examples of data quality metrics by dimension

There’s no one-size-fits-all formula for data quality—what’s “high quality” depends on the types of data, context, and use case. However, several key dimensions form the backbone of any robust data quality program. Below are the most widely used dimensions of data quality, with example metrics and practical ways to apply them.

1. Completeness

Definition: Completeness measures whether all required data is present and populated. Incomplete data leads to gaps in understanding and poor decision-making.

Example metrics:

• Percentage of missing values per dataset

• Ratio of populated fields to total required fields

• Number of null or empty values per record

How to use it: Track completeness across critical systems like your CRM or data warehouse. Dashboards can highlight incomplete fields—such as missing customer IDs or product codes—so teams can intervene early. This allows data stewards to maintain data integrity across the workflow and prevent missing information from influencing analytics.

2. Accuracy

Definition: Accuracy measures how closely data reflects the real-world entities it represents.

Example metrics:

• Percentage of records matching authoritative sources

• Number of data entry or format errors per dataset

• Ratio of correct to incorrect values (based on audits or validation rules)

How to use it: Implement data validation and cross-checks against reference datasets or master records. In AI applications, accuracy ensures training data mirrors reality—preventing biased outcomes or faulty predictions. Regular accuracy checks across data warehouses and transactional systems can dramatically optimize model reliability.

3. Consistency

Definition: Consistency evaluates whether data remains uniform across systems, formats, and processes.

Example metrics:

• Percentage of conflicting values across systems

• Number of schema or format discrepancies

• Count of mismatched values for shared fields (e.g., customer ID, product code)

How to use it: Consistency ensures a single version of truth across the enterprise. Data catalogs and metadata management tools can enforce standardized definitions and naming conventions. Unified, consistent data reduces reconciliation time for data engineers and improves trust in cross-departmental dashboards.

4. Uniqueness

Definition: Uniqueness ensures that each record exists only once within a dataset or system. Duplicates inflate storage costs, skew analytics, and reduce trust.

Example metrics:

• Duplicate record rate (% of redundant entries)

• Number of duplicate IDs or transactions

• Percentage of unique keys or identifiers

How to use it: Run automated deduplication processes and enforce unique identifiers at ingestion. By identifying redundant entries across systems, organizations can streamline data workflows, lower storage costs, and maintain trustworthy key metrics.

5. Timeliness

Definition: Timeliness measures how current and up-to-date data is relative to when it’s used. Stale data limits responsiveness and weakens insights.

Example metrics:

• Data latency (time between creation and availability)

• Percentage of records updated within SLA timeframes

• Number of outdated records detected in a given period

How to use it: Integrate real-time validation and freshness checks. APIs and event-streaming architectures help ensure that time-sensitive data—such as inventory levels or dynamic pricing—is always relevant. Monitoring timeliness improves agility and ensures that dashboards reflect accurate, actionable insights.

6. Validity

Definition: Validity assesses whether data conforms to defined rules, formats, or business logic.

Example metrics:

• Percentage of values outside accepted ranges (e.g., age < 0)

• Ratio of records failing validation rules

• Number of syntax or format errors (e.g., invalid email addresses)

How to use it: Apply rule-based data validation at the ingestion stage. For example, reject transactions with invalid postal codes or ages outside logical bounds. Validity checks reduce manual review cycles and protect the consistency of downstream analytics.

Other dimensions of data quality—including relevance, usability, and duplication control—add depth to your framework. Relevance ensures datasets align with the business context. Usability evaluates how easily stakeholders can interpret and act on data through dashboards or workflows. By integrating all dimensions into a unified quality framework, organizations gain a comprehensive view of data integrity—a perfect segue into implementation best practices.

How should leaders implement data quality metrics?

Establishing a data quality measurement framework takes structure, clarity, and commitment. For enterprises leveraging AI and machine learning, this discipline is non-negotiable. AI models can only perform as well as the data they consume, so tracking metrics ensures continuous improvement and guards against data drift. Follow these three steps to ensure your program delivers results:

1. Define what to measure

Identify the most critical data domains and systems affecting business outcomes—such as customer data, sales transactions, or product catalogs. Map relevant dimensions of data quality (accuracy, completeness, timeliness) to those domains and set measurable thresholds for each.

Consider linking metrics directly to KPIs and key metrics used in executive dashboards—connecting data quality issues to tangible business performance.

2. Monitor and analyze performance

Use automated profiling tools to continuously scan for anomalies. Example analyses include:

• Ratio of valid records to total records

• Error counts per million data points

• Timeliness or latency by data source

Visual dashboards help business and technical stakeholders interpret results quickly, identify weak spots, and prioritize remediation efforts. Regular monitoring also improves collaboration between data stewards, data engineers, and business analysts, creating a shared understanding of how data integrity affects business decisions.

3. Automate where possible

Manual data validation can’t keep pace with modern data velocity. Automate rule-based checks or use AI-assisted engines to flag duplicates, missing values, or anomalies in near real time. Automation not only improves accuracy but also reduces operational costs and speeds issue resolution.

Automation also enhances data lineage visibility—tracing errors back to their source systems—and strengthens governance across the entire workflow.

In short: automation transforms data quality management from a reactive chore into an ongoing optimization cycle that protects the enterprise from low-quality data.

Once metrics are defined and automation is in place, establish a cadence of review. Update thresholds as types of data evolve or new regulatory requirements arise. Regular calibration ensures your quality program continues to align with both business priorities and technological advancements.

How can you improve and maintain data quality?

Improving data quality isn’t a one-time initiative — it’s an ongoing process tied to your broader data governance strategy. Organizations that embed data quality management into their data catalogs achieve measurable improvements in trust, efficiency, and compliance.

Here’s how to sustain success:

• Establish ownership: Assign dedicated data stewards and data engineers to oversee quality for high-impact datasets.

• Integrate governance and quality: Align data validation rules and thresholds with compliance, audit, and operational requirements.

• Educate and empower teams: Promote a culture of accountability and transparency. Encourage teams to explore data lineage to understand how low-quality data propagates through systems.

• Leverage automation: Set up continuous monitoring pipelines that alert users to shifts in key metrics, anomalies in data values, or SLA breaches in real time.

A modern data catalog helps unify these processes, connecting metadata, data warehouse assets, and governance policies into one view. With this integrated approach, teams can monitor data quality, trace lineage, and optimize quality workflows across the enterprise.

How can you use AI to improve data quality metrics?

AI and machine learning are transforming how organizations measure and maintain data quality. In 2026, leading enterprises are applying AI-driven quality frameworks that can:

• Detect anomalies automatically by learning normal data patterns.

• Recommend corrections for duplicate or inconsistent data.

• Predict data degradation before it impacts analytics or AI models.

• Score datasets for AI readiness, ensuring only trustworthy data feeds generative models.

These intelligent systems help teams focus on high-impact issues while reducing manual work — creating a proactive, self-improving data ecosystem.

Alation’s Data Quality Agent

In Alation’s thought-leadership ecosystem, the Data Quality Agent (DQ Agent) is a powerful AI-driven tool that helps enterprises automate validation and remediation. The agent integrates directly with Alation’s Data Catalog to:

• Detect anomalies across structured and unstructured data.

• Provide contextual lineage to pinpoint the origin of data quality issues.

• Continuously monitor data quality and flag deviations in real time.

• Recommend fixes to optimize data accuracy and maintain trusted analytics pipelines.

To explore how intelligent automation can elevate your data integrity strategy, learn more about Alation’s Data Quality Agent and its role in creating AI-ready, resilient data ecosystems.

Assess and improve your organization’s data quality

As data ecosystems grow more complex and AI becomes central to business strategy, maintaining high-quality data is no longer optional — it’s essential.

Tracking and improving data quality metrics allows your organization to make confident decisions, comply with evolving regulations, and train reliable AI models.

By combining governance, automation, and intelligence, enterprises can transform data quality management from a reactive process into a competitive advantage.

Start by identifying which dimensions matter most to your business — and use data quality metrics to turn that insight into measurable, lasting impact.

Keen to learn more? Book a demo today.