Build Data Quality Rules for AI Success: Types, Examples, and 2026 Implementation

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 18th, 2026 | 17 min read

Quick answer: What are data quality rules?

Data quality rules are standardized criteria used to validate if data is fit for purpose. They act as "automated checkpoints" within your pipelines, preventing bad data from entering downstream applications. Organizations use these rules to automatically check data against business requirements before being used in analytics or AI.

Most data quality rules cover six essential data quality dimensions, including accuracy, completeness, consistency, validity, uniqueness, and timeliness.

Key characteristics of effective data quality rules:

  • Business-centric: Each rule connects to specific business requirements like "customer emails must contain @ symbol" or "order dates cannot be in the future".
  • Measurable outcomes: Each rule must produce a binary "Pass/Fail" result to allow for automated quality scoring.
  • Automated and proactive: Rules should be embedded directly into data pipelines to catch errors before they impact downstream systems.
  • Context-aware: Logic must adapt to the field's importance (e.g., a null value is critical for "Customer ID" but optional for "Middle Name").
  • Traceable: When a rule fails, it must provide sufficient metadata to pinpoint the exact source of the error for rapid remediation.
  • Scalable enforcement: Modern platforms apply rules across entire data domains using metadata tags rather than manual configuration.
  • Collaborative ownership: Data producers and consumers co-create rules through contracts that align expectations, and document definitions centrally.

Below: what data quality rules are and why they matter, types of rules, popular examples, how to create effective rules, common challenges, implementation best practices.

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Data quality rules at a glance

Dimension Rule Definition Practical Example
Accuracy Does the data reflect the real-world entity? Latitude/Longitude must fall within specific country borders.
Completeness Are all required fields populated? Every ‘Lead’ record must have an ‘Email’ or ‘Phone Number’.
Consistency Does the data match across different systems? ‘Product_ID’ in the CRM must match ‘SKU’ in the ERP.
Validity Does the data follow a specific format? ‘US_Zip_Code’ must be exactly 5 or 9 numeric digits.
Uniqueness Are there any duplicate records? No two ‘User’ records can share the same ‘Social_Security_Number’.
Timeliness Is the data sufficiently up-to-date? ‘Stock_Price’ must have been updated within the last 60 seconds.

What are data quality rules and why do they matter?

Data quality rules are the programmable “contracts” that define what high-performance data looks like for your business. They define acceptable values, formats, relationships, and constraints that data must satisfy to be considered trustworthy.

Data quality rules are the primary defense against data downtime and the erosion of trust across the information ecosystem. By codifying business requirements into automated logic, these rules turn abstract “governance” into a tangible, measurable asset.

The financial and operational cost of bad data


Data quality is the bedrock of AI readiness. While traditional rules focused on basic hygiene, the rise of generative AI has made them a prerequisite for AI success.

Gartner’s Distinguished VP Analyst Rita Sallam predicts that “at least 30% of generative AI projects will be abandoned by the end of 2025 because of poor data quality (besides rising costs and inadequate risk controls).” When the underlying data fails, the entire return on investment for high-cost infrastructure and model development disappears.

Poor data quality is also a financial debt, with Gartner estimating that organizations lose an average of $12.9 million annually due to sub-par data quality. This debt leads to project failure at a massive scale, negatively impacting revenue, customer satisfaction, and regulatory compliance.

The purpose of data quality rules


Data quality rules provide the mechanism to prevent these costs through proactive detection rather than reactive cleanup. They serve three primary functions:

  1. Enabling prevention at the source: Catch errors during ingestion before bad data pollutes downstream systems or fine-tuned models.

  2. Shifting toward continuous monitoring: Rather than one-time cleanup projects, rules enable ongoing quality assessment with automated checks that scale with your data volume.

  3. Providing trust signaling: When rules pass consistently, they build confidence that data is reliable for decision-making. The quality metrics derived from these rules provide transparency into data health, allowing teams to understand which datasets meet standards and which require attention.

Modern data quality frameworks integrate rules as a core component alongside profiling, monitoring, and governance. So next, let’s explore the different types of data quality rules.

What are the different types of data quality rules?

Data quality rules are generally organized into the core dimensions of data quality, serving as the automated logic that determines if a record is fit for use in production.

1. Accuracy


Accuracy rules verify that data correctly represents real world entities or events. This category is often the most challenging to automate because it requires a trusted reference source for comparison.

For example, a rule might validate that a customer’s shipping address matches an official postal service database or that a recorded transaction amount aligns with the original bank ledger.

2. Completeness


Completeness rules identify missing or null values in mandatory fields. This is critical because missing attributes can skew analytics and lead to biased outcomes in automated systems.

A typical completeness rule might require that every customer profile must contain both an email address and a phone number to be considered valid for marketing outreach.

3. Consistency and integrity


Consistency rules ensure that data remains uniform as it moves across different systems or storage locations. This prevents contradictory information from reaching decision makers and maintains a single version of the truth.

For instance, if a customer is listed as “Active” in a CRM, a consistency rule ensures the status is not listed as “Inactive” in the billing system.

Meanwhile, referential integrity rules maintain the relationships between different datasets. They verify that a child record always points to a valid parent record, such as ensuring every transaction in a sales table is linked to an existing account ID in the master customer table.

4. Validity


Validity rules ensure that data conforms to specific formats, patterns, or business logic. Common examples include verifying that a Zip Code contains only numeric digits or that a birth date does not occur in the future.

Validity rules generally act as a primary filter during data ingestion to prevent incorrectly formatted data from polluting downstream warehouses.

5. Timeliness and freshness


Timeliness rules measure the delay between a real-world event and its availability in your system. This is especially vital for high-frequency use cases like fraud detection or supply chain optimization.

Meanwhile, a freshness rule might flag a data asset if it has not been updated within a specified time window, such as the last fifteen minutes for inventory stock levels.

6. Uniqueness


Uniqueness rules prevent the creation of duplicate records that can inflate metrics and increase storage costs. These rules ensure that each entity, such as a product or a user, is represented exactly once in a dataset.

For instance, a rule might dictate that no two records in a database can share the same Social Security Number or unique internal employee ID.

7. Custom business logic


Beyond the standard dimensions, organizations increasingly rely on custom business rules. These rules validate data based on specific, high-context domain knowledge.

A custom rule might compare two different fields for logical consistency, such as flagging an error if the “Shipping Cost” exceeds the “Total Product Value.” This catches “silent” failures where data is technically valid in format but operationally incorrect.

What are the most common data quality rule examples?

While dimensions provide the framework, specific rules act as the enforcement mechanism. Organizations typically implement a “starter set” of rules to address the most frequent points of data failure. The following table provides concrete examples across the six core dimensions.

Dimension Rule Category Practical Example
Accuracy Range check A “Sensor_Temperature” reading must fall between -50 and 150 degrees.
Completeness Mandatory field Every “Insurance_Claim” record must contain a “Policy_Number” and “Incident_Date.”
Consistency Cross-system sync A “Customer_Status” in the CRM must match the “Billing_Status” in the ERP.
Integrity Orphan detection Flag any “Support_Ticket” assigned to an “Employee_ID” no longer in the directory.
Validity Pattern matching Every “User_Email” must follow the regex pattern for a standard email address.
Uniqueness Primary key check No two “Employee” records can share the same “Government_ID” or “Email.”
Freshness Freshness SLA The “Stock_Inventory” table must be refreshed every 30 minutes.
Timeliness Delivery window Daily sales reports must be available in the BI tool by 7:00 AM local time.
Custom logic Anomaly detection Alert if “Daily_Signups” is 3 standard deviations above the 90-day average.

Organizations using automated data quality approaches combine these rule types into comprehensive validation suites. Modern platforms suggest rules based on data profiling and metadata analysis, accelerating implementation.

How can organizations create effective data quality rules?

Creating effective data quality rules is a collaborative process that bridges the gap between technical execution and business requirements. Organizations that succeed in 2026 treat these rules as living assets that evolve alongside their data products rather than static, one-time configurations.

1. Profile and assess your current data state


Before defining new rules, you must understand the existing health of your datasets. Use data profiling tools to interrogate your data for patterns, null values, outliers, and schema drift. This assessment benchmarks your current quality levels and identifies the critical data elements that require the most rigorous controls.

2. Collaborate with business and domain experts


Rules created in a technical vacuum often fail to address operational realities. Effective rule creation requires input from the business users who understand the context of the data.

So, once you have a baseline, collaborate with business stakeholders to identify which fields directly affect revenue or compliance. This ensures rules are tied to actual outcomes rather than just technical perfection.

3. Implement contracts and automated thresholds


Shift quality checks “left” by using data contracts. These are formal agreements between data producers and consumers that codify quality standards directly into the ingestion process.

To stay realistic, set appropriate tolerance levels. While a “Customer ID” might require 100% compliance, a “Lead Source” field might only require 95% to balance quality with operational speed.

4. Automate monitoring and remediation


Manual checks cannot scale with modern data volumes. Deploy continuous monitoring by embedding rules directly into your pipelines. When a rule is violated, the system should automatically trigger alerts to the appropriate data steward or even initiate remediation workflows to quarantine the bad data.

Embed rules directly into your pipelines to run automatically during ingestion or specific events. When a rule fails, the system should trigger an immediate remediation workflow, alerting the specific data steward responsible for that domain. This replaces reactive cleanup with proactive, automated governance.

5. Establish clear ownership and accountability


Data quality is a shared responsibility, but it requires clear accountability to be effective. Assign data stewards within business units to own the functional quality rules for their respective domains.

For example, the finance team should own the rules for expense categorization, while sales operations manages the integrity of CRM data. By mapping rules to specific owners, you ensure that when quality drops, there is a clear path for rapid resolution and continuous improvement.

What are the most common challenges with implementing data quality rules?

Implementing data quality rules is often hindered by the following primary challenges:

  • Scaling across fragmentation: Manually managing rules across diverse legacy systems, cloud warehouses, and SaaS apps is nearly impossible, often leaving data quality high in central hubs but poor at the source.

  • Lack of business context: Technical teams often write rules in a vacuum, failing to identify which specific data errors actually impact revenue, compliance, or model performance.

  • Rule decay and fatigue: As schemas and business logic evolve, old rules produce false positives, leading to alert fatigue where teams begin to ignore critical quality warnings.

  • Remediation bottlenecks: Identifying an error is useless without ownership; lack of clear stewardship means failed checks sit in queues for days rather than being resolved.

What are the best practices for implementing data quality rules?

Successful rule implementation requires organizational change management alongside technical deployment.

1. Prioritize rules by business value


Identify 20% of data elements driving 80% of business value and concentrate rule creation there first. Use criticality assessments considering regulatory requirements, revenue impact, and operational dependencies.

2. Implement data contracts


Move away from reactive cleanup by using data contracts. These agreements enforce quality rules at the source, ensuring that data producers are held accountable for the health of the information before it ever hits your warehouse.

3. Create reusable rule templates


Build rule templates defining validation patterns once, then apply templates across multiple instances. Email validation logic applies to customer emails, employee emails, and vendor contact emails.

4. Leverage AI and active metadata


Manual rule creation cannot scale in 2026. Use data quality tools with active metadata management to set up an always-on, intelligent system and leverage AI to suggest rules automatically based on historical patterns.

5. Automate stewardship and remediation


A rule failure must trigger an immediate workflow. Map every rule to a specific data steward so that alerts are sent to the person with the business context to fix them. Transparency is key; make these pass/fail rates visible in a central catalog to build organizational trust.

6. Communicate quality metrics transparently


Make rule execution results visible to all stakeholders. Data quality metrics tracking pass rates, trend lines, and violation patterns help teams understand current state and measure improvement. Some examples include:

  • Data uptime: The percentage of time your critical data assets meet all defined quality rules. This is the ultimate “SLO” for a data team.

  • Time to detection (TTD): The average time between a data quality rule failure and the automated alert being issued to a steward.

  • Data quality ROI: The estimated cost savings from preventing downtime and manual cleanup, measured against the cost of your governance tools and personnel.

Organizations adopting data quality management approaches treat rule implementation as an ongoing program rather than a one-time project.

How do modern platforms streamline data quality rules?

Modern data quality platforms are shifting from manual, labor-intensive setups to metadata-driven automation and intelligent rule management. This transition allows teams to manage thousands of rules with minimal operational burden.

AI-driven rule generation


Instead of writing logic from scratch, modern data quality software uses AI to analyze data patterns and suggest relevant validation checks. Teams simply review and approve machine-generated rules, reducing the setup time from hours to minutes.

Metadata-driven enforcement


Rules are applied automatically based on classifications. For example, tagging a column as “PII” or “Financial” can instantly trigger a suite of pre-defined privacy and accuracy rules across the entire data estate.

Native warehouse execution and observability


Rules now run directly within the cloud warehouse (like Snowflake or Databricks) using native compute. Metadata control planes like Atlan unify these platforms and integrate with observability tools like Monte Carlo or Soda to surface quality signals alongside catalog metadata, creating unified quality dashboards.


They also support automated column-level lineage for root cause and impact analysis. This allows teams to see exactly which downstream dashboards are affected by a failure.

Collaborative data contracts


Modern platforms like Atlan embed data contracts directly within workflows, formalizing quality agreements between producers and consumers. This creates a shared source of truth where both parties are automatically notified if a contract is breached, ensuring immediate accountability.

Atlan’s Data Quality Studio for unified data quality management


Teams using Atlan’s Data Quality Studio report significant efficiency gains compared to manual rule management. Point-and-click rule templates, smart scheduling, and Slack alerts that pinpoint failures with business context reduce operational burden while expanding validation coverage.

See how Atlan's metadata-driven approach reduces manual work

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Real stories from real customers: Transforming data quality with effective rules

Organizations implementing systematic data quality rule programs achieve measurable improvements in data trust and operational efficiency.

General Motors: Data Quality as a System of Trust


“By treating every dataset like an agreement between producers and consumers, GM is embedding trust and accountability into the fabric of its operations. Engineering and governance teams now work side by side to ensure meaning, quality, and lineage travel with every dataset — from the factory floor to the AI models shaping the future of mobility.” — Sherri Adame, Enterprise Data Governance Leader, General Motors

See how GM builds trust with quality data →

Workday: Data Quality for AI-Readiness


“Our beautiful governed data, while great for humans, isn’t particularly digestible for an AI. In the future, our job will not just be to govern data. It will be to teach AI how to interact with it.” — Joe DosSantos, VP of Enterprise Data and Analytics, Workday

See how Workday makes data AI-ready →

Moving forward with data quality rules

Implementing effective data quality rules requires balancing comprehensive validation with operational feasibility. Start by identifying high-impact data elements, engage both technical and business stakeholders in defining criteria, and adopt platforms that automate rule management through metadata intelligence.

The organizations seeing greatest success treat rules as living specifications that evolve with business requirements rather than static technical constraints.

Transform your data quality with Atlan's AI powered rule suggestions.

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FAQs about data quality rules

1. What is the difference between data quality rules and business rules?


Data quality rules validate whether data meets technical and format standards like “email must contain @ symbol.” Business rules encode operational logic like “orders over $10,000 require manager approval.” Quality rules ensure data is structurally correct while business rules determine how data should be processed or used in workflows.

2. How many data quality rules should an organization have?


The appropriate number varies based on data complexity and business criticality rather than following fixed targets. Most organizations benefit from 50-100 foundational rules covering critical data elements, expanding coverage iteratively based on quality issues encountered and business priorities. Focus on depth of coverage for important data rather than breadth across all data.

3. Should data quality rules block data or just flag issues?


The decision depends on data criticality and downstream impact tolerance. Block data for critical fields where errors cause immediate business harm like financial transactions or regulatory reporting. Flag issues for less critical data where some tolerance exists and human review adds value before rejection.

4. How often should data quality rules be reviewed and updated?


Review rules quarterly to ensure alignment with current business requirements and validate that thresholds remain appropriate. Additionally, trigger reviews when business processes change, new data sources integrate, or rule violation patterns shift significantly indicating evolving data characteristics or quality challenges.

5. Can data quality rules be applied to unstructured data?


Traditional rules work best for structured data with defined schemas and formats. Unstructured data like documents, images, or free text requires different approaches including completeness checks for required attachments, format validation for acceptable file types, and metadata rules for classification and tagging rather than content validation.

6. What is the relationship between data quality rules and data governance?


Data governance provides the framework establishing who defines rules, approval processes for new rules, and escalation paths for violations. Rules are the operational implementation of governance policies, translating high-level quality standards into executable validation logic embedded in systems and workflows.

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