What is data validation? Methods and examples

Every organization relies on data being trustworthy. However, inaccurate, incomplete, or inconsistent data tampers with analytics, leading to incorrect insights.

It also affects business outcomes and product decisions. The effects can lead to failed system migrations or risk compliance for your team.

Data validation is the technique or process that ensures clean and accurate data. As a result, it leads to better insights, findings, and decision-making.

In this post, you’ll learn how data validation works, why it is essential, and how to implement it effectively. You’ll also explore related concepts like data cleaning and verification, as well as how modern automated agentic tools can be leveraged to transform this process.

What is data validation?

Data validation is the process of verifying that data complies with defined rules and constraints.

It’s performed before data is stored, processed, or accepted by the system. It ensures accuracy, consistency, and data integrity across systems. For instance, when a user submits a sign-up form, the submitted data is validated as to whether it represents the correct data type.

Similarly, when data is transferred from one system to another, it’s validated to ensure uniformity and consistency. Data validation is a quality parameter that ensures any data that enters a system meets the expected criteria and requirements.

 

What does data validation mean in practice?

In a practical sense, data validation involves enforcing standards or constraints to ensure data passes certain checks.

These rules for validating data come from a variety of sources, such as business requirements, system specifications, data contracts, regulatory standards, and so on.

Together, these rules combine to create an end-to-end data validation framework. This framework then enforces the meaning and usability of data across its life cycle.

For instance, a street address validation might confirm if it follows a recognizable postal format. A transaction amount validation might confirm that it is a positive number or that it falls within an expected range. Some other common examples appear below.

Why perform data validation?

For systems to consistently produce correct outcomes, teams must perform data validation systematically to avoid disrupting the organization’s data architecture.

The following scenarios explain why teams need to perform data validation in different use cases:

1. Humans feed data into systems. However, they can make errors during data entry, which can create additional errors later when the data is transferred to a different system.
2. A machine-learning model can be fed wrong or inconsistent data, leading to biased, inconsistent, or even invalid predictions.
3. Poor quality of data can directly disrupt business operations during system migrations.
4. If a financial system is fed inconsistent data, regulatory scrutiny or even penalties can occur.

Data validation is the primary mechanism through which organizations can protect the integrity of their information assets.

Why do organizations need data validation?

There are three primary reasons why organizations need data validation: operational reliability, regulatory compliance, and competitive intelligence.

Operational reliability

Operational reliability means that the system is expected to produce accurate outcomes throughout its operation.

If validation during operation fails due to inconsistent data, it will result in incorrect orders, failed transactions, or incorrect transactions. This will eventually hamper the organization’s overall functionality.

Operational reliability means that the system is expected to produce accurate outcomes throughout its operation.

Regulatory compliance

Regulatory compliance indicates that the data fed into the system meets quality standards and the expected criteria. If data is not in sync with the standards, it can result in reputational damage, penalties, or even legal problems.

Competitive intelligence

Competitive intelligence means that data is validated to ensure no inconsistent data leads to wrong analytics or biased/incorrect decisions.

In this case, if data is not validated, incorrect analytics can lead to missed market opportunities, affecting the organization’s overall status.

When is data validation performed?

Data validation is performed at multiple stages in the process of the data life cycle:

 

Data validation performed only at the point of entry does not guarantee protection against data quality degradation at later stages in the pipeline.

If data does not pass any validation checks, it may not be permanently rejected. It’s flagged for correction or handling in accordance with defined rules, constraints, and policies.

Why is data validation important?

Modern systems are intricate and complex. When they consume invalid data, the scope of negative consequences expands, leading to damage to the organization’s reputation and its customers.

Each dimension of data quality addressed by validation directly maps to some kind of business risk when validation is absent.

Data validation offers the following benefits, making it crucial for organizations.

Validates data that’s clean and cross-checked can accurately represent the real-world entity or events it describes.

Accuracy

Validates data that’s clean and cross-checked can accurately represent the real-world entity or events it describes. For instance, a customer’s billing address being stored incorrectly causes invoices to be sent to the wrong location, leading to failed payments.

Completeness

Validation ensures that all the required fields are present and that records are not truncated. If a patient record has a missing date of birth, the healthcare system may face issues calculating the correct medication dosages or assigning age-appropriate medication.

Consistency

Cross-system validation ensures that all the data transferred between systems is in sync and meets required standards.

If an order is marked “shipped” in the logistics system but is still “pending” in the CRM, it can create conflicting status reports and cause misdirected customer support.

Timeliness

If any data or records are found to be obsolete, validation can detect that and prevent it from affecting existing records or their operations.

A supplied record with an expired contract date, if undetected, could continue to trigger automated purchase orders to a vendor who is no longer in service.

Uniqueness

Data validation can avoid or remove duplicate data to prevent record conflicts. For example, a customer phone number appearing twice in a CRM can cause operations on that contact to fail or produce invalid results.

Referential Integrity

Relational validation helps ensure that all foreign keys point to their parent records. An invoice referencing a deleted customer ID can cause the billing system to send duplicate or irrelevant emails.

How does data validation work?

Procedurally, data validation is a repeatable process of defining rules, executing checks, capturing results, and remediating failures.

Data validation is performed using a set of rules and constraints called validation rules. They can be applied to a dataset or to certain records and then evaluated for correctness.

The appropriate choice of tool depends on the scale of data, the complexity of validation rules, and the degree of integration required with existing systems.

Data validation tools

To assist with data validation, a number of data validation tools exist, from low-level utilities to complete and extensive end-to-end enterprise platforms.

The appropriate choice of tool depends on the scale of data, the complexity of validation rules, and the degree of integration required with existing systems.

The following list of tools can help different teams understand what and where each tool can be used:

Common data validation rules

Validation rules are the most essential element for data validation. Teams can use different rules in combination to ensure comprehensive coverage of data quality measures.

The following table gives the most commonly applied validation rule types, what they evaluate, and an example that explains those rules:

How to perform data validation

Performing data validation requires a structured approach with the following steps:

Identify data sources and data owners

Teams first document where data originates, who is responsible for it, and how it flows through a system. This is called data lineage documentation. It’s a prerequisite for comprehensive validation coverage.

Teams first document where data originates, who is responsible for it, and how it flows through a system.

Define validation rules with business stakeholders

Validation rules should not be defined by engineers in isolation, but rather alongside business stakeholders.

It’s the business stakeholders who understand the real-life implications and meaning of data and the consequences of quality failures. Rules must reflect both the technical constraints and business logic.

Prioritize rules by risk

Not all validation failures carry an equal amount of risk. For instance, a missing middle name is a much smaller issue in comparison to a missing patient identifier. Prioritizing rules by their risk factor can help streamline resources and make data validation a more efficient process.

Implement validation at the appropriate layer

Validation can be implemented at the:

• Database layer using constraints
• Application layer in the source code
• Pipeline layer through transformation checks
• Testing layer through automated assertions

Best practice is to implement validation at multiple layers to ensure every data inlet or source into the system is safeguarded against validation failures.

Automate and integrate into pipelines

Manual validation does not scale, especially with large projects and enterprises. Validation must be automated and integrated into CI/CD pipelines and data monitoring workflows to ensure consistent coverage.

Establish a remediation and escalation process

Validation without a remediation process is incomplete. Teams should define how failed records are handled, quarantined, corrected, rejected, or escalated to a data steward.

Monitor validation metrics over time

Track validation pass rates, failure trends, and data quality scores over time. Deteriorating validation metrics are early warning signals of upstream data quality problems.

 

Use case: Maintaining data integrity during an enterprise SAP S/4HANA migration

Flower Foods is the second-largest baking company in the United States with 47 bakeries that produce breads, buns, rolls, and snack cakes across the country. They initiated a complex migration from a 20-year-old SAP ECC environment to SAP S/4HANA.

Problem

The QA team faced hundreds of thousands of rows of data to migrate with no scalable automated validation capability.

Existing Excel-based test data management processes were too slow and extremely resource-intensive.

Naming conventions changed for each business unit across both systems, creating a high risk of data mismatches. Manual, ad hoc testing was also not able to scale to the full data set within the required migration timeline.

Solution

Flower Foods implemented Tricentis Data Integrity alongside Tricentis Tosca to automate end-to-end data validation across the migration process.

The model-based test automation enabled their automation to rapidly build tests that continuously verified data quality as environments changed, regardless of data type, source, or format.

Data Integrity maintained a mapping of dozens of specific business units across the ECC-to-S/4HANA transition and centralized test data management so that changes were immediately communicated between the data team and the testing team.

Test coverage was scaled to the complete data set.

Outcome

Flower Foods achieved a 35% reduction in their testing timeline and a significant reduction in time spent validating data during the migration, as test coverage was extended to the entire data set.

The manual stare-and-compare process for business-unit mapping was completely eliminated. Communication between the data and testing teams was streamlined through a centralized data management layer.

The organization completed the SAP S/4HANA migration with data integrity maintained throughout and business operations uninterrupted.

What are the different types of data validation?

Data validation can be categorized by where it is applied, what it’s actually evaluating, and how it’s being implemented. Understanding the different types of data validation can help teams build layered and highly comprehensive validation strategies.

Scope of application

In this category, there are four types of data validation:

1. Field-level validation: Validation checks are applied to individual data fields like type, format, range, and completeness.
2. Record-level validation: Checks are applied across multiple fields within a single record, such as consistency between related fields.
3. Cross-record validation: Checks that span multiple records, such as uniqueness enforcement or aggregate total reconciliation.
4. Cross-system validation: Checks that compare data across two or more systems to confirm consistency, which is critical in integration and migration scenarios.

By timing

Depending on when validation is applied, this category has three types of data validation:

1. Inline or real-time validation: Applied at the point of data entry or ingestion, where any invalid data is flagged immediately as it enters the system.
2. Batch validation: Applied to a complete dataset at a scheduled time, such as an overnight ETL reconciliation.
3. Streaming validation: Continuously applied to data as it flows through a message-based pipeline or an event-driven system.

By the implementation method

Depending on the methods or tools used to carry out data validation, this category has four types of data validation:

1. Constraint-based validation: Enforced by database engine constraints, such as NOT NULL, UNIQUE, CHECK, and FOREIGN KEY.
2. Schema validation: Enforced through schema definitions, such as JSON Schema, XSD, Avro Schema, etc.
3. Programmatic validation: Implemented in the application source code.
4. Rule engine validation: Managed through a centralized rule engine or data quality platform.
5. Automated test-based validation: Assertions are built into automated test suites that execute validation as part of the CI/CD pipelines.

Data validation vs. data cleansing vs. data quality management

Validating, managing, and cleansing data are three distinct but complementary capabilities. They are often confused, and understanding the distinctions can be helpful in designing a coherent data quality strategy.

Data cannot be meaningfully compared to a source if it does not first meet the basic structural and type requirements enforced by the validation rules.

Data validation vs. data verification

Another term that’s often confused with data validation is data verification. To understand the difference between data verification versus validation, consider the example of a system migration.

Validation will confirm if the records in the target system meet the format and constraint requirements. Verification will confirm if the values in the target system match what was actually present in the source system.

Both are necessary, but one doesn’t substitute for the other.

Validation can be understood as a prerequisite for verification. Data cannot be meaningfully compared to a source if it does not first meet the basic structural and type requirements enforced by the validation rules.

How is data validation used in a business environment?

Data validation is applied across virtually every business function that handles data. The following table maps common business scenarios to the types of validation that are most relevant for each use case:

Rules for consistency in data validation

Consistency validation is one of the most crucial and frequently overlooked components of data validation. Consistency rules ensure that related data fields agree with each other and that data maintains the same meaning and representation across systems.

Examples of consistency rules include:

• End date must be greater than or equal to start date
• Shipping address state must match the shipping address’s postal code state
• Total invoice amount must equal the sum of line item amounts
• Cancelled order must not have an associated fulfilment record
• Customer status “active” must not be combined with an account closure date in the past

Consistency rules encode business logic into the data layer, ensuring that data remains semantically coherent across fields, records, and systems.

Even with a sound strategy, data validation in practice can face several recurring challenges.

Challenges in data validation

Even with a sound strategy, data validation in practice can face several recurring challenges. The following indicates each challenge, what it is, and how you can mitigate it effectively:

Best practices for effective data validation

The most effective data validation strategy ensures the following best practices:

1. Validate data at the source

Validate data at the source, not just at the destination, to ensure validation failures are caught early. As a result, this leads to quicker resolution and also mitigates the consequences of validation failures at later stages.

2. Treat validation rules just as code

Version-control your validation rules alongside your data pipelines and applications. Tracking validation rules and runs will help you devise a more comprehensive validation coverage in subsequent runs.

3. Build validation into your CI/CD pipelines

Data validation should be a mandatory gate in deployment and data pipeline promotion workflows. This will make data validation part of your engineering process.

4. Monitor data validation metrics

Monitor data validation metrics continuously just as you’d monitor operational KPIs. Keep track of data quality scores, validation pass rates, and error trends so that it’s visible to engineering leadership alongside system performance metrics.

It will also help in drafting more comprehensive test reports and enabling teams to devise better validation strategies based on these metrics.

5. Do not embed implicit validation logic

Do not embed implicit validation logic inside transformation code. Make validation an explicit, auditable step. Abstracting validation logic will allow it to be more readable, as well as easier to manage and update, even for team members who do not have complete context about it.

6. Collaborate with business stakeholders

Collaborate with business stakeholders on rule definition. Rules defined without a business context often remain incomplete or become incorrect.

7. Plan well for validation failure

Plan well for validation failure. For example, define remediation paths before they are even needed. As a result, validation without a defined response to failure becomes incomplete governance.

Having remediation paths in place lets you resolve failures quickly, mitigating the consequences of those failures on your business and organization.

Validation without a defined response to failure is incomplete governance.

Tricentis data integrity

When it comes to enterprise-scale data validation, especially in ERP systems, system migrations, and complex multi-system landscapes, automated validation platforms can enhance the effects of validation for your team.

These platforms provide capabilities beyond general-purpose tools and add more structure to the entire validation process.

Tricentis Data Integrity provides automated data validation that can be implemented in complex enterprise environments. It allows you to:

• Build validation tests rapidly without manual scripting, using model-based test design.
• Compare data across SAP, Oracle, Salesforce, Snowflake, and other commonly used enterprise platforms for cross-system validation.
• Automate reconciliation of millions of records and eliminate manual intervention in cumbersome processes.
• Integrate with other tools like Tricentis Tosca for complete functional testing.
• Leverage AI-generated insights to spot data quality trends and prioritize remediation, reducing future validation failures.

Learn how Tricentis helps teams validate data and ensure quality through AI-driven testing solutions.

How agentic AI improves data validation

Agentic AI can enhance data validation. Here’s how:

1. Agentic automation can provide autonomous rule discovery where AI agents analyze data distributions to infer rules, rather than manually defining them.
2. It can create self-healing pipelines by detecting failures and executing remediation measures automatically, rather than having engineers manually respond to validation alerts.
3. It can coordinate validation across multiple systems, APIs, and data stores as a unified workflow, rather than manually maintaining and running scripts for cross-system validation checks.

Tricentis Data Integrity has built AI capabilities into its validation platform, which enables teams to build, execute, and maintain data validation at scale, leveraging the above benefits of agentic AI.

This post was written by Siddhant Varma. Siddhant is a full-stack JavaScript developer with expertise in front-end engineering. He’s worked with scaling multiple start-ups in India and has experience building products in the ed-tech and healthcare industries. Siddhant has a passion for teaching and a knack for writing. He’s also taught programming to many graduates, helping them become better future developers.