Data Validation Guide: Ensuring Accuracy & Integrity in 2025

Updated December 04th, 2024

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Data validation is the process of verifying data accuracy, consistency, and adherence to quality standards.

It detects errors, enforces predefined rules, and enhances reliability across systems. Automated validation tools streamline operations, enabling real-time anomaly detection and scalability.

This approach safeguards data quality, empowers decision-making, and meets regulatory demands.
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Data validation is a crucial process in data management, ensuring the accuracy and quality of data. It involves checking data against specific rules or criteria, such as data types, range constraints, and format specifications, to verify its integrity and correctness.

This process is essential in maintaining reliable and trustworthy data, particularly in data-intensive fields like analytics, database management, and decision-making processes.

In this article,

  • We unravel the intricacies of data validation
  • Explore the three essential types of data validation
  • Processes to achieve data that you can trust.

Let’s take a deep dive into the world of data validation!

Table of contents #

  1. What is data validation?
  2. 3 Types of data validation
  3. The 9 benefits of data validation you cannot miss
  4. The 4-step data validation process: Ensuring data accuracy and integrity
  5. What are the three types of data validation?
  6. How organizations making the most out of their data using Atlan
  7. Summing up
  8. FAQs about Data Validation
  9. Data validation: Related reads

What is data validation? #

Data validation is a process used in data management and database systems to ensure that data entered or imported into a system meets certain quality and integrity standards.

The primary goal of data validation is to prevent inaccurate, incomplete, or inconsistent data from being stored or processed, which can lead to errors and problems in various applications and analyses.

Data validation can be applied at various stages in data processing, including:

  1. Data entry: Validation checks are performed as data is entered to ensure that it conforms to the defined rules before it is accepted into the system.
  2. Batch processing: Data can be validated in bulk as part of batch processing routines to identify and correct errors in existing datasets.
  3. Real-time validation: Some systems perform real-time validation as data is submitted or updated to ensure data integrity at the point of entry.
  4. Database level: Databases often have built-in mechanisms for data validation, such as constraints, triggers, and stored procedures, which enforce data quality and consistency.

Common validation checks include verifying that numerical data falls within an acceptable range, ensuring dates are valid, checking for unique identifiers, and validating data formats (e.g., email addresses or phone numbers).

3 Types of data validation #

1. Pre-entry data validation #


Pre-entry data validation is primarily focused on preventing obviously incorrect or incomplete data from being entered into a system. It sets the initial quality standards for incoming data.

This type of validation occurs before data is entered into the system, at the point of data collection or data entry initiation.

Pre-entry validation helps improve data quality by addressing common data entry errors in real time. It minimizes the need for data correction after the data has been entered and enhances user experience by guiding them to provide accurate information.

Examples

  • Required fields in data validation ensure that all essential information is provided by mandating that certain fields must be filled in before the data can be processed.
  • Data type checks involve verifying that the data entered matches the expected format, such as confirming that a field meant for dates actually contains a valid date.
  • Format checks are employed to validate the structure of the data, ensuring that inputs like email addresses, phone numbers, or postal codes adhere to the standard formatting conventions for these types of data.

2. Entry data validation #


Entry data validation focuses on real-time checks and feedback during the data entry process to ensure that data adheres to predefined criteria and standards. It helps prevent errors at the point of entry.

Entry data validation occurs as data is being input into the system. Users or data entry personnel receive immediate feedback on the quality and accuracy of the data.

Entry data validation improves the accuracy of data input and reduces the likelihood of errors entering the system. It provides real-time guidance to users, ensuring that data meets quality standards before it is stored, which, in turn, reduces the need for subsequent data correction and maintenance.

Examples

  • Providing drop-down menus, auto-suggestions, or pick-lists to guide users in selecting valid values.
  • Flagging errors or inconsistencies as they occur, such as inputting a future date or a negative value in a quantity field.
  • Validating data against predefined rules, such as checking that an employee id is unique or falls within a specific range.

3. Post-entry data validation #


Post-entry data validation is focused on assessing and maintaining the quality and integrity of data already present in the system. It addresses data quality issues after data has been entered or imported.

This type of validation occurs through batch processing routines or periodic validation checks applied to existing datasets.

Post-entry data validation helps maintain data quality over time. It ensures that the data remains accurate and consistent by addressing errors and inconsistencies that may have crept into the system over time.

Examples

  • Data cleansing activities, which include removing duplicate records, correcting errors, and standardizing formats.
  • Checking data for referential integrity to ensure that relationships between data elements are consistent and valid.
  • Validating data against predefined rules on a regular or as-needed basis to identify and rectify data quality issues.

These three types of data validation are essential components of a comprehensive data quality management strategy.

They collectively contribute to the accuracy, reliability, and integrity of data within an organization’s databases and systems, ensuring that data remains a trustworthy asset for decision-making and reporting.

The choice of which type to use depends on the specific data management needs and objectives of the organization.

The 9 benefits of data validation you cannot miss #

Data validation is a critical process that ensures the integrity of your data, safeguarding it from errors, inconsistencies, and inaccuracies.

From improving decision-making to maintaining regulatory compliance, data validation offers a multitude of benefits that organizations simply cannot afford to overlook.

In this section we’ll talk about the nine compelling advantages of data validation, shedding light on why it’s an essential practice for any data-centric enterprise.

The key benefits of data validation include:

  1. Improved data quality
  2. Error prevention
  3. Enhanced data consistency
  4. Data integrity
  5. Improved decision-making
  6. Reduced data entry errors
  7. Faster data processing
  8. Compliance and regulatory requirements
  9. User-friendly interfaces

Let’s look at them in detail:

1. Improved data quality #


Data validation is crucial for maintaining the accuracy and reliability of data within a system.

By applying validation rules, you can ensure that the data entered or imported meets specific quality standards, reducing inaccuracies, inconsistencies, and errors in the data.

This, in turn, results in a higher overall data quality.

2. Error prevention #


Data validation helps prevent errors in the data at the point of entry or during batch processing. When data is validated, it undergoes checks to ensure it adheres to predefined criteria and constraints.

Invalid or erroneous data is flagged and can be rejected or corrected before it is stored. This reduces the need for costly and time-consuming data cleansing and correction procedures later on.

3. Enhanced data consistency #


Validation rules enforce consistency in data elements. This means that data is required to conform to predefined standards, such as data types, formats, and ranges.

Consistent data is essential for reporting, analysis, and data integration, as it ensures that data elements are uniform and can be reliably compared and combined.

4. Data integrity #


Data validation plays a critical role in maintaining the integrity of data.

By preventing the entry of invalid or malicious data, it helps safeguard the security and functionality of the system.

This is especially important in systems where data quality and security are paramount, such as financial or healthcare applications.

5. Improved decision-making #


High-quality, validated data leads to more reliable and informed decision-making. Decision-makers can have confidence in the data they use for analysis, reporting, and strategic planning.

This is resulting in better outcomes and reduced risk of making decisions based on inaccurate or incomplete information.

A 2025 Outlook: Data Integrity Trends and Insights report by Precisely revealed that 64% of organizations identified data quality as their top data integrity challenge, marking an increase from 50% in 2023. The same survey found that 67% of organizations do not completely trust their data for decision-making, up from 55% the previous year. This highlights the need for data validation to build trust and enable confident decision-making.

6. Reduced data entry errors #


When data validation is applied, data entry personnel are less likely to make mistakes.

The system enforces validation rules in real-time, providing immediate feedback to users as they enter data, helping them correct errors as they occur.

This not only improves data accuracy but also enhances the user experience by guiding users through the data entry process.

7. Faster data processing #


Valid data is easier and quicker to process because it meets the predefined quality and consistency standards.

With less need for data cleansing and transformation.

The systems can respond faster to queries and generate reports more efficiently, improving overall system performance.

8. Compliance and regulatory requirements #


In certain industries, such as finance, healthcare, and legal, data validation is essential for meeting compliance and regulatory requirements. Compliance often mandates the use of validated data to ensure data accuracy, security, and privacy.

9. User-friendly interfaces #


Systems that incorporate data validation often provide real-time feedback to users during data entry. This feedback helps users correct errors as they occur, making the data entry process more user-friendly and efficient.

These benefits collectively contribute to the trustworthiness and reliability of data within an organization, enabling better data-driven decision-making, reducing the risk of data-related issues, and ensuring that data remains an asset rather than a liability.

Data validation is essential for maintaining the accuracy and reliability of databases and ensuring that data-driven decisions are based on trustworthy information.

It is a critical component of data quality management and helps prevent data-related issues and errors in various applications and reports.

The 4-step data validation process: Ensuring data accuracy and integrity #

Data validation is a critical component of data management, ensuring that the information within a system remains accurate, reliable, and consistent.

This four-step process is key to achieving these goals. Here are the following four-step process:

  1. Data entry
  2. Validation rules definition
  3. Validation process
  4. Error handling

Let’s understand them in detail.

1. Data entry: the starting point of data quality #


Data validation begins at the data entry stage, where raw information is collected and input into a system. This process involves:

  • Data collection: Information is gathered from various sources, including physical forms, online surveys, sensor readings, and more.
  • Manual vs. Automated entry: Data can be entered manually by individuals or automatically through software tools, depending on the volume and complexity of the data.
  • Data cleansing: Before entry, data may undergo cleansing to remove duplicates, correct errors, and standardize formats, ensuring a clean and reliable starting point.
  • Data validation at entry: Some systems incorporate basic validation checks, such as ensuring required fields are filled, data types are correct, and basic formatting is adhered to.

2. Validation rules definition: Establishing data standards #


The second step in data validation is defining validation rules, which set the criteria for what constitutes valid data. These rules can be highly varied and include:

  • Data type checks: Ensuring that data is of the expected type (e.g., text, numbers, dates).
  • Range checks: Verifying that numerical data falls within acceptable ranges.
  • Format checks: Ensuring that data adheres to specific formats (e.g., email addresses or phone numbers).
  • Referential integrity checks: Guaranteeing that data relationships and references are maintained.

3. Validation process: Assessing data against rules #


Once validation rules are in place, the data undergoes rigorous assessment against this criteria:

  • Comparing data: The data is compared against the defined rules to determine if it’s valid. Data that meets the criteria is deemed valid, while data that falls short is flagged as invalid.

4. Error handling: Dealing with invalid data #


The final step of data validation involves handling data that doesn’t meet validation criteria:

  • Prompting correction: Users or data entry personnel may be prompted to correct invalid data.
  • Automated correction: In some cases, the system can apply automated corrections if the issues are straightforward.
  • Rejection or notification: Invalid data can be rejected or notifications can be sent to relevant personnel for manual review and correction.

By following these four steps, data validation ensures that data remains accurate and reliable, preventing errors, inconsistencies, and inaccuracies that could impact data quality and decision-making. It’s a fundamental practice in maintaining data integrity and trustworthiness.

What are the three types of data validation? #

Data validation can be categorized into three main types based on when and where the validation occurs in the data management process:

The types are:

  1. Pre-entry data validation
  2. Entry data validation
  3. Post-entry data validation

Let us understand each of them in detail.

1. Pre-entry data validation #


  • Purpose: Pre-entry data validation is primarily focused on preventing obviously incorrect or incomplete data from being entered into a system. It sets the initial quality standards for incoming data.
  • When it occurs: This type of validation occurs before data is entered into the system, at the point of data collection or data entry initiation.
  • Examples of checks:
    • Required fields: Ensuring that mandatory fields are filled before data is accepted.
    • Data type checks: Verifying that data types match the expected format (e.g., checking that a date field contains a valid date).
    • Format checks: Validating data formats, such as email addresses, phone numbers, or postal codes.
  • Benefits: Pre-entry validation helps improve data quality by addressing common data entry errors in real time. It minimizes the need for data correction after the data has been entered and enhances user experience by guiding them to provide accurate information.

2. Entry data validation #


  • Purpose: Entry data validation focuses on real-time checks and feedback during the data entry process to ensure that data adheres to predefined criteria and standards. It helps prevent errors at the point of entry.
  • When it occurs: Entry data validation occurs as data is being input into the system. Users or data entry personnel receive immediate feedback on the quality and accuracy of the data.
  • Examples of checks:
    • Providing drop-down menus, auto-suggestions, or pick-lists to guide users in selecting valid values.
    • Flagging errors or inconsistencies as they occur, such as inputting a future date or a negative value in a quantity field.
    • Validating data against predefined rules, such as checking that an employee id is unique or falls within a specific range.
  • Benefits: Entry data validation improves the accuracy of data input and reduces the likelihood of errors entering the system. It provides real-time guidance to users, ensuring that data meets quality standards before it is stored, which, in turn, reduces the need for subsequent data correction and maintenance.

3. Post-entry data validation #


  • Purpose: Post-entry data validation is focused on assessing and maintaining the quality and integrity of data already present in the system. It addresses data quality issues after data has been entered or imported.
  • When it occurs: This type of validation occurs through batch processing routines or periodic validation checks applied to existing datasets.
  • Examples of checks:
    • Data cleansing activities, which include removing duplicate records, correcting errors, and standardizing formats.
    • Checking data for referential integrity to ensure that relationships between data elements are consistent and valid.
    • Validating data against predefined rules on a regular or as-needed basis to identify and rectify data quality issues.
  • Benefits: Post-entry data validation helps maintain data quality over time. It ensures that the data remains accurate and consistent by addressing errors and inconsistencies that may have crept into the system over time.

These three types of data validation are essential components of a comprehensive data quality management strategy.

They collectively contribute to the accuracy, reliability, and integrity of data within an organization’s databases and systems, ensuring that data remains a trustworthy asset for decision-making and reporting.

The choice of which type to use depends on the specific data management needs and objectives of the organization.

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

Summing up #

In the world of data management, data validation emerges as the sentinel of data integrity, safeguarding the accuracy and reliability of information at every stage of its journey.

From pre-entry checks to real-time validation and post-entry maintenance, the commitment to data quality is a commitment to informed, trustworthy decision-making.

As you navigate the data-driven landscape, remember that data validation is the key to ensuring that your data remains a source of unwavering trust and unwavering value.

FAQs about Data Validation #

1. What is data validation? #


Data validation ensures data accuracy, consistency, and reliability. It verifies that data meets predefined rules and quality standards, such as format, range, and completeness, maintaining data integrity throughout processing and storage.

2. How do I implement data validation in my system? #


Implementing data validation involves defining validation rules, integrating automated tools, and establishing workflows to check data at various stages—before entry, during entry, and post-processing.

3. How does data validation improve data quality? #


Data validation improves data quality by ensuring that all data adheres to established standards, reducing errors, and maintaining consistency. This leads to more accurate analytics and informed decision-making.

4. What are the common types of data validation rules? #


Common types include format validation, range checks, schema validation, and cross-field validation. These rules ensure data conforms to specified patterns, falls within acceptable ranges, and maintains consistency across related fields.

5. How can data validation ensure regulatory compliance? #


Data validation enforces compliance by verifying that data aligns with legal and industry standards like GDPR or HIPAA. Automated validation processes help maintain audit trails and mitigate compliance risks.

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