Data quality is crucial for any organization that relies on data to make critical business decisions. Two key components of ensuring data quality are data validation and data verification. Though these terms are sometimes used interchangeably, there are important differences between data validation vs data verification that have implications for overall data integrity.

This article will explain what data validation and data verification are, how they differ, and when each should be used as part of an overall data quality strategy. The focus keywords are “data validation” and “data verification.”

Key Takeaways

Data validation checks data to ensure it meets predefined standards and rules. Data verification compares data across systems to check for inaccuracies.
Data validation occurs early in data entry. Data verification happens after data has been entered and processed.
Data validation prevents bad data from entering systems. Data verification detects errors in existing data.
Data validation uses rules like data types, formats, and ranges. Data verification uses cross-checks between systems.
Data validation and verification are crucial for high-quality data. They complement each other in the data quality process.

Also Read: Block Cipher vs Stream Cipher: What’s the Difference Between Them

A Head to Head Comparison Between Data Validation vs Data Verification

Feature	Data Validation	Data Verification
Definition	The process of checking the accuracy, completeness, and integrity of data before it is used or stored.	The process of confirming the accuracy and reliability of data by cross-checking it against a reliable source.
Purpose	To ensure the data meets predefined standards and requirements.	To confirm that the data is accurate, reliable, and consistent with the original source.
Timing	Occurs during the data entry or data collection process.	Occurs after the data has been collected and stored.
Automation	Can be automated through the use of rules, scripts, or software.	Can also be automated through the use of data reconciliation tools or scripts.
Error Prevention	Helps prevent errors from being introduced into the data.	Does not prevent errors, but rather identifies and corrects them.
Error Detection	Detects errors during the data entry or collection process.	Detects errors after the data has been collected and stored.
Error Correction	Allows for immediate correction of errors during the data entry or collection process.	Requires additional steps to correct errors after they have been identified.
Data Quality	Ensures the data meets predefined quality standards.	Confirms the data is accurate and reliable.
Data Integrity	Ensures the data is complete, consistent, and free from errors.	Confirms the data has not been tampered with or altered.
Reporting	Provides reports on the accuracy and completeness of the data.	Provides reports on the reliability and consistency of the data.
Compliance	Ensures the data meets regulatory or industry-specific requirements.	Confirms the data is in compliance with relevant regulations or standards.
Cost	Generally less expensive than data verification, as it is implemented during the data entry or collection process.	Can be more expensive than data validation, as it requires additional steps and resources to cross-check the data.

What is Data Validation?

Data validation is the process of ensuring data meets specific standards and requirements before it gets entered into a system. It acts as an initial data quality safeguard by checking raw data against a set of predefined rules and parameters.

The main purpose of data validation is to prevent incorrect, inconsistent, or irrelevant data from entering a database or system. Data validation checks that data is complete, accurate, and reasonable in the context of what is expected.

Some examples of data validation checks include:

Data types: Check whether a field contains the expected data type, such as text, number, date, etc.
Data formats: Checking data is formatted correctly, like dates in MM/DD/YYYY format.
Valid options: Checking data matches predefined options, like “Male” or “Female” for gender.
Length: Checking data falls within minimum/maximum length limits.
Range: Checking numbers fall between an allowable minimum and maximum.
Required fields: Checking required fields are not empty.
Consistency: Checking data is consistent across records, like identical customer IDs.

By running data through validation checks, issues like typos, formatting mistakes, missing information, and nonsensical values can be caught early before they reach core systems and reports. This helps improve overall data integrity and reliability.

Data validation is typically implemented at the point of data entry through input masks, dropdown selections, automated editing checks, and other controls. It can also occur through batch processes that scrub larger data sets before they are loaded into target databases and applications.

Also Read: Encryption vs Tokenization: What’s the Technical Difference Between Them

What is Data Verification?

While data validation focuses on checking raw data at the source, data verification focuses on double-checking the accuracy of data that has already been entered and processed in a system.

Data verification involves cross-checking data values across different systems and data sets to identify inconsistencies or errors in existing data. The objective is to detect issues that may have gotten past initial data validation checks and made their way into core databases and reports.

Some examples of data verification checks include:

Cross-system checks: Comparing common data fields across different systems for inconsistencies, like customer last name in a CRM vs order system.
Checksum comparisons: Using checksums or hash values to check datacopied between systems wasn’t corrupted or changed.
Audit checks: Looking for missing sequence numbers, transaction IDs, or other indicators of gaps in data transfer and load processes.
Reference data: Checking codes and categorical values match the allowed options in a reference lookup table.
Historical comparisons: Comparing current data to expected trends and patterns based on historical data.

While data validation operates on individual data fields and records, data verification can span multiple systems and data sets to highlight systemic data inaccuracies. Data verification procedures like cyclic redundancy checks and hash totals are commonly used to monitor large volumes of data moving between systems for completeness and accuracy.

Data verification can output reports on inconsistencies found so that erroneous data can be corrected at the source. It may also initiate workflows to notify relevant departments about data quality issues.

Key Differences Between Data Validation and Data Verification

While data validation and data verification both contribute to overall data integrity, there are some key differences between the two processes:

When it happens

Data validation happens early when data is entered and submitted.
Data verification happens later after data has gone through processing into core systems.

What it checks

Data validation checks individual data points against expected criteria.
Data verification looks for inconsistencies across entire data sets.

How it works

Data validation uses predefined rules and parameters.
Data verification uses cross-checks between systems and datasets.

What it finds

Data validation finds errors by source data submitters.
Data verification finds systemic data inaccuracies.

Purpose

Data validation prevents bad data from entering systems.
Data verification detects errors in data within systems.

While data validation focuses on establishing strong data hygiene at the source, data verification provides downstream checks to ensure information stays accurate as it flows between systems.

Data validation and verification procedures also generate different metrics. Data validation metrics examine measures like rejection rates and required field completion. Data verification metrics examine error rates found through cross-checks and orphan records between systems.

Why Data Validation and Verification Work Together

Though data validation and data verification have some differences, they actually work closely together as complementary components of a complete data quality strategy:

Layers of protection: Data validation and verification provide multiple layers of protection against bad data.
Better decisions: Preventing and finding data issues leads to higher-quality analytics and decisions.
Efficient processes: Early data validation saves costs vs finding/fixing errors later.
Accountability: Data validation at input ensures accountability for quality.
Proactive monitoring: Data verification provides ongoing monitoring of data risks.
Cascading checks: Issues found in verification can flag validation gaps.
Complete picture: Validation and verification metrics offer insights across the data lifecycle.

Data Validation Methods and Techniques

To implement effective data validation processes, some various methods and techniques can be used:

Input Forms

Input forms can apply validation rules like data types, field lengths, input masks, and dropdown values to check data on submission. For example, they can require dates in MM/DD/YYYY format or limit text lengths.

Scripts

Scripts executed during file loading processes can check for issues like properly formatted header rows, valid code combinations, referential integrity, and reasonableness. For example, flagging sales transactions with invalid product codes.

Batch-Edit Checks

Batched validation jobs can run more complex edit checks like testing field ranges, looking for duplicate records, and checking cross-field consistency. For example, validating IDs are unique across a dataset while checking for orphan records missing IDs.

Database Constraints

Database constraints like data types, enums, foreign keys, and CHECK clauses validate data in transit during transactions and query operations. For example, foreign keys can ensure reference data integrity across related tables.

Hashing

Hashing inputs into a fixed-length digest can detect tampering or corruption. Matching hash values indicate no modification, while mismatched hashes reflect changed data.

Regular Expressions

Applying regular expression rules can validate text formats like phone numbers, codes, and equipment IDs. For example, regex can validate VIN numbers against make/model specifications.

Predictive Modeling

Statistical methods like time series forecasting can validate data by predicting expected values for comparison and flagging significant deviations. For example, anomaly detection on usage readings.

By combining these techniques, a layered data validation strategy can be implemented to enhance data quality during input and loading processes before information reaches downstream systems.

Data Verification Techniques and Methods

Some key techniques and methods for verifying the accuracy of data within systems include:

Reference Data Checks

Comparing operational data against authoritative master data can reveal inconsistencies, such as invalid product names or location codes, that require correction in transactional systems.

Cyclic Redundancy Checks

Cyclic redundancy checks compare checksums calculated before and after data transfer to detect changes or corruption during system migration.

Reconciliation

Reconciling transaction counts, amounts, and hashes between systems identifies missing or duplicated data, such as orders or payments that are only present in one system.

Time Sequence Checks

Testing for missing or non-sequential transaction IDs, dates, or codes can reveal gaps that indicate broken processes, missed data, or extraction issues.

Multi-way Matching

Matching values for the same attribute across 3 or more systems can pinpoint discrepancies and sources for data errors. For example, different client addresses across CRM, billing, and fulfillment systems.

Stratification

Analyzing data volumes, totals, distributions, and statistics across subsets over time can uncover unusual patterns that signal potential data issues.

Gap Analysis

Examining periods with missing data where expected can highlight failed batches, broken integrations, or changes in upstream data feeds into a system.

Audit Sampling

Auditing random samples to validate values against source documents or inputs can uncover the accuracy rate and systemic data issues.

Analytics Review

Collaborating with business analysts to identify unexpected changes, outliers, or suspicious values in reporting data can pinpoint problem areas.

What are the Best Practices for Effective Data Validation and Verification

To maximize the effectiveness of data validation and verification processes for enterprise data quality, some key best practices include:

Set organization-wide data quality standards: Maintain consistent validation rules across systems and processes. Use centralized reference data for verification checks.
Validate at point of entry: Catch bad data early before propagation into systems. Use input validation techniques like required fields, formats, ranges, and lookups.
Use both synchronous and batch validation: Provide immediate user feedback on issues, along with robust batch checks.
Automate where possible: Automated rules and scripts enable efficient, scalable data validation.
Create alerts on violations: Alert data owners and users to validation failures for quick resolution.
Monitor verification results: Review ongoing verification checks and failed tests for needed data corrections.
Trace issues back to root causes: Leverage verification exceptions to pinpoint systemic input quality gaps for validation improvements.
Measure progress: Track validation rejection rates and verification error counts over time as key data quality KPIs.
Make incremental improvements: Continually refine rules and checks to adapt to the evolving data landscape.
Communicate data quality discipline: Instill an organization-wide commitment to proactive, preventative data quality through validation and verification at all layers.

Data Validation and Verification Using Talend

Talend offers powerful features to help implement robust data validation and verification across the enterprise, including:

Data Quality Tools

Talend provides an intuitive framework to build and deploy data quality rules, patterns, and algorithms for both batch and real-time data validation. This includes capabilities like:

Out-of-the-box validation routines for dates, emails, credit cards, and more
Matching algorithms to identify duplicates and relationships
Standardization to conform formats and values
Reference data matching
Custom validation rules using scripts and regex

These tools can be leveraged to validate data during migration, integration, and monitoring processes.

Data Preparation App

The browser-based data preparation app allows composing validation steps into data flows with an easy drag-and-drop interface. Steps like schema enforcement, referential integrity checks, custom rules, and outlier analysis can all be integrated.

Data Audit and Reports

Rich audit reporting provides visibility into data validation metrics like rejection rates and verification check failures. Data profiling also helps identify areas that require new validation checks or process improvements.

Version Control

All validation rules and flows are under version control to ensure compliance with regulatory requirements and manage code changes over time.

With its robust toolbox and methodology for end-to-end data health, Talend enables organizations to implement systematic data validation and verification to ensure high-quality, trusted information.

Example Data Validation and Verification Scenarios

To illustrate how data validation and verification work together in real-world situations, some example scenarios include:

Order Processing

Data Validation: When orders are entered into the CRM system, Input masks, required fields, and reference lookups are used to reject bad data during submission.
Data Verification: Orders replicated to ERP are checked for missing IDs, invalid customer numbers, and unmatched addresses against CRM.

Financial Reporting

Data Validation: Batch validation of imported GL account codes against a corporate chart of accounts reference data.
Data Verification: Reconcile transaction totals in the reporting data warehouse against source system extracts to find discrepancies.

Supply Chain Logistics

Data Validation: Regex validation of shipment tracking IDs against carrier numbering specs during scanning.
Data Verification: Cross-check shipment statuses between WMS, ERP, and carrier systems to identify lags/errors.

Clinical Research

Data Validation: Researchers are required to enter values within allowable ranges and formats before submitting subject data.
Data Verification: A statistical review of the study data identifies outliers suggestive of measurement or data entry issues for follow-up.

Manufacturing Quality

Data Validation: Sensor values fall within defined normal thresholds before recording readings in the historian database.
Data Verification: Compare production counts between sensors, historians, MES, and ERP to detect inconsistencies.

What are the Challenges of Data Validation and Verification

While clearly beneficial, some common challenges can arise when implementing data validation and verification processes:

Defining appropriate validation rules and criteria takes knowledge of data sources; business uses, and data science.
Building validation into processes at the source takes upfront work compared to checking later downstream.
Manual verification checks can be time consuming and expensive at large data volumes.
Legacy systems may lack flexible validation capabilities, leading to fixes and rework.
Striking the right balance between tight validations to maximize data quality versus loose validations to avoid impeding business.
Getting user buy-in and compliance with extra validation steps, especially in manually intensive processes.
Tracing failures found in verification back to the root causes to improve validation rules and checks.
Keeping validation and verification logic in sync across ever-changing systems and data flows.

Overcoming Challenges

Ways to help overcome these potential challenges include:

Involving business and technical teams in defining needs and priority areas.
Starting with high-risk domains and incrementally expanding validations over time.
Using declarative tools and automated checks to minimize custom coding needs.
Monitoring verification results closely and iterating on validation rules.
Providing user feedback on record rejections with clear guidance on corrections.
Dedicating data stewards to manage ongoing improvements to validation and verification processes.
Automating as much as possible through orchestration and workflows.

With the right organizational commitment, along with iterative improvements over time, obstacles can be overcome to realize significant data quality gains through robust validation and verification.

Final Thoughts

In summary, data validation and verification are complementary processes that ensure overall data quality throughout the data lifecycle.

Data validation establishes critical quality checks early during data capture and entry using predefined rules, parameters, and constraints. This blocks bad data from entering key systems and propagates quality into downstream flows.

Data verification provides ongoing monitoring by looking for inconsistencies, errors, and anomalies in data already within core systems. Issues found can trigger corrective actions as well as improve validation rules.

Organizations that invest in both robust data validation upfront alongside continuous data verification gain significant advantages in operational efficiency, analytics reliability, and risk reduction through trusted data. By preventing faulty data from entering systems and detecting issues within them, data validation and verification work together to drive overall data integrity and quality.

FAQs on Data Validation and Verification

Here are answers to some frequently asked questions about data validation and verification:

What are the main benefits of data validation and verification?

Data validation prevents bad data from entering systems, while data verification detects errors in existing data to improve overall data quality, reliability, and integrity for downstream usage.

When should you validate vs verify data?

Data validation should occur as soon as possible, such as when data is entered or extracted from a source system. Data verification occurs later after data is loaded into databases and shared across systems.

What tools can you use for data validation?

Input form controls, database constraints, validation scripts, batch edit checks, regex, and predictive checks can all validate data proactively during ingestion.

What techniques help verify data?

Cross-checks between systems, cyclic redundancy checks, reconciliation, reference lookups, time sequence checks, and reviewing analytics outputs can verify data already in systems.

How can you make validation and verification efficient?

Automating rules through scripts and reusable libraries avoids custom coding. Orchestration manages complex flows. Monitoring and issue tracking provides continuous improvement.

How do you calculate data validation and verification metrics?

Data validation metrics include pass/reject rates, missing values, and accuracy percentages. Data verification metrics cover cross-checking failure counts, exception volumes, and accuracy.

How do you get user buy in on extra validation and verification?

Communicate the benefits of quality. Provide self-service tools to check own data. Deliver feedback on issues found with guidance to fix them. Automate where possible.

Can you over-validate and disrupt workflows?

Yes, more validation can improve workflows. Prioritize high-risk validations first. Use both synchronous and batch validations. Measure impact and iterate.

Priya Mervana

Verified Web Security Experts

Priya Mervana is working at SSLInsights.com as a web security expert with over 10 years of experience writing about encryption, SSL certificates, and online privacy. She aims to make complex security topics easily understandable for everyday internet users.

Data Validation vs Data Verification: What’s the Difference Between Them?

What are the Difference Between Data Validation and Data Verification?