A Guide On Data Quality Validation: What It Is, Types, & Best Practices

Machine Learning (ML) and Artificial Intelligence (AI) are transforming businesses of all types and sizes, being no longer restricted to just technology-focused companies. In the digital world, the dependency on these technologies is only increasing.

According to McKinsey’s 2023 State of AI report, 55% of organizations have adopted AI overall for one or more functions.

However, the accuracy and effectiveness of ML models are driven primarily by the quality of data used to test, validate, and train the model. This is where data quality validation can play an effective role.

By implementing robust data validation standards, organizations can safeguard their data integrity, minimize errors, and extract meaningful insights from their data assets.

Let us delve into the fundamentals of data quality validation, exploring various validation types, data quality validation metrics, and best practices to empower organizations to achieve data-driven excellence.

Understanding Data Validation

Data validation is the meticulous process of inspecting and ensuring the accuracy, consistency, and reliability of data. It is a critical step in data management, as it helps to prevent errors, improve data quality, and ensure that data-driven decisions are based on reliable information.

Data quality validation can be performed at various levels, including individual data fields, relationships between data fields, and entire datasets.

Types of Data Validation

There are several different data validation types, each of which serves a specific purpose. Some of the most common types of data validation include:

Field-Level Validation

This type of validation checks individual data fields to ensure that they meet specific criteria, such as data type, length, and value range.

For example, a field-level validation might check that an email address is in the correct format or that a phone number is the correct length.

Cross-Field Validation

This type of validation checks the relationships between different data fields to ensure they are consistent and make sense. A cross-field data validation example includes checks for fields like the order date (making sure it is before the shipment date) or that a customer's age is consistent with their birth date.

Format and Range Validation

This type of validation checks that data is formatted correctly and falls within a predefined range. For example, a format validation might check that a date is in the format YYYY-MM-DD, and a range validation will check that a temperature range is between 0 and 100 degrees Celsius.

Data Quality Metrics and Standards

Data Quality Validation ensures that the organization’s data quality is accurate, complete, consistent, and timely. This is part of the overall data quality management, ensuring that any data field is monitored for quality standards and areas of improvement are identified.

Some of the common data quality metrics that are used in this process include the following:

• Accuracy: The percentage of data values that are correct.
• Completeness: The percentage of required data values that are present.
• Consistency: The degree to which data values are consistent across different sources and systems.
• Timeliness: The degree to which data is up-to-date.
• Validity: Verifies that the data conforms to defined business rules and meets the relevant parameters.
• Duplication: Ensures that the same data is not duplicated in multiple fields, helping avoid multiple representations of the same data within the system. This is crucial to save you time and prevent errors that can crop up due to multiple records of your customers or products.
• Uniqueness: The degree to which data values are unique and not duplicated.
• Lineage: Captures the trail of where your data comes from. This can help in data quality assessment and improvement, clearly understanding how data flows through the system and what modifications or updates were made.

Data Validation vs Data Quality

Data validation and data quality are closely related concepts, but they are not the same thing. Data validation is a specific process used to verify the accuracy, completeness, consistency, and timeliness of data.

On the other hand, data quality is a broader concept that encompasses all aspects of data that affect its usefulness, including its accuracy, completeness, consistency, timeliness, uniqueness, and relevance.

In other words, data validation is a tool that can be used to achieve data quality. By implementing data validation techniques, organizations can improve the quality of their data and make it more useful for decision-making.

Best Practices for Data Validation

For the ML model to be effectively trained, there are several best practices that organizations can follow to implement effective data quality validation. This includes:

• Establishing clear and measurable standards for data quality.
• Validating data as close to the source as possible to prevent errors from propagating.
• Using automated tools to validate data whenever possible.
• Regularly monitoring data quality metrics to identify and address problems.
• Train users on how to validate data and how to avoid errors.

Example of Data Validation

Data validation techniques in some form are already being used for several aspects, such as online web forms. But to make the best use of ML models for data quality validation, one area could be for verifying personal identification. As per Experian, 30% of contact information for financial usage may be inaccurate. This can be due to several reasons, such as human mistakes, errors, and incomplete data.

To ensure the accuracy and completeness of the collected data, data validation techniques can be implemented as follows:

• Name validation: Check if the name field contains valid characters and is not empty.
• Email address validation: Verify that the email address format is correct and the domain name is valid.
• Phone number validation: Ensure the phone number format is consistent with the selected region or country code and that the length is within the expected range.
• Date of birth validation: Check if the date of birth format is correct and that the date is within a reasonable range.

By applying these validation rules, the registration form can prevent invalid or incomplete data from being submitted, ensuring the integrity of the collected customer information.

Conclusion

To sum up, data quality validation plays a crucial role in ensuring data quality and reliability, empowering organizations to derive actionable insights from their data assets. By implementing robust data validation practices, organizations can:

• Enhance data accuracy
• Promote data completeness
• Maintain data consistency
• Ensure data timeliness
• Protect data integrity

MarkovML is a platform that helps data scientists, ML engineers, and students harness the power of ML models for their projects using intelligent data management. This includes no-code auto-EDA to identify gaps and outliers in data, collaborative reporting, and intelligent data catalog to ensure that all data validation processes can happen seamlessly in a centralized place.

So, if you want to supercharge your ML projects and ensure data quality and validation, think MarkovML.