Key takeaways

• Data validation is a critical process for maintaining data accuracy and integrity across applications. It helps businesses prevent errors, save time, and make informed decisions by revealing unknown patterns and ensuring compatibility with other data sets and applications.
• There are different types of data validation techniques that target specific error types, including format, range, and consistency checks, among others. Automated software tools are available to ease and tailor these validation processes for business needs.
• Despite its benefits, data validation faces challenges such as the handling of failed validation tests and user compliance with validation warnings. Best practices involve defining clear rules, using automated tools, validating at multiple stages, and updating processes continuously.

What is data validation?

Data validation is a procedure that ensures the accuracy, consistency, and reliability of data across various applications and systems. It is a prerequisite for leveraging datasets in machine learning and other data-driven initiatives.

The process of validating data not only ensures the accuracy of data but also fosters confidence in data integrity, which is significant for any business to ensure accurate outcomes of its analysis. Invalid data not addressed early in the process can result in higher costs downstream due to issues caused by poor-quality data.

Beyond that, data validation plays a pivotal role in revealing unknown patterns, providing additional insights for better decisions. Validating the structure and standards of a data model ensures compatibility with applications and other data sets, preventing structural issues when applications use the data.

How is data validation used in a business environment?

In a business environment, data validation serves as the backbone for maintaining data quality, enhancing customer experiences, and streamlining operational processes.

Data validation in a business offers numerous advantages:

• Improving regulatory compliance
• Streamlining operations
• Strengthening marketing efforts and building better customer value
• Supporting financial accuracy
• Optimising resource management
• Easier data integration and data migration

Imagine a retail scenario where correct data type or acceptable values speeds up checkout processes, leading to improved operational efficiency. Or consider how the reduction in failed deliveries and minimisation of cart abandonment rates due to validated data directly boosts business performance by retaining customers and smoothing transactions.

These scenarios bring to life the practical applications and impact of data validation in a business setting. And if you’re looking for other examples – more suited to your company profile – take a look here:

• Data-driven insurance: exploring the latest trends
• How Big Data revolutionises the financial industry?
• 14 reasons why your organisation is missing out when not using data it possesses

Data validation isn’t a one-size-fits-all process. Tailoring data validation processes to an organisation’s specific goals ensures that the data validation is relevant and efficient. This is where business data analysts come into play. Their deep understanding of data models is fundamental to their ability to contribute to the development of validation criteria.

Validated data, therefore, becomes a valuable asset that’s easier and quicker to process because it meets the predefined quality and consistency standards, integral for prompt business intelligence.

How does data validation differ from data verification?

While data validation and data verification may seem similar, they serve different purposes in the data quality assurance process. Data validation ensures data meets specific criteria before processing, acting like a bouncer checking IDs at the door.

On the other hand, after the data input has been processed, data verification steps in, confirming that the data is accurate and consistent with source documents or prior data – akin to a supervisor double-checking work for accuracy.

What are the common data validation techniques?

There are several data validation techniques designed:

• Format checks: ensure that data is in a specific format, like the YYYY-MM-DD format for dates
• Range checks: validate that a numerical value falls within a specified range
• Consistency checks: ensure that data is consistent across different fields or tables
• Uniqueness checks: verify that data is unique and does not contain duplicates
• Presence checks: confirm that data is present and not missing

Each of these techniques targets specific types of errors to ensure data meets predefined criteria, often providing an error message to help identify the issue.

The implementation of data validation checks, including the use of fuzzy matching and unique identifiers, serves to ensure the uniqueness and logical consistency of data inputs and stored records.

Custom validation rules in data validation can include a variety of types of data validation, such as:

• numbers
• text
• dates
• times

These rules ensure that the input data entered is accurate and consistent.

How to perform data validation?

Performing data validation is a multi-step process essential for maintaining data integrity, especially during Extract, Transform, Load (ETL) operations where data is transferred from a source to a data warehouse.

Here’s a step-by-step guide to conducting data validation effectively:

1. Extraction verification: Ensure that the data extraction from the source is complete and accurate. This involves checking that all the intended data is retrieved without any loss or truncation.
2. Transformation rules validation: Apply and verify transformation rules to the extracted data. This includes format normalisation, data cleansing, deduplication, and conversion processes that the data must undergo before loading it into the warehouse.
3. Load consistency check: During the loading phase, ensure that the data being inserted into the target system is consistent with the transformed data. This involves checking for any errors or discrepancies that may have occurred during the transfer.
4. Integrity constraints enforcement: Enforce integrity constraints such as foreign key relationships, unique constraints, and not-null constraints to ensure that the data conforms to the logical structure of the target data model.
5. Post-Load auditing: After loading the data, perform an audit to compare source data with the data now in the warehouse to ensure completeness and accuracy. This can involve record counts, checksums, or sample data verification.
6. Error handling and logging: Implement a robust error handling and logging mechanism to capture any validation failures, allowing for prompt correction and reprocessing of the affected data.

Each step in this process is important to verify that the data is not only transferred correctly but also meets the predefined quality standards necessary for reliable analysis and reporting in the data warehouse environment.

What are some common data validation tools?

While Excel and Google Sheets are common data validation tools, there are specialised software programs designed to address different validation needs and automate the validation process. These enterprise tools are equipped with advanced features that provide robust data validation capabilities beyond the basics.

Some data quality tools that you can consider are:

• Astera: lauded for its agile data cleansing and correction capabilities and provides rigorous data validation checks customised to specific requirements
• Informatica: offers tasks like deduplication, data standardisation, enrichment, and data validation, with the ability to handle data both in the cloud and on-premises
• Talend: uses machine-learning algorithms to provide recommendations for data quality improvement
• Datameer: focuses on data preparation and transformation for Snowflake

These tools can help you ensure the quality and accuracy of your data by providing a well-organised data tab.

Other notable tools for data quality management include:

• Alteryx, which uses AI for data quality recommendations
• Data Ladder, which offers real-time data quality validation
• Ataccama One, which provides continuous data quality management with AI to automatically detect anomalies.

See related articles on the different steps and tasks involved in data workflows:

• Data Transformation: the complete guide for effective data management
• Data automation for business growth: everything you need to know
• Data Cleaning: benefits, process and best practices
• Data Modelling: a guide to techniques and best practices

Challenges in data validation process

Despite the many benefits of data validation, it’s not without its challenges.

One significant challenge is handling large volumes of data, which can be resource-intensive and time-consuming. As businesses generate and collect vast amounts of data, ensuring each data point is accurate and valid requires robust validation tools and scalable infrastructure.

Integrating data from multiple sources further complicates the validation process. Different data sources often have varying formats, structures, and standards, making it difficult to ensure consistency and accuracy. This issue can be mitigated by using data integration tools and establishing standard data formats and protocols.

Real-time data validation adds another layer of complexity, as it requires immediate processing and verification of data as it is entered or received.

The complexity of validation logic can make it difficult to implement and maintain effective validation processes. Simplifying validation logic where possible and clearly documenting the rules can help manage this complexity. Additionally, extensive validation processes can impact system performance, slowing down operations and affecting user experience.

Moreover, challenges such as unvalidated business logic or key metrics impede effective data validation, which is necessary for reliable insights. This underscores the importance of data validation procedures to ensure the overall reliability of the data.

What are the best practices for data validation?

As with any process, there are best practices in data validation that can help streamline the process and enhance the quality of results:

• Defining clear validation rules – these rules serve as a guide for what constitutes valid data and help identify and correct errors and inconsistencies,
• Using automated tools throughout data management processes – using automated tools can help to streamline the validation process, reducing the likelihood of human error and improving efficiency,
• Validating data at multiple stages – this ensures that any errors or inconsistencies are detected early in the process, reducing the likelihood of them causing issues later on,
• Continuously monitoring and updating validation processes – it helps to ensure that they remain relevant and effective as the data and business needs evolve.

The future of effective data management lies in robust validation processes, and having the right partner is key. Future Processing, with its extensive experience in IT solutions and data services, is ready to help ensure that your business can trust its data. Contact us today!