Data Validation: The What, Why, and How

What Is Data Validation?

Data validation is a data quality management technique that ensures the data you import or feed into a system meets specific quality and integrity standards. Ideally, you perform a series of documented tests validating data against code lists, data types, or other set thresholds.

Data Validation Examples

In one example of data validation, you can create criteria for the date to be not greater than, for example, today's date. You also can create a validation check that only allows whole numbers, not those between 30 and 43.

If a user inputs data that fails to meet the validation criteria, a warning displays with an error message.

Why Is Data Validation Important?

Data validation enhances data reliability and accuracy by preventing the input of invalid data.

Validation is a way of automating data control, and validated data reduces the need for manual checks while saving time for cleaning bad data.

Data checks prevent malicious data entry, safeguarding the security of sensitive systems. They help you meet regulations that mandate the use of validated data.

Best Practices for Effective Data Validation

Some best practices for effective data validation are:

• Use consistent rules for your dataset to ensure all entries follow the same standards.
• Keep data validation rules simple. Simple rules are easier to implement, maintain, and be understood by other users.
• Use automation platforms to perform most of the repetitive data validation tasks and automate validation scripts.
• Validate data in real-time to correct errors before they spread through systems. Also, validate data as close as possible to the data source.

Industry Applications of Data Validation

Data Validation in Health Care

The quality of data used in patient diagnosis and treatment must be validated for reliability, accuracy, and consistency. Errors in health care data threaten the quality of care and treatment outcomes.

Data Validation in Financial Services (Finserv)

Data validation in financial services checks the accuracy of transaction data, customer records, and financial reports. In this highly regulated industry, regulators require players to closely monitor and report their data, such as constantly updating foreign exchange rates.

Data Validation in Retail

In retail, data validation confirms the accuracy of pricing, supplier, consumer, and product information. Errors in retail data may impact the creation of effective targeted marketing, customer service, and supply chain management strategies.

Data Validation in High-Tech (Hitech)

High-tech environments handle large volumes of complex data. Data validation in this industry confirms that the data used in technology systems and processes, such as software development life cycle and artificial intelligence model training and development, is accurate, reliable, secure, and meets quality standards.

Data Validation in Health Care

Importance of Data Validation in Health Care

Health data affects treatment decisions, and the use of validated data enhances patient care. Doctors can rely on validated patient information to create personalized treatment plans.

Validation also ensures data safety, a requirement under compliance regulations such as HIPAA and GDPR.

When claims data is validated, it promotes the proper use of funds by preventing misallocation. Also, a lack of errors helps avoid delays while ensuring the timely payment of claims.

Challenges Specific to Health Care Data

With healthcare data coming from varied sources, it's challenging to ensure quality. Also, clinical data is coded in various systems, including ICD, LOINC, CPT, and SNOMED. It is challenging to maintain standardization and consistency across such datasets.

Medical technologies like telemedicine and emergency care situations require the use of data in real-time. Validating data in real-time presents difficulties, such as complex data structures, schemas, and data coming in large volumes.

Techniques and Best Practices for Health Care Data Validation

Convert data from various healthcare data sources including electronic health records (EHRs), medical devices, administrative claims databases, and patient self-reports into a standardized format to reduce inconsistencies.

Conduct routine data audits to ensure that data is up to date to reduce discrepancies and errors.

Data Validation Examples

You can confirm the validity of claims data by checking if the current procedural terminology (CPT) code matches the patient's diagnosis while verifying that it falls within the patient's coverage limits.

You can validate appointment scheduling data for consistency by ensuring that the scheduling system allocates shorter time slots for follow-up appointments than new consultations.

Data Validation in Financial Services (Finserv)

Importance of Data Validation in Financial Services

Validating financial data helps achieve regulatory compliance, such as anti-money laundering directives. Besides, validating transactions can help detect fraudulent activities. It also ensures that only validated payments are processed to avoid losses.

Finserv data is used in forecasting, analysis, and reporting. Validating this data helps manage risks and record better outcomes regarding market volatility predictions and credit risk assessments.

Common Discrepancies in Financial Data

Data entry errors, fraud, and poor record-keeping result in inconsistencies in financial data.

False financial disclosures like overstating or understating revenues and profits lead to an inaccurate presentation of the financial status of an organization.

Misclassification, that is, incorrectly categorizing items such as revenue and expenses, also may occur. For example, miscategorizing expenses may inflate and deflate declared profit.

Techniques and Best Practices for Financial Data Validation

Invest in tools that validate data in real time, correct errors automatically, and remove duplications.

You can also audit transactions to detect inconsistencies and redundancies.

Data Validation Examples

Rage validation in financial services ensures that a transaction amount falls within the allowed maximum transaction amount, preventing fraud.

Additionally, transaction IDs are unique to prevent the duplicate processing of payments and to ensure traceability.

Data Validation in Retail

Importance of Data Validation in Retail

Validating stock-keeping unit (SKU) and product quantity data helps maintain accurate inventory records.

With validated consumer information, such as purchase history and contact details, a retailer can create personalized shopping experiences and marketing promotions.

Common Data Issues in Retail

Duplication is common in customer datasets if information from different data sources such as billing, sales, marketing landing page forms, and marketing records is not correctly merged.

Data silos (with structured and unstructured data coming from different sources) create roadblocks to achieving accuracy and consistency in retail data.

Techniques and Best Practices for Retail Data Validation

Regularly reconcile data across all sales channels to ensure consistency in inventory levels. Also, you can build a custom data integration system that accommodates and merges unstructured and structured data from all sources.

Data Validation Examples

One example involves ensuring home addresses, phone numbers, and email addresses are valid to improve customer communication and logistics, including home deliveries.

A retailer can confirm that product information on their e-commerce platform—such as images, descriptions, and pricing—matches the actual product and is consistent with what is available in physical stores.

Data Validation in High-Tech (Hitech)

Importance of Data Validation in High-Tech

Data validation in high-tech prevents corrupted data from introducing malware such as SQL injection and cross-site scripting.

Blocked invalid data also doesn't reach the server for processing, reducing the server load.

Unique Challenges in Hitech Data

Data interoperability—the exchange and integration of electronic data—is still a challenge in hitech.

High technologies handle big amounts of data, and it is challenging to validate this data through cleaning, curation, and labeling. Additionally, inaccurately recorded training datasets may introduce bias in AI models.

Techniques and Best Practices for Hitech Data Validation

Validate training data through range validation, type validation, and format validation.

Build data validation checks on both the server side and the client side.

Data Validation Examples

If you're creating a database of athletes, you can create a validation that checks that only if the "name" and "body weight" fields are not left empty, and you can continue creating the record.

Common Data Validation Techniques

Range Checking

This verifies if the input data falls within a predetermined range. For example, in geographic data, you can create a validation that ensures longitude values fall between minus 180 and 180.

Format Checking

Format checking ensures that data is entered in the correct format to help maintain a consistent database. You can set the date to follow the MM/DD/YYYY format.

Consistency Checking

Consistency checking ensures data across related fields is logically consistent, with all the data being noncontradictory and using consistent terms. For a project timelines database, you can validate if the start date precedes a completion date.

Uniqueness Checking

Data such as Social Security numbers and product codes are unique, and duplicates could lead to errors. This technique checks against duplicates.

Completeness Checking

Completeness checking verifies that there are no missing values in a dataset and that all the data from different sources reaches the intended destination containing all its necessary values and data points.

Presence Check

The presence check confirms that all critical fields in a dataset are filled in. A user cannot leave the fields with the presence check blank, as an error message will appear, blocking the user from proceeding to the next step.

Code Check

This confirms that input into a dataset is from a list of accepted values and follows an acceptable formatting criterion. For instance, you can verify a country code's validity by checking against a list of valid country codes.

Common Data Validation Challenges

Data bias—You may need to periodically revalidate data, update it, and remove bias while observing responsible AI, explainable AI, and fairness-aware learning principles.

Data complexity and volume—Validating big data containing heterogeneous datasets is a tedious task. You may need to use data validation tools.

Data source variability—Data comes from varied sources, including web scraping, APIs, surveys, and databases in different formats, quality levels, and structures.

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This post was written by Caroline Wanjiru. Caroline is a software developer and a technical writer. In her work, she has developed interests and worked on many machine learning and artificial intelligence projects.

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