What is data validation? Why, when, and how to use it

Data validation ensures that data is accurate, complete, and consistent when it is collected and stored. By identifying any errors or discrepancies that may have occurred during the data collection process, the validation of data allows businesses to avoid the pitfalls that result from using inaccurate, messy, or invalid data, including wasted resources, skewed business decisions, and poor forecasting.

In this article, we will explain data validation, including why it is so important, when it should be performed, and the different data validation techniques you can use. We will also share the three essential data validation steps so that by the end of this article, you will have a clear understanding of the process and be able to confidently perform data validation checks yourself.

What is data validation?

Data validation is a form of data cleansing that involves checking the accuracy and quality of data before using, importing, or processing it. The goal of data validation is to ensure that data is high quality, free from errors, and fit for its intended use. The process involves reviewing the collected data for completeness, consistency, and accuracy to identify any errors or discrepancies.

Several types of data validation might be applied to a dataset depending on destination constraints or objectives. Some common data validation types include range checks, consistency checks, and logical checks. Range checks ensure that the values are within the expected range, while consistency checks ensure that the data is consistent across different variables. Logical checks validate the relationships between different data points to ensure they are logically sound. Other data validation techniques include uniqueness checks, which ensure that each record is unique, and format checks, which ensure that the data is in the expected format.

Data validation is essential to data quality and accuracy. By performing data validation checks, you can be sure that the data you are using is reliable and of high quality. This is particularly important in decision making, where the quality of data can have a significant impact on the validity and trust in the decisions made.

Data validation is a crucial data management process that ensures datasets collected from various data sources are high-quality and free from errors. Completing data validation means that datasets will be consistent, accurate, and complete as well as protected from data loss or errors during their life cycle.

Why is it important to validate data?

By validating data, businesses can gain insights into market trends, customer behavior, and product performance. This information can be used to improve decision making in marketing, product, and business decisions, leading to increased efficiency, more accurate insights, and greater revenue and profits.

Data validation also ensures that data is secure, reducing the risk of data breaches and leaks. It can save businesses time and money by reducing the need for manual data cleaning before loading data into data warehouses. Data validation also ensures data uniqueness, reducing the time-consuming and expensive process of manual data cleaning.

Data validation is an essential workflow for businesses that rely on data to drive decision making. It ensures that data is consistent, accurate, complete, and fit for the intended use. By performing validation checks and using machine learning techniques, businesses can create a high-quality dataset that meets the overall business requirements and leads to improved business decisions.

When is data validation performed?

One key aspect of data validation best practices is to perform validation checks at two stages: before the ETL process in data warehousing and after the data has been collected and loaded.

Before the ETL process, data validation is performed to identify any potential issues with the data before it is loaded into the data warehouse. This ensures that the data is clean and consistent, making it easier to analyze. It helps to identify missing data, data formatting issues, and incorrect data types.

After the data has been collected, data validation is performed to identify and resolve any issues that might have occurred during the collection process. This allows analysts to get more accurate insights from the data, leading to more informed decision making. Common validation checks include uniqueness checks, range checks, and logical checks.

By performing data validation before ETL as well as after data collection, businesses can ensure that their data is clean and accurate, leading to better insights and decision making.

Common data validation techniques

There are multiple types of data validation checks. The right checks depend on the type of data being validated and the specific requirements of the project. By using one or more of these data validation types, organizations can ensure the quality and accuracy of their data, leading to more accurate insights and informed business decisions.

Here are some of the most common data validation examples:

Data type check

A data type check verifies that data entered into a field is of the correct data type, such as a number, date, or text. For example, in a database containing customer information, the data type for the "Age" field would be a number. A data type check would ensure that the data entered in this field was a number and not text.

Code check

A code check ensures that the codes used in the data are valid and conform to specific standards. For example, a code check can verify that country codes conform to ISO standards or that currency codes are correctly formatted.

Range check

A range check verifies that data falls within an acceptable range of values. For instance, if a database contains the age of customers, a range check can ensure that all ages are within the specific range of 18 to 100 years.

Format check

A format check ensures that data is entered in the correct format. For example, a format check can verify that phone numbers are correctly formatted, including the correct number of digits, dashes, or parentheses.

Null values check

A null values check verifies that data is not missing in mandatory fields. For instance, if a database contains customer information, the "Name" field cannot be left blank. A null values check would flag any missing data in mandatory fields.

Consistency check

A consistency check compares data across multiple fields or tables to ensure that they are consistent. For example, in a database containing customer information, a consistency check can verify that the same customer ID does not have different addresses or phone numbers across different tables.

Uniqueness check

A uniqueness check ensures that each record is unique and not duplicated. For example, in a database containing customer information, a uniqueness check would verify that no two customers share the same ID or email address.

How to validate data

Following this simple, three-step data validation workflow will help you to easily and confidently ensure data validity.

Step 1: Use a data sample

Using a data sample is a good way to make the validation process more manageable. The sample should contain enough data to be representative of the entire dataset but small enough to be easily validated. This step is also useful for identifying any potential issues with the data before moving on to the full dataset.

Step 2: Apply data validation checks

This step involves applying one or more data validation checks. The checks applied will depend on the type of data being validated and the specific requirements of the project. Some of the common checks include data type checks, code checks, range checks, format checks, null values checks, consistency checks, and uniqueness checks.

Step 3: Check against the schema

In this step, the source data is matched against the destination schema. This is important because it ensures that the data meets the requirements of the project and can be integrated into the larger dataset. The schema should be well-defined and clearly documented to ensure that the data is properly structured.

By following these data validation steps, you can ensure that your data is validated and of high quality, complete, and accurate. This is essential for making informed business decisions and avoiding costly mistakes.

Data validation challenges

Even if you follow all the correct data validation rules, there can still be obstacles to smoothly validating data.

Outdated data

One of the challenges of data validation is dealing with outdated data. When data is stored in silos, it becomes difficult to validate it against current source information. In such cases, researchers have to spend time searching for updated data, leading to delays and increased costs.

Risk of errors

Manual data validation processes increase the risk of errors. Human errors such as typos, incorrect entries, and missing data can lead to inaccurate results. Such errors can result in bad decisions and negative impacts on the business.

Time-consuming

Data validation can be time-consuming, especially when dealing with large datasets. Manually checking every record for accuracy and consistency can take a significant amount of time, leading to project delays and increased costs.

Lack of understanding

Another challenge is the lack of understanding of data management. Many businesses lack an in-house expert who properly understands data management. As a result, there may be outdated or inaccurate data, which makes it difficult to meet data validation requirements. Businesses may need to invest in training or hiring an expert in data management to avoid such issues.

Data validation best practices

However, these obstacles can be mitigated by using the following best practices:

Define your data rules

Consistent rules are essential for ensuring consistency across your data. Make sure you have a thorough understanding of your data needs first. This will help you to create an effective data guide. Your data rules should also be as clear and simple as possible to facilitate implementation, monitoring, and maintenance.

Some examples of data rules include minimum and maximum values for numerical fields or requirements for non-ambiguous data formats. Be sure to document everything, and make your data guide available to everyone tasked with working on your data so they have a single source of truth to draw from.

Lean on automation

Automation tools streamline the data validation process, increasing efficiency and saving money. Automation is ideal for handling the most repetitive data validation tasks and reduces the risk of introducing human errors into your data. For example, if you implement real-time validation checks during data collection, you can prevent basic mistakes from entering your dataset.

Audit your processes regularly

Test your data validation processes once you have set them up to ensure they are working as intended. Continue to monitor and check these processes to ensure that they remain suitable for your data needs and to catch any inaccuracies or errors as soon as possible.

Run basic training sessions

You don’t need every employee to possess an encyclopedic knowledge of data validation, but all the members of your team should know the basics: roughly what it means, why it’s important, and what they’re expected to do to support it. Even an occasional reminder to be careful while entering or moving data can be beneficial.

Performing data validation with RudderStack

Data validation is an essential process for ensuring the accuracy and quality of data before it is used or processed. By validating data, businesses can avoid costly errors, gain more accurate insights, improve efficiency, and increase data security.

We have covered the different types of data validation, including data type check, code check, range check, format check, null values check, consistency check, and uniqueness check. Additionally, we have discussed the steps involved in ensuring data validity, such as using a data sample, applying one or more data validation checks, and checking against the schema.

However, data validation is not without its challenges, such as dealing with outdated data, manual errors, time constraints, and a lack of understanding of data management.

RudderStack is committed to providing a platform that can deliver clean, reliable, and trustworthy data to all your teams. We have worked with our customers to develop two APIs–our Transformations API and our Data Governance API–specifically designed to make incorporating data validity checks into your CI/CD workflow easier.

Sign up for a free trial and discover how you can simplify data validation with RudderStack.

Data validation FAQs

Why is data validation important?

Data validation is a vital workflow for any business that relies on data. By ensuring that data is consistent, accurate, complete, and fit for the intended use, data validation provides high-quality datasets that increase efficiency, improve decision-making, and, ultimately, grow revenue.

What is data validation used for?

Data validation is used to ensure that when data is moved or consolidated from different sources it is not corrupted or made inaccurate due to different formatting types or rules. By validating data against a consistent set of rules, businesses can guarantee that the data in their warehouses is consistent, accurate, and complete.

What are the benefits of data validation?

The main benefit of data validation is that business data is error-free and high-quality. Enhanced data quality and consistency lead to increased usefulness, improved decision-making, and lowered costs.