What is Data Validity?

What is data validity? Why is it important?

Nowadays, more companies are beginning to recognize the value of big data and the many different functions it can serve in an organization. Data and insights can help an organization develop solutions and improve its processes so that it can get the edge over its competitors. However, what if the data isn’t valid? In this scenario, the decisions made based on this data will be invalid as well. This can lead to huge losses in both time and money as you are left scrambling to repair the damage of a failed initiative or project. 

Data accuracy could also be considered data validity. This is where the idea of the validity of data is significant. What is data validation? Basically, it’s the process of checking the integrity, accuracy, and quality of data before it is used for a business purpose.

The idea is to compare a data set against certain defined rules to ensure the correctness of the data both in structure and content. These rules or checks vary in style. There are several different types of data validation checks, including data type checks, code checks, range checks, format checks, consistency checks, and uniqueness checks. These are the main validation checks examples that demonstrate just how many ways there are to validate data. The specific checks to use is up to the business and depends on their goals as well as the nature of the data they are managing. 

One area where data validation is most crucial is in the transfer point between the source systems that have collected the raw data and your central data depository. Before the data is loaded into this central location, it’s imperative that you have validated the data and ensured that it is completely consistent in type.

There are many examples of data validation; one of them is the ETL validation script. ETL stands for Extract, Transform, Load. These scripts are often manually created for various data sets and then used by data engineers to import data. Within these scripts are often rules that consist of the kinds of checks we’ve already described to ensure data validity throughout the transition. 

Why is data validation important? Data-driven initiatives and projects are only as good as the data going into them.

If there are defects in the data, there will be defects in the project’s results. For large, expensive endeavors, problems like this can be disastrous. That’s why good data validation matters.

Data validity vs. data accuracy

In any discussion of data validity, you’ll probably also hear the words “accuracy” and “reliability” thrown around. Let’s consider validity vs. accuracy vs. reliability.

When looking at data validity vs. accuracy, there are certainly similarities. However, the two terms are not interchangeable. Both data accuracy and validity seek to describe the quality or usability of the data. However, data accuracy refers to how well the data corresponds to the real-world or true value of an entity. Data validity can be defined as a term to refer to how well data values are consistent, based on defined rules.

For example, an accurate data point would be a street address that is actually where the survey respondent lives. Basically, a valid data point could be a correctly formatted street address. As you can see, there is a pretty big difference between the two concepts. Data can be valid but inaccurate. For example, if a real address was given but was not the actual habitation of the respondent.

It’s impossible for data to be both accurate and invalid. Let’s also look at data validity vs. reliability. Again, both terms are measures of how useful the data is, but there are differences. Reliable data is data that can be reproduced consistently, given the same conditions. Once again, data could be valid but not reliable. It’s important to understand the differences between these terms.

How to validate data

At this point, you’re probably wondering how to validate data. There are a couple of ways organizations go about it. The first method is one we’ve already mentioned – scripting.

Scripting is a low-risk, versatile, and popular way to go about validating data. As long as your script complies with data validation best practices, this can be a great way to ensure that your data is valid. However, there are some downsides to a scripting strategy. For one, validation scripts are not capable of validating real-time data streams coming in from complex data pipelines. 

Validating real-time data is a growing need in modern cloud-based architectures. Also, validation scripts are not easily scalable. Scripts can take time to be updated and can lead to increased costs whenever technology is changed. Fortunately, there are other data verification and validation methods. One of them is using an enterprise solution to automatically validate data streams. One of the best data validation examples of a tool like this is Acceldata.

Acceldata is a data observability platform that can be used alongside a system like Kafka to automatically validate data streams in real-time. Eliminate repetitive, tedious scripting and free up time for your data engineering team to focus on innovations that can grow your business.

Data validation rules

Data validation rules are the specific controls that define the format of the data. We’ve already listed various data validation rules examples above. Let’s take a closer look at a few data validation examples.

The type check is a data validation rule that looks to confirm the data type (integer, string, or some other format). A code check is a way to ensure that the data comes from a valid list of values or follows certain other formatting rules. For example, a code check could be applied to zip code values to ensure that each one can be found on an official list of zip codes.

When it comes to data validation rules for access control, these are fairly common and consist of things like character limitations and length requirements for passwords. Whatever kinds of data validation rules apply to your data set; it is vital that your data is checked against them so that your organization can confidently make data-driven decisions.