Data validation is an essential step in any data workflow. Here’s everything you need to know about data validation types and how to do it yourself.
Written by Sara A. Metwalli Published on Mar. 07, 2023
Data validation is the process of ensuring your data is correct and up to the standards of your project before using it to train your machine learning models. Data validation is essential because, if your data is bad, your results will be, too. Errors in the data lead to faulty results and can cost companies (and individuals) money, time and resources.
When dealing with data — whether you’re collecting, analyzing or preparing it for a data-handling algorithm (such as machine learning algorithms) — you first need to validate the different characteristics of the data.
Given the amount of data that algorithms have to handle today, manually validating the data is infeasible. As a result, most data workflows now have automated data validation processes that can make your work faster, more efficient and more accurate.
There are different ways to automate your data validation. You can use a cloud service like Arcion , or download an open-source tool such as the Google Data Validation Tool , DataTest , Colander or Voluptuous , which are all Python packages. Moreover, continuous integration and deployment tools, like TravisCI offer automated data validation whenever you add new data to the project.
Validating your data helps avoid any risk of false results. In tech, we often hear the phrase “garbage in = garbage out,” which refers to how inaccurate input data leads to incorrect results in the system. When we use the same flawed data to make business-critical decisions, faulty insights will cost companies time, money and resources. In medical applications, inaccurate data can even have fatal consequences.
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Data has different characteristics so when we validate the accuracy of our data, we need to validate those additional characteristics. The characteristics of the data include the data type, its range, format and consistency. We perform these types of validation using code or specific data validation tools. Depending on the application and the data, we can perform some validation tests, but not all of them.
Data comes in different types. One type of data is numerical data — like years, age, grades or postal codes. Though all of these are numbers, they can be either integers or floats. For example, a year can’t be 2010.14 because years must be integers. On the other hand, grades can be either an integer (99) or a float (90.5). Another type of data is text data — names, addresses or emails, for instance.
Type validation often refers to checking whether or not an entry matches the field. For example, you might try entering text in the age field, which should only allow numerical data types. If the user inputs a text in a numerical type field, the algorithm we use may crash or the results will be faulty. So, if we’re creating a system to calculate the average age of participants in a specific sport, if some of the entries are text, they will either break the code, or will be ignored in the calculations. Either instance will lead to a non-optimal result. Moreover, the more faulty entries we have in our data, the less accurate the results will be.
Format checking validates the data’s structure. For example, birthdays have a specific format (say, YYYY-MM-DD). Having the data in this format is essential for the project’s next steps, so checking that your data has the correct structure is vital. When you’re validating the data structure, you should have a clear understanding of the correct structure in order to make the validation process consistent and straightforward.
