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.
Why Is Data Validation Important?
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.
Types of Data Validation
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.
Type Check
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 Check
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.
Correctness Check
Sometimes the data may be in the correct format but may need to be corrected. For example, a birthday entry may be 1990-13-06. Although the format is valid, there’s no month 13. This step in the validation ensures that your values are logical and meaningful. Another example is checking if a postal code or a phone number is valid. Sometimes this is referred to as the range check.
Consistency Check
Depending on the target application, there might be specific rules for the data. For example, some websites have different conditions for the length of a password and the type of characters it may contain. In this type of validation, we check if all the data follow these rules consistently and that there are no null or invalid values in the data.
Uniqueness Check
Another type of data validation is the uniqueness check, which checks for the uniqueness of some data entries. This is often used to check for specific data, like company employee ID or bank account numbers. These values must be unique. Otherwise, problems may occur when we process and handle the data.
Benefits of Validated Data
- Ensure accurate results. Data validation is the first step to ensuring the accuracy of your results. When you validate your data, you can immediately eliminate inaccuracies as a possible cause when you get unexpected results.
- Ensure compatibility of data from various sources. We often collect data from different channels and resources. In order to analyze and process your data, it needs to be consistent regardless of where it came from.
- Save time down the line. Data validation can be a time-consuming task at first, but when you do it correctly, you can save time on the project's next steps or when you inevitably add new data to the database.
Challenges of Data Validation
Though data validation is essential and has many benefits to ensuring smooth data flow throughout the project, it also has its challenges.
- Data validation is complex. In general, ensuring data’s accuracy is difficult. That difficulty increases as the database begins pulling from multiple sources, which is often the case with today’s applications.
- Data validation is time consuming: As we already mentioned, data validation can be time consuming, especially for more complex databases and those that collect data from different sources. Nevertheless, it remains essential for every project to ensure good results.
- Data validation is tailored for specific requirements. When we design a data validation system, we often do so with a particular set of requirements in mind. If that set of requirements ever changes, we need to modify our data validation system to fit the new requirements.
How to Perform Data Validation
You can perform data validation in one of two ways.
1. Validation by Scripts
You’ll follow this method if you can program and know how to design and write code to validate your data based on the application and the given requirements. In this case, you will need to write and use a script to validate your data. For example, a simple way to validate whether or not a variable is an integer is by using a Python script. You can do this by creating a flag that indicates “true” if the data type is incorrect. In that case, the program will send an error message to the user or the programmer to fix the type.
intFlag = False
while not intFlag:
if isinstance(var, int):
intFlag = True
else:
print(‘Type Error!’)You can apply the same process to check different conditions in the data and ensure its validity using packages like Pydantic.
2. Validation by Programs
In this case, you can use an existing program to validate your data. You’ll provide the program with your data and the requirements you need to verify. Using this approach doesn’t require any programming knowledge at all. You can either use a tool like the Google Data Validation Tool, any other open-source tool or a paid tool like FME.
