Data Validation

What is data validation

Data validation is the process of checking data that meets requirements by comparing it to a set of rules that have already been set up or defined. This procedure entails performing a series of checks known as check routines. Simple checks ensure that a date of birth only has numbers, while more complex checks include structured conditional checks.

Validating data makes sure that data is clean, accurate, and usable. Only validated data should be imported, saved, or used; otherwise, programs may stop working, results may be erroneous (for example, if models are trained on bad data), or other potentially disastrous problems may arise.

Importance of data validation

Data validation can help you find bugs faster, so you don’t have to play a cat-and-mouse game to find them. It can also save you time later when cleaning up bad data. Besides this, validating data is very important in so many ways. In this section, we will discuss some of the most important aspects of it:

Analysts can limit the quantity of inaccurate data in their warehouse by validating their data. Organizations should work together to validate data to get the most out of the process.
Validating the accuracy, clarity, and specificity of data is necessary to fix any project problems. You risk making decisions based on inaccurate, unrepresentative data without validating data.
Data Validation is used in the ETL (Extraction, Translation, and Load) process and data warehousing. It allows an analyst to understand the scope of data conflicts better.
It is also important to test the data model. If the data model is set up and structured correctly, you can use data files in different programs and applications.
Validating data can also be performed on any data, including data contained within a single application, such as MS Excel, or simple data mixed together in a single data store.
Types of data validation

Validating data comes in many forms. Most Validating data processes perform one or more of these checks before storing data in the database. These are some common types of data validation checks:

Data type check

A data type check makes sure that the type of data entered is correct. For example, a field may only accept numeric data. If this is the case, the system should reject any data containing other characters, such as letters or special symbols.

Code check

A code check ensures that a field’s value comes from a valid list or is formatted correctly. For example, it’s easier to know if a postal code is correct when you compare it to a list of correct codes.

Range check

Range checks are used to validate data that must fall within a certain range. There is a defined lower and upper boundary for reasonable values. For example, a primary school student is most likely between 10 and 14 years old. The computer can be set up to only take numbers from 10 to 14.

Format check

Many types of data follow a format that has already been set. Date columns that are stored in a fixed format, like YYYY-MM-DD or DD-MM-YYYY, are a common example. A data validating process that checks that dates are in the correct format helps keep data and time consistent.

Consistency check

A consistency check is a type of logical check that makes sure the data entered makes sense. One example is ensuring that the delivery date is after the shipping date.

Uniqueness check

Email addresses and IDs are two examples of data that are naturally unique. These fields should only have one entry in a database. A uniqueness check ensures that an item is not put into a database more than once.

Pros and cons of data validation

With Validating data testing, businesses can check that their databases are correct and valid and make better decisions. If you are deciding validating data for your business, here are the pros and cons of each:

Pros
Check the data’s accuracy

Validating data does a lot of the heavy lifting to ensure data integrity. Validation won’t change or improve your data, but it will ensure it serves its intended purpose if it’s set up correctly.

Helps Manage Multiple Data Sources

Data validation becomes increasingly important as the number of data sources increases. Suppose you are importing customer data from different channels; you will need to validate all of this data simultaneously against the same tracking strategy. Otherwise, conflicts and errors could appear between the datasets.

Save Time

Validating data takes time, but once it’s done, you won’t have to change anything until your inputs or requirements change.

Cons
Complexity

Validation is tough with several complex data sources. Many enterprise platforms, such as Segment, include powerful validation tools for large multi-source applications, which can help in this situation.

Data Validation Errors

This validation can lead to errors; not all validation software is perfect. Almost certainly, there will be validation errors that need to be fixed.

Changing Needs

One of the biggest problems with validating data is that it needs to be re-validated after certain changes are made. Schema models and mapping documentation must be updated as data types and inputs are provided.

Conclusion

We learned about data validation, its importance, types, and pros and cons from the talk above. Validating data is an important step in managing it, and it is often done as part of data cleansing. The goal of validating data is to ensure that it is of high quality and can be trusted and used confidently.

QuestionPro can guide you in your validating data process. QuestionPro offers various data validation features, including setting data types, ranges, patterns, and mandatory fields for survey questions.?