Data validation is a process of ensuring that data is accurate, complete, and usable, usually by automatically comparing stored data with reliable sources. The data validation process is often carried out before a dataset is used for various purposes such as lead generation, marketing, and more.
What is the purpose of data validation?
The main aim is to eliminate incorrect data input and ensure that the data collected is appropriate for use, and doesn't cause errors. Examples of errors that can occur are incorrect contact details (emails or phone numbers) for marketing campaigns, incorrect revenues for analyzing business growth, GDPR-related issues, etc.
If Data is not validated, what are the implications?
If data is not validated, it is more likely to contain errors, inaccuracies, and inconsistencies, leading to poor decisions, incorrect analysis, system failures, and wasted time and money for your organization.
Automated and Manual ways of performing data validation
Here’s a breakdown of how data validation typically works:
- Automated Tools
Automated data validation involves the application of several algorithms, tools, and scripts to facilitate the steps needed for validation. This is well appropriate for big data validation where the procedure would cost lots of time and effort to complete manually.
These tools are mainly used to verify the data by highlighting such aspects as duplication, missing values, and errors. They can work with different data formats and can be modified.
Examples: IMPROVE app, Company Information API, and other relevant specialized software. View all our data validation services here. - Manual Review
Where automation fails to probe them, humans can complement the validation process by spot-checking the data or reviewing them for added sensitivity. This involves the use of people who manually check on data. This method is appropriate for small datasets or when automated procedures are inadequate.
It is one of the most important steps in data analysis where different entities of data are scrutinized to gain a deeper understanding of their format, content, and correlation patterns. This is a good first step before applying the data validation rules.
Common types of data validation
- Format Validation
Ensures that data is in the right format, for example, that phone numbers aren't too short, that email addresses match the company's web domain, or simply that the data isn't outdated or unrealistic.
- Range Validation
Ensures that values are numerical and checks that they lie within reasonable ranges of values. For instance, age should be between 0–120.
- Uniqueness Validation
Helps avoid such issues as one record having more than one link to the same URL or duplicated customer ID or email address.
Every type of data validation guarantees the proper documentation of data for the corporate processes.
How to prepare for and perform data validation?
Validation rules can be used within a system to validate data. Others include automated validation software, and custom scripts that are designed to compare data against certain set criteria.
Here are practical steps to build a successful data validation system:
- Set Clear Rules Early
Find out, to the finest degree possible, what makes data ‘’valid’’, or suitable for your business. This might include such limits as format, range, or requirements that must be met, such as the requirement to fill in some fields.
- Use the Right Tools
Some of the software is designed with data validation solutions inherent within them. For form validation in real-time, daily, weekly, and monthly data, or even for any batch data checks, select the right data validation tools that are suited to you.
- Test Regularly
Validating your processes helps to check whether they are operational as planned and to check for any chance that may have been missed.
Challenges
Data validation, which is equally significant, has its problems. The most obvious challenge is the volume of data that has to be coped with, especially when merging the data collected from various sources. It is possible to have data validity in one arena but not for new, mes,y, structured data that comes from other sources.
The existence of new and changing compliance issues is the other problem area. Validation rules should be adjusted over time because various acts and regulations, such as GDPR or CCPA, may be modified over time.
Conclusion
Good data validation is one of the crucial factors in any business plan that involves data. Yes, it’s about catching mistakes, but there is so much more to it–it is about each piece of information being prepared to fulfill its intended use, be it helping a customer, supporting a financial decision, or meeting regulatory requirements.
The best management practices ensure that businesses monitor the process for error indicators and apply them to automate the business to arrive at accurate results, thus increasing operational efficiency while avoiding costly mistakes.
Find here good examples of data validation.
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