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EDUCATION

Data Verification Differs from Data Validation

Data verification and data validation look similar, but actually, they differ from each other.

What is data validation?

Data validation refers to the process of examining if the specifications meet customers’ requirements. Simply put, it is the process of filtering every piece of information and checking if it falls within the predefined & acceptable range of values for a given field.

Example

Let’s consider an example of data validation. Every four-wheeler is assigned unique alphanumeric characters, which include the prefix of the state. For example, DL for Delhi, or GC for Gold Coast, denotes the abbreviations used for defining the location or Postal Service where it comes from.

There is a list of all states in India consisting of these types of prefixes. The data validation process will be carried out to check if the entered Postal Codes of vehicles are valid or not. For instance, if Z0 is the case to check, the validation function will determine it wrong, as the prefix needs two letters from the alphabet. These characters should match the one in the list of all postal codes. If it does not match, this will be a case of invalid data entry.

Certainly, you should have a list of all postal codes to match with. Then, you may apply validation functions to a field, or a range of fields, for checking the accuracy of data entries.

Application or Uses

Data validation services are primarily used in performing various application systems, or website form functions. Let’s say, you want to discover the email ID of the inquirer. You may put in the validation to check the accuracy of the email ID. If it’s not from any domain name, mark it invalid. A message will pop up, stating that the entered email ID is wrong. Then, you may prompt to fill in the correct one.

What is data verification, and how is it different?

Data verification is a little different from validation. It is associated with the correctness, or accuracy of the current data, or identifying if the datasets meet quality parameters.

It’s like verifying what data you have to ensure that they are all fixed, consistent, and conveying the purpose that they intend to. Verification can occur at any time. This is a recurring process to ensure the benchmark quality of your information.

However, this is not the case with validation. It can only happen when you have created and updated the database.

Application or Uses

Data verification is important, especially when data migration took place. The movement or relocation of any information may cause some errors in its formatting or visibility. Sometimes, it’s a browser or server that may make a difference to its format. This is where cleansing is required.

Example

A website owner shifts his web content to the cloud server from local storage systems. This may interfere with the quality of the existing data because the content would be shifted over the server. This can affect the appearance of the data or their appearance because the source of storage would be a virtual place.

Before migration, the entire content is prepared for moving to another location. It may involve freezing cells or converting file formats into PDFs or others. Else, the migration can assign the incorrect value to a field in the spreadsheet.

This is why it is important to verify every piece of information once the shifting is over. Match the consistency of all files with the source system. You may select a sample from both storage and manually check the accuracy. Or, you may use any software or application to automate this checking process.

Verification Is a Continuous Process

Verifying data is not associated with data migration only. It is vital to ensure the accuracy and consistency of the entire database over time. You may miss out on updating all databases with a new entry. With this process, this can mistake can be prevented.  Schedule updates as a part of regular, monthly, or quarterly activity.

Duplicity is another thing that can be eliminated by verifying data. Duplicate data entry means having the same data twice, thrice, many times in a sheet. This is no less than a blunder when it comes to analyzing and making decisions accordingly. The duplicity may lead to negative and inflexible solutions.

This challenge can be administered through operator-led quality testing of data entry. Or, we can have logical tools to find and correct the entry with the perfect match data.

Quality is Common in Verification & Validation

Data enable insights to speak up and deliver values. Various analysts, solution architects, and data scientists find them to understand, extract, and integrate the value with a decision funnel to maximize benefits & returns.

Fortunately, we have artificial intelligence or AI together with machine learning to draw intelligence in no time. Certainly, these technologies involve validations, which are powered by verified data.

It means that both cleansing methods are interlinked and valuable to make our decision-making funnel highly effective and extremely valuable. They enable easy management and effective use of information that we have.

Summary

Data validation and verification look a little similar process, but actually, they differ from each other. Data validation is carried out after verification, which requires quality to match specifications. On the flip side, data verification means checking the usefulness and errors to make a dataset consistent.

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