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Cloud Data Analytics - Sep 19 2021

Importance of Data Validation and Different Methods of Data Validation

Data validation helps organizations check the accuracy and quality of their data. Data validation is typically carried out before data is imported or processed. Ensuring the accuracy of data and establishing its quality on a routine basis also allows organizations to perform a data cleanse – an essential step, considering the massive amount of data collection happening today. 

Incomplete data, duplicate data, or incorrect data can be extremely damaging, and it can negatively impact business analysis and predictions. This in turn can impact effective business decision-making both in the short and long run. 

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Data Validation Process

The process of data validation involves introducing multiple checks into a system or into reports that work to ensure logical data consistency – both at the input stage and also in data storage. 

If you use an automated system, then human supervision and intervention will be minimal. However, it’s critical to ensure the data entered into the system is accurate, complete, and addresses all the established quality standards. 

If your data is corrupted in any form (inaccurate, incomplete, and duplicate) it will cause downstream reporting issues, which in turn impact business results. Even when it comes to unstructured data, if the data is incorrectly entered, it can result in costs incurred by way of not only cleaning the data but also in data transformation, and storage of data. 

How to perform data validation?

Validation by Scripts

If you are fluent in coding languages, then validation by scripts can be one option for data validation. This method allows you to easily compare data values and data structure to check if they match or fit within a set of predefined rules. This helps in ensuring that your data entered falls within the necessary quality parameters. 

However, if you’re managing complex data or dealing with extensive data quantity, data validation using validation scripts can be a lengthy and exhaustive process.

Validation by Programs

There are several software solutions available which can be used to perform data validation. If you’re using a software program for data validation, the process can be easier and can help increase data accuracy. Data validation programs are designed to follow set checks and to understand the type of file structures you are working with. 

What are the types of data validation?

It’s vital to note that data validation is critical for driving data workflow efficiency within any organization. And while data validation does take time and effort, it cannot be ignored since accurate data is necessary in delivering the best possible outcome or results for your business decisions.

Data validation can be performed using a variety of advanced tools and procedures, or done manually. The process involves the use of different procedures to carry out multiple data checks as a way of ensuring data accuracy and quality prior to storing of the data. 

Some of the most commonly used types of data validation checks include the following:

  1. Data Type Check

A data type check helps in ensuring that the data entered is of the correct data type or form. So, for example, if a data entry field in the system used is built only for accepting data in the form of characters, then any other form of data, such as data in numerical value or symbols should ideally be rejected. 

For example, ensuring the correct data type or form is entered in the corresponding data entry/capture field is one of the first checks in the data validation process.  

  1. Code Check

Using a code check can help ensure that a data field is chosen only from an established or valid list of values, and that it is structured for specific formatting rules. 

For example, if while entering data, the field requires inputting of a postal code or a country code, then it is necessary to ensure the code entered is valid by checking it against an established or valid list of postal or country codes. 

  1. Range Check

Conducting a range check is again a commonly used data validation method to help verify a predefined range within which the entered data falls.  

Consider the following:

Range validations can help in ensuring that the data received is within or matches the expected range limitations that have been created within the system. So, for example, if your system has a form field which accepts only values between 1 and 99, a range validation check will automatically reject any data number above 99 or less than 1. 

You can establish range validation checks for a wide range of data values including date, age, geographical range (longitude and latitude), among others values. 

  1. Format Check

Entries of certain data types are often done using a specific predefined format. A common example of this is date columns (DD-MM-YYYY or YYYY-MM-DD). 

When fixed formats like this are used for entering and storing data, it can help in ensuring consistency and accuracy across your data as well as ensuring capture of accurate timelines. 

  1. Consistency Check

A consistency check is another commonly used type of data validation. Here, the idea is to establish a logical check – this helps in ensuring that the data entered has been done in a logical and consistent manner. 

For example, the delivery date of a package will obviously be after the shipping date. A consistency check will ensure that this happens. In other words, it allows for a logical and consistent entry and validation of the data type.

  1. Uniqueness Check

Often data such as names or email IDs or even addresses can get mixed up, or even entered multiple times leading to data duplication. 

One way to prevent this from happening is by using a uniqueness check in your data validation process. This can be done by ensuring your database has unique entries for the relevant fields thereby ensuring that data is not entered multiple times. 

What is a data validation tool?

Data validation can help ensure your data is clean, accurate, logical and consistent. However, data can easily get corrupted due to several factors such as complexities of user interface, redundant, inflexible, or faulty business processes, poor master data maintenance, and inefficiency within your data entry and storage processes and this in turn can lead to financial losses. This is where a data validation tool can play a critical role. 

A data validation tool can help you efficiently manage and master data validation at all stages via the use of specified validation rules, or check routines as they are also called, to help check your data accuracy, consistency, and relevance.

The results of conducting data validation heavily rely on the meticulous implementation of the predefined validation routines or checks all through the data lifecycle.

Challenges In Data Validation

Data validation can present challenges for several reasons. Two of the most common challenges include the following:

    1. The spread of the data: If the data within your organization is spread or distributed across multiple databases, then validating the data can be challenging. You also have to consider the fact that the data can be redundant or outdated or the data could be stored in isolated groups. 
  • The process of validating data formats can be an exhaustive and time-consuming process. This can be the case if you are dealing with massive amounts of data or if you follow a manual data validation process. 

Having said this, data validation does not have to be time consuming. There are several data integration solutions which can help you automate your data validation processes. 

The Right Viewpoint

Data validation is an essential aspect of ensuring your data is reliable, relevant, consistent, and accurate. You can perform data validation using several different types of data validation checks that not only help you maintain and manage your data better, but also help in a data cleanse. 

Given the sheer size of data that organizations collect, data validation should be considered a way of optimizing data workflows as opposed to a cumbersome and time-consuming process.  

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About NextPhase.ai

NextPhase.ai is a data cloud services provider specializing in Snowflake, cloud data management and analytics technologies. We accelerate enterprise digital transformation initiatives by leveraging our innovative cloud data management technology, “NextPhase.ai DATAFLO” to optimize and rationalize disparate enterprise data into relevant insights. “DATAFLO” is designed to automate the lifecycle of data management transformation using AI and ML along with expeditious on-ramps to the Snowflake data cloud infrastructure. NextPhase.ai provides a range of technology consulting services for the Financial Services, Biotech and Technology industry sectors combining our platform-based services, seasoned talent, and industry proven methodology so our customers can harness more from their data. We are a Silicon Valley based company with global presence having delivered high value service engagements for numerous Global 2000 enterprises.

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