Data integrity becomes increasingly more important as more B2B firms use data-driven techniques to enhance revenue and improve operational efficiencies. The inability to trust business data gathered from a variety of sources can sabotage an organization’s efforts to fulfill critical business objectives. The sheer volume of data can be overwhelming for businesses.

Data standards, heterogeneous data systems, a lack of data governance, manual processes, and so on are all issues they encounter. As a result of this inability to trust data, data validation is required. Data validation allows businesses to have more confidence in their data.

In this article, you will gain information about Data Validation. You will also gain a holistic understanding of the importance of Data Validation, the types and methods of Data Validation, the steps to perform Data Validation, and its benefits and limitations. Read along to find out in-depth information about Data Validation.

What is Data Validation?

Data Validation is the process of ensuring that source data is accurate and of high quality before using, importing, or otherwise processing it. Depending on the destination constraints or objectives, different types of validation can be performed. Validation is a type of data cleansing.

When migrating and merging data, it is critical to ensure that data from various sources and repositories conforms to business rules and does not become corrupted due to inconsistencies in type or context. The goal is to generate data that is consistent, accurate, and complete in order to avoid data loss and errors during the move.

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Why Data Validation is Important?

Data Validation: Importance of Data Validation | Hevo Data
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Validating the accuracy, clarity, and specificity of data is essential for mitigating any project flaws. Without validating data, you risk making decisions based on imperfect data that is not accurately representative of the situation at hand. Structures and content in datasets determine the results of the process, and validation techniques cleanse and eliminate unnecessary files from it, as well as provide an appropriate structure to the dataset for the best results.

Data Validation is used in Data Warehousing as well as the ETL (Extraction, Translation, and Load) process. It makes it easier for an analyst to gain insight into the scope of data conflicts. While it is critical to validate data inputs and values, it is also necessary to validate the data model itself. If the data model is not properly structured or built, you will encounter problems when attempting to use data files in various applications and software.

Data Validation can also be performed on any data, including data in a single application, such as MS Excel, or simple data mixed together in a single data store.

What are the Types of Data Validation?

Every organization will have its own set of rules for storing and maintaining data. Setting basic data validation rules will assist your company in maintaining organized standards that will make working with data more efficient. Most Data Validation procedures will run one or more of these checks to ensure that the data is correct before it is stored in the database.

The following are the common Data Validation Types:

1) Data Type Check

A Data Type check ensures that data entered into a field is of the correct data type. A field, for example, may only accept numeric data. The system should then reject any data containing other characters, such as letters or special symbols, and an error message should be displayed.

2) Code Check

A Code Check ensures that a field is chosen from a valid list of values or that certain formatting rules are followed. For example, it is easier to verify the validity of a postal code by comparing it to a list of valid codes. Other items, such as country codes and NAICS industry codes, can be approached in the same way.

3) Range Check

A Range Check will determine whether the input data falls within a given range. Latitude and longitude, for example, are frequently used in geographic data. Latitude should be between -90 and 90, and longitude should be between -180 and 180. Any values outside of this range are considered invalid.

4) Format Check

Many data types have a predefined format. A Format Check will ensure that the data is in the correct format. Date fields, for example, are stored in a fixed format such as “YYYY-MM-DD” or “DD-MM-YYYY.” If the date is entered in any other format, it will be rejected. A National Insurance number looks like this: LL 99 99 99 L, where L can be any letter and 9 can be any number.

5) Consistency Check

A Consistency Check is a type of logical check that ensures data is entered in a logically consistent manner. Checking if the delivery date for a parcel is after the shipping date is one example.

6) Uniqueness Check

Some data, such as IDs or e-mail addresses, are inherently unique. These fields in a database should most likely have unique entries. A Uniqueness Check ensures that an item is not entered into a database more than once.

7) Presence Check

A Presence Check ensures that all mandatory fields are not left blank. If someone tries to leave the field blank, an error message will be displayed, and they will be unable to proceed to the next step or save any other data that they have entered. A key field, for example, cannot be left blank in most databases.

8) Length Check

A Length Check ensures that the appropriate number of characters are entered into the field. It verifies that the entered character string is neither too short nor too long. Consider a password that must be at least 8 characters long. The Length Check ensures that the field is filled with exactly 8 characters.

9) Look Up

Look Up assists in reducing errors in a field with a limited set of values. It consults a table to find acceptable values. The fact that there are only 7 possible days in a week, for example, ensures that the list of possible values is limited.

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What are the Methods to Perform Data Validation?

There are various methods for Data Validation available, and each method includes specific features for the best Data Validation process.

The methods to perform Data Validation are as follows:

1) Validation by Scripts

In this method, the validation process is carried out using a scripting language such as Python, which is used to write the entire script for the validation process. To ensure that all necessary information is within the required quality parameters, you can compare data values and structure to your defined rules. This method of Data Validation can be time-consuming depending on the complexity and size of the data set you are validating.

For example, if you want to validate whether a variable is an integer or not in a particular dataset, it can be done using the below Python script.

intFlag = False
while not intFlag:
 	 if isinstance(var, int):
 		intFlag = True
 	 else:
 		print(‘Type Error!’)

The validation code can check the variable type and set the flag to true if it is not an int. The program can then raise an error, log the invalid data, or take other appropriate action based on the validation failure.

2) Validation by Programs

Many software programs are available to help you validate data. Because these programs have been developed to understand your rules and the file structures you are working with, this method of validation is very simple. The ideal tool will allow you to incorporate validation into every step of your workflow without requiring a deep understanding of the underlying format.

The different programs that can be used are:

A) Open Source Tools

Because open-source options are cost-effective, developers can save money if they are cloud-based. However, in order to complete the process effectively, this method necessitates extensive knowledge and hand-coding. OpenRefine and SourceForge are two excellent examples of open-source tools.

B) Enterprise Tools

For the Data Validation process, various enterprise tools are available. Enterprise tools are secure and stable, but they require infrastructure and are more expensive than open-source tools. For instance, the FME tool area is used to repair and validate data.

What are the Steps to perform Data Validation?

The steps carried out to perform Data Validation are as follows:

Step 1: Determine Data Sample

If you have a large amount of data to validate, you will need a sample rather than the entire dataset. To ensure the project’s success, you must first understand and decide on the volume of the data sample as well as the error rate.

Step 2: Database Validation

You must ensure that all requirements are met with the existing database during the Database Validation process. To compare source and target data fields, unique IDs and the number of records must be determined.

Step 3: Data Format Validation

Determine the overall data capability and the variation that requires source data for the targeted validation, and then search for inconsistencies, duplicate data, incorrect formats, and null field values.

Data Validation Use Cases

Spreadsheet programs like Microsoft Excel and Google Sheets offer basic built-in data validation features. Both Excel and Sheets include a “Data Validation” option under the Data menu. This allows users to specify validation rules like acceptable data types or value ranges for selected cells.

More advanced data validation capabilities are found in ETL (Extract, Transform, Load) and data integration tools. These solutions apply validation policies and constraints during the process of extracting data from a source, transforming it as needed, and loading it into a target system. By integrating data validation into the ETL workflow, these tools help ensure data integrity and quality throughout the integration process.

What are the Benefits of Data Validation?

Some of the benefits of Data Validation are as follows:

  • It is cost-effective because it saves the appropriate amount of time and money through dataset collection.
  • Because it removes duplication from the entire dataset, it is simple to use and is compatible with other processes.
  • With improved information collection, data validation can directly help to improve the business.
  • It is made up of a data-efficient structure that provides a standard database and cleaned dataset information.

What are the Limitations of Data Validation?

Some of the limitations of Data Validation are as follows:

  • Because of the organization’s multiple databases, there may be some disruption. As a result, data may be out of date, which can cause issues when validating the data.
  • When you have a large database, the process of data validation can be time-consuming because you have to perform the validation manually.

What are the Challenges of Data Validation?

  • Data is often distributed, siloed, or even outdated across an organization. It becomes challenging to validate such data, given its scattered nature.
  • Data validation is time-consuming. Even though there are tools that perform data validation, data practitioners often face challenges when dealing with larger datasets.
  • Data validation systems are designed with a particular set of requirements. When the requirements change, the system has to be modified, which is a big challenge given the constant changes in datasets.

Data Validation vs Data Verification

While data validation and data verification are closely related concepts in data management, they differ significantly from each other. Let’s see how.

Data Validation:

  • Data validation ensures that the data input into a system or database is accurate, complete, and meets predefined rules and constraints.
  • It involves checking for errors, inconsistencies, or invalid values before the data is accepted or processed.
  • Data validation typically occurs at the point of data entry or data collection, often through input validation rules, format checks, range checks, or other automated mechanisms.
  • Data validation aims to prevent introducing incorrect or incomplete data into the system, ensuring data quality and integrity from the outset.

Data Verification:

  • Data verification is the process of confirming the accuracy and completeness of data after it has been entered or processed by a system.
  • It involves comparing the data against a known source or reference point to verify its correctness.
  • Data verification often involves manual processes, such as reviewing reports, auditing samples of data, or cross-checking against external sources.
  • Data verification aims to identify and correct any errors or inconsistencies that may have been introduced during data entry, processing, or transformation.

In summary, data validation focuses on ensuring the accuracy and completeness of data as it’s being migrated from one source to another, while data verification focuses on confirming the accuracy and completeness of data after it has been processed or stored in a system. Both processes are essential for maintaining data quality and integrity, but they address different stages of the data lifecycle.

Conclusion

In this article, you have learned about Data Validation. This article also provided information on the types and methods of Data Validation, the steps to perform Data Validation, and its benefits and limitations. Now that you learned about it, you should also understand solutions to replicate data as the data volume increases. Otherwise, your efforts to clean data through validation wouldn’t help much for making data analysis-ready.

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Share your experience of understanding Data Validation in the comment section below! We would love to hear your thoughts.

Manisha Jena
Research Analyst, Hevo Data

Manisha Jena is a data analyst with over three years of experience in the data industry and is well-versed with advanced data tools such as Snowflake, Looker Studio, and Google BigQuery. She is an alumna of NIT Rourkela and excels in extracting critical insights from complex databases and enhancing data visualization through comprehensive dashboards. Manisha has authored over a hundred articles on diverse topics related to data engineering, and loves breaking down complex topics to help data practitioners solve their doubts related to data engineering.

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