Data validation is an essential preliminary step to migration. Any organization can benefit from making sure that data ...
Data validation is an essential preliminary step to migration. Any organization can benefit from making sure that data is as accurate and complete as possible in advance of pursuing extract, transform and load processes or starting a transformation journey. While validation previously required manual methods or scripting, automated tools now make validating sample datasets or large volumes of data easier and faster than ever before. Find out how to validate data and learn about ways to promote accuracy and integrity.
Data validation is a process of checking and cleansing data to confirm quality. High-quality data is correct and useful. Validation involves the use of routines, which are also referred to as checks, constraints or rules, to ensure that data is as valid and complete as possible. The specifications that serve as the basis for data validation may either be manually scripted or built into enterprise or open-source applications.
Validation is essential for ensuring the accuracy and integrity of data. The entry or storage of inaccurate data in systems can undermine the veracity of analytics and data-driven decision-making. It is particularly important to validate data in advance of extract, transform and load projects, or ETLs. These projects involve extracting data from a source and verifying it during a testing phase prior to transforming the data and loading it to a target storage destination.
In the past, manual data validation took a lot of time and effort and was prone to human error at every stage of the process. Automated tools now make it possible to validate large volumes of data based on consistent standards. The stakeholders of organizations should determine how to validate data in terms of the checks applicable to the types or formats in use. Validation checks are likely to be most useful during the testing phase that precedes ETL or migration projects.
There are many types of data validation or checks for data. Before describing some of the most common types of validation, it is worthwhile to differentiate between data validation and verification. These terms are often used interchangeably, but grasping the distinction between these methods is essential for comprehending the scope of validation.
Validation applies to original copies of data input by users or originating from external systems. These checks take place during the creation or updating of records. Verification typically takes place after backups or loading to guarantee matches between the original states and copies of data. With a clearer sense of the purpose and timing of data validation, it is useful to consider how to validate data. Here are 10 of the most common types of data validation:
Data type checks are a basic validation method for ensuring that individual characters are consistent with expected characters as defined in data storage and retrieval mechanisms. For example, a data type check on a numeric data field would return an invalid result if the field contains letters or symbols.
Data validation can ensure that data provided by a user or external system is in the specified format or template for the target system. A simple example of this check might be checking to make sure that dates contain the proper number of integers and correspond to expectations for the order of the day, month and year.
A presence check ensures that data is present. Most internet users are familiar with forms that return errors for null or empty fields. This data verification measure promotes the completeness of data by ensuring that users or external systems do not omit required information.
Checking data for consistency can ensure that entries are valid. This type of verification can be useful for ensuring that data is accurate and meets system requirements. For instance, the date of an entry for a past record should not be in the future. Consistency checks can also confirm matching data.
One of the most useful techniques for how to validate data involves checking ranges and constraints. This method is useful when input must fall within a specified minimum and maximum range. Constraints can also set rules that input must meet for validation, such as requirements for the minimum length or types of characters in passwords.
Checking for uniqueness can be useful for systems that administer user accounts. This check can ensure that data is unique from other input on a form or within a system. Validating the uniqueness of data can ensure that multiple accounts do not have the same username or that the email address or phone number associated with an account is not already in use, which can reduce redundancy across accounts.
Validating syntax makes it possible to check the structure and format of submissions to ensure that input matches the expected pattern. Syntax validation is also useful for making sure that the data type or length of input in a field meets set requirements. One example of syntax validation could be a requirement to include an at sign in an email address field.
Integrity validation is essential for checking the consistency of data in a set or across relational datasets. This method can validate a foreign key against primary keys. Internal mechanisms do not constrain the values in a foreign key field but make it possible to check the validity of cross-field or cross-table references.
Validating the existence of particular files is useful for programs or systems that process or store files. This type of check can ensure that a file with a provided name exists in the specified location and is in an accepted format.
Structured data validation is a broad category that allows for a combination of various types of validation with complex processing. Stakeholders can use this type of validation to test conditional restraints or sets of process operations within systems.
The process for validating data typically involves several steps that should ideally take place during the testing phase of an ETL or migration project. Depending on the type of data, validation may require additional measures. Here are five general steps that stakeholders should plan to take when validating data:
The first step toward validation is to clearly define the requirements for data and data models. This step is particularly important when source data must undergo any type of transformation prior to loading to the target database. Assessing the volume of datasets is also an important preliminary step that can help stakeholders determine whether selecting a sample could be useful for defining an acceptable error rate.
A clear understanding of data sources is helpful for validating a sample set or full data with scripts or automated tools. Considering the nature of data sources can be useful for determining the scope of a migration project and could prevent data loss. This step can also include determining specifications for datasets that will undergo transformation.
The format, structure and value of migrating data must match the schema of the target database. This stage of testing factors in requirements for accessing and using migrated data and could also involve format checks. Considering destination requirements can provide insight into how to validate data and help stakeholders determine which measures to take at the transformation stage of an ETL project.
Issues with data health or quality may become evident at any step of the validation process but are most likely to pose an obstacle after establishing data, source and destination requirements. Data might not meet organizational standards or target database specifications and may require cleansing or conversion prior to migration. In addition to validation, verification at the testing stage can identify quality issues by comparing source and destination data.
Enforcement, verification or advisory actions are necessary after unsuccessful validation attempts. Enforcement actions identify why validation failed. Some automated systems will attempt to correct data and validate the conformant version. Verification actions need input from users and are not a strict validation process. Advisory actions allow for the provisional entry of unvalidated data but note validation issues. Stakeholders can also log validation issues for future processing.
These steps make it possible to identify data that requires cleansing and any other measures necessary for validation during testing. Waiting until the end of the ETL process to cleanse data can prove more time-consuming and costly. The enterprise transformation experts at Cloudficient can recommend the best approach to data retention and expiration for any organization.
Taking measures to ensure the accuracy and integrity of data can allow for successful validation. Clean datasets are more useful and valuable than sets that contain erroneous, incomplete or unstandardized data. Integrity refers to the preservation of data quality across modifications and transfers. Find out how to validate data and verify accuracy while laying a foundation for physical and logical data integrity.
Verification procedures can promote accuracy and consistency across all copies of data. Data verification is particularly helpful for identifying errors introduced by systems. Stakeholders can check the consistency of data across source and target databases or multiple sources by matching records to indicate and resolve errors, redundancies and other issues that could undermine accuracy.
Physical data integrity encompasses standards for the storage and accessibility of data, including the physical security of on-premises systems and the development of a disaster recovery plan with reliable backups. Enterprises pursuing transformation projects should also consider the security posture and redundancy features of cloud storage providers.
There are four categories of logical data integrity. Entity integrity ensures that data elements are not repeated or missing. Referential integrity sets standards for data storage and use. Domain integrity corresponds to accepted formats and types of data. Lastly, user-defined data integrity covers any additional rules or constraints to prevent the loss of data integrity.
A combination of data validation and verification procedures can promote accuracy and integrity in source and target databases. In general, verification should occur prior to ETL processes. Validating data during the early stages of workflows can save time and money downstream.
Cloudficient specializes in helping prepare for cloud migration. We use next-generation technology to change how organizations retire legacy systems and transform them into the cloud. Our affordable product offerings and scalable services respond to client needs. We also have the expertise necessary to help stakeholders determine how to validate data with the best legacy data management and digital transformation resources. Contact us to find out how you can bring Cloudficiency to your migration project.
With unmatched next generation migration technology, Cloudficient is revolutionizing the way businesses retire legacy systems and transform their organization into the cloud. Our business constantly remains focused on client needs and creating product offerings that match them. We provide affordable services that are scalable, fast and seamless.
If you would like to learn more about how to bring Cloudficiency to your migration project, visit our website, or contact us.
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