Understanding The Concept of Data Validation in 2023

       August 2, 2023

Understanding The Concept of Data Validation in 2023

What is the concept of Data Validation?

       You might have come across the term “Data validation” plenty of times in relevance to your data analytics and data intelligence activities. So, what exactly is data validation? Data validation is the process of verifying the integrity, accuracy and framework of data before it gets implemented into operations. The results coming out of Data validation has multiple business applications, they are critical for data analytics, business intelligence and also in some cases implementation of outcomes to influence AI/ML models in a process. Data validation is also widely practiced across financial systems in conjunction with accounting and compliance.

      Data validation overall is relevant to data quality, and is an important element of data quality measurement. It is critical for measuring accuracy and authenticity of the data set.

Why do you need Data Validation?

Why do you need Data Validation

       Business operations are increasingly based on data these days and for data scientists, analysts and other teams working with data, even marketing and sales, validated data is important. The output of any business operation is critically influenced by valid data. Your business intelligence and data analytics models’ accuracy and efficacy depend on a validated data set. So, data validation is very important in driving business operations efficiently.

Understanding the 3 important types of Data Validation

      Although there are multiple types of data validation to enable seamless usage of data for operations, let us look at the three most common types of data validation.

Understanding the 3 important types of Data Validation

      Data Type Validation – This is one of the most common forms of data validation. This type confirms data in each field of a data set matches any specified data format or type.

      Structured Data Validation – As the name suggests, this form of data validation focuses on ensuring that the data is compliant with a certain framework of schema.

      Consistency Data Validation – Consistency is key everywhere and for data too, this type of validation ensures data styles are consistent across data sets and the values are listed as specified.



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Reviewing Data Validation Techniques & Methods

       There are several Data Validation techniques but some of the most followed techniques and methods are as follows. Remember, data validation techniques essentially do depend on the type of business you are involved in.

Reviewing Data Validation Techniques & Methods

      Form Validation – this is a simple technique which takes into consideration completion, accuracy and specific criteria met in the data form before use and submission.

      Client end Validation – Before server submission this technique enables client-side data validation. This is important for faster, and immediate output to the user and helps in overcoming malevolent users.

      Server end Validation – As the name suggests, the technique is used to authenticate data on the server side immediately after client submission. This is a more secure form of client dependent data validation which circumvents client end validation.

      Regular Expression Technique – This technique follows a regular pattern format to validate data. The technique is all about following specific patterns such as phone numbers, email address, mailing addresses etc.

Some other common techniques include:

      Data type validation, which checks for correct data types such as dates, integer etc

      Range validation technique, which checks for specific range, such as specific demographics of the data sets

      Length validation technique, check for data length as specified by set parameters, based on characters

      Format validation technique, essentially dependent on following a specific format such as a date format or decimal format for numerical values

Critical Data Validation Tools

Critical Data Validation Tools

      The most basic tools that people use for Data Validation has to be Microsoft Excel and Google Spreadsheets. It is a straight forward and simple tool, both of them and comes at no cost. However, there are some other open source tools too which can be implemented to support your data validation exercise. One such tool is DBT, which is used for validation and data transformation. But, if your data validation needs are to define an entire business process with multiple operational and business implications then it is best to choose from the below list of top Data Validation Tools & Software that are commercially available.

      Ataccama ONE – Is an augmented data management platform that features data discovery and profiling, metadata management and a data catalog, data quality management, master and reference data management, and big data processing and integration.

      Innovative Systems Synchronos - Is an enterprise MDM solution used for operational or analytical purposes.

      Oracle Cloud Infrastructure Data Catalog - Is a metadata management service that helps organizations find and govern data using an organized inventory of data assets.

      SAP Master Data Governance - Enables users to consolidate and centrally govern master data and support for all master data domains and implementation styles, pre-built data models, business rules, workflow, and user interfaces.

      Syniti Master Data Management - Enables the creation of a single point of reference to master and application data from multiple domains.


A Complete Guide to Creating a B2B Database

      Data validation is an important element of data management and should not be over looked. Data validation is critical for accuracy, authenticity and efficiency of data and its contribution to the business process. Understanding data validation, the techniques of data validation and the different types of data validation helps your data management team to work with the necessary best practices for effective implementation of a data management strategy for your company. With data validation, data errors and quality problems improve drastically, thus helping smooth operations.