Data validation is a critical step in the analytics world to ensure a smooth data workflow. Any inconsistencies in data at the beginning of the process may impact the final results, making them inaccurate. Therefore, checking the accuracy and quality of data before processing it is extremely important. Data validation is an essential step in the data handling task to create data that is consistent, accurate, and complete to prevent data loss and errors. It allows users to check that the data they are dealing with is valid by means of end-to-end testing such as testing for data accuracy, data completeness, and data quality.
While validation is a critical step, it is often overlooked. Businesses can perform various types of validation depending on the constraints and objectives. This article will discuss the importance of data validation, approaches to carry data validation, benefits, challenges, and more.
Importance of Data Validation
Data validation provides accuracy, cleanness, and completeness to the dataset by eliminating data errors from any project to ensure that the data is not corrupted. While data validation can be performed on any data, including data within a single application such as Excel creates better results. Inaccurate and incomplete data may lead the end-users to lose trust in data.
Data validation is essentially a part of the ETL process (Extract, Transform, and Load), which involves moving the source database to the target data warehouse. In doing so, performing data validation is required to enhance the value of the data warehouse and the information stored there.
Various data validation testing tools, such as Grafana, MySql, InfluxDB, and Prometheus, are available for data validation.