What is Data Validation?
In data analytics, if your data is not correct from the starting point, then it can definitely impact the results too. Hence it is essential to validate the data before using it in the process, and this can be done through data validation. It is one of the most crucial parts of the data handling task, whether it is for the field of information collecting, presenting, or analyzing the data.
According to the basic definition, it is a method to check the quality and accuracy of data, or it is defined as the data cleaning to ensure that data is complete, unique, and it is present in the required range. Data validation used in the process like Extract, Transform, and Load (ETL), in which you have to transfer data from database source to a targeted data warehouse for joining it with other sets of data for analysis to increase the accuracy.
This process is essential because it helps to gain the best results possible, but it slows down the complete analysis. These days data validation becomes a much quicker process than usual due to the automated validation process, and data validation is becoming the essential ingredient of the workflow.
Importance of Data Validation
Data validation provides accuracy, details, and clarity because it is necessary to eliminate issues from any project. Risks occur in the decision making if you don’t validate your data by appropriate process. In datasets, structures and content decide the results of the process and validation technique cleanse and eliminate the unnecessary files from it and provide an appropriate structure to the dataset for best results. Data validation is used in data warehousing as well as it is also used for the ETL (Extraction Translation Load) process. It provides convenience to an analyst for getting insight inside the scope of data conflicts. Data validation can also be performed on any data, including the data in a single application like MS excel or mixing simple data in a single data store. We have used a term ETL, so it is highly time-consuming to validate the data via scripting or manually. Still, a modern ETL tool can be beneficial for you to expedite the process of validating your data. You can easily integrate, transform, and clean the data if it is moved to your data warehouse. As a part of your assessment of your data, you can determine which errors can be fixed at the source, and which errors an ETL tool can repair while the data is in the pipeline.
Methods of Data Validation
There are different types of ways available for the data validation process, and every method consists of specific features for the best data validation process, these methods are:
In this method validation process is performed through the scripting language like python for writing the entire script for the validation process. For example, the creation of XML files needs sources and table names, columns, and target database names for comparison, then python takes the XML file for input and provides the results. However, this method is time-consuming because it needs a writing script and its verification.
2. Open Source Tools
Developers can save money if the open-source options are cloud-based because open source options are cost-effective. However, this method requires excellent knowledge and hand-coding to complete the process effectively. Some of the best examples of open source tools are OpenRefine and SourceForge.
3. Enterprise Tools
There are different enterprise tools available for the data validation process. Enterprise tools are secure and stable, but it requires infrastructure, and it is costlier as compared to open source tools. For example, the FME tool area used to repair and validate the data.
Steps of Data Validation Process
1. Determine Data Sample
If you have a large amount of data for the data validation, then you need a sample rather than a complete dataset. You have to understand and decide the volume of the data sample and find the error rate to assure the success of the project.
2. Database Validation
For the process of validation of the database, you have to ensure that all requirements are fulfilled with the existing database. Determination of unique IDs and the number of records are required to compare source and target data fields.
3. Data Format Validation
Determine the overall capability of data and the variation that requires source data for the targeted validation, and then search the incongruent, duplicate data, null field values, and incorrect formats.
Best Data Validation Tools and Software
There are various tools available that can provide the best data validation process with the best performance and appropriate outcomes, so these tools are:
- Datagaps ETL Validator
- Data-Centric Testing
Benefits of Data Validation
- It is cost-effective because it saves the right amount of time and money through the collection of datasets.
- It is easy to use and compatible processes because it removes duplication from the complete dataset.
- Data validation can directly help to improve the business with enhanced information collection.
- It consists of a data-efficient building that gives the standard database and cleaned dataset information.
Challenges for Data Validation
- There can be some disturbance occurring due to the multiple databases across the organization. Therefore data may be outdated.
- The process of data validation can be a highly time-consuming process when you have an ample amount of database because you have to perform the validation manually.
The data validation process is a significant aspect to filter the large datasets and improve the efficiency of the overall process. However, every technique or process consists of benefits and challenges, so it is crucial to have the complete acknowledgment of it. Data validation can improve quality and accuracy to provide the best work process. In this article, we have discussed some of the essential key factors that can clear your mind regarding data validation. Data handling can be easier if an analyst adapts this technique with the appropriate process, then data validation can provide the best outcome possible for big data.
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