Top 5 Must Haves In Your Data Testing Platform

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No matter how many new features an application has, how clean the user interface is, or even how fast the content is delivered across the Internet, if the data is inaccurate on incomplete, the product will fail. A company that uses corrupt or incorrect data cannot function: errors will disrupt your business processes, hinder accurate decision-making, hurt business operations between departments, increase overall costs due to returns and administration overhead, and—depending on which part of the world the company operates—create huge regulatory compliance issues, not to mention potential privacy violations. Businesses cannot afford to have their reputations suffer, and thorough data testing should be at the forefront of a data management strategy.

Building the right kind of data testing platform can be a time-consuming and challenging task. The goal is to identify and fix data issues before releasing them to a larger audience. But with so many potential options, what matters most? On the one hand, you want a robust platform that can handle large volumes of data and is scalable enough to handle future requirements. On the other hand, you want an easy-to-use platform that doesn’t require specialized programming skills to execute testing tasks. With so many software vendors offering a vast array of product features, how do you know what to look for when choosing a data testing platform?

Each round of testing is different, so there’s no “one size fits all” solution. But there are some standard features that every data testing platform should have to perform reliable tests and provide valuable insight into your data. Here are the top five you would want to have when buying a data testing platform.

Read: How To Evaluate ETL Testing Tools

Data testing platforms should allow you to run tests quickly and get results without requiring complicated configuration setups or esoteric code understood only by a few. The goal is to allow as diverse a group as possible to build and evaluate test scripts.

A good data testing platform will create easy-to-understand and share results across your team and other departments. Good communication will encourage identifying and fixing new and existing data issues.

Data rarely exists on just one data source. Your platform should support input and transformation among structured and unstructured databases housed in the cloud and on-premises.

Depending on the size of your datasets, speed will be a critical feature in your data testing platform. There is no point in trying to test across millions or recordsets if the tests take several hours, or even days, to fully execute.

You should be able to customize your data testing platform to meet your specific needs and requirements. This might include building custom profiling metrics, data visualization reports, and designing source-to-target rules

How ETL Validator makes a difference?
ETL-Validator

You are not limited only to these options; using them as a starting point will help you choose the best data testing tool. ETL Validator is one possibility. It offers easy-to-use functionality and intuitive user interfaces for robust data testing and validation capabilities. It is compatible with all major database platforms and comprises intuitive features for managing data at scale. It also allows you to automate data extraction, loading, and transformation during the migration process. Let’s reevaluate each must-have criterion from above and see how the ETL Validator would meet these requirements.

Data testing platforms should allow you to run tests quickly and get results without requiring complicated configuration setups or esoteric code understood only by a few. The goal is to allow as diverse a group as possible to build and evaluate test scripts.

A good data testing platform will create easy-to-understand and share results across your team and other departments. Good communication will encourage identifying and fixing new and existing data issues.

Data rarely exists on just one data source. Your platform should support input and transformation among structured and unstructured databases housed in the cloud and on-premises.

Depending on the size of your datasets, speed will be a critical feature in your data testing platform. There is no point in trying to test across millions or recordsets if the tests take several hours, or even days, to fully execute.

You should be able to customize your data testing platform to meet your specific needs and requirements. This might include building custom profiling metrics, data visualization reports, and designing source-to-target rules

Regardless of the product you choose, you want to select a tool that your team can work with and will integrate into your daily workflows. Although I list what I believe are the five must-haves for your data testing platform, there may be other factors you will want to consider depending on the particular use cases and requirements. You may want to consider advanced data analytic capabilities, the level of automation level support provided, version compatibility, available discount pricing, the technical support offered, and other qualifications. With that in mind, the best tool is the one that meets your specific needs, helps you deliver high-quality data at a reasonable pace, and gives you the tools you need to be more agile and efficient in your work.

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Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL ValidatorDataFlow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.  www.datagaps.com 

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