The most valuable feature lies in the capability to assign data quality issues to different stakeholders, facilitating the tracking and resolution of defective work.
IT Manager at a insurance company with 10,001+ employees
Real User
2021-03-05T20:22:02Z
Mar 5, 2021
It is saving a lot of time. Today, we can mask around a hundred million records in 10 minutes. Masking is one of the key pieces that is used heavily by the business and IT folks. Normally in the software development life cycle, before you project anything into the production environment, you have to test it in the test environment to make sure that when the data goes into production, it works, but these are all production files. For example, we acquired a new company or a new state for which we're going to do the entire back office, which is related to claims processing, payments, and member enrollment every year. If you get the production data and process it again, it becomes a compliance issue. Therefore, for any migrations that are happening, we have developed a new capability called pattern masking. This feature looks at those files, masks that information, and processes it through the system. With this, there is no PHI and PII element, and there is data integrity across different systems.
It has seamless integration with different databases. It has components using which you can easily integrate with different databases on the cloud or on-premise.
It is a drag and drop kind of tool. Instead of writing a lot of Java code or SQL queries, you can just drag and drop things. It is all very pictorial. It easily tells you where the job is failing. So, you can just go quickly and figure out why it is happening and then fix it.
The data quality tools in Talend Open Studio for Data Quality enable you to quickly take the first big step towards better data quality for your organization: getting a clear picture of your current data quality. Without having to write any code, you can perform data quality analysis tasks ranging from simple statistical profiling, to analysis of text fields and numeric fields, to validation against standard patterns (email address syntax, credit card number formats) or custom patterns of...
The most valuable feature lies in the capability to assign data quality issues to different stakeholders, facilitating the tracking and resolution of defective work.
It offers advanced features that allow you to create custom patterns and use regular expressions to identify data issues.
The solution is customizable.
It is saving a lot of time. Today, we can mask around a hundred million records in 10 minutes. Masking is one of the key pieces that is used heavily by the business and IT folks. Normally in the software development life cycle, before you project anything into the production environment, you have to test it in the test environment to make sure that when the data goes into production, it works, but these are all production files. For example, we acquired a new company or a new state for which we're going to do the entire back office, which is related to claims processing, payments, and member enrollment every year. If you get the production data and process it again, it becomes a compliance issue. Therefore, for any migrations that are happening, we have developed a new capability called pattern masking. This feature looks at those files, masks that information, and processes it through the system. With this, there is no PHI and PII element, and there is data integrity across different systems.
It has seamless integration with different databases. It has components using which you can easily integrate with different databases on the cloud or on-premise.
It is a drag and drop kind of tool. Instead of writing a lot of Java code or SQL queries, you can just drag and drop things. It is all very pictorial. It easily tells you where the job is failing. So, you can just go quickly and figure out why it is happening and then fix it.
The features that I find to be the most valuable are the extensibility, the integration, and the ease of integration with multiple platforms.
With its frequency function, we were able to pick a line of business to be addressed first in one of our conversion projects.