What is your primary use case for Informatica Cloud Data Quality?

We use the solution to validate emails, telephone numbers, and postal codes. It can also create some data quality rules using the Cloud app's data quality function.

We've 300-400 data quality rules and different kinds of rules for completeness, conformity, and timeliness. We're using Informatica to implement those rules in Cloud Data Quality.

The solution is used for data quality implementation. We handle the whole life cycle of data quality from end to end, including rule specification and exception handling.

I use Informatica Cloud Data Quality in my company, as most of our customers have data from different systems and just want to standardize and enrich that data. If you have data like cell phone numbers, it is important to ensure that there is a consistent format to adhere to, like the area code, IDs, or social security numbers. The tool also helps users check email addresses because sometimes a user may find that their data has an incorrect email address. A lot of the use cases related to the tool involve understanding the data from all of the systems and starting the journey to unify some of those data fields, having some consistency in how the data is being captured, and helping look at the completeness of the data. In short, the use cases are to understand the data from different systems and different sources generally. Data could come from the CSV, a web service, or out of the database, but Informatica Cloud Data Quality allows you to extract from all those sources to give you a unified interface to decide how you want to customize and standardize the data.

We have a customer with a lot of data. We see a lot of quality issues, so we have implemented a data quality solution on-premises. Since everything is moving to the cloud, we are also moving our data quality solution to the cloud. We are building data quality goals for profiling, matching, and enrichment. There are other use cases, one example is matching data in our on-premises MDM system. To do this, we need to clean the data and profile it to understand its quality. We can then use data quality rules and matching modules to identify and resolve data quality issues. We also use it to validate customer email addresses and phone numbers on the fly in our in-house customer applications. Other use cases include data integration and data governance.

We are working with our clients to help them build a data governance product for their enterprise data warehouse. By using Informatic EDC, Informatica Axon, and Informatica Data Quality, they can ensure the quality of their data governance. Here, we are sharing the data with the consumers. We are using automated tools via Axon to ensure that the quality of data is good and that it is trustworthy. The average threshold for good-quality data is above 85 percent, so we have set a conformance threshold. Data quality is important when scanning data in Axon. We can determine the quality of the data and, if necessary, reach out to the data storage to correct it. Cloud data quality is important in this process because it can correct or reference data.

We are using Informatica Cloud Data Quality for data quality operations.

My primary use case of this solution is to cleanse data quality.

We have a lot of data from multiple vendors spanning multiple years. We want to have one unified final data standard, rather than searching around to do the quality check. We have a data governance process to make sure we have the proper data. In order to do that, we use some tools, such as Informatica Cloud Data Quality to bring the data together and to do the quality checks.