I worked on a project where we had to use these tools to execute the data quality requirements from assessment perspectives to scorecards and similar tasks. I haven't used any other tools, so I cannot really compare. However, anything that was related to data quality from an implementation perspective was available. I haven't used it extensively, but it has its own use cases and could be potentially useful depending on the context. The main work I did was benchmarking the data sets that were available at the time to complete the seven dimensions of data quality.
Data and Analytics Manager at a insurance company with 10,001+ employees
Real User
Top 5
2025-04-19T00:08:00Z
Apr 19, 2025
The main use case we had with Informatica Cloud Data Quality was real-time data quality monitoring. When customer information was entered into a tool in real time, we were able to run data quality rules using Informatica Cloud Data Quality before the data was passed on to the database. This was integrated with Service Bus, and the rules were executed successfully.
IT Manager - Data Quality and Migration at a manufacturing company with 10,001+ employees
Real User
Top 5
2025-02-11T20:59:39Z
Feb 11, 2025
I use Informatica Data Quality for data profiling before starting any migration projects to understand what the data looks like. I also run quality reports on it, with mappings written for different rules to see what data needs fixing and to identify data issues.
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.
Data & Analytics Practice at Tech Mahindra Limited
Real User
Top 20
2024-07-05T03:08:00Z
Jul 5, 2024
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.
Learn what your peers think about Informatica Cloud Data Quality. Get advice and tips from experienced pros sharing their opinions. Updated: July 2025.
Data Quality Director at a financial services firm with 5,001-10,000 employees
Real User
Top 20
2023-12-15T16:36:48Z
Dec 15, 2023
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.
Lead Data Engineer at a tech services company with 51-200 employees
Real User
2023-02-14T13:21:59Z
Feb 14, 2023
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.
Principal at a computer software company with 11-50 employees
Real User
2021-08-24T20:48:13Z
Aug 24, 2021
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.
Informatica Cloud Data Quality Radar is a cloud application that quickly identifies, fixes, and monitors data quality problems in your business applications—wherever they are, in the cloud or on-premise.
This easy-to-use, browser-based tool empowers line-of-business managers to take ownership of the data quality process so business can maximize the return on trusted data. Informatica Cloud Data Quality Radar enables you to quickly assess the strengths and weaknesses in your data, to track...
I worked on a project where we had to use these tools to execute the data quality requirements from assessment perspectives to scorecards and similar tasks. I haven't used any other tools, so I cannot really compare. However, anything that was related to data quality from an implementation perspective was available. I haven't used it extensively, but it has its own use cases and could be potentially useful depending on the context. The main work I did was benchmarking the data sets that were available at the time to complete the seven dimensions of data quality.
The main use case we had with Informatica Cloud Data Quality was real-time data quality monitoring. When customer information was entered into a tool in real time, we were able to run data quality rules using Informatica Cloud Data Quality before the data was passed on to the database. This was integrated with Service Bus, and the rules were executed successfully.
We use Informatica Cloud Data Quality ( /products/informatica-cloud-data-quality-reviews ) in a medium business environment.
I use Informatica Data Quality for data profiling before starting any migration projects to understand what the data looks like. I also run quality reports on it, with mappings written for different rules to see what data needs fixing and to identify data issues.
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.