We recently deployed it for one of our clients, who use it to enhance the quality of their government-related customer data. The primary focus is on ensuring compliance with government policies, and it serves as a crucial component in achieving data quality improvements.
Talend Data Quality helps me find and fix problems in my data. It checks for errors and follows rules to ensure my data is accurate. If it finds issues, it works together with me and the data stewards to fix them. It is like a team effort to make sure my data is good quality from the start.
IT Manager at a insurance company with 10,001+ employees
Real User
2021-06-03T14:05:28Z
Jun 3, 2021
Talend DQ module is more focused around data profiling, so we have some teams in our organization from data governance who use it to profile the data to find anomalies in the data.
IT Manager at a insurance company with 10,001+ employees
Real User
2021-03-05T20:22:02Z
Mar 5, 2021
Talend has different modules. Talend has Talend Data integration (DI), Talend Data Quality (DQ), Talend MDM, and Talend Data Mapper (TDM). We have Talend DI, Talend DQ, and TDM. Our use cases span across these modules. We don't use Talend MDM because we have a different solution for MDM. Our EDF team is using an Informatica solution for that. We have a platform that deals with MongoDB, Oracle, and SQL Server databases. We also have Teradata and Kafka. The first use case was to ensure that when the data traverses from one application to another, there is no data loss. This use case was more around data reconciliation, and it was also loosely tied to the data quality. The second use case was related to data consistency. We wanted to make sure that the data is consistent across various applications. For example, we are a healthcare company. If I'm just validating the claim system, I need to see how do I inject the data into those systems without any issues. The third use case was related to whether the data is matching the configurations. For example, in production, I want to see: * If there is any data issue or duplicate data? * Is the data coming from different states getting fed into the system and matching the configurations that have been set in our different engines, such as enrollment, billing, and all those things? * Is it able to process this data with our configuration? * Is it giving the right output? The fourth use case was to see if I can virtually create data. For example, I want to test with some data that is not available in the current environment, or I'm trying to create some EDA files, which are 834 and 837 transaction files. These are the enrollment and claims processing files that come from different providers. If I want to test these files, do I have the right information within my systems, and who can give me that information. The fifth use case was related to masking the information so that in your environment, people don't have access to certain data. For example, across the industry, people pull the data from production and then just push it into the lower environment and test, but because this is healthcare data, we have a lot of PHI and PII information. If you have your PHI and PII information in production and I am pulling that data, I have everything that is in production in the test environment. So, I know your address, and I know your residents. I can hack into your systems, and I can do anything. This is the main issue for us with HIPAA compliance. How do we mask that information so that in your environment, people don't have access to it? These are different use cases on which we started our journey. Now, it is going more into the cloud, and we are using Talend to interact with various cloud environments in AWS. We are also interacting with Redshift and Snowflake by using Talend. So, it is expanding. We are using version 7.1, and we are migrating to version 7.3 very soon.
Practice Manager (Digital Solutions) at a computer software company with 201-500 employees
MSP
2020-08-30T08:33:35Z
Aug 30, 2020
Our use cases vary, but mainly we are using it for implementing a master data management platform. We get data from multiple sources and create a golden ticket record that can be used for ingesting the data from that single source to any of the platforms.
ETL/SQL Developer at a insurance company with 201-500 employees
Real User
2018-03-06T07:53:00Z
Mar 6, 2018
We have a legacy system (Wins + DB2), which stores all our data. For reporting purposes (from SQL), we need to analyze data. We use it for making decisions, for example, if we want to display data elements in our reports based on if a column ever gets a value entered by user or what are distinct values that we are receiving for transformation purposes. We use it to check patterns, like zip code, state codes, and phone numbers. We also check data value frequency for business decision in mapping from one system to another.
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...
We recently deployed it for one of our clients, who use it to enhance the quality of their government-related customer data. The primary focus is on ensuring compliance with government policies, and it serves as a crucial component in achieving data quality improvements.
Talend Data Quality helps me find and fix problems in my data. It checks for errors and follows rules to ensure my data is accurate. If it finds issues, it works together with me and the data stewards to fix them. It is like a team effort to make sure my data is good quality from the start.
Talend DQ module is more focused around data profiling, so we have some teams in our organization from data governance who use it to profile the data to find anomalies in the data.
Talend has different modules. Talend has Talend Data integration (DI), Talend Data Quality (DQ), Talend MDM, and Talend Data Mapper (TDM). We have Talend DI, Talend DQ, and TDM. Our use cases span across these modules. We don't use Talend MDM because we have a different solution for MDM. Our EDF team is using an Informatica solution for that. We have a platform that deals with MongoDB, Oracle, and SQL Server databases. We also have Teradata and Kafka. The first use case was to ensure that when the data traverses from one application to another, there is no data loss. This use case was more around data reconciliation, and it was also loosely tied to the data quality. The second use case was related to data consistency. We wanted to make sure that the data is consistent across various applications. For example, we are a healthcare company. If I'm just validating the claim system, I need to see how do I inject the data into those systems without any issues. The third use case was related to whether the data is matching the configurations. For example, in production, I want to see: * If there is any data issue or duplicate data? * Is the data coming from different states getting fed into the system and matching the configurations that have been set in our different engines, such as enrollment, billing, and all those things? * Is it able to process this data with our configuration? * Is it giving the right output? The fourth use case was to see if I can virtually create data. For example, I want to test with some data that is not available in the current environment, or I'm trying to create some EDA files, which are 834 and 837 transaction files. These are the enrollment and claims processing files that come from different providers. If I want to test these files, do I have the right information within my systems, and who can give me that information. The fifth use case was related to masking the information so that in your environment, people don't have access to certain data. For example, across the industry, people pull the data from production and then just push it into the lower environment and test, but because this is healthcare data, we have a lot of PHI and PII information. If you have your PHI and PII information in production and I am pulling that data, I have everything that is in production in the test environment. So, I know your address, and I know your residents. I can hack into your systems, and I can do anything. This is the main issue for us with HIPAA compliance. How do we mask that information so that in your environment, people don't have access to it? These are different use cases on which we started our journey. Now, it is going more into the cloud, and we are using Talend to interact with various cloud environments in AWS. We are also interacting with Redshift and Snowflake by using Talend. So, it is expanding. We are using version 7.1, and we are migrating to version 7.3 very soon.
Our use cases vary, but mainly we are using it for implementing a master data management platform. We get data from multiple sources and create a golden ticket record that can be used for ingesting the data from that single source to any of the platforms.
We have a legacy system (Wins + DB2), which stores all our data. For reporting purposes (from SQL), we need to analyze data. We use it for making decisions, for example, if we want to display data elements in our reports based on if a column ever gets a value entered by user or what are distinct values that we are receiving for transformation purposes. We use it to check patterns, like zip code, state codes, and phone numbers. We also check data value frequency for business decision in mapping from one system to another.