Informatica Cloud Data Quality Reviews

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.

Informatica Cloud Data Quality was most valuable for our real-time data quality monitoring needs. We could run data quality rules as part of Service Bus, which ensured the integrity of customer information before it was entered into our database. This system was quite successful in maintaining data quality in real time.

We use Informatica Cloud Data Quality for data profiling and data quality. We also use the intelligent system of Informatica, lineage, and the data catalog.

We are using Informatica to get the data profiling. For example, if we are planning data analytics or data science projects, we first plan to ingest all the data that we are planning to use in that project into Informatica. Then we check it and get the profile. We also get the catalog of the data, and then we start.

The most important benefit of the data quality product is the data quality side. When we started, the data quality level was maybe 17% for that domain. After we ingest the data into the data quality tool, it is more than 70%.

The initial setup takes approximately less than two months for one domain. It can be easier, but it also takes a lot of time on the process side, such as creating the KPIs for data quality. The system is a little bit complicated. It has too many features.

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.

I have used Informatica Cloud Data Quality and PowerCenter for a bit, though I worked on the data quality tool mostly.

I tried both versions, the one on the data center and the one on IDMC, which is the Informatica Cloud.

Regarding infrastructure setup, I was responsible for installing Informatica Cloud Data Quality on Google infrastructure, which was straightforward. I don't recall having significant issues. The challenges were mainly related to firewalls, security rules, and allowing network connectivity.

We are using Oracle, Informatica IDQ, EDC, and Axon. These three tools are currently being used on one of our projects in the Middle East.

We recently started a project with one of our clients in Saudi Arabia, and we are using Informatica Cloud Data Quality for data quality management.

The customer required us to use Informatica Cloud Data Quality. They insisted on having it and were willing to pay more, which is fine because we tried to ensure that the tool is just a mechanism to automate activities, not the backbone for implementation.

EDC stands for Enterprise Data Catalog, IDQ stands for Informatica Data Quality, and Axon is for governance.

Mainly with data quality, we have a full data quality management suite through Informatica Cloud Data Quality itself, starting from documentation of business rules, profiling, getting any kind of issues with data quality, doing assessments, and we do these kinds of frequent measures for data quality, as well as resolving any kind of issues.

Regarding the features, the dashboards related to data quality profiling are the most powerful tool that we are using, and the integration between EDC and the data quality is very important. Having this feature provides a real understanding of the environment and also aids in quality management for whatever kind of data sources within the organizations.

Data quality is missing enterprise-wide in our bank. We use the solution for all the customer data to judge the metrics of software data quality from the source systems.

Server maintenance, server hosting, backup, restoration, and DB capabilities are not needed in the cloud. There is no crashing of the servers. The admin part is very seamless in the cloud, while administration is a big task for the on-premises version.

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.

The user-friendliness and performance of Informatica is quite impressive. It’s easier to grasp and understand data quality, and how data governance tools work, as they are built on the same underlying architecture. The QCA addresses performance issues, whether it’s related to data, PowerCenter, or any other aspect. The shared underlying architecture enables me to pinpoint parameters for optimization. Informatica can handle large volumes of data without compromising performance. While other tools may offer similar capabilities, Informatica consistently delivers impressive results.