Informatica Cloud Data Quality Reviews

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.

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.

The most valuable features of the solution stem from the fact that the product's integration capabilities are easy to work with, along with another feature, the name of which I forgot, but I know that it helps users to compare the quality of results from two different points in time just to see if there have been any improvements.

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.

Informatica has CDI. So, if you find some issues, you can always integrate your CDI and then fix your data.

We utilize it for two tasks. The first involves collaborating with our business team to identify specific data quality issues, and this part of the process runs smoothly. However, the second task is focused on using Informatica to investigate data issues that aren't entirely clear. This aspect doesn't yield satisfactory results, and we struggle to effectively pinpoint the problems using the tool.

When discussing data quality within IDMC (Informatica Data Management Cloud), it involves several modules, including CDI (Cloud Data Integration), CDP (Cloud Data Profiling), and Cloud Data Governance. Initially, we focused on Cloud Data Integration, which was the aspect we were already familiar with. We encountered challenges when trying to explore data quality issues using Informatica. We primarily used Cloud Data Profiling to connect with Cloud Data Governance, a tool also used by Teva. This integration allowed users to access data quality results within the data governance catalog.

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.

We're seeing more productivity. The tool provides accelerated libraries that we can use instead of building things from scratch. It's looking very promising from a productivity perspective.

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.