What is your primary use case for Informatica Data Quality?

Informatica Data Quality offers functionalities such as profiling and address standardization. It identifies discrepancies in data through profiling, providing statistical insights similar to other data quality tools. What sets IDQ apart is its integration with PowerCenter, Informatica's flagship data integration tool. IDQ includes a developer tool with a wide range of transformations like Cost, Standardizer, and Cleanser, which are essential for data quality management. It operates as a batch-based tool, distinguishing itself in the market. While IDQ focuses on these aspects, it doesn't directly relate to tools like TDM or MDM, which are separate solutions.

We had proof of concepts where certain test cases were given to improve the functionality of the vendor regarding the supply chain. So, it was a proof of concept. It was not applied in production. It was just illustrating the capabilities of the platform, and we were comparing it with other platforms.

I use Informatica Data Quality to consolidate master data across our organization, ensuring consistent and high-quality data. Combining MDM and data quality features, we address issues like customer data inaccuracies. Additionally, we document business terms in our data catalog for a unified view across the company.

We use it for various use cases, including data provisioning, data validation, continuous monitoring of data quality, and data standardization.

Data Quality involves quality checks and data profiling. It scans through the data, providing metrics like the number of nulls and unique values. Informatica Data Quality profiles the data.

Our organization operates in the financial industry and we utilize the solution to gather and incorporate high-quality data, making it available in the data marketplace.

We don't use profiling as much, however, we do use it, in certain cases, for profiling. We use the Analyst tool to do out-of-box, high-level profiling of data to see high-level quality of completeness, and uniqueness, et cetera. Mainly, we use the Developer tool to connect to the sources and to write data quality rules.

A lot of times, we use it for basic profiling. That's its most common use case. Currently, we are also in the process of establishing a set of ongoing processes around Data Quality that would feed into and augment our current metadata. So, from that standpoint, our usage is primarily around some of the basic dimensions of data quality, such as completeness, conformity, consistency, timeliness, accuracy, etc. We measure each of those or at least create quality rules that measure each of those aspects. We're in the process of doing this for all of the data that's currently feeding into our analytics engine. These are some use cases that we're currently doing on a daily basis.

I starting with the design and discovery phase to detect data issues by using the profiling service, then started to analyze the data to determine and fix issues with it.

We are using it for extraction purposes for some of our projects. We extract and test the data.

Our primary use case of this product is for data management relating to customers and contact information. It's a global resource, we ensure the quality of our customer information and primary attributes. I'm a principal data architect and we are customers of Informatica.

We use Data Quality for healthcare projects, and although it's not its prime purpose, we also use it to extract data. Compared to PowerCenter, it's a little easier to use — we designed the mapping ourselves. In short, we use Data Quality primarily for extraction purposes. Within our organization, there are 10 employees using this solution.

Our primary use case of this solution is to improve issues of security, quality and accuracy. We are partners of Informatica and I'm an architect.

We use this solution for data quality management.

Our primary use case for this solution is purely for data quality analysis.