Informatica Data Quality

Automation of technical and business data quality checks on data from over 10 different data sources across various sectors. Access to end-users on the data profiles makes the company proactive in resolving data quality issues which in turn improves the trust in data. Eventually promotes business decisions backed by data.

Provides simple, logical interfaces and connectors to eliminate the need for complex coding. Offers intelligent operations gnat ensure resilience, business continuity, and efficiency. Democratizes integrations by enabling self-service for nontechnical citizen integrators. Allows us to easily scale up or down to accommodate a variety of workloads without impacting performance. Secure everything effectively.

Informatica Data Quality is a solution that pushes companies to take a vibrant step in managing data, enhancing and reinforcing security, and activating all the analytical demands in the business. Further, Informatica Data Quality focuses on data collaboration and standardization, a form that improves the quality and reliability of the database system. Finally, the whole validation and cleansing process is effectively supported by Informatica Data Quality, from address verification to content filtering.

[Informatica Data Quality] has reliable data quality powered by AI. Find and fix quality issues that mean the difference between business initiative success and failure with our leading industry data quality solution. Ability to manage the quality of multi-cloud and on-premises data for all use cases and workloads. Manage and transform data with data standardization, validation, enrichment, and consolidation capabilities.

IDQ is used for data cleansing and address validation. Passing Invalid Addresses and getting output as valid address and using these address to send mail to employees. Data cleansing rules while loading data from source to warehouse.

Informatica Data Quality is a tool that supports business users (non-technical) and developers(technical) in an equal way. It can be enhanced through the customization of rules and policies, and supports seamless integration with Informatica Power Center, with the help of Informatica Data Quality Mapplets. These mapplets can be imported to Informatica Power Center, and can be used within the Data Integration Process.
Data profiling can also be made within the tool, and profiling results give you insight into the data that you are about to use to perform a data quality operation.
However, the more data you profile, the more memory and CPU it needs.

Customer creation process - using web services for matching and address cleansing. Managing exception record management for both bad and duplicate record management. Currently using across the organization for scorecarding to cover DQ problems.

We use Informatica Data Quality IDQ to validate customer and vendor addresses and to insure there are no duplicates. We also use it to verify consistency in product descriptions.

I used Informatica in my previous company for a Finacial Service provider for Data extraction from flat files. Data from various transfer agents (TA) was gathered using Informatica and loaded into Database. I can handles millions of records at one time.

We are sourced from heterogeneous relational database's and also from legacy systems like SAP and SAP BW for our data integration project. After addressing multiple bugs and errors on the production data, we learned that Data Profiling and Data Quality was really missed during the analysis stage. We found many data inconsistencies, foreign character problems and issues with simple formats and formulas. Special characters were also one of the many bugs we failed to address in the beginning. Now that we start all the projects with IDQ and only after its help in analysis and fixing the bad guys, it helped us with quite a few production tickets. It also helped us with best end user experience. We are still on top of the learning curve using IDQ, but we are holding up strong with it as is now.

Informatica is used to integrate to various applications like Salesforce, SAP etc. with Oracle and SQL databases. It's used by various departments across the Americas. It helps to optimize the data for various reporting tools.

We found many data inconsistencies, foreign character problems and issues with simple formats and formulas. Special characters were also one of the many bugs we failed to address in the beginning. Now that we start all the projects with IDQ and only after its help in analysis and fixing the bad data, it helped us with quite a few production tickets. It also helped us with best end user experience.

IDQ is used to update the customer data with correct address, name, phone numbers etc. It's used in the Digital IT department at my current organisation. It identifies the duplicate customers and maintains the up to date details of a customer.

In our organization that I was at, IDQ was being used only by a single department. We used IDQ to first help us standardize customer addresses and then eventually started using the matching capabilities to find duplicate records within our database. Before this product we used SQL statemtns to standardize data and find duplicates.

By using IDQ, it greatly reduced the time and effort to perform the above mentioned items.

IDQ is being used across our organization. Specifically, we use IDQ for contact and address validation. As part of the sales and marketing domain, we need to enrich data for better campaigns. IDQ is a key player in accomplishing this process for us. We are using the Address Doctor engine for our standardization. Address Doctor standardizes contacts and addresses.

IDQ is used by a department at my organisation to ensure and enhance the data quality. We started with address standardization and now it had been brought to a next level of quality check where it fixes duplicates, junk characters, standardize the names, streets, product descriptions. In the past we had issues mainly with duplicate customers and products and this was affecting sales projections and estimates.

Informatica Data Quality is being used to monitor the overall qualtiy of the data organization wise. We have created mapplets which filters / ensures the data quality has been met and shows how much percent of hte data has passsed Company Data Quality policies and how much percent data has failed also it provides detailed granular level view of the data which has not met data quality standarads.

IDQ is used by a department at my organisation to ensure and enhance the data quality.
The usage was started with address standardization and now it had been brought to altogether a next level of quality check where it fixes duplicates, junk characters, standardize the names, streets, product descriptions.
In the past we had issues mainly with duplicate customers and products and this were affecting the sales projection and estimates.

IDQ was purchased at Apollo about a week before I was hired and my first primary duty was to get the tool up and going (infrastructure, install, config, staff). It was used to present quality scorecards to data stewards/owners

To implement data quality to meet compliance, IDQ was selected as the tool. Currently it is being used by one department, and will gradually be used across other departments.