“The vision I describe to my colleagues is that they’ll be able to implicitly trust the data that informs them, no matter where in our organization it comes from – that goes for our cloud data stored on Microsoft Azure.”
Use fit-for-purpose data at enterprise scale
Automate and scale data profiling
Perform continual analysis to better understand your data and detect problems.
Improve data accuracy and reliability at scale
Integrate data cleansing, standardization, address verification and more for multiple use-cases.
Prebuilt AI-powered rules and accelerators
Autogenerate common data quality rules across virtually any data from practically any source.
Boost data observability for better insights
Understand the health of your data through the multiple lenses of data, pipelines, and business.
Pay only for what you use with our flexible pricing.
Explore related Data Quality and Observability services
As a leading part of the AI-powered Informatica Intelligent Data Management Cloud (IDMC), Data Quality and Observability works with a range of complementary services.
Key Data Quality and Observability Resources
Data Observability: The Key to Successful Data and Analytics
White Paper
Data Observability: The Key to Successful Data and Analytics
FAQ for Data Quality and Observability
Data quality is the degree to which data is accurate, complete, consistent, timely and relevant for its intended use.
Data quality is important because it can have a significant impact on the accuracy of AI and analytics results, decision-making, operational efficiency and overall business performance.
Common types of data quality issues include data duplication, missing values, incorrect formatting, outdated information and inconsistent data across systems.
The main benefits of improving data quality are that it enhances decision-making by providing accurate, complete, consistent, timely and relevant data to enable more accurate AI and analytics results and ultimately more-informed decisions.
