• Data Quality


Data Quality

Get accurate, reliable and consistent data

Up to 40% of all strategic processes fail because of poor data quality. Join the 60%.

Make better decisions using data you can trust, and build a data-driven business. Data quality is part of our DNA, so we can help you put it at the core of everything you do. We support traditional relational databases and emerging big data technologies such as Hadoop with enterprise-grade support and scalability. Whether your data is in-stream, in-database, in-memory or in-batch, we help you get it right. Data Quality helps you assess, improve, monitor and manage the quality of all your data, structured and unstructured.

Data quality isn’t something you do just once; it’s a process. We help you at every stage, making it easy to profile and identify problems, preview data, and set up repeatable processes to maintain a high level of data quality.

As a result, you can rely on it to make strategic decisions and improve core operational processes. Designed and priced for small and midsize businesses, the easy-to-use solution is suited for both business and IT users. Highly flexible, the solution is available in desktop and client/server environments so you can start small and grow as your needs change.

Explore Solutions

Unify data from different databases and source systems. Merge multiple files or duplicate records from a single file into one entity or record with industry-leading matching technology and innovative clustering logic.


Establish data hierarchies and reference data definitions to create a unified view of a particular data entity, such as customer or product.


Perform gender analysis using an advanced gender analysis algorithm.


Intelligently break multi-value fields into parsed elements such as address, city, first and last name, date, phone number, etc. Standardize data and eliminate duplications and inconsistencies by using advanced standardization (mapping) routines such as element, phrase and pattern standardization. Replace an original field with a new value or append a standardized value directly onto the source record. Support house holding through sophisticated match keys that enable the engine to group records and assign an integer-based unique identifier. Use natural language parsing to separate values as required.



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