TS Quality is the part of Trillium Software® System™  improving and maintaining data quality across the enterprise. You can actively process data with a rules-based data quality engine to ensure that it meets established standards defined by the organization. Processes can be deployed across all platforms and applications to ensure consistency and accuracy wherever business users rely on data to meet their goals.

TS Quality cleanses, standardizes, and matches any data: name and address data; product data; asset, material, and location data; transactions; etc. It validates worldwide address data against authoritative sources and identifies relationships among records. World-class global capabilities and automated, rules-based intelligence give organizations a simple but complete solution to handle massive volumes of data—out of the box. Organizations can further customize rules and adapt to meet changing business needs, responding quickly to competitive challenges and improving operational efficiencies.

Where do organisations implement Data Quality software?
Organizations implement data quality products in a number of different systems since data changes rapidly and is often reused or recaptured across many disparate systems. Data quality processes are regularly incorporated in:

CRM systems to provide the foundation for a unified customer view.

ERP systems to eliminate transaction errors especially before the information becomes replicated in other systems.

Data warehouses to enforce standards and synthesize data for downstream applications.

E-commerce applications to ensure that data coming into the enterprise sytems is accurate and complete from the start.

Service-oriented architectures to offer reusable, rules-based data standards that can be incorporated into complex business processes, in real time.

Data integrations to reduce the risks involved in consolidating large volumes of data and moving it from one system to another.
Organizations often start addressing data quality with a system or application known to have bad data or as part of a systems development effort. The highest gains are achieved when the same data quality processes are subsequently rolled out to all source systems receiving new data.