Data Management and Integration
Consolidate and manage customer data for enhanced insights.
Capabilities include visual data modelling as knowledge graphs, information quality, data visualisation, data governance workflows and policy management, data integration and analytics.
Master Data Management and Integration Portfolio
Spectrum Business Steward Module
A collaborative framework for effective information governance
The Pitney Bowes Spectrum Enterprise Business Steward Module is a browser-based tool for manually reviewing exception records. Correct, approve and reincorporate exceptions into your data quality process for information integrity that your business can trust.
Spectrum Data Federation
Gain insight by eliminating the cost, risk and effort of physically combining different data sets.
To gain insight on your customers, you need to traverse multiple data sources that often don’t easily work together. With Data Federation, you can democratize access to information without the risks associated with physically combining those sources.
Spectrum Data Hub Module
Gain a robust repository for understanding and managing your most critical data assets.
Effective data model development requires a robust hub that can tap into a full range of processes, interactions, hierarchies, roles and domains for better insight and business results.
Spectrum Enterprise Data Integration Module
Access, connect, integrate and analyse data from a wide array of applications
Data integration, in batch or real time, requires the ability to connect to data from multiple sources either directly or through integration with existing data access technologies. The ability to join and query data from a variety of resources, while integrating with SAP and Siebel is critical.
Spectrum® Metadata Insights
Unlock the value in your data assets. Discover, enrich and optimize across the data lifecycle.
Spectrum Metadata Insights helps bring out the best in your data resources. It builds a comprehensive data glossary for your business, accelerates data discovery and prioritizes data-quality improvements.