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February 12, 2013 in Big Data, Business Analytics, Business Intelligence (BI), Cloud Computing, Customer Performance Management (CPM), Information Applications (IA), Information Management (IM), IT Performance Management (ITPM), Location Intelligence, Operational Performance Management (OPM), Sales Performance Management (SPM), Social Media | Tags: Data Governance, DG, Information Management, Master Data Management, MDM | by Mark Smith | Leave a comment
Managing data efficiently across the enterprise continues to be a large challenge for both business units and IT. Organizations need data supplied in a consistent format and timely manner to help manage their activities and processes, but some do not look beyond conventional approaches to improvement. Today’s large volumes of data make it more difficult to understand the relationships among data and the role of location-related data. Our 2012 benchmark research on information management found that most organizations need to advance their data initiatives and take steps to integrate them.
In 2012, Pitney Bowes enhanced its Spectrum technology for data management to help organizations realize the full value of data entities. Its relationship discovery technology for graph modeling accelerates association of data in the enterprise and with social media. The latest version, Spectrum 8, brings together data integration, data quality and master data management in an integrated approach to enterprise information management. Architecturally it uses data federation and data integration interfaces to its master data management hub to bring together core capabilities including a portfolio of data quality and data enrichment capabilities. One of Spectrum 8’s unique elements is its support for location and spatial data. Our research on cloud computing reveals the rising importance of data management in these environments; I hope that Pitney Bowes will address this in future versions.
In announcing version 8, the company detailed a spectrum of improvements. Its latest data integration release takes steps forward with a visual query builder and deeper support for XML sourcing, synchronization and schemas; it also helps the enterprise with scheduling, loaders and transactional support for databases and XML. In data quality, Spectrum 8 supports location and address certifications and validation through forward and reverse geocoding, which is now expanded to include China, India and many other countries, as well as providing a new module for transliteration. In master data management, it adds a range of modeling techniques, hierarchy management including visual analysis and social network analysis, reliability advancements in clustering, fault tolerance and hot backup of the system. Further it has advanced support for what we call location analytics and spatial management of business data. Our current research into location analytics shows that these improvements are timely, as organizations need better support for making data location-aware, which Pitney Bowes is doing across the entire Spectrum offering. I also am impressed by the usability of the system, which as well as IT specialists also will suit analysts who are familiar with data relationships.
Pitney Bowes is not well-known for its information management technology, but this release has a robust and integrated approach, and organizations should include it in their evaluations. Its graph-oriented technology can help streamline customer data for easier interactions and contribute to improving the customer experience throughout an organization’s business processes. Our research agenda for 2013 outlines the information management competencies needed to support the new generation of big data and information optimization, and Pitney Bowes is relevant in this regard. Its Spectrum offering will appeal to those looking to use their customer information more fully in business processes, although it can be used for other types of data as well. Spectrum surpasses other offerings with its customer and social relationship mapping, making data more location-aware and enriching it through its tools. As indicated by our big-data benchmark research, Pitney Bowes will need to expand further to address big data through integration with Hadoop, data appliances and in-memory computing; that will be necessary for it to work with systems that CIOs and data management professionals are architecting now. Pitney Bowes also should foster alliances with the range of applications providers in big data, analytics, customer service and contact centers to ensure it is seen as part of the overall technology ecosystem. Last year I pointed out the critical need for this company to provide tighter alignment among its own efforts in customer communications. In addition I expect that as its product offerings evolve, Pitney Bowes will position itself in the new evolution of information management called information optimization. Those looking at a new generation of data management that is integrated and uses discovery and analytics should consider Pitney Bowes Spectrum.
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