Data and Software Solutions Through Location-Based Insights
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Data and Software Solutions Through Location-Based Insights

Pitney Bowes has expanded its data and software solutions that will enable organizations to better understand customers with context to location. The expansion includes updates to the Pitney Bowes Spectrum solution, which incorporates address, location and customer data information to create a single view of a customer; unique customized data sets that organizations can deploy to supplement existing investments in business intelligence (BI) infrastructure; and the U.S. debut of the Confirm solution suite, a popular infrastructure management solution used by hundreds of government agencies around the world to improve employee efficiency and constituent services.

According to Bob Guidotti, EVP & President, Software Solutions, Pitney Bowes, in today’s market, customer experience is dependent on having a complete, single view of the customer. That isn't possible if location isn't factored into the equation. Today, Pitney Bowes is building on its world-class Spectrum technology and our leadership position within location intelligence to deliver a powerful set of capabilities for turning consumer, business and infrastructure data into rich insights that public and private organizations can use to provide better experiences for people they serve.

Adding Location to Big Data

Data generated through consumer mobile devices, sensors, social media and transactions is growing exponentially. Gartner forecasts that 8.4 billion connected things will be in use worldwide in 2017, up 31 percent from 2016, and will reach 20.4 billion by 20201. That’s why business and government organizations around the world need powerful location tools to include within their Big Data framework to perform location analytics that reveals actionable business and operational insights.

To meet these needs Pitney Bowes is announcing a new Big Data module to its Spectrum solution, which delivers the ability to validate and cleanse customer and location-based data natively within Apache Hadoop and Spark, before applying analytics. Clients benefit from the combination of market-leading address validation, GeoEnrichment and analysis to derive more meaningful insights from structured and unstructured data. For example, organizations can use these location-based insights to gain a 360-degree view of property to streamline the mortgage process, and property and casualty underwriting; offer coverage mapping for real-time mobile networks; and understand customers, merchants and ATM locations.

To help deliver the new Spectrum solutions to market, Pitney Bowes has partnered with several of the largest Big Data software providers, including Cloudera and Hortonworks, and certified its data quality and advanced geospatial capabilities on both Cloudera Enterprise and Hortonworks Data Platform. Furthermore, it has joined the Hortonworks Partnerworks in the Modern Data Solutions (MDS) partner program. Through these partnerships, clients that have invested in Big Data frameworks can easily add Pitney Bowes data quality and location capabilities to their data lakes and business processes.

Enriching Data with Location Intelligence

Many enterprise organizations invest heavily in database or BI infrastructure and know those capabilities provide a better return with the addition of location-based data. But many companies do not have the time or wherewithal to accumulate and enrich the address information to take these deployments to the next level.

To alleviate that challenge, Pitney Bowes is announcing a new Addressing and GeoEnrichment Data Portfolio that will include industry-specific data sets organizations can quickly and affordably deploy to better understand their customers through Pitney Bowes-delivered flat files, or within Big Data environments such as Apache Hadoop and Spark. The portfolio will initially include 24 pre-built data sets for the insurance and real estate markets. More data sets are expected to be released over time.

These GeoEnrichment data sets are associated with a U.S. address through a simple and fast look-up process using a proprietary and persistent ID, called the pbKey. Detailed location attributes, such as property information, area demographics, proximity to hazards, availability of services and more, now enable clients to quickly build a more complete “golden record” of their customers in context of their location.

Transforming Public Infrastructure Lifecycle Management

Today, local and state governments are faced with the challenge to become more responsive to citizens with better services for basic needs. Aging infrastructure, deferred maintenance and lagging investment have led to a decline in the condition of public infrastructure. In fact, a recent assessment from the American Society of Civil Engineers (ASCE) gave U.S. infrastructure a D+ or “poor” rating. Whether they repair, replace or upgrade failing infrastructure, state and local governments are under extreme pressure to optimize spending and allocate resources efficiently.

To help state and local government agencies cope with such challenges, Pitney Bowes is now offering Confirm, a cloud-based, asset data and Intelligent Infrastructure Management solution, to U.S.-based public and private sector organizations. Confirm is an end-to-end integrated platform that effectively manages asset data across all phases of the lifecycle from current condition records management to maintenance and future investment planning. This globally-renowned solution is already supporting 45 percent of road infrastructure management in the U.K., and more than 140 million citizens worldwide.

An IoT-integrated and location-based solution, Confirm helps accelerate both strategic and operational business processes, while supporting workforce mobility and legislative compliance, such as MAP-21 and FAST. Clients also have access to a global community of product users and the Confirm development team via the Li360 Collaboration Community, which facilitates rapid time-to-value and product innovation.

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