Location analytics begins only when GeoEnrichment is completed. It’s the starting point through which all location-based data flows. The problem is that most geospatial analysts spend 70% of their time on data preparation, data cleansing, address validation and geocoding. Only then can users be confident that the analysis phase of projects can begin in earnest. There must be a better way.
GeoEnrichment is the ability to add authoritative and descriptive attributes to customer, transaction or other data at a known location. As such, Pitney Bowes has developed a unique process, shortcutting the time between data prep and data analytics. The foundation of Pitney Bowes GeoEnrichment process begins with the Master Location Data (MLD) data product. MLD is a pre-geocoded dataset of more than 170 million U.S. addresses. Using multiple sources of data and a process to apply a confidence index to the resulting geocode, MLD is highly accurate dataset.
As one example of the GeoEnrichment process, Pitney Bowes is developing the Property Attributes dataset, which uses MLD as the foundation. This new data product will include 205 property attributes for over 148 million U.S. properties. Attributes will includeproperty type, owner occupied indicator, residential use indicator, total buildings on parcel, number of stories, number of apartments and more. This dataset is particularly useful for insurance companies for risk analysis or for emergency response and public safety agencies for pre-fire planning, for example.
Location Matters … and Accurate Location Data Saves Money
As geospatial technologists, we often emphasize how important accurate location data is to the analysis, which often starts with geocoding. Just how important is illustrated in the diagram below. To insurance underwriters, the sequence shows the difference between building level and street interpolated geocoding. The yellow dot on the building in panel #1 represents a building geocode; in panel #2 the purple line represents the flood plain; panel #3 is a street interpolated geocode. The difference to insurers could be thousands of dollars of inappropriately-priced (under or over-valued) policy premiums. If premiums are over-priced, customers find a better deal; for premiums that are underpriced, clients who might be a poor risk could submit claims that ultimately result in lost revenue.
In addition to MLD, Pitney Bowes is launching a series of software and data products as well as geospatial solutions that combine the company’s IT and analytical services.
- Powerful location analytics capabilities for business intelligence– Pitney Bowes’ Spectrum Spatial for BI software integrates with the most popular BI solutions to provide advanced visualization and analysis.
- A global library of over 350 geospatial data products– Pitney Bowes data portfolio includes demographics, points of interest and industry-specific data for over 240 countries and territories.
- Multi-resolution raster (MRR) technology– This new raster file format of Mapinfo Pro Advanced adds speed and performance to handle complex modelling projects and enables the analysis of large amounts of data, including complex digital imagery and terrain models.
- GEO APIs – Six application programming interfaces are now available on the Pitney Bowes Commerce Cloud that provide resources to encourage both commercial and corporate developers, as well as customers, to enrich their Mobile and Web applications with Pitney Bowes’ location technologies through the use of APIs. In addition, we are announcing our Global Geocoding API that allows customers and partners to integrate geocoding with their applications. These APIs expose Pitney Bowes core geospatial technology and data for enterprise software application development.
- Applied Analytics – Pitney Bowes maintains a global consulting practice focused on employing predictive analytics to provide businesses with actionable insights around channel investment, identification of market opportunities and targeted customer communications.
Through the combination of GeoEnrichment and location analytics, users can not only develop a more comprehensive location data warehouse but have the tools to go deeper with geospatial analysis. The goal is always to find “the answer” within the data.
Today, transactions can be physical or virtual and in the world of mobile commerce. Customers seamlessly transit the Web, physical stores and mobile apps. They have the power to express their opinions and provide product information to other possible consumers across a wide range of social media. Businesses want this information and appending these more dynamic, location-based data types to customer records provides a rich, and “geoenriched” set of data.