BROADLY, ANALYTICS ARE ABOUT ADDING MEANING TO DATA. In the near future, there will be a lot of data to analyze. IDC estimates that by 2015, available digital data will explode from 2.8 Zettabytes (1 ZB = billion tera- bytes) to nearly 8 ZB, and dealing with huge data flows will generate a lot of money. According to IDC, annual revenues for information and communications technology (ICT) will grow by $1.7 trillion to about $5 trillion by 2020. About 80% of the growth in both data and revenue will come from newer phenomena like mobile, sensors, social interactions, and clouds. Much of this new social and economic data will nec- essarily be location-specific, but little will be managed or analyzed in traditional geospatial systems.
This presents a problem for the traditional GIS industry and for the BI (Business Intelligence) industry seeking to geospatial- enable systems. In particular this raises questions for govern- ments that have invested heavily in spatial data infrastructures (SDI). The question for the Open Geospatial Consortium, the geospatial industry’s open standards organization, is how to offer value to the BI marketplace with as little impact as possible on existing systems and business practices? For the geospatial industry (GI), this evolving environment creates three distinct, but related issues, which will require traditional geospatial technology and services companies to:
- Provide and promote a framework of standards and best practices that help governments incorporate social and economic data into spatial data infrastructures.
- Understand the strategic use of location-specific data within mainstream analytics.
- Enable user-defined geospatial requirements and models for mainstream analytics at process and individual levels.
The OGC has initiated an effort to enhance the geospatial industry’s ability to participate in analytics markets. This effort is led by the OGC’s Geospatial Business Intelligence Domain Working Group (GeoBI DWG). The DWG proposes three specific initiatives to address these issues.
Dr. Michael Sanderson, 1Spatial
Ben Searle, Australian Bureau of Statistics
Dr. Raj Singh, Open Geospatial Consortium
David Sonnen, Integrated Spatial Solutions, Inc.
Matthew White, U.K. Ordnance Survey
Initiative 1. Adding Social and Economic Data to SDIs
Spatial data infrastructures are at the center of most government geospatial efforts, and have great potential to power better BI. Adding socio-economic data to SDIs will be essential for thousands of regional planning and monitoring efforts, including the World Bank and U.N. A repeatable approach is needed for integrating burgeoning masses of social and economic data to those SDIs. Statistics and spatial integration work underway in the U.K. Ordnance Survey, and the Statistical Spatial Framework (SSF) proposed by the Australian Bureau of Statistics offer well-considered starting points. Without social and economic data, SDIs fall short of their infrastructure missions. In today’s dynamic technology and policy environment, the original view of SDIs as rela- tively static data frameworks needs to change to a view that recognizes SDIs as windows on a changing world.
Initiative 2. Understanding the Strategic Use of Mainstream Analytics
The analytics space is large, complex and rapidly changing. This broad area includes low-latency routing/messaging, in-stream analytics, complex event processing, operational analytics and business intelligence. A number of related technologies like discovery, content delivery, machine-readable data, machine learning and Big Data will continue to influence the analytics space. There are many roles for location-specific elements within various analytic areas. Those roles are significantly different from current GI roles in SDI. (See Figure 1 for a comparison.) To ensure accurate geospatial information is used in analytical engines, the GI and BI industries need to understand and participate in the creation of relevant location frameworks for mainstream analytic processes.
Initiative 3. Enabling User-defined Geospatial Requirements and Models for Mainstream Analytics
At a practical, process level, we see a significant understanding gap. Analytics developers/users do not understand geospatial technologies. GI technologists do not understand the process-specific issues and requirements for mainstream analytics. The GeoBI DWG is defining ways for analytics users to...
The complete article is available in the Summer 2012 Digital Edition of LBx Journal.