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The GeoWeb has exposed millions of live data feeds. We’ve been marveled at how they are being combined into visualizations and mashups to help users find their ways around cities, spot crime, and coordinate community activities. However, with the increased wealth of data it’s very easy for a user to be overwhelmed with information and unable to understand or take action.
Analytics provide users with the ability to refine, query and derive knowledge from all of this information. Traditionally access to analytics has been restricted to complicated tools that require hours, days, or even years of training. The models are limited and expensive, often desktop-only, tools where individual results are posted onto websites or data portals that preclude further investigation from users.
Like the Web moved from static publishing to bi-directional conversations, collaborative analytics are turning static data publishing into multi-directional conversations. Users can now individually analyze data for meaningful insights, and share these insights and models with other users. Further, users can tweak each others analysis by changing data set variables or evolving an equation. This creates a collective intelligence refining data and algorithms for deeper understanding of our evolving data landscape.
This presentation will discuss how organizations are utilizing crowd-sourced data combined with analytics to provide their users with individual metrics as well as improved insight across the entire community.
For example, the United Nations Environmental Programme Grid-Arendal is utilizing crowd-sourcing to gather climatological data from global researchers and provide models for citizens to run their own analysis and provide feedback. Through open access to analytics, citizens can understand how global science models apply to their local environments. Similarly, mobile application developers are utilizing collaborative analytics to understand real-time data usage of location-based applications. This enables enterprise to fuse this emergent data with their key performance metrics such as point of sale indicators and marketing efficiency.
As an industry, we’re just beginning to move beyond our own amazement of publishing huge volumes of accessible data and it’s time to move into deeper, communal understanding of global and local issues and how each individual’s insight can greatly enhance collaborative intelligence.
Stores, Malls, Bars – there locations are facts. Not owned by anyone, but gathering that data and just as importantly keeping it accurate is expensive. What can a startup do? Can they depend on larger players’ APIs? Should they buy the data? Should they depend on their users? Or should companies pool together and just maintain one canonical database of places?
19th–21st April 2011