Here is a good post from Rio+20, the United Nations Conference on Sustainable Development, where Google Earth (GE) Engine is presented as a system to monitor deforestation in the Amazon and promote sustainable development.

This part seduced my attention:

…the system demonstrates the potential for Google Earth Engine to become a multi-sensor, multi-algorithm, multi-technology, crowdsourcing environmental monitoring platform.

Why on earth would anyone want to use Google Earth Engine as business intelligence (BI) software?  The map!  The map (or globe for GE) provides a common platform for all users.  There is a power in the map that provides an intuitive interface for drilling down, discovering, and visualizing data while maintaining the context needed to see how everything fits together (data relationships).  This is difficult to do using just color-coded data tables, graphs, and pie charts.   In GE, embedding or linking to data typically shown in BI dashboards is as easy as adding it inside the mouse click bubble.

Traditional BI software tries very hard to put a “map in the app” for those clients who need it, but the software is grown from non-spatial roots.  Almost as an afterthought, BI software ties the data back to some sort of boundary that may or may not make sense, such as a state, zip code, sales region, etc.  To make the paradigm shift required to match the ability of GE as a mapping platform/map interface would require the granularity to tie data down to it’s geographic coordinates and the flexibility to represent that data with a point, line, polygon, 3D polygon, etc.  The data also needs to inherit parent-child relationships to be intelligent.  It definitely saves time when the data is originally created with this in mind.

BI and GE represent the reverse approach to the same goal:  BI shows the data, then lets users drill down to a map, while GE shows the map, then lets users drill down to the data.  They need to merge, allowing users to choose any approach without compromising the effect of the data.  Recognizing the power in both approaches is why I have such a zeal to see this happen.  We have come a long way, but have yet a long way to go.