Examples of Geographic Business Intelligence

This is a continuation of the transcript of a Webinar hosted by InetSoft entitled "Geographic Business Intelligence." The speaker is Mark Flaherty, CMO at InetSoft.

Mark Flaherty (MF): So now let’s talk about a few examples. First, let’s think about what a purely GIS example looks like. Take a forestry agency, for instance. They use GIS for a number of cases, including reporting to various stakeholders. So when they choose to plant or sell areas of the forest, they need to inform a number of other environmental bodies, other government departments, as well as the general public.

So before they had this system they would take digital photographs of the landscape and doctor them in certain ways to show what the landscape model might look like after the planned changes. So naturally that’s a manual, albeit artistic process, that is done in one-off use-cases.

With GIS, the process is much more efficient, and the communication can be more effective. The image is now generated from the actual organizational data. So in a way, this is already a form of business intelligence report.

Take another example from the same organization. In this case, we’re talking about 2D mapping. They need to depict different areas of profitability of a forest depending on planting and harvesting cycles of the business plan. There might also be a chart showing changes in cash flow over time. This now is really a type of financial report, not just a GIS report.

Geographic Business Intelligence for Finance

This information is easily understood by the finance director or the chief executive of that organization. I think this highlights something really important. When organizational information like this, about the profitability, cash flow, or efficiency of an organization, is rendered in this way, it’s quite eye-opening. It certainly catches the attention of senior managers.

The range of applications that you can imagine from an integrated geographic business intelligence solution is broad. From one end of sophistication, which is also likely limited in the number of users in an organization, likely the information analysts, you have statistical and data mining in a visual analytic, map-based way. This is used for hypothesis testing and predictive analysis. Next on the spectrum of sophistication and number of users, you have ad hoc query and analysis, which can include canned and parameterized reports, drill-down reporting, and OLAP functionality across multiple databases.

After that comes OLAP cube analysis which can include pre-defined analytical views, but also speed-of-thought slice and dice analysis. Next comes enterprise reporting which includes batch production of printed and Web-based reports, pixel-level control of field placement, and multi-report scorecards and dashboards, and this is geared towards managers and staffers alike. Lastly, you have report delivery and alerting which includes report subscription and delivery, exception-based alerting, and information delivery services, including delivery to consumers or customers or citizens of a local authority.