Now I am going to start by giving some examples of how customers are using geospatial analytics. It's very exciting particularly in the finance and insurance and health care industry which is a large industry for business intelligence. In these industries the technology is being applied in a variety of different ways.
It's going to the heart of the insurance industry, for example, where geography questions have been asked particularly in the property area and the automobile area forever. But previously actuaries have been constrained by having to use postal data to make those kind of geographic comparisons.
So they would try to determine if a zip code area was close to the shoreline, the coast. It might in greater jeopardy for hurricanes coming ashore than zip codes further back, and that necessarily wasn’t always the case. And so now with spatial analytics and geospatial analytics we can make that type of analysis much more precise for actuaries.
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Geospatial Intelligence Example for Actuaries
So the classic example is for years actuaries always wanted to know whether a property was in or out of a flood plain, and if you were in the flood plain you paid more for your insurance, and if you were out of the flood plain you paid less for your insurance. But now with the geospatial analytics, we can actually make determinations along the lines of to what degree a property is in the flood plain.
So now properties that are further in the flood plain now can pay higher insurance, and properties that are not as far in the flood plain can pay less and then of course continually the least, those that are out of the flood plain can pay even less. But that goes right to the heart of the insurance business because it's about property. It’s about pricing and marketing, and so all of our major insurance carriers are finding that to be critically important.
In the retail industry, siting of retail locations has been a project. Most large retailers and retail banking operations have property divisions to make determinations where their next retail location should be. And of course many of our retail customers are now looking at this, but particularly in retail banking with all the merger and acquisitions that have taken place over the last two years, they are particularly interested in using geospatial analytics to make determinations in terms of how they rationalize their retail locations throughout North America.
When it comes to deploying this technology, there are a couple of considerations that we see coming up time and time again. Geospatial analytics are computationally intensive. While the amount of data, itself, that's stored in the database is not significantly greater than it has been in the past, performing geospatial comparisons in terms of like the one I mentioned a moment ago, that comparison in terms of the degree to which a property is in a flood plain requires the immediate real time comparison perhaps in some cases it could be millions of points that exist within a database because the polygon or the geographic representation of that flood plain actually can be a large boundary area that could have thousands of coordinates on it.
Geopspatial Analytics in Retail
And making comparisons between hundreds of properties and those thousands of coordinates requires quite a bit of computation. And of course, the way we have implemented geospatial analytics, the geometry data is spread across a massively parallel architecture just like any other piece of data. So what we can bring to bear in is the complete computing capability of InetSoft's data grid cache architecture.
We have seen significant differences when we have been asked to use geospatial analytics for existing geospatial computations that customers were already using, and when we have applied InetSoft's Style Intelligence to it, they have gotten immediate performance improvements. So we are really excited about that.
And the second major aspect of it is as you know InetSoft is not exactly like a conventional business intelligence application. So whether it be MicroStrategy or Business Objects or Cognos, whatever the flavor that people are familiar with, we offer a more flexibly deployed solution. Because our application can mashup data from all operational data stores, it can help create that single source of the truth for all of your business intelligence applications whatever they might be, right.
And so what we can do right now is we can actually extend that theme not only to your BI applications but to your GIS applications as well. We have taken the approach that facts and dimensions in a business intelligence application equal the features that are displayed on a map.
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Easier Geopspatial Analytics
So by tagging your facts and dimensions with the geometry data type that I mentioned a moment ago, if you look at a business intelligence report and look for sales for some particular location, you are going to see a number and now because InetSoft is the single source of the truth for your BI and your geospatial applications, if you go over and click on a pushpin on a map like we are all accustomed to doing with Google Earth or Google Maps or whatever it might be, the data or the sales numbers that you see associated with that location on the map are going to be exactly equal to the numbers that you would see on a conventional business intelligence report.
So now with geospatial analytics, people no longer have to extract data out of their data warehouse in some cases or other databases and load them into geospatial data marts, they can actually use InetSoft as a single source of the truth for both their BI reports and their GIS or mapping applications.
It’s interesting how all of our customers are asking geographic questions of their data or their information so it's not like geospatial analytics is anything new to them. So what we provide really is an opportunity to improve the performance and improve the precision of questions that they are already asking of their data. And secondly, we are just at the beginning of this geospatial revolution as it’s being called because with the introduction of GPS devices in each one of our pockets, we are really driving down the cost and improving scale economies for geospatial technologies and analytics. So, with this, we are just at the beginning of what some are actually calling a geospatial revolution.