Sophisticated Data Aggregation Using GIS
Cloud computing and GIS platforms are a match made in heaven for the modern enterprise. Perhaps even a better match than your favorite cloud based CRM, HR/Finance system, or social media platform. Why? because even Facebook started small and grew, one college campus at a time. But for a global corporation, the fundamental data set covers most of the Earth, which occupies an area of 197 million square miles!
Today’s maps contain the vastness of our environment, resources, property, and populations. We have maps of all types with enough precision to support navigation systems. Satellite images are taken from miles up to achieve resolutions of a few dozen centimeters. Birdseye views are taken by circling planes to give us pictures from several perspectives. Planes and drones take lidar images using lasers to create high precision 3Dmodels of buildings and objects.
Maps track mountain tops and ocean depths, rural areas, and cities. We track properties, points of interest, the locations of phone towers, their signal coverage, and the wires, and gas pipes under our streets. And all of these have metadata to track how things are used, owned, proposed, and what they’re contained within. Containership leads to databases’s of virtual boundaries for continents, countries, regions, states, counties, zip codes, and parcels. There are databases containing the location of every significant point on the planet and algorithms to interpolate between them togeocode andadd new locations. Andamazingly much of this information is changing all of the time!
On top of this, with the advent of IoT, we’re poised to generate more data than has ever been seen before as devices provide constant streams of data about where they are and what they’re doing at any given moment. The cloud offers ways to store and analyze this vast amount of data and we have access to it from the devices in our lives and the apps we use throughout our enterprises.
But it wasn’t always so. In the early days of GIS technology, the tools were relatively niche and required skills akin to specialized engineering disciplines. Not surprisingly folks with such skills often learned them in the military or the federal government. The military applications for GIS are obvious and one can imagine the amount of work required to manage the country’s various resources.
At that time, GIS software was expensive, proprietary, and had limited scalability. Mapping applications were fat client tools which required large file shares of data and images-something very difficult to extend across a WAN for example, which often caused mapping departments to be fairly centralized. Adding GIS features to an order processing system to embed a map or to geocode an address was often difficult and required complex low level code.
The software itself was only part of the problem. The bigger complexity was data. Not just the amount but the logistics of coordinating disparate sources: Local and federal governments, 3rd party, in house -often overlapping, inconsistently tagged, and released at varying schedules. Mapping vendors bundled datasets to hide much of this but their data often required being supplemented with additional data–such as higher quality images. Coordinating the data sources was critical for any application so there was a huge need for careful release management, often requiring an entire team.
Fast forwarding to today’s cloud apps, the accessibility problems of GIS technology is solved by web tools, thin client apps, and centralized systems that can be configured once and shared by many.
The challenge of consolidating data still exists but the cloud vendors hide this from us. Pan along Google or Bing maps and you see the faint logos of various data providers in the corner along with version information. And on top of that it’s all changing all the time. The land under our feet seems stable enough but the data about it is far from fixed and is constantly being improved.
Cloud beautifully solves problems of immense data sets requiring massive storage and infrastructure, data distribution problems, high velocity updates, and setup costs. And when you consolidate data for one customer, you can leverage it for many customers while economies of scale make it cost effective to provide the best data possible.
There will always be premium data, but thanks to cloud the benefits are entirely on the side of the consumer. Yesterday’s premium data is practically free. Today’s premium data, such as 3D virtual building models, will be cheaper tomorrow.
What started for Microsoft and Google as a way to add location based servicesto their search productswas considered fairly limited compared to the more mature features of ESRI.But Microsoft and Google gradually started to address this–as did the aftermarket and open source community.But vendors like ESRIwere watching and started to adjust their offerings to be complimentary rather than competitive with Google and Microsoft.
One could access Bing or Google’s webservices in MapInfo or ESRI’s desktop tools and no longer need a massive trove of images stored on the network. But not stopping there, ESRI adapted their server tools to be cloud friendly. So one could provision a full ArcGIS Server instance on the cloud provider or your choice and leverage immense data sets, choose additional premium data, setup gateways to your own data, and still use the high end desktop tools in a subscription model.
So all in all, this is the absolute best and most exciting time for companies to adopt advanced GIS tools and move beyond the basics of out of the box Google and Bing. Like other Cloud tools, you can start small and grow to any size. And technologies like IoT will create datasets, the likes of which we’ve never even seen that could only be wrangled using cloud tools like Hadoop.
We’re certainly seeing huge growth in several areas: the high end premium mapping tools are easier to get into the hands of end users thanks to subscription models. And premium tools are adding new features and data enabling our users to create sophisticated interactive visualizations and analytics.
With the use of web apps and subscription models, these sophisticated data aggregation and presentation tools no longer require a heavy load on our desktop hardware. This also gives our revenue producers the flexibility to use and present with the device of their choice, which is key to keeping our sales professionals from outselling.
Due to the proliferation of easy to use web services, it’s never been easier to embed maps and spatial functions to LOB applications–even legacy ones. For brand new apps, we’re often adding mapping front and center to the interfaces, where everything is oriented around location. We’re also seeing amazing integrations of GIS features in our various 3rd party apps.
However the most significant area of growth we’re seeing, thanks to both cloud BI and GIS tools, is the growth of end user self-service solutions. This is aided by many of the younger members of our workforce coming in comfortable with the tools to create mashups and data integrations on their own.
We’re at a tipping point for a next generation GIS technology, where users will have access to so much location based data, and analytical tools that can handle it, the insights possible will provide significant advantages to those who’ve made the investment.