Today, GIS is not just software. It’s data. It’s people. It’s getting to the answer. Quicker. Data is expanding. More users demand access to information but don’t consume the information in the same way as a GIS professional would. But as curators and managers of geospatial information, GIS teams are looking to support the entire organization and are looking beyond their own departments to enable the broader community of potential users of geospatial information.
Now, the challenge turns to leveraging these data for smarter, more effective solutions in both government and commercial endeavors. There are a few pre-existing conditions that are found where geospatial technology teams exist:
- Geospatial data are not always seen as an asset
- GIS departments are now more integrated with the IT department led by a Chief Information Officer and sometimes a Chief Data Officer; they are acutely aware of how much geospatial data is being collected.
- Data warehouses and other data resources are still siloed
So how did we get here? To be sure, it’s not just the availability of more data that has become challenging for organizations that are investing in geospatial technology. It’s not a case where data needs to be first digitized and geo-rectified, as in years past. Artificial intelligence is increasingly supporting this task. Today’s GIS users have shifted work from being data creators to more data managers and analysts. But there are many more sources of geospatial data such as from weather sensors, mobile devices and a variety of new imaging platforms like unmanned aerial vehicles. This leads to databases becoming larger hence the need to engage with IT management. And finally, to the last point, will current geospatial software and databases suffice to handle the variety, volume, and velocity of new geospatial data?
Can your organization exist without geography?
As an example, certainly municipal governments recognize that a city is a collection of assets and these assets have a location. From vehicle fleets to manhole covers to fiber optic cables, data are collected from assets that are stationary, dynamic and mobile, or in some cases the asset is subterranean.
And now, each asset can be enabled with sensors. Those sensors broadcast data continually thus creating a data deluge, where it falls to the responsibility of the CIO or likely the GIS department director to devise with a data management plan. Recognition of the challenge is always good. Because, when city leaders can identify, track, monitor and manage the data from these assets, the full value and possibilities of geospatial data and connected technologies result in the location intelligent city.
Two Basic Premises
Let’s start with two basic premises. First, some organizations have geography as the foundation of their business. Uber, as an example, doesn’t exist as a company without geospatial technology. Government, in general, is a naturally geographic business. That is, fundamentally, the underpinnings of government require a tax base and infrastructure, both of which are rooted in geographic information. It’s a basic conclusion, then, that every city needs to become location intelligent. In addition, businesses that find their roots in geographic data will consume as much data as necessary to gain a competitive advantage.
Second, other organizations are “challenged” to use geospatial data and may not understand all use cases that are directly applicable, yet find that because of the increasing availability, high frequency, and highly accurate data, they may seek ways to exploit these new data sources. Retailers, as an example, while not underpinned by geography, seek a competitive advantage by having superior locations of their brick and mortar establishments. And, as retailers transition to balance ecommerce with physical locations, geospatial data creeps into the many facets of logistics, fulfillment, catchment areas, footfall traffic and mobile marketing.
Five Pillars of the Data-driven organization
So, if organizations recognize the need for more location intelligence, they have to build a foundation of what constitutes the building blocks of the solution architecture. That is, what must they do from an IT infrastructure and hence a GIS architecture to be successful? Herein are the five, primary architecture pillars.
1. Usability…It isn’t just software usability, though that is important, but rather as more users see the advantages of leveraging geospatial data and hence can drive competitive rewards, it is the quality of data that becomes more important.
2. Extensibility - Here it’s not only the ability to customize workflows and applications but it’s important to recognize that the platform must be customizable for users beyond the GIS department. And this is an important point. If you want to empower people with geospatial data, a desktop GIS will likely not be the solution…it should be a different platform, customized to the workflows of knowledge workers.
3. Flexibility – The next generation of geospatial solutions must add connectivity services to databases and other data types, and be ready to support the desktop, cloud, SaaS as well as mobile platforms. GIS must adapt to the user’s preferred IT environment…not the other way around.
4. Compatibility - GIS must work in mixed environments of open source and commercial software and ingest data from myriad sources. Interoperability with other GIS and IT systems is critical to productivity.
5. Expandability – With data volumes increasing, can the current geospatial architecture process the volume and velocity of data? That is, as presented above, will current IT infrastructures handle larger volumes of device and sensor data? And, will it essentially break your current GIS system, thus requiring you to look at different platforms that are spatially-enabled, but not your current desktop or server- based GIS?
What’s the result of standing up the five pillars of the data-driven organization? It’s the freedom to adapt to a dynamically shifting technology and data environment and accommodating the demands of more users. GIS is a fantastic tool for many disciplines. However, for too long it’s been confined to a framework that emphasizes map-centric output. While maps and the ability to visualize proximity relationships are important, they not the only methods to utilize location-based data.
Geospatial technology is highly effective when used in conjunction with other geospatial and non-spatial data. The ability to append myriad data to a single address location, as an example, creates better context and thus more informed decisions. Geospatial technology can also be effective as an embedded solution within other enterprise applications. For example, insurance companies that need to calculate the distance to certain perils, such as earthquakes or high crime areas, to properly underwrite policies require “just the answer” to the question of “where” or “how far.”
So, to become a more data-driven organization requires a different perspective. It’s a perspective that understands the complexities of the software, the plethora of new data types and the needs of the organization. Together, this new perspective can drive a powerful shift it analytics and operational success.
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