Geospatial Rising and Falling Stars for 2017. Predictions from Ryan Goodman, CEO of CMaps Analytics


In 2017, we will see continued growth in geospatial technology in all forms. The technological advancements we are experiencing on a day to day basis is helping advance new waves of clever innovations for enterprise. In a world with self driving cars, artificially intelligent home assistants, and popularity of games like Pokemon Go, the world of GIS and Location Based services is on a convergence path with macro-technology trends.

Rising Solutions for Enterprise

Indoor and micro-location intelligence

Leading up to 2017, we have seen a huge emphasis on geospatial / mobile engagement for retail and mobile marketing. However, there are less visible successes in the world of automation and optimization for manufacturing, logistics, and healthcare. Real-time fencing and large scale asset engagement will mature as asset and traffic dense spaces foster new innovations beyond the one-way communication mechanisms that sparked the location based services market.

As I mentioned in the introduction, this is a key area where I see geospatial data and technology converging with machine learning, IOT, and big data analytics. One company that has publicly demonstrated its mastery of this is Amazon within their warehouse operations.

Expansion of Location Analytics Scenarios Beyond Full Stack GIS Platforms

Location Intelligence and was once relegated to a finite number industries and scenarios where it made sense to invest in GIS technology and skills. In 2017, those skills are critical, but will require professionals to broaden their horizons into new areas. One of my favorite champions for this revolution for GIS is Matt Sheehan who also contributed his 2017 predictions. In 2017 lines of business like sales, marketing and HR (not traditional GIS users) will ask more sophisticated “where” questions as they pertain to their jobs, requiring modern breed of business user and analyst friendly solutions. Most enterprise analytics tools will continue to commoditize a broad set of geospatial analysis and visualization capabilities. However, there is still a need for more advanced, customized solutions and there is no shortage of commercially available geo-analytics platforms like Carto and the embedded geo-analysis APIs work we do at CMaps Analytics.

Lead Distribution Analysis with CMaps Analytics

New Gold is Big Geo-Data Products

For example, high resolution aerial photography and the machine generated data (sensor and image post processing based) will create a new wave of interesting business and analytics driven solutions for construction, mining, utilities and other industries. While these techniques and technologies have been around for a while, the expanding commercial drone industry and platforms forming around these solutions will allow organizations that operate at massive scale are creating powerful derivative data products. Both community and commercial initiatives will form and we are already have success stories to bench mark. The first example of 2017 already dropped from Uber, who released Movement, a data product which demonstrates what is possible from mining massive volumes of data to provide derivative information/data products.

Losing Steam in the Enterprise

While these new trends picking up steam some past year’s trends will flatten or slow down, pending any new innovations that come up this year.


The first wave of embedded GIS in BI solutions were either too complex for IT stakeholders to maintain or resulted in simple integrations that lacked important geospatial data and functions that makes GIS so useful. As GIS on BI lost steam, some GIS vendors like ESRI took the bull by the horns to develop their own analytical tools which looks quite promising. It’s still too early to tell if they have a hit on their hands, but we will should have an idea the deeper we get into 2017.

Google Maps Losing Steam in Enterprise

Google is a force in consumer apps and mobile, but has lost ground in enterprise to open-source and commercial platforms. From MapBox, ESRI and IBM, to a recent announcement with Microsoft, HERE, and TomTom, Google Maps is the one missing mapping provider from a lot of recent enterprise partnerships. Google Maps provides an exceptional cost/value proposition and has mountains of data that it can mine for geospatial services. It will be interesting to see if there is a resurgence in Google’s marketing and release cadence for enterprise class geo-services in 2017.

“Location Intelligence” Tag

While the term “Location Intelligence” is popular among industry pros, a large portion of enterprise stakeholders are not actively searching for “Location Intelligence”. Looking at recent Google trends Twitter activity I was surprised that this term is not a blip on the relative geospatial social-sphere. With that said, as the pervasiveness and importance trends mentioned above grow, we could see higher awareness of related terms.


  1. Great article Ryan. I’ve long suspected location intelligence is little searched for in Google (rather like location strategy and other such terms being banded around). I wonder how the business community view location questions. Do they see the map capabilities built into the likes of Tableu adequate?

    • Folks are focused primarily on the end result which is a map or the kind of analysis they need in the map. So at a surface level, I would say that many organizations would find Tableau maps adequate for basic mapping requirements, if the goal is to take the results of an analytical process and display it on a map. Tableau does not have robust spatial functions, which could easily be supplemented by Alteryx. I know retailers doing the same store planning / cannibalization analysis with these combined solutions. Ultimately to your point it is the end result that folks are searching for.

      • Thanks Ryan. Your response leads naturally to a follow up question. GIS has its core user base in the public sector and orgs managing infrastructure. Does GIS have a place in the BI dominated commercial sphere, and If it does in what capacity?

  2. Nice article. Working in the Utilities-industry, I think we’ll see more focus on Big Data and use of GIS to visualize and analyse these kinds of data. There are a lot of sensors spread out in the water/sewer Networks which sends realtime data, and this has much potential!

    • Thanks for the comments. I do agree that GIS, or at minimum open standards originating from GIS industry are needed to understand the massive volumes of spatial data that we create as a society. For real-time and IOT, these are fascinating problems to be solved. Edge computing is one of many interesting opportunities to re-think and optimize some IOT scenarios you describe. Ultimately we will see smarter interconnected devices and geospatial micro-services to automate real-time action from the device. Exciting but also a little scary 🙂


Please enter your comment!
Please enter your name here