Everyone has a love-hate relationship with location. Everyone sees it as widely important and critical — be it in industrial automation, 3D Printing or analytics. As everything has become digital, location has become something that is embedded in digital assets.
However, at the same time everybody faces problems in the accuracy of location — the ability to use that location reliably, whether it is in automated map making, to day-to-day end user applications. The merging of data from different sources, from multiple geospatial sources is still very difficult. We don’t have good universal point standards for mapping information or merging information together for using the same featured language across multiple sources.
There is a lot of promise, but at the same time there is also a lot of concern that the industry is not quite living up to that promise.
Setting standards is crucial
The geospatial industry continues to provide classic maps and imagery. We have talked about standards for a long time, but I am not sure whether they really have defined the standards that are required to easily incorporate geo-data into other datasets.
The expectations of the user community when it comes to accuracy are growing faster than our technology to keep up. People expect it to happen instantaneously and be 100% accurate all the time. Customer expectations are increasing faster than the technology is. Here again, standards play a crucial role since that would help us integrate geo-data with non spatial data to deliver better accuracy.
Further, standards among geo sources enable a set of patterns, tools, capabilities for merging the data sources together. Therefore, the question is does a customer have to go to five different vendors, get their data and figure out how to use it together, or can the industry actually support patterns and collaboration among different suppliers to deliver what is needed.
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Cooperation is necessary
The ongoing digital transformation is leading to everything becoming a digital asset. Given this, the general pattern for a successful company in the IT and geospatial space has to be rapid innovation and built around integration.
The industry has realized that there is tremendous value in cooperation, mostly because no single company has all the information. We know that success in this space is going to be driven by end-users who are using geospatial data and technologies outside the traditional geospatial industry.
The other important enabler that is going to be super important going forward is the ability to handle real-time event flow — receiving real-time data streams from a variety of sources, sensors, IoT devices — and to be able to operate on those streams in real time, take action, deliver events to other systems. Today, as in many cases, merging multiple streams together in order to recognize information or recognize patterns is of utmost importance. In some sense, the integration of multiple partners and providers will happen not just at the physical data level but also at the advanced stream level.
Another dimension that is going to be critical is focusing on very fast delivery, very fast information processing, and very fast information action as opposed to traditional long-term processing of information. The world is becoming a lot more real time.
Cloudification of geo is a continuing trend. What is also emerging right now is that people are learning how to develop really good machine learning models that are stable and can be reused. The other key trends that I think are important are: democratization of analytics, which includes the ability to take huge datasets, use standard sequel to manipulate those datasets, and visualize and maintain those large datasets.
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