In addition, they are influenced by broader developments both inside and outside the geospatial ecosystem. This includes technology such as Google Maps and other consumer mapping applications, modern mobile technology including smart phones, tablets and wireless networks, and advances in sensing technology and feature recognition, guided from many angles, ranging from face recognition to self-driving cars.
This initial blog is an overview of these ten key requirements that I believe are critical for any modern geospatial platform. In subsequent posts, I will discuss each individual topic in more detail.
1. Mobility
Mobile applications are essential to leveraging geospatial data in infrastructure companies. A major portion of the network companies’ need for geospatial data is in the field and capturing data at the source is critical to improving data quality. The traditional GIS approach is failing to deliver good data quality and currency. Significant improvements in team collaboration and the digitization of the design process can also be enabled by the ability to design in the field.
Even though wireless communications continue to improve, it remains essential for mobile geospatial applications to be able to run offline as well as online. In addition to everyday gaps in coverage, a key use case for all infrastructure companies is responding to disasters, when wireless communications may be totally down. Offline data sync needs to be highly scalable and robust for use in large enterprises, with large data volumes and many users.
Mobile applications need to have the same capabilities as office-based applications, and really need a common architecture so that customization works in the same way across all platforms. Support for all the major mobile platforms (Android, iOS and Windows) and form factors (tablets, phones and laptops) is essential to realizing the potential of a truly mobile platform.
Google Maps changed expectations on usability and performance of geospatial applications. Enterprise applications need to meet the same standard as consumer applications in this respect. It is easy to underestimate the difficulty of delivering applications that are simple to use for non-technical users with minimal or no training, but this is critical to widespread adoption of geospatial applications across the enterprise, and to delivering the benefits that these systems can provide. Business and process transformation cannot be achieved without widespread adoption and this will never happen without easy-to-use applications.
An enterprise geospatial platform must be open in multiple respects. It must be easy to integrate data from other enterprise systems, which may or may not be explicitly geospatial such as jobs, outages, crew locations, network status and more. Supporting a range of technical approaches is important, including rest services, database level integration, JavaScript APIs and embedding of maps or other widgets. All these have pros and cons and different approaches may be needed depending on the situation.
In addition, a modern geospatial platform needs to have good interoperability with other geospatial or GIS platforms. While there can be advantages to using one platform for multiple functions, companies must also have the flexibility to mix platforms where appropriate.
4 - Modern architecture
Geospatial platforms tend to have a long life within infrastructure companies. They often have extensive customizations and integration with other systems that makes them very difficult and costly to replace. For this reason, it is important for any new platform that is introduced to have a modern architecture, with a clear focus on web and mobile applications. Web applications are far easier to deploy to a large user base than traditional desktop software, and easier to integrate with other applications. As was mentioned earlier, mobile applications are critical to a modern geospatial platform and need to be a primary focus, not a second-class citizen, as is the case with all the traditional GIS vendors.
Systems need the ability to be deployed either in the cloud or on premise. The cloud is clearly the future, and a desktop GIS model just doesn’t fit with a cloud strategy. However, utilities especially have been more conservative about moving to the cloud than many industries, so systems must also be able to run on premise in the short-term.
Another important architectural principle is for the platform to be built with loosely coupled components, so it can be easily extended over time and elements can be replaced when needed. This is fundamental to maximizing the useful lifetime of the platform.
While there is commonality between the work performed by network operators, all companies vary in the details of their work processes and their existing systems. This means it is essential that geospatial applications can be modified to support the specific needs of any given organization. There are two main approaches to modifying the system.
The first is that the platform needs to support a “low code” configuration environment that enables many aspects of the system to be configured without requiring programming. This increases the speed at which new applications can be deployed and lowers cost of ownership.
However, any “low code” environment will ultimately hit some limitations, so it is important that, when necessary, applications can be extended by writing additional code. This needs to be done in a way that cleanly separates custom code from core code, and uses well defined APIs, to minimize any support or upgrade issues due to custom code. A common platform for customization across both office and field applications is critical to be able to deliver new functionality quickly and to reduce development, testing and support costs.
Delivering good performance and scalability is particularly challenging for geospatial applications, because of the data volumes involved and the complexity of the display requirements. Excellent performance is critical for widespread adoption of geospatial applications and for maintaining productivity. Google Maps, and other consumer focused systems, set a benchmark for application performance, and enterprise systems need to match those expectations.
Data modeling for infrastructure networks is quite complex, progressively more so as you move from gas and water, to electric and telecoms. It is important to look at the capabilities that a platform has for modeling complex network devices, and in particular, how easy it is to extend the network model to handle new device types. Communications and electric networks are evolving very rapidly, and platforms must adapt their network models accordingly.
One important technique for doing this is to be able to define aspects of a device’s behavior in code where needed, rather than just at the data level. This makes it much easier to handle complex behavior and new device types.
A key requirement for infrastructure companies is the ability to manage and analyze not just their network assets as they are today, but as they will be in the future. This is vital to managing planning, design and construction processes.
Version management is the industry standard approach to tackling this problem, which was pioneered by Smallworld in the 1990s and subsequently adopted in some form by all the major vendors. While capabilities vary somewhat between platforms, there hasn’t been significant innovation in this area over the last thirty years. There is clear scope for some major improvements to the versioning model, including the idea of active version management that identifies potential conflicts much earlier in the planning and design process, delivering significant productivity benefits.
Security is of course a key consideration for any enterprise IT platform. As far as possible, security should be handled with common mechanisms across all applications in an enterprise, so much of the requirement here is to integrate with or leverage existing security systems and practices. Integration with established directory systems like LDAP / Active Directory as a basis for authentication and authorization is a key foundation, as is supporting multi-factor authentication.
Mobile applications have a number of additional requirements, especially in a geospatial environment where significant amounts of data may be stored on the mobile device to support offline use. These requirements are best handled by supporting established MDM (Mobile Device Management) and MAM (Mobile Application Management) applications that implement common requirements such as the ability to remotely wipe data from a device that has been lost or stolen.
An important emerging area for geospatial applications is reality capture and reality data modeling. A rapidly growing range of imaging and sensing devices, from smartphones to specialized scanning devices, enable the automated capture of real-world data to produce highly realistic models that include imagery and 3D data such as point clouds and meshes. A modern geospatial platform must support the storage, display and capture of these new rich data types in addition to more traditional geospatial data types. It’s certain that the use of reality capture in day-to-day field operations will grow rapidly in the coming years. This reality revolution will see a dramatic increase in the automation of data capture, resulting in significantly improved data quality and reduced costs for data maintenance.
Products from the traditional GIS vendors, even recently developed ones, remain stuck in the twentieth century, centered around a desktop GIS that is designed for specialized technical users. Recent developments in the broader technology world enable us to reimagine what an enterprise geospatial platform can deliver. It must be a platform for everyone that runs in any web browser and on any mobile device, that is agile in adapting to the organization’s needs and to evolving network technologies. Pushing more and more data updates out to the field, with increasing automation, will dramatically improve data quality and reduce the cost of data maintenance. Without radically improved geospatial data quality, organizations cannot succeed in the digital transformation projects that they need to remain profitable and competitive.
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