The challenges facing those innovating with location tech and digital maps
Today, our world relies on maps - think about how many apps and services you use daily, both personally and professionally, that use a location-based component.
Given how much of the world relies on maps, you'd think there are lots of maps designed to allow businesses and their developers to solve specific problems. Surprisingly, there are few maps for businesses to build with and integrate into their own applications and use cases.
While proprietary maps do come with much-needed quality and reliability, they also come with the huge sacrifice of not being able to combine useful data from other map ecosystems, providers and open sources. They're not interoperable. As a result, most maps will never be as rich as they could be for their specific use case.
So, what challenges does this pose for organizations and developers innovating with digital maps and location data? And how can they find the right commercial mapping solution to enable new services and products to flourish?
In most cases, the digital maps we have today resulted from a single use case. However, when digital maps are built with a single end use, they lose their dynamism, they become static and rigid -- more akin to paper maps of old than the powerful, data-rich tools they can be.
This has meant that all kinds of organizations across the private and public sector have had to make do with limitations imposed upon them. Companies that build with map data have had to develop and maintain their own map stacks, balancing data from disparate sources that all reference different base maps and somehow, make it all work.
They've had to invest significant time, money and resources into adapting their maps, fitting their data to its structure and making it work for their use case. Over the years, these maps have been modified and adapted to work for other use cases and have become large and unwieldy.
Ultimately, something that's adapted to solve a single problem is never going to be as good as something bespoke and purpose built to solve many problems - but what is the solution?
Now, organizations and their developers must select from the available mapping providers to determine which solution will meet their unique requirements. What has been missing from the market, however, is a solution in which all companies and devices can collaborate and communicate through a single digital representation of the physical world.
In a fast-paced and competitive landscape, companies shouldn't be restricted on how to build for their customers, rather they should be empowered to utilize maps in the best way possible. They need a geospatial standardized map; one they can add their own data to and innovate on top of.
Think of it like the Internet - if every tech company created its own Internet and data couldn't be moved between these systems; there would be a huge cost in moving that data around and the Internet wouldn't have developed into what it is today.
This layered approach, built on an open standard, will ensure that all parts of the digital stack work together, without the need to resolve or conflate data from one platform to another. This level of interoperability saves time, effort and a lot of headaches later down the line when the businesses try to meld data from another source or add additional functionalities.
Most importantly, this will free up resources so developers can focus on creating new services and products that are specific to customers' needs and wants. With everyone working from the same standard, data becomes much easier to share and work with, acting as a catalyst for innovation.
AI and machine learning is turning that traditional approach to maps on its head - allowing businesses to create new services faster, more accurately and with fewer developer and operator hours. With AI and machine learning, developers are better equipped to process data and turn observations into edits, updates and features as quickly and accurately as possible.
Humans are still required to check for errors, continually improve algorithms and ensure the AI is doing what it's supposed to do. However, machines can now do the heavy, laborious lifting. It's increasing the accuracy and freshness of maps and making developers far more effective and productive.
What does this look like in practice? For the automotive industry, a standardized AI-enabled base map will allow carmakers to integrate real-time traffic data, vehicle-to-infrastructure communication and even electric vehicle charging stations into a cohesive system that supports the future of mobility.
In the public sector, those developing smart cities will benefit from the privacy, precision and flexibility offered by a standardized, AI-driven base map. With real-time data at their disposal, city planners can create more efficient transport networks, improve infrastructure, and develop smart systems that respond to the changing needs of their citizens. Furthermore, with the ability to add their own data into a private layer, it's incredibly valuable to applications where data protection is paramount.
Meanwhile, in logistics, the ability to quickly adapt to changes in road conditions, optimize delivery routes, and integrate external data - from fuel consumption to environmental impact - into a map is a game-changer for companies seeking to streamline operations and reduce costs.
In the future, maps will continue to be a core tool in the functioning of global business, navigation and our daily lives. However, maps - specifically, the way they are made - need to adapt to give organizations the flexibility and scalability needed to make everything work well together.
When an orchestra is all playing from the same sheet music, guided by an expert conductor, symphonies are created. In the context of maps, standardization brings enhanced accuracy, freshness and interoperability. Only through this unified, collaborative approach will innovation and end user satisfaction skyrocket.
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