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Productizing Geospatial Analytics for Developing Markets

The Slow Growth of the Geospatial Industry

At the beginning of the last decade, Marc Andreseen from the famed Silicon Valley venture fund Andreseen Horowitz famously said that software was eating the world. Even a visionary like him wouldn’t have imagined that by the end of the decade, the same software industry would be biting its nail as it risks of being eaten by the juggernaut called AI.

While software continues to provide business value offering enterprises the ability to scale quickly, the rise of big data, cloud computing and the Internet of Things (IoT) has created a hyper-acceleration in the capabilities that AI can offer, such as assisted decision making, diagnostics, and predictive analytics.

Geospatial technology has been around for over two decades now, with governments and public agencies being the primary target users. The issues around data access and movement of large chunky geospatial datasets inhibited leveraging geospatial data products, leading to the underutilization of its potential and the opportunity to integrate it with mainstream software products. Also, the geospatial industry has been traditionally obsessed with mapping and images. Thankfully, the AI Hyperwave has started infiltrating this industry, which offers a unique opportunity for expanding the realm of what can geospatial data do for your business.

Stepping Stones to Growth

The advent of open data programs such as the Landsat and Copernicus projects from NASA and ESA respectively happened around the same time when the commercial Earth observation industry got disrupted by the CubeSat standard that made it possible to have satellite imagery align with he 3Vs aspect of Big Data —Volume, Velocity, and Variety.

These developments by the mid of last decade led to a sudden increase in the cadence of data capture from space, which in turn aligned with the capability and affordability of cloud computing for data store and distribution.

So what does this industry’s convergence mean for the software, AI, and all other industries?

At a very high level, it has now become possible to create products that describe our changing world in close to real-time as we can acquire data of any part of the world, from any part of the world, move it around, push it into any data pipeline and visualize it all on the cloud at scale.

Let us look at a few new-age applications of geospatial data:

  • Precision directing your salesforce to target locations for customer acquisition and making their operations more cost-efficient.
  • Ability to identify credit risk of small-holder farmers by creating a history of their land and region, enabling financial inclusion programs.
  • Your home insurance company paying you even before you filed a claim post-flooding due to heavy rainfall in your city because they had indemnified the risk basis the history of flooding in your area.

These use cases are not needed in the form of a map or an image, which represents only a tiny fraction of the geospatial data applications.

However, these examples were meant to illustrate the opportunity that geospatial data offers to deliver tremendous business value. But this geospatial revolution will most likely take place gradually and unlike the adoption of AI perhaps even unnoticeable. It would form an important part of the Decision Intelligence that is required by enterprises who are set with the war-chest of AI tools for their digital transformation.

At the end of the day, businesses need actual numbers to support their decision-making processes and not just a map. But the million-dollar question is how does one create products from datasets that have potentially unlimited applications depending on the ability to extract information from it?

The Quest of Geospatial Products

Unfortunately, there is no straight answer for it today as the geospatial analytics industry is moving in different directions and at different paces, depending on which part of the world we are looking at. While targeting hedge funds to provide deep insights on commodities movement might have worked for some U.S. based startups in this space initially, the real exploitation and scaling of geospatial analytics is yet to be tested in markets where the data infrastructure is missing for validating the AI models. While this happens, creating products that fit the market requirements becomes a unique task since AI is growing at an accelerated rate in other areas and geospatial cannot afford to be a slow mover anymore.

This is what shall drive the product innovation for the industry — finding business applications for geospatial analytics and making it an integral part of mainstream technology strategy such as AI and digital transformation programs or risk being pushed again into the ‘outlier zone’ by the technology players as the world witnesses convergence of AI, cloud computing, Big Data, and predictive analytics at a breakneck pace.

The innovations that have happened during the last decade on the technology now needs to be translated into product, process, and business model innovations for extracting the maximum value for organizations that are adopting geospatial technology.

At SatSure, we have adopted a customer development model to overcome the chasm of moving from services to product-oriented business models as the global industry comes to terms with the possibilities that geospatial analytics has to offer. To serve developing markets, enterprises need to collaborate not just on product development but also on the resources, shared skill sets, data sharing, and other expertise to jointly flourish.

Such co-innovation helps in creating a closer relationship with the clients, consumers, and users to foster and develop products that creates a sustainable competitive advantage for startups who are into building products that have some component of geospatial analytics.

And we at SatSure have benefitted from such a customer and service orientation strategy for developing our products for the financial services sector, with the results being an increased user connect leading to a reduction in the churn rate over the last one year.

However, an important aspect of working in the developing markets with geospatial analytics at the center of our offering has taught us that it is still too early to standardize the product specifications since the value of geospatial analytics touches different business stakeholders and helps them solve sometimes a very diverse set of problems, leading to continuous development in frameworks such as AGILE.

The inherent challenge of building products with geospatial analytics as a core component is that it doesn’t fit in any technology buckets because of its infinite potential, and yet it can fit anywhere and everywhere. Thereby, it is very improbable that this market will ever be an oligopoly. Depending on the target applications and geographies, geospatial analytics will continue to be in the ‘early adopters’ as per Professor Moore’s theory of technology adoption.

While investors globally are being shown big fat numbers on the total addressable market, but there still is no confidence in the serviceable obtainable market numbers for geospatial products because the industry is still in the ‘early adopters’ stage.

Hence, there is a need for both patient capital to fuel continuous customer engagement in order to make geospatial analytics truly an integral part of the AI Hyperwave and productizing the services that meet the needs of the world beyond the U.S. and Europe and be more open to co-innovation initiatives to reduce the costs, spreading the risks and getting to the market faster.

[Written by Rashmit Singh Sukhmani, Co-Founder & Chief Data Officer and Prateep Basu, Co-founder & CEO at SatSure. This story was first published on Sparta Blogs.]


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