In this, the third article in this mini-series, I am going to give you a window into my mind. How I think and how I approach problems. What I have learned over 25 years from both stunning business successes and dramatic failures.
My end goal is to provide you with the building blocks to take advantage of a unique time in our geospatial history – what I have been calling Geospatial 2.0 – so you can construct an incredible Blue Ocean geospatial business.
Let’s get started.
Setting the Stage
In case you did not read the previous two articles in the series, here is a brief summary.
- Most geospatial companies have services based business models, they are sales-led, with existing relationships being critical to generating revenue.
- Most companies operate in so called Red Oceans, or highly competitive geospatial marketplaces. These include sectors within Federal, State, and local government, utilities, transportation, oil and gas.
- Thanks to limited investment in marketing, few companies clearly differentiate themselves in these Red Oceans.
- The Geospatial 2.0 technology revolution characterised by new data sources thanks to new sensors (lidar, hyperspectral, imagery, bathymetric etc), IoT and real-time data, the evolution of multi-dimensional geospatial platforms and artificial intelligence has opened up a world of Blue Oceans.
- Blue Oceans are markets associated with high potential profits, characterized by uncontested market space and a lack of competition.
Building a Blue Ocean Hypothesis based on Geospatial Market Trends
So where do I start these types of big, new opportunity explorations?
I construct a high level hypothesis based on trends in the geospatial market. Above I summarized the technical base for Geospatial 2.0. The high level business value Geospatial 2.0 delivers is the ability to model the real world to drive better understanding and from that improved insight to make better decisions. In essence using digital technology to make ours a better world.
Okay, next we need to drill deeper.
I believe, at its heart, the immediate Geospatial 2.0 Blue Ocean opportunity is centred on intelligent, dynamic digital twins (IDDT). Intelligent suggests artificial intelligence. Dynamic means real time. Digital twins; these are 3D digital representations of the real-world.
So which sectors have clear use cases for IDDT, and might be early adopters of the technology? I believe AEC/infrastructure.
First phase of our hypothesis complete.
Hypothesis Validation with Generative AI
You might know I am a prolific user of Generative AI, and have encouraged my readers to be the same. So, it is time to check our first phase hypothesis with our favourite LLM (ChatGPT, Claude, Morphic, Perplexity etc). Here is our prompt:
“I am building a Blue Ocean business case. Based on a web search, I want you to critique this hypothesis: I believe we are in the midst of a geospatial revolution, what I have been calling Geospatial 2.0. This has been driven by new data sources thanks to new sensors (lidar, hyperspectral, imagery, bathymetric etc), IoT and real-time data, the evolution of multi-dimensional geospatial platforms and artificial intelligence. In real terms this means we can now construct intelligent, dynamic digital twins. Intelligent = artificial intelligence. Dynamic = real time. Digital twins = 3D digital representations of the real-world. I believe AEC/infrastructure have clear use cases for IDDT, and might be early adopters of the technology?”
I’ll not print the full response from ChatGPT (feel free to try this prompt yourself), but here is the first sentence in the reply:
“Your hypothesis aligns with current industry trends and makes a strong case for a Blue Ocean strategy in the geospatial revolution, particularly within the architecture, engineering, and construction (AEC) sectors.”
We’ve made a good start.
Ideation, Discovering Opportunities and Partnerships
Though still at a high level, we have an initial focus: IDDT in the AEC/infrastructure sector.
This gets us started but we need to dig much deeper, to get laser focused or as Geoffrey Moore in his book Crossing the Chasm puts it: ‘a highly focused, targeted approach to capturing a specific segment of the early majority’, see his D-Day analogy for more details.
The ‘getting deeper focus’ or ideation phase we are now in, from my experience, is by far the most difficult. We are still overcoming friction and trying to gain momentum. So how do we come up with ideas? Here is not how to do it, and something I watched with horror. Lock a small group of techies in a room. Have them come up with 5 ‘great’ ideas. Then have them choose the ‘best’ idea and present that to management in the form of a go-to-market strategy,
Epic fail!
Your goal in this phase is to discover real, significant pain point problems, which have high value ($$$) and can potentially be solved by your own geospatial tech/data/expertise in combination with your partners (existing and new).
Any chance of success in this difficult phase requires a guiding framework. And that framework is design thinking.
Design thinking is a human-centered approach to problem-solving that emphasizes empathy, creativity, and iterative testing to develop innovative solutions.
I will briefly describe design thinking below, then apply it to our problem. For those wanting to find out more on this framework see this article from MIT: Design thinking, explained.
There are 5 phases to design thinking:
1) Empathize – See through the eyes of your customer
2) Define – Discover a problem
3) Ideate – Explore potential solutions
4) Prototype – Build a high level solution
5) Test – Get customer feedback on the prototype
Before I discuss a design thinking scenario, I’m going to add another layer of focus to our exploration. Given recent weather events in the US we will turn our attention to:
Disaster risk management and resilience planning – IDDT targeted at utilities with a focus on severe weather events such as hurricanes.
Here are our 5 design thinking steps:
Empathize – Engage with utility companies in hurricane-prone areas to understand their disaster preparedness and response challenges. Ask them about their pain points, such as power outages, infrastructure failures, and slow recovery times.
Define – Problem statement: Utilities in hurricane-prone areas struggle with real-time visibility into power grid health, making it difficult to predict damage and allocate resources efficiently during a hurricane.
Ideate – Real-time grid monitoring with IDDT: Use IoT sensors and satellite imagery to feed live data into a digital twin, monitoring critical infrastructure (power lines, substations) during hurricanes. Resource allocation optimization: AI-driven models within the digital twin could suggest optimal locations for deploying repair teams, equipment, and resources in response to real-time damage.
Prototype: Create a dynamic digital twin that provides real-time visualization of infrastructure, including power lines, substations, and transformers. Integrate AI to provide automated risk assessments and recommend preemptive measures, such as shutting down vulnerable sections of the grid to prevent cascading failures.
Test – Show the IDDT prototype to key staff within the utility to gather feedback on its functionality and effectiveness. Questions might include: How effectively does the system provide real-time insights into infrastructure damage during a hurricane? What additional features would improve the tool’s utility use during hurricane events (e.g., integration with existing systems, mobile access for field workers)?
In the design thinking diagram above you will notice that steps 3 to 5 are iterative. Arriving at a customer validated solution often takes many iterations. Remember customer feedback is critical, they are helping you design the solution. They may actually end up be your first customers!
Blue Ocean Opportunity?
One side question – how do we know we have come up with a Blue Ocean opportunity? This is a question which needs some level of research. That can start with Generative AI. In our case I asked ChatGPT the following:
Q. Do a web search and tell me whether we have uncovered a blue ocean opportunity
A. The use of Intelligent Dynamic Digital Twins (IDDT) for hurricane response in utilities appears to represent a strong Blue Ocean opportunity.
Your research should not stop here. But this is an encouraging start.
Closing Thoughts
For your sake dear reader, I will extend this article to a fourth in the series. Again, this article is getting a little long. In our next article we will continue our journey in building out our winning new Geospatial Blue Ocean business. Until then ..
Matt Sheehan is a Strategic Growth Advisor helping companies discover and win new Geospatial 2.0 Blue Ocean opportunities in this age of dynamic geospatial digital twins and AI.