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A simple tool for translating business problems into geo-solutions

The Geospatial Solution Canvas, a simple tool from Esri Canada’s director of management consulting, Matthew Lewin, helps you break down business problems, then devise solutions powered by geospatial technology. Read on to get the full method as well as an example of how to use it.

I love design canvasses. They’re a great way to distill a complex topic down to its component parts, and provide a structured way to think through a problem without getting lost in the weeds. Over the years, I’ve used and benefitted from several design canvasses, including the business model canvas, the value proposition canvas, the AI canvas and others. I even helped create one!

I turn to a design canvas whenever I need to quickly formulate ideas and summarize concepts for a broad audience. As a communication tool, they’re tough to beat.

That spirit of simplification motivated me to look at how a design canvas might alleviate a thorny issue in the geospatial discipline—specifically, helping non-specialists translate business problems into geospatial solutions.

As an industry, we tout the intuitiveness of maps and the innate sense of spatial awareness we all possess. But when it comes to making a connection between a discrete business issue and a solution driven by geospatial technology, it’s still quite difficult. And if we are ever to reach the lofty goal of embedding geospatial intelligence into every business system and business decision, then it needs to be simpler for anyone to translate a business problem into a geospatial solution. That means making it easier to conceive a solution without getting lost in the myriad product offerings and technical jargon.

So if you want to leverage the power of the geographic approach and want a simple, streamlined way of linking a problem to a solution, have a look at the Geospatial Solution Canvas.

Image of the Geospatial Solution Canvas, which shows a business problem, its cause(s) and the goals surrounding it, alongside the solution strategy, the organization’s existing geospatial capabilities, expected outcomes and design assumptions.

To explain how the Geospatial Solution Canvas works, I’ll use an example of an issue from the consumer packaged goods (CPG) industry. The CPG industry consists of items you use every day, including household goods, packaged food, beverages and all manner of consumables. The issue I’ll look at is the struggle among those in the industry to secure new investment capital to fund growth and expansion.

CPG organizations, like most businesses, are under pressure to deliver on ambitious environmental, social and governance (ESG) targets. It’s no longer acceptable to be profit first and only. Businesses in 2022 must demonstrate their commitment to environmental sustainability, social equity and ethical governance lest they risk a hit to their reputations—and their investors take notice. 

In the past, investors flocked to businesses based primarily on strong financial performance. Not anymore. Those same terms are increasingly linked to ESG performance, and firms with poor ESG ratings are seen as risky investments.

How can we use geospatial technology to help CPG organizations improve their ESG ratings and appeal to investors? Let’s use the Geospatial Solution Canvas to run through the problem and devise a solution.

A version of the Geospatial Solution Canvas, filled out with information from a hypothetical consumer packaged goods organization.

The canvas is divided into three parts: the problem domain, the solution domain and the outcome domain. I’ll start with the problem domain first.

The problem domain

First, you specify the core business problem. As noted already, our example is concerned with the CPG industry’s struggle to attract capital investment. Be sure to get this right. The entire solution hinges on your understanding of the business problem, so it’s essential that you take the time to clearly and precisely articulate the problem. Avoid vague language or ambiguous wording—this will only confuse you later on when you get to solution design. You want to make sure you get to the core problem, which means teasing out problems from symptoms. Ask yourself, “If we addressed this issue as described, would our stakeholders declare our solution a success?”

A well-defined problem provides a concrete starting point. The next step is to identify the source of the problem; this means drilling down into underlying causes. Often there are numerous conspiring forces that contribute to a business problem. Your job is to uncover that highly influential cause that we’ll call the priority cause of interest. This is the factor (or set of factors) that is the root of the problem and will subsequently be the target of your solution. Just like with the problem statement, don’t rush this step. If the priority cause of interest is poorly defined, you risk designing a solution that’s a poor fit and fails to address the business problem. And you’ll more than likely waste time on revisions and rework.

In our example, one of the primary reasons CPG businesses struggle to attract investors is a poor ESG rating—particularly with respect to environmental sustainability. CPG businesses typically have large and complex supply chain networks. At each node in the network, they’re exposed to environmental risks, and poor mitigation can significantly impact their ESG rating downstream. This includes how a business manages waste and pollution, water, land use, biodiversity and greenhouse gas (GHG) emissions. Increasingly, investors are looking at a firm’s environmental risk exposure across the entirety of the supply chain and penalizing firms that fail to demonstrate their commitment to sustainability. Businesses that effectively manage these risks improve their standing with investors.

Since we now have a handle on the business problem and the cause of interest, the last step in the problem domain section is to define the goal. The goal distills the business problem and the cause of interest into a single statement. You’ll refer to this statement repeatedly as you work through the solution, and it helps keep the problem and underlying cause front and centre as you develop your concept. In our example, the goal is to establish a system that helps CPG businesses improve supply chain sustainability and attract high-quality investment.

The solution domain

The middle section of the canvas focuses on the core of your solution. In this section, you define the solution strategy, the solution’s geospatial capabilities and the main technology components. 

In the first step, you define the overall approach to solving the problem. This is the solution strategy. This is a vital step. Before you dive into features and functions, you first need to nail down the scope of your solution and provide some rationale for each scoping decision. That means figuring out what combination of external business drivers, internal business processes, core technology and key stakeholders your solution will focus on—and why! A solid solution strategy sets the parameters that your solution operates within. 

In our example, I start the solution strategy with a single value proposition statement, focusing on the purpose of the solution and the primary beneficiaries. I describe the solution as a decision support system (DSS) for procurement specialists and product managers at CPG businesses that helps determine the best mix of upstream suppliers based on key sustainability factors. The point is to be brief and to the point and describe the core of your solution.

Following the value proposition statement, you define the scope and rationale for your solution. The intention is to further refine the value proposition statement by defining the scope of four key areas. In our example:

  • Business scope (i.e. external business drivers): Focused specifically on managing supply chain GHG emissions, as this is an environmental factor with a significant influence on ESG ratings.
  • Process scope (i.e. internal business processes): Focused on upstream suppliers (namely, suppliers of procured products and transporters of supplies), as this is one of the largest emitting segments of the supply chain.
  • Technology scope: Focused on geospatial technology, data and analysis as we target the geographic variation in emissions across the supply chain. 
  • People scope: Focused on the managers and specialists responsible for procurement and product design decisions and executives accountable for ESG performance.

With the scope of the solution set, your next job is to hash out a high-level functional design. That means defining the solution’s specific geospatial capabilities. This is where your concept goes from being a general information solution to an expressly geospatial solution.

The geospatial capabilities define what your solution does. They describe the jobs it performs in service of the solution strategy. In our example, I identified two main jobs that the solution should perform: determining the preferred upstream suppliers based on GHG emissions and a balance of other factors, and determining the preferred supply routes and transporters based on GHG emissions and a balance of other factors. You can identify as many jobs as you like; however, I recommend keeping it to a handful of the most important ones as getting too fine-grained will expand the canvas vertically and defeats the purpose of the canvas as a simplification tool.

For each job, you define the key analytical tasks. Since we’re focused on geospatial analysis in this canvas, I recommend using another tool to help you figure this out: the geospatial lens. The geospatial lens is a simple matrix of twenty-four general-purpose analysis patterns that you can apply to a broad range of business problems. It’s formed by intersecting a set of common geospatial concepts (location, scale, route, distribution, proximity, correlation) with the standard data analytics types (describe, diagnose, predict, prescribe). By combining these concepts and types, you get a powerful set of analysis patterns that are inherently geospatial in nature and can be applied to any problem.

A screenshot of the geospatial lens, a tool made up of twenty-four analysis patterns that are created when the four types of data analytics (describe, diagnose, predict, prescribe) are combined with the types of geospatial context (location, scale, route, distribution, proximity, correlation).

To use the geospatial lens, consider the job to be performed. In our first case, we want to determine a preferred set of upstream suppliers. Scan through the lens and pick out analysis patterns that fit the case. It’s often easiest to start with the “describe” cases since these are the most basic form of analysis. Once you’ve found a pattern that fits, work from left to right, applying the different analytical patterns. Don’t get hung up on minutiae. Focus on identifying the core analysis required to perform the job.

For each pattern you select, rewrite it so that the generic wording is replaced with language specific to your use case. Focus on the outcome achieved after applying the pattern. By the end of the process, you should have a set of tasks that, in combination, complete the job to be performed. In our upstream supplier case, I touch on all four analytic patterns:

  1. The solution maps out and compares supplier emissions by location to see hot and cold zones of GHG emissions.
  2. The solution determines the underlying factors that most influence emission levels.
  3. The diagnostic information from the second step is used to predict emissions levels at supplier sites where emissions levels aren’t directly disclosed.
  4. The models and data from patterns one to three are used to identify and rank preferred suppliers based on known and estimated emissions levels.

It’s not critical that you have a description for each of the four analytical patterns. Sometimes your solution only requires descriptive and diagnostic capabilities. That’s fine. Focus on the job to be performed and the needed functionality—no more, no less.

At this stage, it’s tempting to want to jump to specific technologies and technical requirements—avoid this until the next step. The point of identifying jobs and tasks is to describe the functional capabilities of your solution and abstract out the underlying technology. We want to separate the what from the how so we don’t anchor our thinking to specific products or packaged solutions. Once you’re satisfied with the geospatial capabilities identified, then it’s time to move to the next step and map out the technology components

The technology component section summarizes the specific tools and data required to implement the geospatial capabilities. It’s divided into four parts reflecting different aspects of the information lifecycle. Since the solution we’re designing is fundamentally an information-based solution, breaking it out according to the information lifecycle ensures that we identify the requisite technology from earliest raw data capture to late-stage results communications.

However, before you jump into specific technologies, you need to identify your data. Data is the heart of your solution, so you need to catalogue the layers of information that underpin your solution. You don’t need to identify every data item, but you should identify datasets that are critical to the analytical tasks identified in the previous step. In our example, I noted data related to supplier locations, emissions intensity, procurement volumes and renewables mix (to understand the portion of energy consumption associated with GHG-emitting fuels). There’s also a range of underlying transportation network data required.

Once you’ve identified the key datasets, work through each of the four technology component categories focusing on the tools needed to capture, analyze, map and communicate the data. The four technology components consist of:

  • Data capture and integration: Technologies involved with collection, acquisition, storage and validation of data. In our example, I identified a range of technologies, including ETL scripts for integrating external data sources, natural language processing tools for extracting location-based emissions data locked away in text-based reports and atmospheric satellite emissions scans.
  • Data analysis and modelling: Technologies involved with measuring, interpreting, processing and modelling scenarios based on geospatial data. In our example, I call out statistical and machine learning models for identifying variables that drive emission levels at different supplier sites and across different routes.
  • Mapping and visualization: Technologies involved with visually representing geospatial and non-geospatial information and models. In our example, I identified various thematic maps showing current and projected emissions and time-series maps showing forecasted changes in emissions over time.
  • Sharing and communication: Technologies used to connect and communicate findings and stimulate feedback from target users. In our example, I identified a set of analytical dashboards to show emissions comparisons amongst different suppliers and routes, and story maps to showcase the preferred and optimal supply journey and the positive impact on GHG emissions reduction.

As with the other sections of the canvas, you shouldn’t attempt to call out every piece of software and hardware at this stage. The point is to highlight the relevant technology components that provide the identified geospatial capabilities.

The outcome domain

At this point, we have a decent handle on the business problem and have a conceptual solution based on geospatial technology. The last thing we need to do is summarize the solution at a practical level. The final section of the canvas focuses on the outcomes delivered by the solution and the underlying assumptions that impact its implementation.

Outcomes describe the business results or practical benefits you expect the solution to deliver. From the beginning, I said that the purpose of your solution is to address a business problem. Still, you want to be clear on how—and to what degree—your solution addresses the original business problem. You need to be frank about the results to expect. You don’t want to give the impression that your solution is a silver bullet that pretends to solve all your business woes. This is especially true when addressing a challenge as large and complex as the ESG-investment funding example we’ve discussed. In that case, we can say that with reasonable certainty that our solution will help to improve ESG ratings related to GHG emissions—assuming the solution’s forecasts and recommendations are implemented. And doing so will enhance the business’s standing with investors and provide a reputational boost in the market.

Of course, the more you can quantify the expected outcomes, the stronger your case. If you have reliable data and can model the outcomes effectively, by all means, include a measurable metric for each outcome you define.

Offsetting the outcomes are the assumptions that underpin your solution. Assumptions represent factors you assume to be true. Your job here is to call out critical constraining factors—specifically ones that, if not true, would prevent your solution from succeeding. These are the barrier conditions, and it will be incumbent on you to determine if these barriers can be overcome should your solution be greenlit to the next stage. 

To identify key assumptions, ask yourself, “what would have to be true?” This is the same question recommended in the book Playing to Win by Roger Martin when developing a business strategy—and it works well at the solution level too. What factors or conditions have to be true for our solution to deliver the expected outcomes? Keep in mind you’re not focused on project management constraints like cost, resources and schedule at this point—that’s for steps beyond the solution canvas. Your focus is conceptual and strategic. What are the business, process, technology and people assumptions that could create realistic barrier conditions? In our example, I called out the availability of key emissions datasets as an assumption and potential barrier condition.

Practical guidance

The geospatial solution canvas bridges a gap between a back-of-a-napkin idea and a fully spec’d out technical specification. Clarifying the different aspects of the canvas will help you think through how geospatial technology can address a given business problem and figure out key elements of the design before diving into the details.

In this article, I discussed a problem at a whole-industry level. Most likely, you’ll want to apply the canvas to your unique business situation. To get started, identify the issues impacting your business and where you feel better spatial awareness could help. Next, work top-down. Step through the problem and identify the underlying causes. Devise an appropriate goal for your solution. After that, think through the solution strategy—what’s the scope and rationale behind your solution? From there, define your solution’s geospatial capabilities and main technology components—focus on the end-to-end analysis, data and technology required. Finally, articulate the expected outcomes and constraining assumptions that underpin your solution—what would have to be true for your solution to succeed?

If you’re methodical and diligent, I have no doubt you’ll unearth opportunities to leverage geospatial technology that you would otherwise have missed. And that’s half the battle—conceptualizing your great ideas. Once you’ve got a completed canvas, review it with those you trust. Iterate on it and make it better. If you’re struggling, oftentimes it’s because the business problem is still muddy. Go back to the beginning and adjust each section until you’re crystal clear on the problem, causes, goals, strategy, capabilities, technology, outcomes and assumptions.

Finally, present your solution to the decision makers, and get the go-ahead for a business case and a proof of concept—bringing your vision one step closer to reality.

Outpace your competition with further readings from my free e-book, Geospatial Strategy Essentials for Managers.

This post was translated to French and can be viewed here.

About the Author

Matthew Lewin is the Director of Strategic Advisory Services for Esri Canada. His efforts are focused on helping management teams optimize and transform their business through GIS and location-based strategies. As a seasoned consultant, Matthew has provided organizations in the public and private sectors with practical strategies that enable GIS as an enterprise business capability. At the intersection of business and technology is where Matthew’s interests lie, and he thrives on helping organizations bridge the gap to achieve their most challenging GIS ambitions.

Profile Photo of Matthew Lewin