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The Geospatial Edge: Issue 7, Fall 2023

The Geospatial Edge is Esri Canada’s periodic newsletter for managers and professionals tasked with growing their organizations’ geospatial capabilities. In this issue, Matt Lewin discusses the question of whether, in a time where specific transformational technologies like generative AI and digital twins have high visibility, the general concept of digital transformation should still be a priority for organizations.

The idea for this issue of The Geospatial Edge started with a question I received during a Q&A panel at the Esri User Conference. An audience member asked about digital transformation and why talk of this once-hot business buzzword seems to have faded—supplanted by terms like “digital twin” and “generative AI”. Is digital transformation still a thing? Is it still a priority for organizations? If so, how do digital twins and gen-AI fit in? What about GIS? It was an interesting discussion, but we only scratched the surface during the panel. Thus, a new Edge was born!

Yes, digital transformation is still a thing; it always will be.

If digital transformation talk has, in fact, faded, then I think it's due to lingering confusion about the term itself. Many treat and discuss digital transformation as a single tech-driven initiative—an IT undertaking primarily focused on replacing or upgrading an organization's core technology stack. This is off in my books.

Digital transformation is about business transformation. I think of it as business transformation through digital means. That means that as technology evolves, the opportunity to transform digitally persists. The problem with thinking that digital transformation is a one-time event or even a multi-year initiative is that it implies that a digital transformation can somehow be complete, as though it’s a project with a start and end date.

In fact, digital transformation is a process—an ongoing, continuous evolution in how an organization leverages digital technology to run its business, interact with customers and deliver its products and services. And as long technology keeps advancing, the minute you finish one transformation effort, you’re presented with a new world of digitally driven opportunities.

To quote Jeff Bezos, it’s always day one for digital transformation.

Sadly, success with digital transformation has been modest.

While digital transformation is never dead, results may vary, and unfortunately, transformation successes have been few and far between.

In a recent study, McKinsey took a fresh look at digital transformation in the banking sector to determine who's succeeding in their efforts and who isn’t. The results were disappointing. While 89% of organizations surveyed indicated they actively pursue digital transformation, only 31% indicated that they've realized a significant return on their investment—not exactly a resounding success.

Much of this shortfall gets back to the problem of perception. Many of the organizations surveyed focused their digital transformation efforts on keeping up with technology rather than leaping ahead in their business. And lagging organizations, in particular, were focused on implementing “table-stakes” digital tools—tools such as a customer-facing mobile banking app. Ten years ago, this might have been transformational, but not so anymore, especially from a competitive standpoint.

As the study noted, “As soon as one bank introduces a mobile feature, others see it and follow suit relatively quickly.” Organizations that play a perpetual game of catch-up never truly transform anything about their business relative to the rest of the industry. What was once innovative quickly becomes the cost of doing business.

So, what do those leading the way with digital transformation do differently? In short, they focus on creating value that’s hard to copy.

According to the study, organizations enjoying the greatest benefits from their digital transformation efforts go beyond table-stakes apps and focus on transforming core functions of their business. In particular, they focus on re-engineering or optimizing complex processes and workflows. This involves integrating dozens of use cases, multiple stakeholders, volumes of data and numerous external systems.

In the banking sector, leaders go far beyond customer-facing mobile apps and transform the entire digital sales process. They leverage foundational digital technologies (such as cloud, mobile, analytics and social) to optimize the entire customer journey, including developing personalization analytics to improve marketing campaign uptakes, providing omnichannel customer contact center access, enabling real-time financial product approval and offering extensive customer self-service tools to support day-to-day banking.

Digital leaders don’t just change technology; they fundamentally change how business is done.

Technology that enables and optimizes complex integration will drive the next wave of digital transformation.

What stands out from the trends in digital transformation identified by McKinsey is how central the concepts of integration and connectivity have become. Re-engineering complex business processes is essentially an exercise in integration, where you undergo a deep re-wiring of systems, workflows and behaviors. Modern digital transformation rests heavily on achieving broader and tighter integration inside and outside an organization, and technologies that enable and accelerate this deep connectivity will compete for mindshare. That’s why conversations around digital twins, generative AI and geospatial technology have reached a fever pitch.

Digital twins are virtual replicas of physical systems and are, in essence, the ultimate integrative concept. Whether it’s a digital twin of a manufacturing production line, a transportation network or a river basin, implementing a digital twin involves integrating numerous datasets, systems and processes to model a real-life functioning system. That includes how humans interact with and maintain these systems. Often in real-time and in 3D, depending on the degree of sophistication. Below, I’ve summarized a general digital twin maturity model.

Digital Twins and Geospatial Technology

Geospatial technology is a foundational component of digital twins and plays a vital role at each level of maturity.

Maturity level*

Level 0: Static Twin

Level 1: Design Twin

Level 2: Connected Twin

Defining characteristics

A compiled dataset comprised primarily of existing landscape features and physical asset geometries

Augments a static twin (level 0) with design capabilities enabling a user to create and add new asset elements to an existing static twin

Augments a design twin (level 1) with embedded metadata or linked datasets stored in external systems

Major benefits

Provides a visual snapshot of a landscape or physical system at a given point in time

Enables a user to modify the digital snapshot of the physical system and generate designs that are ready for real-world implementation

Supports scenario planning, enabling a user to analyze how changes to the physical system impact the attributes of related phenomena (and vice versa)

Role of geospatial technology

Reality capture and map production

Acquire, process and integrate imagery and spatial features into the compiled data model.

Create 2D/3D maps from the static data.

Model creation and design

Design new spatial features that respect the topological rules of the digital twin and incorporate into the static data model

Spatial analysis and static data integration

Connect metadata or external attribute data to associated spatial features and conduct cross-factor impact analysis (a change in x, impacts y)

Key technologies

Remote sensing

Satellite/UAV imagery

Mobile data collection

Map production tools

2D/3D map authoring tools



Spatial ETL, virtualization

Spatial analytics

Geo data exchanges

Maturity level*

Level 3: Real-time Twin

Level 4: Two-way Twin

Level 5: Autonomous Twin

Defining characteristics

Augments a connected twin (level 2) with real-time asset data updates delivered by sensors, connected devices and the IoT

Augments a real-time twin (level 3) by enabling updates to the state or condition of the physical asset from the digital twin

Augments a two-way twin (level 4) with machine intelligence enabling the twin to sense and adapt to changing conditions and take corrective action

Major benefits

Enables a user to monitor the changing state and behavior of the physical system as it happens and take timely and accurate corrective action

Allows a user to manipulate the state of the physical system without the cost or difficulty associated with interacting with physical environment directly

Reduces required human intervention and increases the predictive and prescriptive power of the digital twin

Role of geospatial technology

Real-time data integration (one-way)

Ingest spatial data into the twin in real-time from external sources and process massive volumes into spatial features and associated attributes

Real-time data integration (two-way)

Update components of the physical system at accurate locations/areas according to instructions sent from the twin

Geospatial AI

Predict changes in spatial phenomena, evaluate the impact on the physical system and generate a corrective response in the twin

Key technologies

Real-time data integration

Real-time dashboards

Big data location analytics

Image classification


Intelligent routing

Computer vision

A twin with a high degree of integration and richness provides the basis for complex digital transformation. Managers can confidently model scenarios across complex workflows and identify process improvements. Essentially, digital twins are a vehicle for modern digital transformation.

Underpinning digital twins are two critical technologies: generative AI and geospatial technology. These are central to digital twin systems as they provide the means to generate, simulate, and adapt digital representations that closely mirror real-world systems. Gen-AI delivers the predictive and prescriptive powers; geospatial technology provides the spatial context. Put them together, and you get an integrated, intelligent digital twin that can tell you where, when and why to take action or make changes. Below is a summary of how I see these technologies relating.

Generative AI and Geospatial Technology

Generative AI has the potential to accelerate outcomes at every stage of the geospatial data life cycle, shifting human involvement toward data-driven interpretation, decision making and innovation

Geospatial Data Life Cycle Stages

Acquisition & Collection

Validation & Management

Discovery & Access

Desired outcomes

Acquire best-fit geospatial data from relevant and trusted sources

Ensure the quality, structure and integrity of geospatial data

Enable timely and relevant access to curated geospatial content and maps

Current technologies (not exhaustive)


UAV/Drone tech

In-situ sensors, AVL

Survey stations

Social media

Field data collection

Spatial ETL

Spatial virtualization

Native geodatabases

QA/Data reviewers

Geo data exchanges

Data marketplaces

Spatial search

Geo-enrichment services

Generative AI-driven enhancements

Enhance data resolution, coverage area and attribute richness at source

Gen-AI advancements:

Data augmentation

Image super-resolution

Semantic segmentation

Data fusion

Enhance quality assessment, error detection & integration of diverse datasets

Gen-AI advancements:

Auto QA assessment

Anomaly detection

Attribute gap filling

Enhance metadata generation and personalize data curation and data search recommendations

Gen-AI advancements:

Auto metadata gen

Intelligent search

Auto curation

Impact on human operators

Shift from data compilation work toward data fit assessment and value-add product dev

Shift from manual data QA and integration work to data exploration and modelling work

Shift from manual content wrangling to creative curation tailored individual or community preferences

Geospatial Data Life Cycle Stages

Analysis & Modelling

Mapping & Visualization

Sharing & Collaboration

Desired outcomes

Understand spatial phenomena, interpret spatial relationships, predict patterns, and model geo-scenarios

Visually represent spatial and temporal relationships in 2D/3D/4D

Communicate and share geospatial content and stimulate feedback from users and stakeholders

Current technologies (not exhaustive)



Intelligent routing

Temporal analysis

2D/3D spatial analysis

Map prod software Integrated Dashboards Plotters

3D Printers


Federated geo-hubs

Open data sites

Story maps

Generative AI-driven enhancements

Enhance pattern recognition, feature extraction & simulation through AI determined variable selection

Gen-AI enhancements:

Complex feature detect

Synthetic data sim.

Uncertainty quantification

Enhance map production speed and consistency, and provide contextual or personalized visuals

Gen-AI advancements:

High-quality map gen

Map style transfer

Contextualized visuals

Real-time visuals

Enhance data privacy and accessibility, accelerate sharing through standardization and automate feedback

Gen-AI advancements:

Data anonymization

Collab. data fusion

Chat assistance

Impact on human operators

Shift from focus on the process of analysis to novel interpretations of the products of analysis

Shift from map production work to novel, creative visual representation work

Shift from content production to novel narrative forms involving geospatial data

How do I see the digital twin-driven transformation era unfolding? It is hard to say overall, but here are a couple of examples from city government organizations that are already under way:

Infrastructure development and investment

  • Uses generative AI to assess economic indicators, demographic trends and urban growth projections to recommend strategic infrastructure investments
  • Uses geospatial technology to map existing infrastructure and growth areas 
  • Integrates both technologies to create a planning twin to guide city officials in making informed decisions about where and when to invest in new infrastructure projects

Citizen engagement and services

  • Uses generative AI to analyze citizen feedback, service requests and sentiment analysis data to identify trends and improve service delivery
  • Uses geospatial technology to provide location-based insights into service demand
  • Combines these technologies to enhance citizen engagement, tailor services to specific needs and optimize resource allocation for improved public satisfaction

The bottom line

Digital transformation is not dead; it's thriving, evolving and adapting to the changing times. The integration of digital twins, generative AI and geospatial technology (and other innovations) into business operations is a testament to its continued relevance. Organizations that embrace these technologies are better equipped to innovate, stay competitive and meet the demands of an increasingly digital world.

So, let’s not bury digital transformation prematurely. Instead, let’s recognize its enduring importance and harness modern digital advances to shape a more efficient, creative and sustainable future for businesses and societies alike.

Let’s talk

I’d love to know about how you’re approaching digital transformation and the next wave of digital innovation. If you have an interesting story, send me an email or connect with me on LinkedIn. I’d like to hear about your experiences!

All the best,


The Geospatial Edge is a periodic newsletter about geospatial strategy and location intelligence by Esri Canada’s director of management consulting, Matt Lewin. This blog post is a copy of the issue that was sent to subscribers in October 2023. If you want to receive The Geospatial Edge right to your inbox along with related messages from Esri Canada, visit our Communication Preference Centre and select “GIS Strategy” as an area of interest.

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.

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