Digital twin success lies in organizational capability

Like a living organism, a digital twin is an ever-evolving, dynamic system that needs to be nurtured and maintained to ensure its health and longevity. Once your digital twin is up and running, how do you ensure its long-term sustainability? In this post, Esri Canada management consultant Alexandre Guy guides you through governance and process considerations for keeping your digital twin in top shape.

The key takeaway

• To enable organizations to accurately simulate future conditions, digital twins need to be continuously supplied with the latest data to remain relevant as reality evolves. But this requires more than technology alone: an accurate digital twin depends on effective governance, a supportive geospatial culture, fit-for-purpose resources and skills, continuous technology integration, reliable, high-quality data, and well-defined processes.

Introduction

Digital twins have been gaining interest in recent years, but what does it take to truly bring them to life? Moreover, once they’re in place, how can we ensure their long-term sustainability? After all, just like a living organism, a digital twin is a dynamic, ever-evolving system that must be fed and maintained to ensure its health and longevity.

In a recent interview with xyHt, Jack Dangermond, president and co-founder of Esri, explained that digital twins are becoming “the living synthesis of GIS layers.” He went on to explain that “digital twins should not be treated as static deliverables” and, as such, that they must be maintained and continuously updated—particularly by ingesting sensor data and adapting to changes in the real world.

Digital twins enable organizations to simulate future scenarios with greater accuracy and make smarter decisions. By strengthening their predictive and prescriptive analytics capabilities, organizations can generate deeper insights that drive strategic action.

Here are some Canadian examples:

• The City of Kelowna, B.C. uses a comprehensive GIS-based digital twin for wildlife risk management and to support its rapid growth, enabling it to simulate its infrastructure needs up to 2041.

• The City of Montréal implemented a digital twin as part of a smart city initiative to improve urban mobility and access to food.

• The City of Ottawa built a 3D digital twin to support its new Official Plan and zoning by-laws.

Realizing these benefits, however, requires more than technology alone. It depends on a strong, well-managed geospatial program anchored in the seven fundamental principles of a geospatial strategy: a clear vision and leadership, effective governance, a geospatial culture, fit-for-purpose skills and capacity, continuous technology integration, reliable and high-quality data, and clearly defined processes.

In our experience working with organizations of all shapes and sizes, digital twins succeed when these core elements are consistently implemented and operationalized across the organization. When one or more of these building blocks are weak or misaligned, digital twin initiatives risk becoming fragmented, outdated or underutilized, and ultimately losing credibility and stakeholder support.

Conversely, when a geospatial strategy is mature and balanced across these dimensions, organizations can scale their digital twins with confidence and deliver sustained value over time.

Strong governance practices

Well-established, robust governance practices are essential to any geographic information system (GIS) program. But these practices become an absolute necessity when it comes to sustaining a digital twin. Moreover, they add other layers of complexity, such as the following:

Temporal validity
To be effective, digital twins need to be a near-real-time representation of the world they describe, which requires the continuous integration of datasets. They need to remain fresh to maintain accuracy.

From a governance standpoint, this means not only that new data integrations have to be managed, but also that the organization needs to define acceptable latency thresholds. These are temporal rules that define when data is considered expired and how this affects the digital twin’s ability to produce accurate insights.

Staying on course
Digital twins can be used for multiple reasons. They can reflect the status quo, a future state, what-if scenarios and historical events. All of these different use cases require an understanding of the underlying data that constitutes the digital twin.

As a result, the governance body overseeing the program needs to define specific data management processes that support specific decision-making to ensure that the quality insights can be extracted from the twin.

Building trust
Like generative AI, what makes digital twins work is the reliability of the data. Without proper data behind it, the digital twin’s reasoning will be flawed. Therefore, the data’s provenance, its curation process and its validation methods are all essential for building an accurate representation of the world upon which to base informed decisions.

Governance programs need to guarantee that the users called upon to make these decisions—who are most likely not GIS specialists—can trust the insights presented by the digital twin. This is not an easy feat to achieve.

An organizational enabler
The outcome of strong governance practices is that they build organizational credibility: credibility in the systems and in the processes involved, but also confidence in the people who are called upon to make decisions when necessary. When sound governance practices are in place, decision-making can occur quickly, consistently and based on shared principles.

In sum, strong governance reduces ambiguity and organizational friction and ensures trust in the validity of the end product.

A continuous data delivery and ingestion process

The importance of clean, well-managed data can't be overstated, as it paves the way for predictive analytics, business intelligence insights and artificial intelligent analysis.

To reflect reality, digital twins rely on the continuous integration and ingestion of new datasets as they become available. Consequently, digital twins should not be viewed as timeboxed projects but rather as an ongoing real-world operational model.

Organizations need formalized processes for developing standards, frameworks and methodologies that enable the continuous deployment of the digital twin. Such processes require assessments and audits of delivery mechanisms to offer quality and performance measurements at various stages. This is what allows for consistent and repeatable outcomes. It gives decision-makers the confidence they need that the digital twin is operating from a safe, secure and coherent environment.

IT footprint
IT plays a major role in preparing the organization for the implementation of a digital twin, whether by helping the organization adopt data standards such as ISO 19650 for managing information throughout the lifecycle of a physical asset, or by implementing other data management practices, such as COBIT, or IT service management (ITSM) practices, such as ITIL.

These standards and practices need to be firmly established and run smoothly to ensure that data is treated as an enterprise asset that carries risks and requires quality and compliance audits.

Organizations that have implemented standards and practices like these report greater efficiency in searching for data and fixing errors, as well as overall higher data quality and integrity. These translate to more time spent analyzing data and acting on the insights gained because the digital twin is accurate, consistent and accessible.

Automation and QA
Considering the amount of data involved, automation is a no-brainer for maintaining data quality. Quality assurance (QA) practices must be planned from the beginning and integrated into your organization’s various workflows.

A key aspect of QA in this context is to define specific thresholds where the digital twin is considered valid from a data consistency standpoint. These considerations must be clearly communicated to the user so that they can properly understand the quality aspects of the insights generated and the reliability of the represented data.

Having good data management practices enables the digital twin to run various scenarios to identify risks, trends and the tradeoffs necessary for optimal solutions. It also allows for greater project visibility, enhanced communication and seamless collaboration among stakeholders. These practices need to be maintained across the entire lifecycle: from acquisition through governance, integration and storage.

Fit-for-purpose resource capacity and skills

Digital twins require staff who are dedicated, trained and have access to specialists. To do this, organizations need to build cross-functional teams with capabilities in in data science, GIS and systems integration, as well as subject matter experts in a wide range of fields specific to the organization.

Establishing a geospatial skills matrix, combined with a training program designed to upskill personnel where needed, is an important step in ensuring the agility and responsiveness of the digital twin program. This will enable your organization to quickly adapt to changing priorities, pursue new initiatives and respond appropriately to emerging risks. While not all organizations can maintain in-house expertise in specialized fields such as real-time event processing, IoT integration engineering or spatiotemporal data management, having access to vendor expertise in these areas remains a key aspect of successful programs.

A key benefit of optimizing your workforce is that it allows you to deploy your resources wisely rather than pushing them beyond their limits. It paves the way for sustainable growth as your organization takes on more challenging projects or expands into new capabilities.

A long-term endeavor that’s well worth the effort

Embarking on the digital twin bandwagon is an exciting journey filled with many possibilities for any organization. However, to truly benefit from such an initiative requires a strategy with a strong foundation in governance, delivery processes, data management practices and upskilled personnel. This is a long-term endeavor that requires constant investment and dedication to raise the organizational strength of the GIS program, which has benefits that extend far beyond the initial implementation of a digital twin.

Esri Canada’s Management Consulting practice can help you assess risks and bridge the gaps between your current state and your strategy so that you can advance your digital twin initiative with confidence. If you have any questions, feel free to reach out.

This post was written in French by Alexandre Guy and can be viewed here.