How do you start your own DTO

Starting your own Digital Twin of an Organisation (DTO) is not a simple, off-the-shelf purchase, but rather an ongoing, evolutionary process . It requires a strategic approach, foundational investments, and a cultural shift towards a mentality based on continuous improvement and fact based decision-making.

The DTO belongs to the Business and should be available across the business.

1. Understand the Core Principles of DTO Construction

To successfully build a DTO, it’s essential to follow five key principles that underpin its construction and drive its emergence and growth:

Principle 1: Start with what you have.

Since there’s no pre-existing template for a unique organisation, the initial digital representation of your business is built from your existing business information.

This starts by capturing information on all your key elements; people, processes, systems and data.

This involves a comprehensive survey of all digital technologies in use, including assets, processes, and interactions within your organisation. Pay attention to data and analytical models embedded in existing software, such as Enterprise Resource Planning (ERP) systems.

While industry data templates (e.g., IBM’s “Industry Data Models”) can provide common elements and best practices, they require extensive consulting expertise to adjust for your specific organisation’s idiosyncratic assets, processes, and interactions.

Gather external data from demand-side (market, consumer, digital marketing systems) and supply-side (supply chain management software, third-party complementors) sources, even if not under your direct control.

These sources should be interpreted through three models: the information model (what data artefacts exist), the context model (expected behaviour and states of these artefacts), and the impact model (interlinkages and correlations between artefacts).

It’s crucial to resist the desire to create a complete representation from the outset, as the DTO will emerge gradually through the interaction of all principles.

The first step is to conduct a full audit of existing sources, connections, data, and analytical models to create an initial entity model.

Principle 2: Set the data free.

Most organisations already possess sources, data collection, and storage capabilities, but much of this is often trapped in information silos.

To build a DTO, this information must be freed from these silos so it can be integrated into the digital twin.

Focus on “dense” data that is unique, inalienable from its originator (generating ongoing implications), and fresh (current and relevant).

While making information accessible is vital, it requires careful consideration of legal and technical protections against unauthorised uses, especially for sensitive data subject to regulations like GDPR. A DTO should not need to carry sensitive information outside the organisation as it mirrors generic Processes and Systems. RACI relationships are an important part of a DTO

Principle 3: Increase the scope.

A DTO’s accuracy depends on actively seeking opportunities to digitise sources and processes that are identified, but are not or only partially documented.

This continuous push for digitisation creates new digital artefacts (software, observations, content).

Ensure that the protocols and formats of newly created digital artefacts are compatible and integrated into your DTO’s information, context, and impact models. This may require harmonisation and potentially creating internal standards.

This principle involves ongoing monitoring of processes and assets within the organisation even where digitisation is not yet pervasive.

Principle 4: Seek new opportunities.

As more artefacts, processes and interactions are identified, documented and digitised within the organization and represented within the digital twin, then new opportunities become increasingly feasible.

Underpinning these new digital opportunities is the notion of generativity, defined as a digital technology’s capacity to enable change

Principle 5: Increment the models.

The DTO is not a static entity; it’s a living digital simulation model that continuously updates and changes as the organisation evolves.

The information, context, and impact models that comprise the DTO must be constantly updated and reconfigured to accurately model new relationships, dependencies, processes, and interactions.

The impact model, in particular, should be designed to integrate historical data and continuously learn and update itself from connected data sources and emerging relationships. This learning can come from continuously updating operational data, human experts, other digital twins, and the wider environment.
This principle demands active extension of the digital twin, beyond simple maintenance, to respond to new opportunities, insights, or external changes.

(Note: Principle 4, “Seek new digital opportunities,” is crucial for leveraging value from the DTO once it’s established, rather than a primary starting point principle. It describes how increasing digitisation and the DTO itself reveal new business opportunities.)

2. Establish Organisational Readiness and Leadership

Successful DTO implementation is a significant change management effort that requires strong leadership support.

Assess Organisational Readiness: Understand your current digital maturity, data infrastructure, and the talent needed to build and maintain the DTO.
Build a Dedicated Leadership Team: It is crucial to establish a leadership team specifically for designing an enterprise digital twin strategy. Bringing in experienced experts, possibly led by a Chief Digital Officer, can be pivotal in educating leadership on the nuances and potential of this technology.
Cultivate a Data-Driven Culture: A cultural shift towards data-based decision-making is necessary, fostering greater reliance on advanced analytical tools. Stakeholders, who may be accustomed to simpler analyses, need to be brought along the development journey and engaged from the onset to understand the power of these tools.
Define Clear Goals and Use Cases: Building a successful DTO starts with clear goals. While a DTO can support all use cases, identifying specific, high-value initial use cases will help demonstrate a rapid return on investment (ROI), which can often be measured in months, not years.

3. Address Skill Requirements and Challenges

Building a DTO is not just a technology acquisition; it’s a process that requires a mix of people, machines, and organisational processes.

Acquire Essential Skills: Organisations need foundational investments in skills. People issues are often the main obstacle (93% of organisations cite this). Essential roles include:
Data Engineer: Understands data capture (sensors, processes) and where data exists internally and externally.
Data Scientist: Identifies new, cross-cutting digital innovations with a blend of organisational and technical skills.
Source System Engineer (Digital Modeler): Understands and integrates digital artefacts (data, analytical models, software) into the DTO’s information, context, and impact models.
Software Engineer (Information Architect): Creates robust data and analytical models and systems for the DTO.
Digital Police (Business Expert): Guides the team on legal, ethical, and privacy aspects of data and the DTO, mitigating confidentiality risks.
Anticipate and Mitigate Challenges:
Technical Debt: Be aware of the accrual of “technical debt” during development, which can increase the cost of future improvements and potentially reduce agility.
Ethical and Policy Issues: Address potential social, ethical, and policy issues arising from data biases and ensure alignment with emerging human rights frameworks and ethical practices.
Technical Risks: Ensure the resilience and stability of your digital infrastructure, as the DTO will become an operational core. The cost of technical failures can be substantial.
Data Quality and Governance: These are significant challenges that must be planned for. The DTO enforces consistent definitions and quality control, acting as a master reference for business terms and processes.
Cost, Complexity, and Scalability: These are inherent concerns that need to be navigated with a phased approach.

4. Implement and Evolve Incrementally

Given the complexity, a phased approach to DTO adoption is recommended.

Start with Immediate Opportunities: Leverage existing data for intelligent analytics and dashboards to gain quick insights and demonstrate value.
Iterate and Expand: Continuously integrate more data, increase the sophistication of AI applications, and expand the DTO to cover more aspects of the organisation.
Partner Strategically: Consider looking beyond your organisation’s boundaries to build necessary technical and talent infrastructure, potentially partnering with universities or cloud providers to enhance computational capabilities and secure talent.

By following these steps, an organisation can systematically capture value from its data and progressively build a robust Digital Twin of an Organisation, leading to improved decision-making, enhanced agility, and sustained competitive advantage.

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