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What a water utility digital twin is
A water utility digital twin is a virtual representation of physical assets and processes, connected to data, models and analytics. In Autodesk’s description, hydraulic and hydrologic models can simulate network behavior as conditions change, while analytics support planning, prediction and monitoring. Those are vendor-described capabilities, not independently measured results.
A twin can represent elements such as treatment facilities, reservoirs, pumps, pipes and collection networks. Its usefulness depends on the quality, timeliness and interoperability of the data and models feeding it. A static 3D model or an isolated dashboard is not automatically a digital twin.
How digital twins can improve management
Planning and investment
Simulation can help teams examine proposed projects, demand changes or operating strategies before committing field resources. A utility can compare modeled scenarios and connect them to target performance indicators, provided the underlying network model and data are fit for that purpose.
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Prediction and monitoring
Connected analytics may make changing system conditions easier to observe and investigate. Potential applications include monitoring operational performance, identifying unusual conditions and testing how a network could respond to different inputs. The announcement and vendor material do not establish a specific percentage improvement, reliability gain or cost reduction.
Coordinating decisions across teams
Bringing asset information, models and operational data into a shared environment can support conversations among engineering, operations, maintenance and leadership. The benefit is organizational as much as technical: teams still need agreed definitions, processes and authority to act on the information.
Info-Tech’s three-step roadmap
Info-Tech’s Build a Water Utility Digital Twin Roadmap blueprint is a planning resource. Its announcement summarizes the following sequence; the complete blueprint was not reviewed for this article.
- Identify desired outcomes. Establish baseline and target organizational key performance indicators (KPIs), then shortlist use cases tied to strategic goals.
- Prioritize use cases. Assess candidate initiatives across people, process and technology, and rank them by potential impact and feasibility.
- Create a tactical roadmap. Identify capability gaps and plan iterative actions to close them.
The order matters. Starting with a platform or a detailed model before defining the decision it must improve can produce an expensive demonstration with no operational owner.
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Use a scored decision sheet for each candidate. The available sources do not prescribe numerical weights, so utilities should set them with stakeholders.
| Question | What to examine |
|---|---|
| What outcome will change? | Baseline KPI, target state, decision owner and time horizon. |
| Is the data ready? | Sensor coverage, data quality, update frequency, historical depth and access rights. |
| Are the models credible? | Network representation, calibration practice, assumptions and validation responsibilities. |
| Can people use the result? | Skills, operating procedures, training, change sponsorship and response authority. |
| Will it integrate? | Compatibility with telemetry, GIS, asset management, work management and control systems. |
| Is the lifecycle affordable? | Implementation, cloud or infrastructure, licensing, integration, support, refresh and model-maintenance costs. |
A small, decision-critical use case with an accountable owner is generally a more defensible starting point than attempting to twin an entire utility at once. The selected KPI should be measurable before deployment and reviewed after each iteration.
Readiness factors that change the roadmap
Info-Tech says an organization’s starting point can depend on leadership sponsorship, technology maturity and cultural readiness. It also notes that utilities have different business drivers, so there is no universal sequence beyond adapting the roadmap to local circumstances.
Leadership sponsorship
Executives need to supply a strategic objective, funding authority and cross-department access. Without sponsorship, a pilot may not gain the data access or process changes required for production use.
Technology maturity
Review existing telemetry, data platforms, network models, GIS, asset registers and integration interfaces. A twin project may first require data governance, connectivity or model calibration work.
Cultural readiness
Operators and engineers must trust the information and understand when it informs—or does not replace—professional judgment. Define training, escalation and accountability before changing procedures.
Software examples and procurement cautions
Autodesk lists Info360 Insight for cloud-based operational performance analytics, modeling and alerting, and InfoWorks ICM for hydraulic and hydrologic network modeling. These products illustrate specialist software categories; their inclusion is not a recommendation or a claim that either is suitable for every utility.
Before selecting a product, verify current capabilities, supported data formats, APIs, deployment model, security and residency requirements, model-management workflow, integration effort, licensing structure and total lifecycle cost. Product details can change, so obtain current vendor documentation and a utility-specific evaluation.
What the evidence does—and does not—show
Jing Wu, principal research director at Info-Tech Research Group, describes digital twin development as “far from a binary concept” and as “a journey of continuous learning and development across multiple capabilities within the digital twin domain.” That framing supports an incremental program rather than a one-time implementation.
The reviewed Info-Tech announcement and Autodesk overview provide no named quantitative outcome showing how much digital twins improve water utility management. They establish a planning approach and describe potential uses; they do not prove a guaranteed operational, safety or financial result. Utilities should therefore publish their own baseline, target and evaluation method for each project.
A practical implementation sequence
- Define the decision. Name the operational decision the twin will support and the KPI that will indicate improvement.
- Document the baseline. Record current performance, data sources, model assumptions, process owners and known gaps.
- Test feasibility. Check data access, model calibration, integration, security and staff capacity before buying a broad platform.
- Run a bounded iteration. Deliver one use case with an accountable owner, review results against the baseline and capture lessons.
- Close capability gaps. Improve data quality, processes, skills or technology identified during the iteration.
- Scale selectively. Add use cases only when their expected impact and feasibility justify the additional lifecycle cost and complexity.
Frequently Asked Questions
Is a digital twin the same as a hydraulic model?
No. A hydraulic model can be one component of a digital twin. A twin also connects relevant physical-asset representations with operational data, analytics and processes; the exact scope depends on the utility’s use case.
Does Info-Tech report a measured improvement from digital twins?
No. Its May 29, 2024 announcement presents a roadmap and potential benefits but does not report a named percentage or other quantitative outcome from a specific implementation.
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Should a utility buy software before defining its use case?
The roadmap points in the opposite direction: define outcomes and shortlist use cases first, assess people, process and technology, then plan the capabilities and tools needed.
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