Analytics & Digital Twins
Turning FM's growing data volume into decision-usable insight, from simple dashboards through to live digital-twin models.
A digital twin without live operational data feeding it is a 3D model with a misleading name.
Why this service exists
As FM's data volume grows (sensors, CAFM/IWMS, BMS telemetry), the gap between having data and using it productively widens unless a dedicated capability actively closes it.
What the service covers
- Dashboard and reporting design.
- Predictive/diagnostic analytics development.
- Digital twin development and maintenance (live-data-fed models, not static 3D visualisation).
Assets and objects
- Dashboards and reporting sets.
- Predictive/diagnostic models.
- Maintained digital twin models.
Who takes part
- Analytics specialist/data scientistRequired
Mandatory for anything beyond basic reporting.
- FM information managementRequired
The data-quality foundation.
- Decision-owning stakeholdersSituational
Across every consuming capability.
What the service needs
- Governed data from FM Information Management.
- BIM models where digital twins are in scope.
- Sensor/IoT feeds.
How delivery runs
- 01Design analytics/dashboards against specific decisions they're meant to support, not as general-purpose visualisation.
- 02Develop digital twins where live operational data genuinely justifies the investment beyond a static BIM model.
- 03Attach every recurring metric to an explicit decision-owner and action threshold before deployment.
- 04Maintain and refine models against actual outcomes over time.
What is delivered
- Dashboards.
- Predictive/diagnostic analytics outputs.
- Maintained digital twin models.
From output to outcome
Dashboards, models and digital twins.
Insight that genuinely changes decisions — explicitly not just visibility.
This capability's success should be measured by decisions influenced, not dashboards shipped.
Where it gets tense
- Analytics investment measured by dashboards built or data volume processed rather than decisions demonstrably changed.
Strategic, tactical, operational
Analytics/digital-twin investment should be justified against specific decision use-cases, not pursued as a generic technology upgrade.
Every dashboard or model needs an assigned decision-owner defined before launch, not retrofitted after low engagement is noticed.
Model/dashboard maintenance (keeping them current and accurate) is an ongoing cost too often unbudgeted at initial deployment.
Performance indicators
- Decisions demonstrably influenced
Not usage/login metrics alone.
Risks
- Sophisticated analytics producing the appearance of management rigour without any of its substance.
Statutory context
No statutory obligation.
Sourcing options
- In-house
Platform/technology often vendor-sourced; analytics design and decision-integration work is best kept as an internal capability closely connected to the decision-owners it serves.
- Single service
Specialist analytics/digital-twin services are sometimes bought in externally.
Technology and data
- BI/analytics platforms.
- Digital twin platforms.
- Machine learning tools for predictive applications.
Competencies required
- Data science.
- Analytics design.
- Decision-facilitation skill.
Common mistakes
- Building dashboards and models without a defined decision-owner and action threshold attached before launch.