Early stages of the asset lifecycle such as feasibility studies, options analysis or business case development.

Commencement of engineering and architectural design.

Construction and Handover phase for new/refurbished assets.

The operational phase of an asset through to end of life.

Data that does not change on a regular basis, typically updated through manual import/export processes.

Data that is updated on a regular basis in an automated fashion. Examples could include data from IOT devices or web-services.

You use statistical, machine-learning (ML) or Artificial Intelligence (AI) methods to capture and help you understand historical data. You have tools which predict future events, or detect anomalies.

The ability to answer "what-if?", conditional questions about your asset using AI/ML tools. You can measure asset condition and performance in hypothetical scenarios. You can explore past and future asset behaviour under different conditions.