Asset - "a useful or valuable thing or person"
Predictive- "relating to or having the effect of predicting an event or result."
Analytics - "the systematic computational analysis of data or statistics"
When combined, our definition of asset analytics is:
"The systematic computational analysis of data or statistics relating to an asset and it's future operation and performance."
The terms "asset analytics" and "predictive analytics" are very broad, and take many forms. An analysis has a specific objective, and it's own set of requirements and constraints. The purpose of performing an analysis is usually to answer one or more questions about the asset in its business context.
Some examples of these questions could be:
Each of the above questions can be answered by one or more types of analysis, however we believe that they should ALL be analyzed using the same working knowledge base (See knowledge management here)
Each type of analysis can be performed using any number of methods to reach the desired answers. Some approaches utilize basic statistics, machine learning, big data, Artificial Intelligence, Causal Diagrams, or any other method currently available to achieve its purpose or answer specific questions.
Asset predictive analytics is about making predictions. These predictions are data and/or assumption based, and forecast future events. These forecasts can then be used to make informed decisions around an organisations operations, and subsequent performance.
Some use cases would be:
The ability to see what is coming, and make strategic adjustments is what separates the reactive businesses of today, from the industry leaders tomorrow.
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