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Last edited 09 Oct 2020
Analytics is a form of logical analysis that can be used to interpret large quantities of data, for monitoring, assessment and prediction. It is the link between data and informed decision making and can be used as a form of predictive intelligence to help shape future plans.
 What is predictive analytics?
Thanks to integrated software programmes, machines can perform some tasks intelligently, thinking and working in a similar way to natural intelligence. This ability is called artificial intelligence (AI) or machine intelligence.
Predictive analytics is a category of data analytics linked to AI, machine learning and data mining that gathers information about past events to make predictions about the future. Based on existing data, predictive analytics identifies patterns - both positive and negative - to uncover relationships and trends that can have an impact on the success or failure of a project, process or business.
 Examples of predictive analytics
Predictive analytics can be used to identify risk and streamline construction workflows. This form of construction technology (ConTech) can use real-time and historical data as well as weather and environmental information to help construction companies use their resources more efficiently. For example, construction firms can prevent unscheduled downtime or make machinery last longer while also improving equipment dispatching and planning.
Predictive analytics can also help companies manage resources by forecasting the exact quantity of a specific material that will be needed at a certain time, improving supply chain management by determining where and when materials should be allocated.
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