CIO OPINION
Predictive analytics is developed using statistical collection , preparation and evaluation techniques . To this process , Machine Learning ( ML ) and Artificial Intelligence ( AI ) techniques are added to generate value from the data . While it is common to wait for assessment documents to consult , there are more and more software applications that are geared towards delivering predictive models to solve various demand response needs .
Regardless of the context in which these predictive models are applied , it must be clear what you want to forecast and the information that is required for this . Recently , events such as the pandemic have dealt a heavy blow to predictive models . Before the coronavirus , the collection of historical information was essential , after this event consumption habits changed and vary daily , changing the conception of the value of data .
The application of predictive models is also benefiting the oil industry . In this case , the companies , through sensors located in the machinery and in the terminals , collect amounts of information that allow establishing the opportune moment to carry out preventive maintenance on the equipment . Here the data analysis becomes relevant because maintenance carried out on time prevents the machine from failing , a fact that generates economic savings for companies compared to having production stopped for hours .
When determining which data is relevant or not for these models , it is important to establish those qualities focused on the success of a business .
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