Post by account_disabled on Feb 22, 2024 0:10:30 GMT -5
Predictive analysis, machine learning and forecasting
Predictive analysis, also known as statistical analysis, is the logical evolution of simple business analysis and its most basic version, the descriptive modality. It represents the shift of the strategic focus from business intelligence to business analytics, which is nothing more than the challenging transition from stability to sustained (and sustainable) dynamism. Understanding what predictive analysis consists of is knowing how to differentiate it from machine learning or forecasting , but also knowing the areas where they complement each other, providing the organization with that vision, a source of knowledge and value generation. Predictive analysis and machine learning This form of analytics, predictive, is still user driven, that is, it involves human interaction, it requires the guidance of an expert who: - Confirm hypothesis. - Determine data requirements. - Set priorities. predictive analysis One step beyond predictive analysis is machine learning , which for many is the core of where predictive analytics starts.
However, in this case the driver is not the human mind and its expertise but rather the data itself. Based on them, and only them, hypotheses are generated, the information is deepened and individual predictions are obtained. Machine learning requires much less data preparation , and also fewer assumptions . It is also completely results-oriented, something that makes it easier to monitor in a Chinese Student Phone Number List business environment. Predictive analytics vs forecasting Confusion between predictive analytics and machine learning is common, but it is also common to assume it is equivalent to a forecast, when in reality predictive analytics is much more than simple forecasting. Predictive analytics is something completely different, beyond normalized forecasting that focuses on assigning a prediction score for each customer or any other organizational element that you seek to value.
Instead, the forecast provides global aggregate estimates, such as the total number of acquisitions in the coming quarter or the profits expected to be generated over a one-year period. Using forecasting techniques, it is possible, for example, to estimate the total number of cars that will be sold in a certain region, while thanks to predictive analysis it is possible to delve deeper into this information to find out the characteristics of the individual customers most likely to buy a car. . If predictive analytics was considered a branch of machine learning, forecasting is undoubtedly a component of any predictive model. Both essential elements to inspire change with a dose of reality. Hence the power of Business Analytics, which refers to the exploration of historical data from many source systems through statistical analysis, quantitative analysis, data mining, predictive modeling and other predictive analysis techniques that, in a way or On the other hand, they make it possible to identify trends and understand information that can drive business change and sustained support for successful corporate practices.
Predictive analysis, also known as statistical analysis, is the logical evolution of simple business analysis and its most basic version, the descriptive modality. It represents the shift of the strategic focus from business intelligence to business analytics, which is nothing more than the challenging transition from stability to sustained (and sustainable) dynamism. Understanding what predictive analysis consists of is knowing how to differentiate it from machine learning or forecasting , but also knowing the areas where they complement each other, providing the organization with that vision, a source of knowledge and value generation. Predictive analysis and machine learning This form of analytics, predictive, is still user driven, that is, it involves human interaction, it requires the guidance of an expert who: - Confirm hypothesis. - Determine data requirements. - Set priorities. predictive analysis One step beyond predictive analysis is machine learning , which for many is the core of where predictive analytics starts.
However, in this case the driver is not the human mind and its expertise but rather the data itself. Based on them, and only them, hypotheses are generated, the information is deepened and individual predictions are obtained. Machine learning requires much less data preparation , and also fewer assumptions . It is also completely results-oriented, something that makes it easier to monitor in a Chinese Student Phone Number List business environment. Predictive analytics vs forecasting Confusion between predictive analytics and machine learning is common, but it is also common to assume it is equivalent to a forecast, when in reality predictive analytics is much more than simple forecasting. Predictive analytics is something completely different, beyond normalized forecasting that focuses on assigning a prediction score for each customer or any other organizational element that you seek to value.
Instead, the forecast provides global aggregate estimates, such as the total number of acquisitions in the coming quarter or the profits expected to be generated over a one-year period. Using forecasting techniques, it is possible, for example, to estimate the total number of cars that will be sold in a certain region, while thanks to predictive analysis it is possible to delve deeper into this information to find out the characteristics of the individual customers most likely to buy a car. . If predictive analytics was considered a branch of machine learning, forecasting is undoubtedly a component of any predictive model. Both essential elements to inspire change with a dose of reality. Hence the power of Business Analytics, which refers to the exploration of historical data from many source systems through statistical analysis, quantitative analysis, data mining, predictive modeling and other predictive analysis techniques that, in a way or On the other hand, they make it possible to identify trends and understand information that can drive business change and sustained support for successful corporate practices.