Introduction
Business Intelligence (BI), a vital tool for building competitive advantage, aids organizations in learning and the creation of knowledge. Examples of predicative modeling employed by BI include regression and neural networks. As with any process, BI has negatives as well as positives. Some positives of BI include enhanced efficiency, a more focused customer value proposition, and the creation of value from an organization’s data, which otherwise would be a cost center. Examples of negatives include cost, complexity, and the possible abuse of data. Data science increases effectiveness enhancing productivity which drives the future competiveness of American business.
The Importance of Business Intelligence
BI is important because …show more content…
The first step in creating a predictive model based on regression is to select independent variables that have demonstrated an explanatory relationship to the dependent variable (Waljee, Higgins, and Singal, 2014). The quality of this predictive model is based on the quality of the selection of independent variables.
Neural networks emulate biological networks of neurons. These predicative models learn on their own through training with sample input and output datasets and although neural networks build accurate predicative models, the rules for creating these predictions are not visible to the end users, in other this knowledge is hiding within the neural network (Loshin, 2012). Accordingly, neural networks make powerful predictive tools, but do not aid in the creation of knowledge as much as other predictive models might.
Regression and neural networks are common types of predictive modeling. Regression employs statistical methods to predict a dependent variable based on its past relationship to one or more independent variables. Neural networks build accurate predicative models, but do not add much to the knowledge discover …show more content…
Adding to this complexity are numerous ethical issues that also require consideration.
How Will Data Science Transform American Business
Data science will transform American business by increasing effectiveness, which will increase productivity, and in turn increase competitiveness. BI aids organizations in building competitive advantage, but this is a constant process as competitive advantage is constantly shifting. Data science amplifies the organizational learning process enabling the creation of economies of knowledge and learning.
America is a highly advanced economy and much of the country’s competitive advantage is based on high productivity and innovation. Migliore and Chinta (2017, p. 49) argued that “data is everywhere” and this data represents a valuable resource in creating competitive advantage. Data science aids in building both greater efficiency in business processes leading to increased productivity, and building competitive advantages based on innovation. Data science accomplishes this through the creation of actionable knowledge.
Business Intelligence (BI), a vital tool for building competitive advantage, aids organizations in learning and the creation of knowledge. Examples of predicative modeling employed by BI include regression and neural networks. As with any process, BI has negatives as well as positives. Some positives of BI include enhanced efficiency, a more focused customer value proposition, and the creation of value from an organization’s data, which otherwise would be a cost center. Examples of negatives include cost, complexity, and the possible abuse of data. Data science increases effectiveness enhancing productivity which drives the future competiveness of American business.
The Importance of Business Intelligence
BI is important because …show more content…
The first step in creating a predictive model based on regression is to select independent variables that have demonstrated an explanatory relationship to the dependent variable (Waljee, Higgins, and Singal, 2014). The quality of this predictive model is based on the quality of the selection of independent variables.
Neural networks emulate biological networks of neurons. These predicative models learn on their own through training with sample input and output datasets and although neural networks build accurate predicative models, the rules for creating these predictions are not visible to the end users, in other this knowledge is hiding within the neural network (Loshin, 2012). Accordingly, neural networks make powerful predictive tools, but do not aid in the creation of knowledge as much as other predictive models might.
Regression and neural networks are common types of predictive modeling. Regression employs statistical methods to predict a dependent variable based on its past relationship to one or more independent variables. Neural networks build accurate predicative models, but do not add much to the knowledge discover …show more content…
Adding to this complexity are numerous ethical issues that also require consideration.
How Will Data Science Transform American Business
Data science will transform American business by increasing effectiveness, which will increase productivity, and in turn increase competitiveness. BI aids organizations in building competitive advantage, but this is a constant process as competitive advantage is constantly shifting. Data science amplifies the organizational learning process enabling the creation of economies of knowledge and learning.
America is a highly advanced economy and much of the country’s competitive advantage is based on high productivity and innovation. Migliore and Chinta (2017, p. 49) argued that “data is everywhere” and this data represents a valuable resource in creating competitive advantage. Data science aids in building both greater efficiency in business processes leading to increased productivity, and building competitive advantages based on innovation. Data science accomplishes this through the creation of actionable knowledge.