ABSTRACT
One of the most significant challenges to succeeding with augmented intelligence is the need to consistently manage data so that it can be applied to critical business problems. Data is needed to build and test the predictive models that are core to augmented intelligence. Traditional business applications and data management practices cannot support augmented intelligence. The data cycle continues after model development to data acquisition and data preparation. After initial model development, additional data preparation may be required. By contrast, augmented intelligence relies on predictive analytics intended to discover patterns in historical data so that this data can be used to predict the future. Machine intelligence allows businesses to deliver a personalized experience to customers, employees, and suppliers. This personalization is based on an analysis of data on each person’s communication style and preferences. The use of machine learning algorithms for predictive analytics is powerful and can be used for both collaboration and knowledge transfer and personalization.
