Technology's impact on Predictive Analytics. Write a short paper that identifies and explains the changes in technology that are increasing our ability to develop predictive analytics. Discuss any...

Technology's impact on Predictive Analytics
Predictive analytics is a version of advanced analytics, which makes use of past and present data for predicting and forecasting future activity and behaviour trends. It is working on wide range technologies such as statistical analysis technique, automated machine learning and analytical queries for determining chances or likelihood of happening of a particular event or activity. For example, a retail store is using customers’ purchase information for developing future promotional strategies and predicting whether customers would willingly participate in it or not.
This process of prediction and forecasting is facilitated by several technological advancements, in terms of regression models, decision tree, neural networks, etc (Siegel, 2016). On one end, these technological models assisted predictive analytics in predicting accurate values and reliable predictions, however, on the other hand, it give rise to certain ethical concerns as discussed below:
Ethical concerns arising on using technology in predictive analytics
1) Data integrity:
This is considered as one of the major ethical issue arising at wider scale on using predictive analytics. This ethical issue is explained in context to healthcare. The health care professionals are using predictive analytics in order to have a proper technology framework in place for deciding clinical priorities, measuring events such as cost effectiveness, formulating clinical protocols, expecting patient outcomes, etc.
In other to perform these activities successfully, data modelling and algorithm development technique of predictive analytics is used. For this, data analytics personnel are getting internal access to millions of de-identified patient information and hospital records available in both inpatient and outpatient settings. This accessibility of data is crucial for generalising findings...