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Avasthi, Aditya (2013) Individual Report: Predictive Analytics. [Dissertation (University of Nottingham only)] (Unpublished)

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Abstract

Today’s economy can be defined as knowledge based economy. Survival in such type of an economy depends on the ability to convert information into knowledge. It is estimated that in the next three year more data or information will be generated than last one thousand years. Large amount of data or information is available online. The available information must be converted to knowledge to improve decision making and stimulate innovation.

The aim of the report is to explore Predictive Analytics and conduct a profound analysis of all its features and characteristics. This will help in understanding its merits and demerits and how it can be beneficial to Perceptive Informatics and pharmaceutical industry in future. Perceptive Informatics provides e-clinical solutions and software-as-a-service (SaaS) to its clients. It enables the client to optimize the drug development process and clinical trial technology.

Analytics can be defined as science of analysis. It involves discovery and communication of meaningful relations and patterns in data. Traditional analytics had the ability to find and analyse old and present data as well as to find the hidden nature of data. Traditional analytics provided information about what went right and what went wrong while decision-making. This was done by analysing past and present data. The latest shift in Business Intelligence sector is the shift from traditional analytics to predictive analytics. It has emerged as the new and distinct software sector.

Predictive Analytics makes use of statistical methods that were developed in 1920s. Predictive Analytics helps in increasing the value offered to clients. It has revolutionised the decision making process by providing scientific and logical basis for it.

Item Type:	Dissertation (University of Nottingham only)
Depositing User:	EP, Services
Date Deposited:	15 Dec 2021 14:35
Last Modified:	15 Dec 2021 14:35
URI:	https://eprints.nottingham.ac.uk/id/eprint/26470

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