Data Analytics Essay

4. Analytics-as-a-service(AaaS) Analytics-as-a-Service is a service of delivering the insights based on your own data, focusing exclusively on business value and opportunities.[5] 4.1 Role of Decision making and Data Insights Decision making - no longer the preserve of middle managers or business analysts-occurs throughout the business, from boardroom to front office.[6] ”Enterprises need to be able to quickly respond to an ever-changing world in order to stay competitive. A constantly changing

troduction Every day, we produce 2.5 quintillion bytes of data. 90% of all data in the world was produced in the past two years. Data has been around forever; we have always gathered information. Paleolithic cavemen recorded their activities by carving them in stone or notching them in sticks. Egyptians used hieroglyphics to record significant events in history. The Library of Alexandria was home to half-a-million scrolls of the ancient world. Less than hundred years ago, we used punch cards to

¬Big Data Analytics Driven Enterprise Asset Management for Asset Intensive Industries. Abstract “Information is the oil of the 21st century, and analytics is the combustion engine.” was introduced by Peter Sondergaard during Gartner Symposium/ITxpo 2011. In fact, data is like oil! It has value, but it needs to be extracted and refined to get the true value from it. In today’s business climate, organisations across various sectors are realising the importance of collecting data from different business

Competing on analytics is currently one of the most essential qualities for companies looking to gain a larger market share in their given industry. This is due to the fact that the easiest way for corporations to differentiate themselves from their direct competitors is to maximize efficiency through cohesive processes and decision making. Analytics is defined as, “The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact based management to drive

JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY SCHOOL OF COMPUTING AND INFORMATION TECHNOLOGY Optimized Dynamic Latent Topic Model for Big Text Data Analytics NAME: Geoffrey Mariga Wambugu REGISTRATION NUMBER: CS481-4692/2014 LECTURER: Prof. Waweru Mwangi A thesis proposal submitted in partial fulfilment of the requirement for the Unit SCI 4201 Advanced Research Methodology of the degree of Doctor of Philosophy in Information Technology at the School of Computing and Information Technology

complicate effective application of business analytics to business intelligence and knowledge management data, and the prospects for improvement Businesses have collected data for many years. Most of the data they collect has been for historical purposes, such as how much of an item has sold and what are the profits gained from those sales. Business intelligence allows one to take that data, manipulate as you see fit and generate reports. The data then has to be extracted and trends analyzed

involving ecosystems to enable users to purchase goods and services and suggest items based on past online behaviors. Integrated customer information will progress into electronic identities. A. How does Amazon use Big-Data to monitor customer habits Amazon doesn’t sell their customer data, nor do they have any intent on doing so in the future. On the contrary, Amazon collects web-based consumer information about user trends, in most cases with the explicit knowledge and agreement of the user. (Beuker

professional level. In this paper, the author will attempt to define data analytics in general and provide a brief overview of the evolution of utilizing data analytics in business. Next, analyze the main advantages and disadvantages of using data analytics within the industry or company that you have chosen. Determine the fundamental obstacles or challenges that business management in general must overcome in order to implement data analytics. Next, suggest a strategy that business management could use

management and supporting system like Data Warehousing, Business Intelligence, Analytics, and/or Big Data has produced a remarkable moment in the history of data analysis. Researching on this topic is very interesting for me. Thank Professor Kraft that gives me opportunity to explore more on these topics. Taking this opportunity, I would like to provide a brief summary of the book that discuss about the Profitable Data Warehousing, Business Intelligence and Analytics. The book published by Technics Publications

MIS 5375 580 SU15 Data Mining & Business Analytics Midterm Exam Summer 2015 by Tamma Shanthipriya A00128661 DATA MINING AND BUSINESS ANALYTICS Data Mining is the computerized acknowledgment of diverse patterns in extensive data sets that are past analysis. It utilizes diverse mathematic algorithms to locate the right information as well as foresee the probability of future events. Some key properties that I learned in this topic are: • discovery of useful patterns • predictions