4. 4.1 Big Data Introduction In 2004, Wal-Mart claimed to have the largest data warehouse with 500 terabytes storage (equivalent to 50 printed collections of the US Library of Congress). In 2009, eBay storage amounted to eight petabytes (think of 104 years of HD-TV video). Two years later, the Yahoo warehouse totalled 170 petabytes1 (8.5 times of all hard disk drives created in 1995)2. Since the rise of digitisation, enterprises from various verticals have amassed burgeoning amounts of digital
Words: 22222 - Pages: 89
MGMT600-1404B-03 November 18, 2014 Professor Smith Phase 1 Discussion Board Quantitative research methods are a collection of data that involves the use of numbers, graphs, and charts. With the quantitative method, questionnaire that consists of close ended questions can be used for analysis. Quantitative research method can be expressed by the use of variables. These variables can be continuous or discrete. A continuous variable is a variable that may take on any value between
Words: 544 - Pages: 3
a set of theories, methodologies, processes, architectures and technologies that transform row data into meaningful information for business processes. The most important functions of BI are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. BI can be applied in the following business processes, in order to add business value:
Words: 1365 - Pages: 6
Name: Professor’s Name: Date: Define data analytics Data analytics is the art of examing raw data with an aim of analyzing the information for evaluation and research. Data analytics as used in industry is to allow companies and organizations to analyze their data in order to improve their production. It focuses on inference, the process of reaching a conclusion based on the known by a researcher. (Lavalle & Kruschwitz 2013) Evolution of data analytics Some years ago when we talked about modeling
Words: 1442 - Pages: 6
www.elsevier.com/locate/techsoc Data mining techniques for customer relationship management Chris Rygielski a, Jyun-Cheng Wang b, David C. Yen a,∗ a Department of DSC & MIS, Miami University, Oxford, OH, USA b Department of Information Management, National Chung-Cheng University, Taiwan, ROC Abstract Advancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have
Words: 8031 - Pages: 33
serious about embracing the opportunity of big data.” — Craig Vaughan Global Vice President at SAP “This timely book says out loud what has finally become apparent: in the modern world, Data is Business, and you can no longer think business without thinking data. Read this book and you will understand the Science behind thinking data.” — Ron Bekkerman Chief Data Officer at Carmel Ventures “A great book for business managers who lead or interact with data scientists, who wish to better understand the
Words: 146629 - Pages: 587
MSc. Information System Management Kyaw Khine Soe (3026039) Data Mining and Business Analytics Boston Housing Dataset Analysis. Table of Contents Introduction 3 Problem Statement 3 The associated data of Boston 5 Data pre-processing / Data preparation 8 Clustering Analysis 11 Cluster segment profile 17 Regression Analysis 18 Predictive analysis using neural network node 19 Decision tree node 21 Regression node analysis 23 Model Comparison 24 The recommendation and conclusion
Words: 2101 - Pages: 9
cu MOVEIN BUSINESS INTELLIGENCE AND ANALYTICS REPORT me nt ap A Business Plan on the Role of Business Intelligence and Analytics for MoveIn Pty Ltd Th ink sw Do TABLE OF CONTENTS Executive Summary ........................................................................................................................ 2 1 -‐ Introduction
Words: 4854 - Pages: 20
access to data to help enterprises make better decisions. It can provide information of various “Information Assets” in an organization and how they interact with each other. These assets include Customer Databases, SCM Information, Personnel data, Manufacturing, Sales & Marketing Activity. Applications of BI: BI can be applied to MARCKM * Measurement - performance metrics, benchmarking etc., * Analytics - data mining, process mining, predictive modeling * Reporting - Data visualization
Words: 1444 - Pages: 6
solution. Indeed, we believe that this architecture will stimulate research, and more importantly organizations, to invest in Analytics and Statistical Fraud-Scoring to be used in conjunction with the already in-place preventive techniques. Therefore, in this research we explore different strategies to build a Streambased Fraud Detection solution, using advanced Data Mining Algorithms and Statistical Analysis, and show how they lead to increased accuracy in the detection of fraud by at least 78% in
Words: 56858 - Pages: 228
