Session 10 : E-business models, Big Data, Data Mining, Cloud Computing

Size: px
Start display at page:

Download "Session 10 : E-business models, Big Data, Data Mining, Cloud Computing"

Transcription

1 INFORMATION STRATEGY Session 10 : E-business models, Big Data, Data Mining, Cloud Computing Tharaka Tennekoon B.Sc (Hons) Computing, MBA (PIM - USJ) POST GRADUATE DIPLOMA IN BUSINESS AND FINANCE 2014

2 Internet Five Forces

3 Internet Value Chain

4 Data Mining

5 Data Mining Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides.

6 Data Mining ERP CRM SCM

7 Data Mining Relationships and Patterns Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials. Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities. Associations: Data can be mined to identify associations. Example : beer-diaper purchasing pattern. Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.

8 Data Mining Major components/ steps in Data Mining. 1. Extract, Transform, and Load (ETL) transaction data onto the data warehouse system. 2. Store and manage the data in a database system. 3. Provide data access to business analysts and information technology professionals. 4. Analyze the data by application software. 5. Present the data in a useful format, such as a graph or table. Different levels/ techniques of analysis o Artificial neural networks o Genetic algorithms o Decision trees o Nearest neighbor method o Rule induction o Data visualization

9 Data Mining Decision Tree : Credit Risk

10 Data Mining Decision Tree : Waiting Time

11 Data Mining Personal Loan offer

12 Data Mining - Benefits Basket Analysis - predict future customer behavior by past performance, including purchases and preferences o Credit Card usage fraud, limits, promotions o Telecom services usage innovators, early adopters o Fraudulent insurance claims Sales Forecasting when customers will buy again (realistic, optimistic and pessimistic) Database Marketing create consumer profiles Merchandise Planning product selection, balancing stock, pricing Call Detail record analysis customer service hotline o Improve customer experience o Average time per call o Common issues - Interactive voice response solution (leads to cost savings) Customer Loyalty predict when customers switch to competition, LCV, what keeps them from churning Segment customers Segment consumers (STP), identify segment competitors Product mix which product to which segment, new features Warranties how many are availed (e.g. 110% money back guarantee)

13 Cloud Computing

14 Cloud Computing Support Applications Upgrades

15 Cloud Computing

16 Cloud Computing = Utility = Software as a Service (SaaS) Infrastructure as a Service (IaaS) Platform as a Service (PaaS) Network as a Service (NaaS)

17 Cloud Computing

18 Cloud Computing Good vs. Bad Advantages 1. Cost Efficiency 2. Convenience and continuous availability 3. Backup and Recovery 4. Cloud is environmentally friendly 5. Resiliency and Redundancy 6. Scalability and Performance 7. Quick deployment and ease of integration 8. Increased Storage Capacity 9. Device Diversity and Location Independence (collaboration) 10.Increased Competitiveness Disadvantages 1. Security and privacy 2. Dependency and vendor lock-in 3. Technical Difficulties and Downtime (e.g. nonavailability of internet) 4. Limited control and flexibility 5. Increased Vulnerability

19 Tharaka Tennekoon, B.Sc (Hons), MBA (PIM - USJ)

Session 11 : (additional) Cloud Computing Advantages and Disadvantages

Session 11 : (additional) Cloud Computing Advantages and Disadvantages INFORMATION STRATEGY Session 11 : (additional) Cloud Computing Advantages and Disadvantages Tharaka Tennekoon B.Sc (Hons) Computing, MBA (PIM - USJ) POST GRADUATE DIPLOMA IN BUSINESS AND FINANCE 2014 Cloud

More information

CHAPTER 3 DATA MINING AND CLUSTERING

CHAPTER 3 DATA MINING AND CLUSTERING CHAPTER 3 DATA MINING AND CLUSTERING 3.1 Introduction Nowadays, large quantities of data are being accumulated. The amount of data collected is said to be almost doubled every 9 months. Seeking knowledge

More information

A Survey on Web Research for Data Mining

A Survey on Web Research for Data Mining A Survey on Web Research for Data Mining Gaurav Saini 1 gauravhpror@gmail.com 1 Abstract Web mining is the application of data mining techniques to extract knowledge from web data, including web documents,

More information

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms Data Mining Techniques forcrm Data Mining The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. Extremely large datasets Discovery of the non-obvious Useful knowledge

More information

Hexaware E-book on Predictive Analytics

Hexaware E-book on Predictive Analytics Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,

More information

Data Mining. Shahram Hassas Math 382 Professor: Shapiro

Data Mining. Shahram Hassas Math 382 Professor: Shapiro Data Mining Shahram Hassas Math 382 Professor: Shapiro Agenda Introduction Major Elements Steps/ Processes Examples Tools used for data mining Advantages and Disadvantages What is Data Mining? Described

More information

not possible or was possible at a high cost for collecting the data.

not possible or was possible at a high cost for collecting the data. Data Mining and Knowledge Discovery Generating knowledge from data Knowledge Discovery Data Mining White Paper Organizations collect a vast amount of data in the process of carrying out their day-to-day

More information

15.564 Information Technology I. Business Intelligence

15.564 Information Technology I. Business Intelligence 15.564 Information Technology I Business Intelligence Outline Operational vs. Decision Support Systems What is Data Mining? Overview of Data Mining Techniques Overview of Data Mining Process Data Warehouses

More information

Data Mining + Business Intelligence. Integration, Design and Implementation

Data Mining + Business Intelligence. Integration, Design and Implementation Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution

More information

ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies

ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

More information

CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics

CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CII Institute of Logistics Session map Session1 Session 2 Introduction The new focus on customer loyalty CRM and Business Intelligence CRM Marketing initiatives Session

More information

Data Mining Submitted in partial fulfillment of the requirement for the award of degree Of Computer Science

Data Mining Submitted in partial fulfillment of the requirement for the award of degree Of Computer Science A Seminar report On Data Mining Submitted in partial fulfillment of the requirement for the award of degree Of Computer Science SUBMITTED TO: www.studymafia.org SUBMITTED BY: www.studymafia.org Preface

More information

Data Warehousing and Data Mining in Business Applications

Data Warehousing and Data Mining in Business Applications 133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business

More information

Data Mining Solutions for the Business Environment

Data Mining Solutions for the Business Environment Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over

More information

Data Mining System, Functionalities and Applications: A Radical Review

Data Mining System, Functionalities and Applications: A Radical Review Data Mining System, Functionalities and Applications: A Radical Review Dr. Poonam Chaudhary System Programmer, Kurukshetra University, Kurukshetra Abstract: Data Mining is the process of locating potentially

More information

Advanced Analytics. The Way Forward for Businesses. Dr. Sujatha R Upadhyaya

Advanced Analytics. The Way Forward for Businesses. Dr. Sujatha R Upadhyaya Advanced Analytics The Way Forward for Businesses Dr. Sujatha R Upadhyaya Nov 2009 Advanced Analytics Adding Value to Every Business In this tough and competitive market, businesses are fighting to gain

More information

Session 7 : Information Systems

Session 7 : Information Systems INFORMATION STRATEGY Session 7 : Information Systems Tharaka Tennekoon B.Sc (Hons) Computing, MBA (PIM - USJ) POST GRADUATE DIPLOMA IN BUSINESS AND FINANCE 2014 CRM CRM - Customer Relationship Management

More information

Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management

Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management Paper Jean-Louis Amat Abstract One of the main issues of operators

More information

IT and CRM A basic CRM model Data source & gathering system Database system Data warehouse Information delivery system Information users

IT and CRM A basic CRM model Data source & gathering system Database system Data warehouse Information delivery system Information users 1 IT and CRM A basic CRM model Data source & gathering Database Data warehouse Information delivery Information users 2 IT and CRM Markets have always recognized the importance of gathering detailed data

More information

BUSINESS MANAGEMENT SUPPORT

BUSINESS MANAGEMENT SUPPORT BUSINESS MANAGEMENT SUPPORT Business disadvantages using cloud computing? Author: Maikel Mardjan info@bm-support.org 2010 BM-Support.org Foundation. All rights reserved. EXECUTIVE SUMMARY Cloud computing

More information

Data Mining Techniques in CRM

Data Mining Techniques in CRM Data Mining Techniques in CRM Inside Customer Segmentation Konstantinos Tsiptsis CRM 6- Customer Intelligence Expert, Athens, Greece Antonios Chorianopoulos Data Mining Expert, Athens, Greece WILEY A John

More information

Importance or the Role of Data Warehousing and Data Mining in Business Applications

Importance or the Role of Data Warehousing and Data Mining in Business Applications Journal of The International Association of Advanced Technology and Science Importance or the Role of Data Warehousing and Data Mining in Business Applications ATUL ARORA ANKIT MALIK Abstract Information

More information

Understanding Your Customer Journey by Extending Adobe Analytics with Big Data

Understanding Your Customer Journey by Extending Adobe Analytics with Big Data SOLUTION BRIEF Understanding Your Customer Journey by Extending Adobe Analytics with Big Data Business Challenge Today s digital marketing teams are overwhelmed by the volume and variety of customer interaction

More information

CLOUD ARCHITECTURE DIAGRAMS AND DEFINITIONS

CLOUD ARCHITECTURE DIAGRAMS AND DEFINITIONS CLOUD ARCHITECTURE DIAGRAMS AND DEFINITIONS April 2014 Cloud Conceptual Reference Model The ease of use a Cloud Consumer experiences results from a complex, behind-the-scenes, orchestration of interchangeable,

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association

More information

Cloud Computing. Cloud computing:

Cloud Computing. Cloud computing: Cloud computing: Cloud Computing A model of data processing in which high scalability IT solutions are delivered to multiple users: as a service, on a mass scale, on the Internet. Network services offering:

More information

Beyond listening Driving better decisions with business intelligence from social sources

Beyond listening Driving better decisions with business intelligence from social sources Beyond listening Driving better decisions with business intelligence from social sources From insight to action with IBM Social Media Analytics State of the Union Opinions prevail on the Internet Social

More information

Q1 Define the following: Data Mining, ETL, Transaction coordinator, Local Autonomy, Workload distribution

Q1 Define the following: Data Mining, ETL, Transaction coordinator, Local Autonomy, Workload distribution Q1 Define the following: Data Mining, ETL, Transaction coordinator, Local Autonomy, Workload distribution Q2 What are Data Mining Activities? Q3 What are the basic ideas guide the creation of a data warehouse?

More information

Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry

Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry Advances in Natural and Applied Sciences, 3(1): 73-78, 2009 ISSN 1995-0772 2009, American Eurasian Network for Scientific Information This is a refereed journal and all articles are professionally screened

More information

Big Data. Fast Forward. Putting data to productive use

Big Data. Fast Forward. Putting data to productive use Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize

More information

ebusiness Web Hosting Alternatives Considerations Self hosting Internet Service Provider (ISP) hosting

ebusiness Web Hosting Alternatives Considerations Self hosting Internet Service Provider (ISP) hosting ebusiness Web Hosting and E-Business Software Web Hosting Alternatives Self hosting Internet Service Provider (ISP) hosting Commerce Service Provider (CSP) hosting Shared hosting Dedicated hosting Considerations

More information

The Cloud for Insights

The Cloud for Insights The Cloud for Insights A Guide for Small and Midsize Business As the volume of data grows, businesses are using the power of the cloud to gather, analyze, and visualize data from internal and external

More information

Cloud Computing: IaaS & PaaS

Cloud Computing: IaaS & PaaS Cloud Computing: IaaS & PaaS Thomas Kurian Executive Vice President Product Development 62 Safe Harbor Statement "Safe Harbor" Statement: Statements in this presentation relating to Oracle's future plans,

More information

Maximize Sales and Margins with Comprehensive Customer Analytics

Maximize Sales and Margins with Comprehensive Customer Analytics Q Customer Maximize Sales and Margins with Comprehensive Customer Analytics Struggling to connect the dots between Marketing, Merchandising and Store Ops? With the explosion of customer interaction systems,

More information

MBA 8473 - Data Mining & Knowledge Discovery

MBA 8473 - Data Mining & Knowledge Discovery MBA 8473 - Data Mining & Knowledge Discovery MBA 8473 1 Learning Objectives 55. Explain what is data mining? 56. Explain two basic types of applications of data mining. 55.1. Compare and contrast various

More information

An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

More information

Predictive Analytics: Turn Information into Insights

Predictive Analytics: Turn Information into Insights Predictive Analytics: Turn Information into Insights Pallav Nuwal Business Manager; Predictive Analytics, India-South Asia pallav.nuwal@in.ibm.com +91.9820330224 Agenda IBM Predictive Analytics portfolio

More information

Worldwide Advanced and Predictive Analytics Software Market Shares, 2014: The Rise of the Long Tail

Worldwide Advanced and Predictive Analytics Software Market Shares, 2014: The Rise of the Long Tail MARKET SHARE Worldwide Advanced and Predictive Analytics Software Market Shares, 2014: The Rise of the Long Tail Alys Woodward Dan Vesset IDC MARKET SHARE FIGURE FIGURE 1 Worldwide Advanced and Predictive

More information

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS Mrs. Jyoti Nawade 1, Dr. Balaji D 2, Mr. Pravin Nawade 3 1 Lecturer, JSPM S Bhivrabai Sawant Polytechnic, Pune (India) 2 Assistant

More information

Despite the hype, cloud adoption is still in its early days for most companies, but cloud is coming.

Despite the hype, cloud adoption is still in its early days for most companies, but cloud is coming. J U L Y, 2 0 1 3 Despite the hype, cloud adoption is still in its early days for most companies, but cloud is coming. TABLE OF CONTENTS Executive Summary Key Findings Key Conclusions Recommendations Survey

More information

Automated Predictive Analysis. Tomer Steinberg

Automated Predictive Analysis. Tomer Steinberg Automated Predictive Analysis Tomer Steinberg Analytics solutions from SAP SAP Analytics Portfolio Cloud Mobile Agile Visualization Advanced Analytics Big Data Enterprise Business Intelligence Collaboration

More information

The NREN s core activities are in providing network and associated services to its user community that usually comprises:

The NREN s core activities are in providing network and associated services to its user community that usually comprises: 3 NREN and its Users The NREN s core activities are in providing network and associated services to its user community that usually comprises: Higher education institutions and possibly other levels of

More information

Emerging Technologies CEOS/WGISS

Emerging Technologies CEOS/WGISS Emerging Technologies CEOS/WGISS CEOS Plenary 2014 Tromso, Norway Tuesday, October 28 th 16:00-17:00 Image Source: http://beck-technology.com/ Agenda WGISS Technology Exploration Interest Group Introduction

More information

Use of Data Mining in Banking

Use of Data Mining in Banking Use of Data Mining in Banking Kazi Imran Moin*, Dr. Qazi Baseer Ahmed** *(Department of Computer Science, College of Computer Science & Information Technology, Latur, (M.S), India ** (Department of Commerce

More information

SAP Predictive Analysis: Strategy, Value Proposition

SAP Predictive Analysis: Strategy, Value Proposition September 10-13, 2012 Orlando, Florida SAP Predictive Analysis: Strategy, Value Proposition Thomas B Kuruvilla, Solution Management, SAP Business Intelligence Scott Leaver, Solution Management, SAP Business

More information

Key Considerations for Libraries

Key Considerations for Libraries Software as a Service and Cloud Computing: Key Considerations for Libraries 1 I ncreasingly, libraries are considering technology as a strategy. Rather than treating technology as merely tools for completing

More information

CHAPTER 8 CLOUD COMPUTING

CHAPTER 8 CLOUD COMPUTING CHAPTER 8 CLOUD COMPUTING SE 458 SERVICE ORIENTED ARCHITECTURE Assist. Prof. Dr. Volkan TUNALI Faculty of Engineering and Natural Sciences / Maltepe University Topics 2 Cloud Computing Essential Characteristics

More information

PREDICTIVE DATA MINING ON WEB-BASED E-COMMERCE STORE

PREDICTIVE DATA MINING ON WEB-BASED E-COMMERCE STORE PREDICTIVE DATA MINING ON WEB-BASED E-COMMERCE STORE Jidi Zhao, Tianjin University of Commerce, zhaojidi@263.net Huizhang Shen, Tianjin University of Commerce, hzshen@public.tpt.edu.cn Duo Liu, Tianjin

More information

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP Data Warehousing and End-User Access Tools OLAP and Data Mining Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities. Key

More information

Chapter 2 Literature Review

Chapter 2 Literature Review Chapter 2 Literature Review 2.1 Data Mining The amount of data continues to grow at an enormous rate even though the data stores are already vast. The primary challenge is how to make the database a competitive

More information

Nine Common Types of Data Mining Techniques Used in Predictive Analytics

Nine Common Types of Data Mining Techniques Used in Predictive Analytics 1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better

More information

Case Study Two. Customer Relationship Management Head To The Cloud. Sifei Liu & Yaqing Ma

Case Study Two. Customer Relationship Management Head To The Cloud. Sifei Liu & Yaqing Ma Case Study Two Customer Relationship Management Head To The Cloud Sifei Liu & Yaqing Ma 2 Question 1 Types of companies are most likely to adopt cloud-based CRM software services are companies that want

More information

Enhanced Boosted Trees Technique for Customer Churn Prediction Model

Enhanced Boosted Trees Technique for Customer Churn Prediction Model IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 03 (March. 2014), V5 PP 41-45 www.iosrjen.org Enhanced Boosted Trees Technique for Customer Churn Prediction

More information

Technology-Driven Demand and e- Customer Relationship Management e-crm

Technology-Driven Demand and e- Customer Relationship Management e-crm E-Banking and Payment System Technology-Driven Demand and e- Customer Relationship Management e-crm Sittikorn Direksoonthorn Assumption University 1/2004 E-Banking and Payment System Quick Win Agenda Data

More information

Data Analysis. Management Information Systems 13

Data Analysis. Management Information Systems 13 Data Analysis Management Information Systems 13 166137-01+02 Management Information Systems Spring 2014 Sync Sangwon Lee, Ph. D D. of Information & Electronic Commerce WONKWANG University Prof. Dr. SSL

More information

DATA MINING AND WAREHOUSING CONCEPTS

DATA MINING AND WAREHOUSING CONCEPTS CHAPTER 1 DATA MINING AND WAREHOUSING CONCEPTS 1.1 INTRODUCTION The past couple of decades have seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation

More information

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA ABSTRACT Current trends in data mining allow the business community to take advantage of

More information

Data Mining: A Tool for Enhancing Business Process in Banking Sector Dr.R.Mahammad Shafi, Porandla Srinivas

Data Mining: A Tool for Enhancing Business Process in Banking Sector Dr.R.Mahammad Shafi, Porandla Srinivas International Journal of Scientific & Engineering Research Volume 3, Issue 12, December-2012 1 Data Mining: A Tool for Enhancing Business Process in Banking Sector Dr.R.Mahammad Shafi, Porandla Srinivas

More information

Increasing the Efficiency of Customer Relationship Management Process using Data Mining Techniques

Increasing the Efficiency of Customer Relationship Management Process using Data Mining Techniques Increasing the Efficiency of Customer Relationship Management Process using Data Mining Techniques P.V.D PRASAD Lead Functional Consultant, JMR InfoTech, Sigma Soft Tech Park Whitefield, Bangalore 560066

More information

The Future of Business Analytics is Now! 2013 IBM Corporation

The Future of Business Analytics is Now! 2013 IBM Corporation The Future of Business Analytics is Now! 1 The pressures on organizations are at a point where analytics has evolved from a business initiative to a BUSINESS IMPERATIVE More organization are using analytics

More information

Master Data Management Enterprise Architecture IT Strategy and Governance

Master Data Management Enterprise Architecture IT Strategy and Governance ? Master Data Management Enterprise Architecture IT Strategy and Governance Intertwining three strategic fields of Information Technology, We help you Get the best out of IT Master Data Management MDM

More information

BUSINESS INTELLIGENCE. Keywords: business intelligence, architecture, concepts, dashboards, ETL, data mining

BUSINESS INTELLIGENCE. Keywords: business intelligence, architecture, concepts, dashboards, ETL, data mining BUSINESS INTELLIGENCE Bogdan Mohor Dumitrita 1 Abstract A Business Intelligence (BI)-driven approach can be very effective in implementing business transformation programs within an enterprise framework.

More information

A Review of Data Mining Techniques

A Review of Data Mining Techniques Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,

More information

The Private Cloud Your Controlled Access Infrastructure

The Private Cloud Your Controlled Access Infrastructure White Paper: Private Clouds The ongoing debate on the differences between a Public and Private Cloud are broad and often loud. The bottom line is that it s really about how the resource, or computing power,

More information

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III www.cognitro.com/training Predicitve DATA EMPOWERING DECISIONS Data Mining & Predicitve Training (DMPA) is a set of multi-level intensive courses and workshops developed by Cognitro team. it is designed

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of

More information

Data Mining: Motivations and Concepts

Data Mining: Motivations and Concepts POLYTECHNIC UNIVERSITY Department of Computer Science / Finance and Risk Engineering Data Mining: Motivations and Concepts K. Ming Leung Abstract: We discuss here the need, the goals, and the primary tasks

More information

ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies

ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

More information

DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM

DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM M. Mayilvaganan 1, S. Aparna 2 1 Associate

More information

Data Warehousing and Data Mining for improvement of Customs Administration in India. Lessons learnt overseas for implementation in India

Data Warehousing and Data Mining for improvement of Customs Administration in India. Lessons learnt overseas for implementation in India Data Warehousing and Data Mining for improvement of Customs Administration in India Lessons learnt overseas for implementation in India Participants Shailesh Kumar (Group Leader) Sameer Chitkara (Asst.

More information

Cloud Service Model. Selecting a cloud service model. Different cloud service models within the enterprise

Cloud Service Model. Selecting a cloud service model. Different cloud service models within the enterprise Cloud Service Model Selecting a cloud service model Different cloud service models within the enterprise Single cloud provider AWS for IaaS Azure for PaaS Force fit all solutions into the cloud service

More information

Business Intelligence Osvaldo Maysonet VP Marketing & Customer Knowledge Banco Popular

Business Intelligence Osvaldo Maysonet VP Marketing & Customer Knowledge Banco Popular Business Intelligence Osvaldo Maysonet VP Marketing & Customer Knowledge Banco Popular Starting Questions How many of you have more information today and spend more time gathering and preparing the information

More information

ebusiness Web Hosting Alternatives Self hosting Internet Service Provider (ISP) hosting Commerce Service Provider (CSP) hosting

ebusiness Web Hosting Alternatives Self hosting Internet Service Provider (ISP) hosting Commerce Service Provider (CSP) hosting ebusiness Web Hosting and E-Business Software Web Hosting Alternatives Self hosting Internet Service Provider (ISP) hosting Commerce Service Provider (CSP) hosting Shared hosting Dedicated hosting 1 Considerations

More information

MDM and Data Warehousing Complement Each Other

MDM and Data Warehousing Complement Each Other Master Management MDM and Warehousing Complement Each Other Greater business value from both 2011 IBM Corporation Executive Summary Master Management (MDM) and Warehousing (DW) complement each other There

More information

SaaS data quality deployments. The increasing demand for SaaS technology is creating a need in the data quality market

SaaS data quality deployments. The increasing demand for SaaS technology is creating a need in the data quality market SaaS data quality deployments The increasing demand for SaaS technology is creating a need in the data quality market An Experian Data Quality White Paper November 2014 Summary Software-as-a-service (SaaS)

More information

PROPOSAL ENTERPRISE SWITCH SERVICE

PROPOSAL ENTERPRISE SWITCH SERVICE PROPOSAL ENTERPRISE SWITCH SERVICE CLOUD MARKET SUMMARY The Cloud computing market is growing rapidly. With many organizations starting to benefit from the Cloud, companies of all sizes should evaluate

More information

Intellectual Property / Copyright Material

Intellectual Property / Copyright Material What is Data Mining? Author: BALWANT RAI Organization: Evaltech, Inc. Evaltech Research Group, Data Warehousing Practice. Date: 02/27/04 Email: erg@evaltech.com Abstract: In this paper we will be going

More information

ElegantJ BI. White Paper. The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis

ElegantJ BI. White Paper. The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis ElegantJ BI White Paper The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis Integrated Business Intelligence and Reporting for Performance Management, Operational

More information

Top 10 Predictive Use Cases and Customer Case Studies

Top 10 Predictive Use Cases and Customer Case Studies Top 10 Predictive Use Cases and Customer Case Studies Confidently anticipate and drive better business outcomes Pierre Leroux, Director Predictive Analytics 2015 SAP SE or an SAP affiliate company. All

More information

On Premise Vs Cloud: Selection Approach & Implementation Strategies

On Premise Vs Cloud: Selection Approach & Implementation Strategies On Premise Vs Cloud: Selection Approach & Implementation Strategies Session ID#:10143 Prepared by: Praveen Kumar Practice Manager AST Corporation @Praveenk74 REMINDER Check in on the COLLABORATE mobile

More information

The Business Analyst s Guide to Hadoop

The Business Analyst s Guide to Hadoop White Paper The Business Analyst s Guide to Hadoop Get Ready, Get Set, and Go: A Three-Step Guide to Implementing Hadoop-based Analytics By Alteryx and Hortonworks (T)here is considerable evidence that

More information

IBM SPSS Modeler Professional

IBM SPSS Modeler Professional IBM SPSS Modeler Professional Make better decisions through predictive intelligence Highlights Create more effective strategies by evaluating trends and likely outcomes. Easily access, prepare and model

More information

Enabling Big Data with Cloud. Go faster Reduce risk Scale as you grow Avoid mistakes

Enabling Big Data with Cloud. Go faster Reduce risk Scale as you grow Avoid mistakes Enabling Big Data with Cloud Go faster Reduce risk Scale as you grow Avoid mistakes Dr. Phil Shelley Why Cloud and Big Data? Complexity Speed Cost Skills Support Technology Analytics 2.0 Industry Trends

More information

Data Mining for Everyone

Data Mining for Everyone Page 1 Data Mining for Everyone Christoph Sieb Senior Software Engineer, Data Mining Development Dr. Andreas Zekl Manager, Data Mining Development Page 2 Executive Summary Contents 2 Data mining in the

More information

Ali Marshall 3 rd Sector Telcom

Ali Marshall 3 rd Sector Telcom Ali Marshall 3 rd Sector Telcom Who are 3rd Sector Telecom? We are a specialist provider of all aspects of telecommunications to the charity and not-for-profit sector Voice Mobile Inbound Connectivity

More information

Achieving customer loyalty with customer analytics

Achieving customer loyalty with customer analytics IBM Software Business Analytics Customer Analytics Achieving customer loyalty with customer analytics 2 Achieving customer loyalty with customer analytics Contents 2 Overview 3 Using satisfaction to drive

More information

Data Mining for Fun and Profit

Data Mining for Fun and Profit Data Mining for Fun and Profit Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. - Ian H. Witten, Data Mining: Practical Machine Learning Tools

More information

CLOUD ERP SOFTWARE PREFERENCES STUDY

CLOUD ERP SOFTWARE PREFERENCES STUDY CLOUD ERP SOFTWARE PREFERENCES STUDY A study conducted by IFS North America APRIL, 2013 THE CURRENT STATE OF THE INDUSTRY BASED ON A SURVEY OF MORE THAN 200 EXECUTIVES METHODOLOGY In early 2013, IFS North

More information

A New Approach for Evaluation of Data Mining Techniques

A New Approach for Evaluation of Data Mining Techniques 181 A New Approach for Evaluation of Data Mining s Moawia Elfaki Yahia 1, Murtada El-mukashfi El-taher 2 1 College of Computer Science and IT King Faisal University Saudi Arabia, Alhasa 31982 2 Faculty

More information

Overview, Goals, & Introductions

Overview, Goals, & Introductions Improving the Retail Experience with Predictive Analytics www.spss.com/perspectives Overview, Goals, & Introductions Goal: To present the Retail Business Maturity Model Equip you with a plan of attack

More information

Cloud Application Marketplace 2012-2017

Cloud Application Marketplace 2012-2017 Brochure More information from http://www.researchandmarkets.com/reports/2237770/ Cloud Application Marketplace 2012-2017 Description: The global cloud applications marketplace is driven largely by the

More information

Using Analytics to Drive Customer Profitability Dr Colin Linsky WW Predictive Analytics Retail Leader IBM SPSS Industry Solutions Team

Using Analytics to Drive Customer Profitability Dr Colin Linsky WW Predictive Analytics Retail Leader IBM SPSS Industry Solutions Team Using Analytics to Drive Customer Profitability Dr Colin Linsky WW Predictive Analytics Retail Leader IBM SPSS Industry Solutions Team 2012 IBM Corporation Agenda Business Analytics The Competitive Advantage

More information

SANJEEV GOEL Freelance Trainer. IT Telecom Management Soft Skills

SANJEEV GOEL Freelance Trainer. IT Telecom Management Soft Skills TRAINING COURSES & WORKSHOPS - INFORMATION TECHNOLOGY ( IT ) CORPORATE IT STRATEGIES e-businss STRATEGIES MASTER DATA MANAGEMENT ( MDM ) BUSINESS INTELLIGENCE ( DATA WAREHOUSING & MINING ) IT AUDITING

More information

A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH

A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH 205 A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH ABSTRACT MR. HEMANT KUMAR*; DR. SARMISTHA SARMA** *Assistant Professor, Department of Information Technology (IT), Institute of Innovation in Technology

More information

Predictive Analytics. Noam Zeigerson, CTO

Predictive Analytics. Noam Zeigerson, CTO Predictive Analytics Noam Zeigerson, CTO Agenda The Predictive Analytics Need Innovative Technologies Business Solutions The problem: Inconsistent stream of revenue Available Data Sources ERP data Web

More information

International Business & Economics Research Journal Volume 1, Number 6

International Business & Economics Research Journal Volume 1, Number 6 Data Mining For Customer Relationship Management Savitha S. Kadiyala, (Email: savitha@gsu.edu), Georgia State University Alok Srivastava, (Email: alok@gsu.edu), Georgia State University Abstract Data mining

More information

Business applications: banking. fraud detection. marketing. distribution/sales. research

Business applications: banking. fraud detection. marketing. distribution/sales. research DEFINITIONS DATA MINING Data Mining is used to search for valuable information from the mounts of data collected over time, which could be used in decision making (Keating 33). Business applications: banking

More information

DATA MINING TECHNIQUES AND APPLICATIONS

DATA MINING TECHNIQUES AND APPLICATIONS DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,

More information

Cloud Computing Backgrounder

Cloud Computing Backgrounder Cloud Computing Backgrounder No surprise: information technology (IT) is huge. Huge costs, huge number of buzz words, huge amount of jargon, and a huge competitive advantage for those who can effectively

More information

A Study on Integrating Business Intelligence into E-Business

A Study on Integrating Business Intelligence into E-Business International Journal on Advanced Science Engineering Information Technology A Study on Integrating Business Intelligence into E-Business Sim Sheng Hooi 1, Wahidah Husain 2 School of Computer Sciences,

More information