Sistem (Pengantar) Penunjang Keputusan Perdagangan Elektronik 1/20 PERDAGANGAN ELEKTRONIK Tinjauan Mekanisme Aplikasi B2C Riset pasar, E-CRM dan periklanan.

Slides:



Advertisements
Similar presentations
1 Introduction to Data Management. Understand: meaning of data management history of managing data challenges in managing data approaches to managing.
Advertisements

Distributed Data Processing
10-1 Data and Knowledge Management 10-2 Data Management: A Critical Success Factor The difficulties and the process Data sources and collection Data.
1 CHAPTER 4 Data Warehousing, Access, Analysis, Mining, and Visualization.
Sistem (Pengantar) Penunjang Keputusan Sistem Informasi Perusahaan 1/20 SISTEM INFORMASI PERUSAHAAN Konsep dan definisi Evolusi SI eksekutif dan SI perusahaan.
Sistem (Pengantar) Penunjang Keputusan Kecerdasan Bisnis (KB) 1/20 KECERDASAN BISNIS (KB) Sifat dan sumber data Pengumpulan data, masalah dan kualitas.
ITEC 423 Data Warehousing and Data Mining Lecture 3.
Data Warehousing CPS216 Notes 13 Shivnath Babu. 2 Warehousing l Growing industry: $8 billion way back in 1998 l Range from desktop to huge: u Walmart:
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-1 Chapter 5 Business Intelligence: Data.
1 Chapter 12: Decision-Support Systems for Supply Chain Management CASE: Supply Chain Management Smooths Production Flow Prepared by Hoon Lee Date on 14.
Data Management: Warehousing, Analyzing, Mining & Visualization
Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation.
L The Difference Between Logical and Physical Views of Information l Databases and Database Management Systems l How You Can Develop Database Applications.
Decision Support Systems for Supply Chain Management Chap 10 王仁宏 助理教授 國立中正大學企業管理學系 ©Copyright 2001 製商整合科技中心.
Accelerated Access to BW Al Weedman Idea Integration.
Chapter 2: Data Warehousing
Data Warehousing ISYS 650. What is a data warehouse? A data warehouse is a subject-oriented, integrated, nonvolatile, time-variant collection of data.
1 Data and Knowledge Management. 2 Data Management: A Critical Success Factor The difficulties and the process Data sources and collection Data quality.
TOPIC 1: GAINING COMPETITIVE ADVANTAGE WITH IT (CONTINUE) SUPPLY CHAIN MANAGEMENT & BUSINESS INTELLIGENCE.
Center of Excellence for IT at Bellevue College. IT-enabled business decision making based on simple to complex data analysis processes  Database development.
DASHBOARDS Dashboard provides the managers with exactly the information they need in the correct format at the correct time. BI systems are the foundation.
CHAPTER 4 Data Warehousing, Access, Analysis, Mining, and Visualization.
Lecture-8/ T. Nouf Almujally
Copyright © 2014 Pearson Education, Inc. 1 It's what you learn after you know it all that counts. John Wooden Key Terms and Review (Chapter 6) Enhancing.
Business systems are computer-based information systems that provide organizations with valuable information in a timely and effective manner to allow.
1 Chapter 4 Data Management: Warehousing, Access and Visualization MSS foundation New concepts Object-oriented databases Intelligent databases Data warehouse.
What is Business Intelligence? Business intelligence (BI) –Range of applications, practices, and technologies for the extraction, translation, integration,
Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization.
Exploring Marketing Research William G. Zikmund Chapter 2: Information Systems and Knowledge Management.
CSI315CSI315 Web Development Technologies Continued.
Dr.S.Sridhar,Ph.D., RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc.
OLAP Theory-English version On-Line Analytical processing (Buisness Intelligence) [Ing.Skorkovský,CSc] KPH_ESF_MU.
Data Warehousing, Access, Analysis, Mining, and Visualization
Data Management Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition.
Ihr Logo Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang.
1 CHAPTER 4 Data Warehousing, Access, Analysis, Mining, and Visualization.
Chapter 10  2000 by Prentice Hall Information Systems for Managerial Decision Making Uma Gupta Introduction to Information Systems.
DECISION SUPPORT SYSTEM ARCHITECTURE: The data management component.
OLAP Theory-English version On-Line Analytical processing (Business Intelligence) [Ing.J.Skorkovský,CSc.] Department of corporate economy.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-1 Chapter 5 Business Intelligence: Data.
Case 2: Emerson and Sanofi Data stewards seek data conformity
MTMM Wrap Up–1 Marketing Engineering: A Look Ahead.
Chapter 11 Business Intelligence Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall 11-1.
5-1 McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved.
1 Categories of data Operational and very short-term decision making data Current, short-term decision making, related to financial transactions, detailed.
INTRODUCTION TO GEOGRAPHICAL INFORMATION SCIENCE RSG620 Week 1, Lecture 2 April 11, 2012 Department of RS and GISc Institute of Space Technology, Karachi.
1 CHAPTER 4 Data Management. 2 Data Warehousing, Access, Analysis, Mining, and Visualization n MSS foundation n Many new concepts n Object-oriented databases.
1 Topics about Data Warehouses What is a data warehouse? How does a data warehouse differ from a transaction processing database? What are the characteristics.
Spatial Information Retrieval. Spatial Data Mining + Knowledge Discovery Used for mining data in spatial databases with huge amounts of data Spatial data.
1  Dr. Chen. I n t r o d u c t i o n t o Decision Support Systems Professor Jason Chen School of Business Gonzaga University Spokane, WA 99258
Architecture of Decision Support System
By N.Gopinath AP/CSE. There are 5 categories of Decision support tools, They are; 1. Reporting 2. Managed Query 3. Executive Information Systems 4. OLAP.
DATA RESOURCE MANAGEMENT
CHAPTER 4 Data Warehousing, Access, Analysis, Mining, and Visualization 2 1.
Why BI….? Most companies collect a large amount of data from their business operations. To keep track of that information, a business and would need to.
EXECUTIVE INFORMATION SYSTEMS Executive information system (EIS) – specialized DSSs designed for use by senior-level executives in order to make upper.
Data Resource Management Agenda What types of data are stored by organizations? How are different types of data stored? What are the potential problems.
1 Management Information Systems M Agung Ali Fikri, SE. MM.
The Concepts of Business Intelligence Microsoft® Business Intelligence Solutions.
BUSINESS INTELLIGENCE. The new technology for understanding the past & predicting the future … BI is broad category of technologies that allows for gathering,
Introduction to Business Analytics
INTRODUCTION TO INFORMATION SYSTEMS LECTURE 9: DATABASE FEATURES, FUNCTIONS AND ARCHITECTURES PART (2) أ/ غدير عاشور 1.
McGraw-Hill/Irwin ©2008,The McGraw-Hill Companies, All Rights Reserved Chapter 5 Data Resource Management.
Information Systems and Technologies in Organizations.
Dr.S.Sridhar,Ph.D., RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc.
Data Resource Management
Decision Support Systems
Advanced Applied IT for Business 2
Data Warehousing, Access, Analysis, Mining, and Visualization
Presentation transcript:

Sistem (Pengantar) Penunjang Keputusan Perdagangan Elektronik 1/20 PERDAGANGAN ELEKTRONIK Tinjauan Mekanisme Aplikasi B2C Riset pasar, E-CRM dan periklanan online Aplikasi B2B Perdagangan kolaboratif Perdagangan mobile dan komputasi pervasif Layanan pendukung Persoalan hukum dan etika dalam perdagangan ektronik Referensi lihat SAP : [5] Bab 14

Sistem (Pengantar) Penunjang Keputusan Perdagangan Elektronik 2/20 Multidimensionality 3-D + Spreadsheets (OLAP has this) Data can be organized the way managers like to see them, rather than the way that the system analysts do Different presentations of the same data can be arranged easily and quickly Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry Measures: money, sales volume, head count, inventory profit, actual versus forecast Time: daily, weekly, monthly, quarterly, or yearly

Sistem (Pengantar) Penunjang Keputusan Perdagangan Elektronik 3/20 Multidimensionality Limitations Extra storage requirements Higher cost Extra system resource and time consumption More complex interfaces and maintenance Multidimensionality is especially popular in executive information and support systems

Sistem (Pengantar) Penunjang Keputusan Perdagangan Elektronik 4/20 Geographic Information Systems (GIS) A computer-based system for capturing, storing, checking, integrating, manipulating, and displaying data using digitized maps Spatially-oriented databases Useful in marketing, sales, voting estimation, planned product distribution Available via the Web Can use with GPS Virtual Reality An environment and/or technology that provides artificially generated sensory cues sufficient to engender in the user some willing suspension of disbelief Can share data and interact Can analyze data by creating a landscape Useful in marketing, prototyping aircraft designs VR over the Internet through VRML

Sistem (Pengantar) Penunjang Keputusan Perdagangan Elektronik 5/20 Ringkasan Data for decision making come from internal and external sources The database management system is one of the major components of most management support systems Familiarity with the latest developments is critical Data contain a gold mine of information if they can dig it out Organizations are warehousing and mining data Multidimensional analysis tools and new enterprise-wide system architectures are useful OLAP tools are also useful New data formats for multimedia DBMS Internet and intranets via Web browser interfaces for DBMS access Built-in artificial intelligence methods in DBMS