商業智慧實務 Prac&ces of Business Intelligence Tamkang University 商業智慧導論 (Introduction to Business Intelligence) 1022BI01 MI4 Wed, 9,10 (16:10-18:00) (B113)

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(16:10-18:00) (B113) Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept.

1 商業智慧實務 Prac&ces of Business Intelligence Tamkang University 商業智慧導論 (Introduction to Business Intelligence) 1022BI01 MI4 Wed, 9,10 (16:10-18:00) (B113) Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management, Tamkang University 淡江大學資訊管理學系 tku.edu.tw/myday/

2 淡江大學 102 學年度第 2 學期課程教學計畫表 Spring 2014 ( ) 課程名稱 : 商業智慧實務 (Practices of Business Intelligence) 授課教師 : 戴敏育 (Min- Yuh Day) 開課系級 : 資管四 P (TLMXB4P) 開課資料 : 選修單學期 2 學分 (2 Credits, ElecHve) 上課時間 : 週三 9,10 (Wed 16:10-18:00) 上課教室 :B113 2

3 課程簡介本課程介紹商業智慧 (Business Intelligence) 的基礎概念及技術實務課程內容包括商業智慧導論管理決策支援系統與商業智慧企業績效管理資料倉儲商業智慧的資料探勘資料科學與巨量資料分析文字探勘與網路探勘意見探勘與情感分析社會網路分析 3

4 Course Introduc&on This course introduces the fundamental concepts and technology prachces of business intelligence. Topics include IntroducHon to Business Intelligence, Management Decision Support System and Business Intelligence, Business Performance Management, Data Warehousing, Data Mining for Business Intelligence, Data Science and Big Data AnalyHcs, Text and Web Mining, Opinion Mining and SenHment Analysis, Social Network Analysis. 4

5 課程目標瞭解及應用商業智慧基本概念與技術實務 5

6 Objec&ve understand and apply the fundamental concepts and technology prachces of business intelligence. 6

7 課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) 1 103/02/19 商業智慧導論 (IntroducHon to Business Intelligence) 2 103/02/26 管理決策支援系統與商業智慧 (Management Decision Support System and Business Intelligence) 3 103/03/05 企業績效管理 (Business Performance Management) 4 103/03/12 資料倉儲 (Data Warehousing) 5 103/03/19 商業智慧的資料探勘 (Data Mining for Business Intelligence) 6 103/03/26 商業智慧的資料探勘 (Data Mining for Business Intelligence) 7 103/04/02 教學行政觀摩日 (Off- campus study) 8 103/04/09 資料科學與巨量資料分析 (Data Science and Big Data AnalyHcs) 7

8 課程大綱 (Syllabus) 週次日期內容 (Subject/Topics) 9 103/04/16 期中報告 (Midterm Project PresentaHon) /04/23 期中考試週 (Midterm Exam) /04/30 文字探勘與網路探勘 (Text and Web Mining) /05/07 意見探勘與情感分析 (Opinion Mining and SenHment Analysis) /05/14 社會網路分析 (Social Network Analysis) /05/21 期末報告 (Final Project PresentaHon) /05/28 畢業考試週 (Final Exam) 8

9 教材課本與參考書籍教材課本 (Textbook): 講義 (Slides) 參考書籍 (References): Business Intelligence: A Managerial Approach, Second EdiHon, Efraim Turban, Ramesh Sharda, Dursun Delen, David King, 2011, Pearson Decision Support and Business Intelligence Systems, Ninth EdiHon, Efraim Turban, Ramesh Sharda, Dursun Delen, 2011, Pearson 決策支援與企業智慧系統, 九版,Efraim Turban 等著, 李昇暾審定,2011, 華泰 9

10 作業與學期成績計算方式作業篇數 3 篇學期成績計算方式 R 期中評量 :30 % R 期末評量 :30 % R 其他 ( 課堂參與及報告討論表現 ): 40 % 10

11 Business Pressures Responses Support Model Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 11

12 Business Intelligence and Data Mining Increasing potential to support business decisions Decision Making Data Presenta&on Visualization Techniques Data Mining Information Discovery End User Business Analyst Data Analyst Data Explora&on Statistical Summary, Querying, and Reporting Data Preprocessing/Integra&on, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems DBA Source: Han & Kamber (2006) 12

13 Business Intelligence (BI) BI is an umbrella term that combines architectures, tools, databases, analyhcal tools, applicahons, and methodologies Like DSS, BI a content- free expression, so it means different things to different people BI's major objechve is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis BI helps transform data, to informahon (and knowledge), to decisions and finally to achon Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 13

14 A Brief History of BI The term BI was coined by the Gartner Group in the mid- 1990s However, the concept is much older 1970s - MIS reporhng - stahc/periodic reports 1980s - ExecuHve InformaHon Systems (EIS) 1990s - OLAP, dynamic, mulhdimensional, ad- hoc reporhng - > coining of the term BI Inclusion of AI and Data/Text Mining capabilihes; Web- based Portals/Dashboards 2010s - yet to be seen Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 14

15 The Evolu&on of BI Capabili&es Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 15

16 The Architecture of BI A BI system has four major components a data warehouse, with its source data business analyhcs, a collechon of tools for manipulahng, mining, and analyzing the data in the data warehouse; business performance management (BPM) for monitoring and analyzing performance a user interface (e.g., dashboard) Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 16

17 A High- Level Architecture of BI Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 17

18 Components in a BI Architecture The data warehouse is a large repository of well- organized historical data Business analyhcs are the tools that allow transformahon of data into informahon and knowledge Business performance management (BPM) allows monitoring, measuring, and comparing key performance indicators User interface (e.g., dashboards) allows access and easy manipulahon of other BI components Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 18

19 A Conceptual Framework for DW Data Sources ERP Legacy POS Other OLTP/wEB ETL Process Select Extract Transform Integrate Load Metadata Enterprise Data warehouse No data marts option Access Data mart (Marketing) Data mart (Engineering) Data mart (Finance) A P I / Middleware Applications (Visualization) Routine Business Reporting Data/text mining OLAP, Dashboard, Web External data Replication Data mart (...) Custom built applications Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 19

20 A Taxonomy for Data Mining Tasks Data Mining Learning Method Popular Algorithms Prediction Supervised Classification and Regression Trees, ANN, SVM, Genetic Algorithms Classification Supervised Decision trees, ANN/MLP, SVM, Rough sets, Genetic Algorithms Regression Supervised Linear/Nonlinear Regression, Regression trees, ANN/MLP, SVM Association Unsupervised Apriory, OneR, ZeroR, Eclat Link analysis Unsupervised Expectation Maximization, Apriory Algorithm, Graph-based Matching Sequence analysis Unsupervised Apriory Algorithm, FP-Growth technique Clustering Unsupervised K-means, ANN/SOM Outlier analysis Unsupervised K-means, Expectation Maximization (EM) Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 20

21 Social Network Analysis Source: 21

$Mining the Social Web: Analyzing Data from Facebook, Twi\er, LinkedIn, and Other Social$

22 Mining the Social Web: Analyzing Data from Facebook, Twi\er, LinkedIn, and Other Social Media Sites Source: 22

Interactions Web Analytics Voice of Customer Customer Experience Management Knowledge

23 Web Mining Success Stories Amazon.com, Ask.com, ScholasHc.com, Website OpHmizaHon Ecosystem Customer Interaction on the Web Analysis of Interactions Web Analytics Voice of Customer Customer Experience Management Knowledge about the Holistic View of the Customer Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 23

24 A Closed- Loop Process to Op&mize Business Performance Process Steps 1. Strategize 2. Plan 3. Monitor/analyze 4. Act/adjust Each with its own process steps Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 24

25 RFID for Supply Chain BI RFID in Retail Systems Source: Turban et al. (2011), Decision Support and Business Intelligence Systems

26 Implica&ons of Business and Enterprise Social Networks Business oriented social networks can go beyond adverhsing and sales Emerging enterprise social networking apps: Finding and RecruiHng Workers Management AcHviHes and Support Training Knowledge Management and Expert LocaHon e.g., innocenhve.com; awareness.com; Caterpillar Enhancing CollaboraHon Using Blogs and Wikis Within the Enterprise Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 26

27 Implica&ons of Business and Enterprise Social Networks Survey shows that best- in- class companies use blogs and wikis for the following applicahons: Project collaborahon and communicahon (63%) Process and procedure document (63%) FAQs (61%) E- learning and training (46%) Forums for new ideas (41%) Corporate- specific dynamic glossary and terminology (38%) CollaboraHon with customers (24%) Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 27

28 The Benefits of BI The ability to provide accurate informahon when needed, including a real- Hme view of the corporate performance and its parts A survey by Thompson (2004) Faster, more accurate reporhng (81%) Improved decision making (78%) Improved customer service (56%) Increased revenue (49%) Source: Turban et al. (2011), Decision Support and Business Intelligence Systems 28

29 Source: 29

30 Business Intelligence Trends 1. Agile InformaHon Management (IM) 2. Cloud Business Intelligence (BI) 3. Mobile Business Intelligence (BI) 4. AnalyHcs 5. Big Data Source: 30

31 Business Intelligence Trends: Compu&ng and Service Cloud CompuHng and Service Mobile CompuHng and Service Social CompuHng and Service 31

32 Business Intelligence and Analy&cs Business Intelligence 2.0 (BI 2.0) Web Intelligence Web AnalyHcs Web 2.0 Social Networking and Microblogging sites Data Trends Big Data Plaoorm Technology Trends Cloud compuhng plaoorm Source: Lim, E. P., Chen, H., & Chen, G. (2013). Business Intelligence and Analytics: Research Directions. ACM Transactions on Management Information Systems (TMIS), 3(4), 17 32

33 Business Intelligence and Analy&cs: Research Direc&ons 1. Big Data AnalyHcs Data analyhcs using Hadoop / MapReduce framework 2. Text AnalyHcs From InformaHon ExtracHon to QuesHon Answering From SenHment Analysis to Opinion Mining 3. Network Analysis Link mining Community DetecHon Social RecommendaHon Source: Lim, E. P., Chen, H., & Chen, G. (2013). Business Intelligence and Analytics: Research Directions. ACM Transactions on Management Information Systems (TMIS), 3(4), 17 33

34 Source: Davenport, T. H., & Patil, D. J. (2012). Data Scientist. Harvard business review 34

35 Top 10 CIO Technology Priori&es in 2013 Top 10 Technology Priorities Analytics and business intelligence Mobile technologies Cloud computing (SaaS, IaaS, PaaS) Collaboration technologies (workflow) Legacy modernization IT management CRM Virtualization Security ERP Applications Source: Gartner Executive Programs (January 2013) Ranking

36 Top 10 CIO Business Priori&es in 2013 Top 10 Business Priorities Increasing enterprise growth Delivering operational results Reducing enterprise costs Attracting and retaining new customers Improving IT applications and infrastructure Creating new products and services (innovation) Improving efficiency Attracting and retaining the workforce Implementing analytics and big data Expanding into new markets and geographies Ranking Source: Gartner Executive Programs (January 2013) 36

37 Summary This course introduces the fundamental concepts and technology prachces of business intelligence. Topics include IntroducHon to Business Intelligence, Management Decision Support System and Business Intelligence, Business Performance Management, Data Warehousing, Data Mining for Business Intelligence, Data Science and Big Data AnalyHcs, Text and Web Mining, Opinion Mining and SenHment Analysis, Social Network Analysis. 37

38 Contact Informa&on 戴敏育博士 (Min-Yuh Day, Ph.D.) 專任助理教授淡江大學資訊管理學系電話 : #2347 傳真 : 研究室 :i716 ( 覺生綜合大樓 ) 地址 : 新北市淡水區英專路 151 號 myday@mail.tku.edu.tw 網址 : 38

Data Mining 資料探勘 Tamkang University Introduction to Data Mining ( 資料探勘導論 ) 1022DM01 MI4 Wed, 6,7 (13:10-15:00) (B216) Min-Yuh Day 戴敏育 Assistant Profes

Data Mining 資料探勘 Tamkang University Introduction to Data Mining ( 資料探勘導論 ) 1022DM01 MI4 Wed, 6,7 (13:10-15:00) (B216) Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management, Tamkang