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Practices of Business Intelligence
HusniLab. Riset JTIF UTM
Course Orientation for
Practices of Business Intelligence
Sumber awal: http://mail.tku.edu.tw/myday/teaching/1071/BI/1071BI01_Business_Intelligence.pptx
Business Intelligence (BI)
2
Introduction to BI and Data Science
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
1
3
5
4
Big Data Analytics
2
6 Future Trends
Course Introduction• This course introduces the fundamental concepts and
technology practices of business intelligence.
• Topics include
– Business Intelligence, Analytics, and Data Science,
– AI, Big Data, and Cloud Computing,
– Descriptive Analytics: Nature of Data, Statistical Modeling, and Visualization, Business Intelligence and Data Warehousing,
– Predictive Analytics: Data Mining Process, Methods, and Algorithms, Text, Web, and Social Media Analytics,
– Prescriptive Analytics: Optimization and Simulation,
– SNA, Machine and Deep Learning, NLP,
– AI Chatbots and Conversational Commerce,
– Future Trends in Analytics.3
Objective
• Understand and apply the fundamental concepts and technology practice of business intelligence.
4
Business Intelligence (BI)
5
Introduction to BI and Data Science
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
1
3
5
4
Big Data Analytics
2
6 Future Trends
1. 1 Course Orientation for Practices of Business Intelligence
2. 2 Business Intelligence, Analytics, and Data Science
3. 3 ABC: AI, Big Data, and Cloud Computing
4. Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization
5. Descriptive Analytics II: Business Intelligence and Data Warehousing
6. Predictive Analytics I: Data Mining Process, Methods, and Algorithms
Syllabus
6
1. Predictive Analytics II: Text, Web, and Social Media Analytics
2. Prescriptive Analytics: Optimization and Simulation
3. Social Network Analysis
4. Machine Learning and Deep Learning
5. Natural Language Processing
6. AI Chatbots and Conversational Commerce
7. Future Trends, Privacy and Managerial Considerations in Analytics
Syllabus
7
Referensi• Slides Kuliah• Business Intelligence, Analytics, and Data Science: A Managerial
Perspective, 4th Edition, Ramesh Sharda, Dursun Delen, and Efraim Turban, Pearson, 2017.
• Decision Support and Business Intelligence Systems, Ninth Edition, Efraim Turban, Ramesh Sharda, Dursun Delen, Pearson, 2011.
8
Team Term Project
• Term Project Topics
– Business Intelligence
– AI Challenge Champion
– Social Network Analysis (SNA)
– FinTech
– Short Text Conversation (STC)
9
10Source: https://www.amazon.com/Business-Intelligence-Analytics-Data-Science/dp/0134633288
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition,
Ramesh Sharda, Dursun Delen, and Efraim Turban, Pearson, 2017.
11Source: https://www.amazon.com/Decision-Support-Business-Intelligence-Systems/dp/013610729X
Decision Support and Business Intelligence Systems, Ninth Edition,Efraim Turban, Ramesh Sharda, Dursun Delen,
Pearson, 2011.
12Source: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems,
Aurélien Géron, O’Reilly Media, 2017
13Source: https://www.amazon.com/Practical-Machine-Learning-Python-Problem-Solvers/dp/1484232062
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems,
Dipanjan Sarkar, Raghav Bali, Tushar Sharma, Apress, 2017.
14Source: https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1787125939
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition,
Sebastian Raschka and Vahid Mirjalili, Packt Publishing, 2017.
15Source: https://www.amazon.com/Data-Mining-Machine-Learning-Practitioners/dp/1118618041
Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners,
Jared Dean, Wiley, 2014.
16Source: https://www.amazon.com/Network-Analysis-Applications-Lecture-Networks/dp/3319781952
Social Network Based Big Data Analysis and Applications, Lecture Notes in Social Networks,
Mehmet Kaya, Jalal Kawash, Suheil Khoury, Min-Yuh Day, Springer International Publishing, 2018.
Google Colab
17https://colab.research.google.com/notebooks/welcome.ipynb
Evolution of Decision Support, Business Intelligence, and
Analytics
18Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
Evolution of Business Intelligence (BI)
19
Chapter 1 • An Overview of Business Intelligence, Analytics, and Data Science 43
Organizations have to work smart. Paying careful attention to the management of BI
initiatives is a necessary aspect of doing business. It is no surprise, then, that organizations
are increasingly championing BI and under its new incarnation as analytics. Application
Case 1.1 illustrates one such application of BI that has helped many airlines as well as, of
course, the companies of fering such services to the airlines.
Business
Int elligence
Spreadsheet s
(M S Excel)
DSS
ETL
Dat a warehouse
Dat a mar t s
M et adat a
Querying and
repor t ing
EIS/ESS
Broadcast ing
t oolsPor t als
OLAP
Scorecards and
dashboards
Aler t s and
not ificat ions
Dat a & t ext
mining Predict ive
analyt ics
Digit al cockpit s
and dashboards
Workflow
Financial
repor t ing
FIGU RE 1.9 Evolution of Business Intelligence (BI).
Technical st af f
Build t he dat a w arehouse
- Organizing
- Summarizing
- St andardizing
Dat a
warehouse
Business user s
Access
Manipulat ion, result s
Managers/execut ives
BPM st rat egies
Fut ure component :
Int elligent syst ems
User int er face
- Browser
- Port al
- Dashboard
Dat a Warehouse
Environment
Business Analyt ics
Environment
Performance and
St rat egy
Dat a
Sources
FIGU RE 1.10 A High-Level Architecture of BI. (Source: Based on W. Eckerson, Smart Companies in the 21st
Century: The Secrets of Creating Successful Business Intelligent Solutions. The Data Warehousing Institute, Seattle, W A,
2003, p. 32, Illustration 5.)
M01_SHAR0543_04_GE_C01.indd 43 17/07/17 2:09 PM
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
A High-Level Architecture of BI
20
Chapter 1 • An Overview of Business Intelligence, Analytics, and Data Science 43
Organizations have to work smart. Paying careful attention to the management of BI
initiatives is a necessary aspect of doing business. It is no surprise, then, that organizations
are increasingly championing BI and under its new incarnation as analytics. Application
Case 1.1 illustrates one such application of BI that has helped many airlines as well as, of
course, the companies of fering such services to the airlines.
Business
Int elligence
Spreadsheet s
(M S Excel)
DSS
ETL
Dat a warehouse
Dat a mar t s
M et adat a
Querying and
repor t ing
EIS/ESS
Broadcast ing
t oolsPor t als
OLAP
Scorecards and
dashboards
Aler t s and
not ificat ions
Dat a & t ext
mining Predict ive
analyt ics
Digit al cockpit s
and dashboards
Workflow
Financial
repor t ing
FIGU RE 1.9 Evolution of Business Intelligence (BI).
Technical st af f
Build t he dat a w arehouse
- Organizing
- Summarizing
- St andardizing
Dat a
warehouse
Business user s
Access
Manipulat ion, result s
Managers/execut ives
BPM st rat egies
Fut ure component :
Int elligent syst ems
User int er face
- Browser
- Port al
- Dashboard
Dat a Warehouse
Environment
Business Analyt ics
Environment
Performance and
St rat egy
Dat a
Sources
FIGU RE 1.10 A High-Level Architecture of BI. (Source: Based on W. Eckerson, Smart Companies in the 21st
Century: The Secrets of Creating Successful Business Intelligent Solutions. The Data Warehousing Institute, Seattle, W A,
2003, p. 32, Illustration 5.)
M01_SHAR0543_04_GE_C01.indd 43 17/07/17 2:09 PM
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
21
Three Types of Analytics
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
22
Analytics Ecosystem
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
23
Job Titles of Analytics
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
A Data to Knowledge Continuum
24Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
Business Intelligence (BI) Infrastructure
25Source: Kenneth C. Laudon & Jane P. Laudon (2014), Management Information Systems: Managing the Digital Firm, Thirteenth Edition, Pearson.
Business Intelligence and Data Mining
Increasing potential
to support
business decisions End User
Business
Analyst
Data
Analyst
DBA
DecisionMaking
Data Presentation
Visualization Techniques
Data MiningInformation Discovery
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems
26Source: Jiawei Han and Micheline Kamber (2006), Data Mining: Concepts and Techniques, Second Edition, Elsevier
Business Insights
with Social Analytics
27
Analyzing the Social Web:
Social Network Analysis
28
29Source: http://www.amazon.com/Analyzing-Social-Web-Jennifer-Golbeck/dp/0124055311
Jennifer Golbeck (2013), Analyzing the Social Web, Morgan Kaufmann
AI ChallengeChampion
30
The 14th NTCIR (2018 - 2019)
31http://research.nii.ac.jp/ntcir/ntcir-14/index.html
NTCIR-14 Short Text Conversation Task (STC-3)
32
33
NTCIR-14 STC-3Short Text Conversation Task (STC-3)
Chinese Emotional Conversation Generation (CECG) Subtask
http://www.aihuang.org/p/challenge.html
Short Text Conversation (NTCIR-13 STC2)Retrieval-based
34Source: http://ntcirstc.noahlab.com.hk/STC2/stc-cn.htm
Short Text Conversation (NTCIR-13 STC2)
Generation-based
35Source: http://ntcirstc.noahlab.com.hk/STC2/stc-cn.htm
• This course introduces the fundamental concepts and technology practices of business intelligence.
• Topics include
– Business Intelligence, Analytics, and Data Science,
– AI, Big Data, and Cloud Computing,
– Descriptive Analytics: Nature of Data, Statistical Modeling, and Visualization, Business Intelligence and Data Warehousing,
– Predictive Analytics: Data Mining Process, Methods, and Algorithms, Text, Web, and Social Media Analytics,
– Prescriptive Analytics: Optimization and Simulation,
– SNA, Machine and Deep Learning, NLP,
– AI Chatbots and Conversational Commerce,
– Future Trends in Analytics.36
Summary
Contact Information
Husni
Lab. Riset JTIF UTM
• Kajian: Web (text) Mining and Retrieval, (Big) Data Science, Internet Apps Engineering, Networking dan Distributed Computing
• Email : husni@trunojoyo.ac.id
• Web: husni.trunojoyo.ac.id
37
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