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