Kecerdasan Bisnis Terapan
Business Intelligence, Analytics,
and Data Science
Husni
Lab. Riset JTIF UTM
Sumber awal: http://mail.tku.edu.tw/myday/teaching/1071/BI/1071BI02_Business_Intelligence.pptx
Business Intelligence (BI)
1
Introduction to BI and Data Science
2
Descriptive Analytics
3
Predictive Analytics
4
Prescriptive Analytics
5
Big Data Analytics
6
Future Trends
2
Outline
• Business Intelligence (BI)
• Analytics
• Data Science
3
Business
Intelligence
(BI)
4
Evolution of Decision Support,
Business Intelligence, and
Analytics
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
5
Changing Business Environments and
Evolving Needs for Decision Support
and Analytics
1.
2.
3.
4.
5.
Group communication and collaboration
Improved data management
Managing giant data warehouses and Big Data
Analytical support
Overcoming cognitive limits in processing and
storing information
6. Knowledge management
7. Anywhere, anytime support
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
6
Decision Support Systems (DSS)
(Gorry and Scott-Morton, 1971)
“interactive
computer-based systems,
which help decision makers
utilize data and models to
solve unstructured problems”
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
7
Decision Support Systems (DSS)
(Keen and Scott-Morton, 1978)
“Decision support systems couple the
intellectual resources of individuals with
the capabilities of the computer to
improve the quality of decisions.
It is a computer-based support system for
management decision makers who deal
with semistructured problems.”
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
8
Evolution of Business Intelligence (BI)
Chapter 1 • An Overview of Business Intelligence, Analytics, and D
Quer ying and
r epor t ing
ETL
M et adat a
Dat a war ehouse
DSS
EIS/ESS
Dat a mar t s
Financial
r epor t ing
Spr eadsheet s
(M S Excel)
OLAP
Digit al cockpit s
and dashboar ds
Business
Int elligence
Scor ecar ds and
dashboar ds
Wor kflow
Aler t s and
not ificat ions
Dat a & t ext
mining
Pr edict ive
analyt ics
Br oadcast ing
t ools
Por t als
Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
FIGU RE 1.9Business
Evolution
ofSource:
Business
Intelligence (BI).
Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
9
mining
Br oadcast ing
t ools
Predict ive
analyt ics
Por t als
A High-Level Architecture of BI
FIGU RE 1.9 Evolution of Business Intelligence (BI).
Dat a Warehouse
Environment
Dat a
Sources
Business Analyt ics
Environment
Technical st af f
Business user s
Build t he dat a w arehouse
- Organizing
- Summarizing
- St andardizing
Access
Dat a
warehouse
Performance and
St rat egy
M anagers/execut ives
BPM st rat egies
M anipulat ion, result s
User int erface
Fut ure component :
Int elligent syst ems
- Browser
- Port al
- Dashboard
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
Intelligent
Solutions.
Data
Warehousing
Institute, Seattle, W A,
Source: Business
Ramesh Sharda,
Dursun
Delen, andThe
Efraim
Turban
(2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
10
Business Intelligence (BI) Infrastructure
Source: Kenneth C. Laudon & Jane P. Laudon (2014), Management Information Systems: Managing the Digital Firm, Thirteenth Edition, Pearson.
11
Business Intelligence and Data Mining
Increasing potential
to support
business decisions
End User
Decision
Making
Data Presentation
Visualization Techniques
Business
Analyst
Data Mining
Information Discovery
Data
Analyst
Data Exploration
Statistical Summary, Querying, and Reporting
Data Preprocessing/Integration, Data Warehouses
Data Sources
Paper, Files, Web documents, Scientific experiments, Database Systems
Source: Jiawei Han and Micheline Kamber (2006), Data Mining: Concepts and Techniques, Second Edition, Elsevier
DBA
12
Architecture of Big Data Analytics
Big Data
Sources
Big Data
Transformation
Middleware
* Internal
* External
* Multiple
formats
* Multiple
locations
* Multiple
applications
Big Data
Platforms & Tools
Raw
Data
Hadoop
Transformed MapReduce
Pig
Data
Extract
Hive
Transform
Jaql
Load
Zookeeper
Hbase
Data
Cassandra
Warehouse
Oozie
Avro
Mahout
Traditional
Others
Format
CSV, Tables
Big Data
Analytics
Applications
Queries
Big Data
Analytics
Reports
OLAP
Data
Mining
Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications
13
Architecture of Big Data Analytics
Big Data
Sources
* Internal
* External
* Multiple
formats
* Multiple
locations
* Multiple
applications
Big Data
Transformation
Big Data
Platforms & Tools
Data Mining
Big Data
Analytics
Applications
Middleware
Raw
Data
Hadoop
Transformed MapReduce
Pig
Data
Extract
Hive
Transform
Jaql
Load
Zookeeper
Hbase
Data
Cassandra
Warehouse
Oozie
Avro
Mahout
Traditional
Others
Format
CSV, Tables
Big Data
Analytics
Applications
Queries
Big Data
Analytics
Reports
OLAP
Data
Mining
Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications
14
Analytics
15
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
16
Three Types of Business Analytics
• Prescriptive Analytics
• Predictive Analytics
• Descriptive Analytics
Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012
17
Three Types of Business Analytics
Optimization
Randomized Testing
“What’s the best that can happen?” Prescriptive
Analytics
“What if we try this?”
Predictive Modeling /
Forecasting
“What will happen next?”
Statistical Modeling
“Why is this happening?”
Alerts
“What actions are needed?”
Query / Drill Down
“What exactly is the problem?”
Ad hoc Reports /
Scorecards
“How many, how often, where?”
Standard Report
“What happened?”
Predictive
Analytics
Descriptive
Analytics
Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012
18
Business Intelligence and
Enterprise Analytics
•
•
•
•
•
Predictive analytics
Data mining
Business analytics
Web analytics
Big-data analytics
Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012
19
Data Science
20
Data Analyst
• Data analyst is just another term for
professionals who were doing BI in the form of
data compilation, cleaning, reporting, and
perhaps some visualization.
• Their skill sets included Excel, some SQL
knowledge, and reporting.
• You would recognize those capabilities as
descriptive or reporting analytics.
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
21
Data Scientist
• Data scientist is responsible for predictive analysis, statistical
analysis, and more advanced analytical tools and algorithms.
• They may have a deeper knowledge of algorithms and may
recognize them under various labels—data mining, knowledge
discovery, or machine learning.
• Some of these professionals may also need deeper
programming knowledge to be able to write code for data
cleaning/analysis in current Web-oriented languages such as
Java or Python and statistical languages such as R.
• Many analytics professionals also need to build significant
expertise in statistical modeling, experimentation, and
analysis.
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
22
Data Science and
Business Intelligence
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
23
Data Science and
Business Intelligence
Predictive Analytics
and Data Mining
(Data Science)
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
24
Predictive
Analytics
Data Science
and
and Data
Mining
Business
Intelligence
(Data Science)
Structured/unstructured data, many types of sources,
very large datasets
Optimization, predictive modeling, forecasting statistical analysis
What if…?
What’s the optimal scenario for our business?
What will happen next?
What if these trends countinue?
Why is this happening?
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
25
Profile of a Data Scientist
• Quantitative
– mathematics or statistics
• Technical
– software engineering,
machine learning,
and programming skills
• Skeptical mind-set and critical thinking
• Curious and creative
• Communicative and collaborative
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
26
Data Scientist Profile
Quantitative
Technical
Data
Scientist
Skeptical
Curious
and
Creative
Communicative
and
Collaborative
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
27
Big Data Analytics
Lifecycle
28
Key Roles for a
Successful Analytics Project
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
29
Overview of Data Analytics Lifecycle
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
30
Overview of Data Analytics Lifecycle
1. Discovery
2. Data preparation
3. Model planning
4. Model building
5. Communicate results
6. Operationalize
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
31
Key Outputs from a
Successful Analytics Project
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
32
Example of Analytics Applications
in a Retail Value Chain
Retail Value Chain
Critical needs at every touch point of the Retail Value Chain
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
33
Analytics Ecosystem
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
34
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
35
Google Colab
https://colab.research.google.com/notebooks/welcome.ipynb
36
Summary
• Business Intelligence (BI)
• Analytics
• Data Science
37
References
• Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A
Managerial Perspective, 4th Edition, Pearson.
• Kenneth C. Laudon & Jane P. Laudon (2014), Management
Information Systems: Managing the Digital Firm, Thirteenth
Edition, Pearson.
• Jiawei Han and Micheline Kamber (2006), Data Mining:
Concepts and Techniques, Second Edition, Elsevier.
• Stephan Kudyba (2014), Big Data, Mining, and Analytics:
Components of Strategic Decision Making, Auerbach
Publications.
• EMC Education Services, Data Science and Big Data Analytics:
Discovering, Analyzing, Visualizing and Presenting Data, Wiley,
2015.
38