Business Intelligence, Analytics, and Data Science · Business Intelligence, Analytics, and Data...
Transcript of Business Intelligence, Analytics, and Data Science · Business Intelligence, Analytics, and Data...
Kecerdasan Bisnis Terapan
HusniLab. Riset JTIF UTM
Business Intelligence, Analytics,
and Data Science
Sumber awal: http://mail.tku.edu.tw/myday/teaching/1071/BI/1071BI02_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
Evolution of Decision Support, Business Intelligence, and
Analytics
5Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
Changing Business Environments and Evolving Needs for Decision Support
and Analytics1. Group communication and collaboration
2. Improved data management
3. Managing giant data warehouses and Big Data
4. Analytical support
5. Overcoming cognitive limits in processing and storing information
6. Knowledge management
7. Anywhere, anytime support
6Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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”
7Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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.”
8Source: 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)
9
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
10
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
Business Intelligence (BI) Infrastructure
11Source: 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
12Source: Jiawei Han and Micheline Kamber (2006), Data Mining: Concepts and Techniques, Second Edition, Elsevier
Architecture of Big Data Analytics
13Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications
Data Mining
OLAP
Reports
QueriesHadoop
MapReducePig
HiveJaql
ZookeeperHbase
CassandraOozieAvro
MahoutOthers
Middleware
Extract Transform
Load
Data Warehouse
Traditional Format
CSV, Tables
* Internal
* External
* Multiple formats
* Multiple locations
* Multiple applications
Big Data Sources
Big Data Transformation
Big Data Platforms & Tools
Big Data Analytics
Applications
Big Data Analytics
Transformed Data
Raw Data
Architecture of Big Data Analytics
14Source: Stephan Kudyba (2014), Big Data, Mining, and Analytics: Components of Strategic Decision Making, Auerbach Publications
Data Mining
OLAP
Reports
QueriesHadoop
MapReducePig
HiveJaql
ZookeeperHbase
CassandraOozieAvro
MahoutOthers
Middleware
Extract Transform
Load
Data Warehouse
Traditional Format
CSV, Tables
* Internal
* External
* Multiple formats
* Multiple locations
* Multiple applications
Big Data Sources
Big Data Transformation
Big Data Platforms & Tools
Big Data Analytics
Applications
Big Data Analytics
Transformed Data
Raw Data
Data MiningBig Data Analytics
Applications
16
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
Three Types of Business Analytics
• Prescriptive Analytics
• Predictive Analytics
• Descriptive Analytics
17Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012
Three Types of Business Analytics
18Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012
Optimization
Statistical Modeling
Randomized Testing
Predictive Modeling /
Forecasting
Alerts
Query / Drill Down
Ad hoc Reports /
Scorecards
Standard Report
“What’s the best that can happen?”
“What if we try this?”
“What will happen next?”
“Why is this happening?”
“What actions are needed?”
“What exactly is the problem?”
“How many, how often, where?”
“What happened?”
Prescriptive
Analytics
Predictive
Analytics
Descriptive
Analytics
Business Intelligence and Enterprise Analytics
• Predictive analytics
• Data mining
• Business analytics
• Web analytics
• Big-data analytics
19Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012
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.
21Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
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.
22Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
Data Science and Business Intelligence
23Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
Data Science and Business Intelligence
24Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
Predictive Analytics and Data Mining
(Data Science)
Data Science and Business Intelligence
25Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
Predictive Analytics and Data Mining
(Data Science)
What if…?What’s the optimal scenario for our business?
What will happen next?What if these trends countinue?
Why is this happening?
Optimization, predictive modeling, forecasting statistical analysis
Structured/unstructured data, many types of sources, very large datasets
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 collaborative26Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
27
Curious and
Creative
Communicative and
CollaborativeSkeptical
Technical
Quantitative
Data Scientist
Data Scientist Profile
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
Key Roles for a Successful Analytics Project
29Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
Overview of Data Analytics Lifecycle
30Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
1. Discovery
2. Data preparation
3. Model planning
4. Model building
5. Communicate results
6. Operationalize
31
Overview of Data Analytics Lifecycle
Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
Key Outputs from a Successful Analytics Project
32Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015
Example of Analytics Applications in a Retail Value Chain
33
Retail Value ChainCritical 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
34
Analytics Ecosystem
Source: Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson
35
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
Google Colab
36https://colab.research.google.com/notebooks/welcome.ipynb
References• Ramesh Sharda, Dursun Delen, and Efraim Turban (2017),
Business Intelligence, Analytics, and Data Science: AManagerial Perspective, 4th Edition, Pearson.
• Kenneth C. Laudon & Jane P. Laudon (2014), ManagementInformation Systems: Managing the Digital Firm, ThirteenthEdition, 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, AuerbachPublications.
• EMC Education Services, Data Science and Big Data Analytics:Discovering, Analyzing, Visualizing and Presenting Data, Wiley,2015.
38