Business Intelligence, Analytics, and Data Science · Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson Business Intelligence (BI)

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

Outline

• Business Intelligence (BI)

• Analytics

• Data Science

3

Business Intelligence

(BI)4

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



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”



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.”



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),


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



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

Analytics

15

16

Three Types of Analytics



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


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


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.



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.



Data Science and Business Intelligence

23Source: EMC Education Services, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley, 2015



Predictive Analytics and Data Mining

(Data Science)



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

Big Data Analytics Lifecycle

28

Key Roles for a Successful Analytics Project


Overview of Data Analytics Lifecycle


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


Example of Analytics Applications in a Retail Value Chain

33

Retail Value ChainCritical needs at every touch point of the Retail Value Chain



34

Analytics Ecosystem



35

Job Titles of Analytics



Google Colab

36https://colab.research.google.com/notebooks/welcome.ipynb

https://colab.research.google.com/notebooks/welcome.ipynb

Summary

37

• Business Intelligence (BI)

• Analytics

• Data Science

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

Business Intelligence, Analytics, and Data Science · Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson Business Intelligence (BI)

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