SlideShare a Scribd company logo
1 of 23
© 2018 Arianto Muditomo All Rights Reserved
Copyright Notice:
This presentation is prepared by Author for Perbanas Institute as a part of Author Lecture Series. It is to be used for educational and non-
commercial purposes only and is not to be changed, altered, or used for any commercial endeavor without the express written permission from
Author and/or Perbanas Institute. Appropriate legal action may be taken against any person, organization, or entity attempting to misrepresent,
charge, or profit from the educational materials contained here.
Authors are allowed to use their own articles without seeking permission from any person, organization, or entity.
arianto.muditomo@2018
Referrences:
1) Baltzan, Paige 2014. Business Driven Information Systems. 4th Edition. New York: McGraw-Hill.
2) Pearlson, Keri E. And Saunders Carol S. 2013. Managing and Using Information Systems: A Strategic Approach. 5th Ed.
Danvers: John Wiley & Sons.
3) Turban, Efraim, Volonino, Linda, and Wood, Gregory 2013. Information Technology for Management. 9th Edition.
Hoboken: John Wiley & Sons. (Chapter 11)
4) Turban, Efraim, Strauss, Judy, and Lai, Linda 2016. Social Commerce: Marketing and Technology Management. Hidelberg:
Springer.
5) Xu, Jun and Quaddus, Mohammed 2013. Managing Information Systems: Ten Essential Topics. Amsterdam: Atlantis Press.
6) Turban, Rainer: Introduction to Information Systems Enablig and Transforming Business 2nd Ed., John Wiley & Sons.2009
7) Kenneth C. Laudon and Jane P. Laudon, Management Information Systems, Managing The Digital Firm, Pearson: Prentice
Hall 2006
8) Business information systems : technology, development and management for the e-business / Paul Bocij, Andrew
Greasley and Simon Hickie. – Fifth edition., © Pearson Education Limited 2015
1
• Session #1: Information System in Business
• Session #2: IT Strategic Planning
• Session #3: Business Information System
• Session #4: Business Intelligence & Decision Support
• Session #5: Ethics, Privacy and Security
• Session #6: e-Business and e-Commerce
• Session #7: Knowledge Management
• Session #8: Enterprise Information System
arianto.muditomo@2018
2
§ Understand organizations’ need for business intelligence (BI), BI technologies, and how to
make a business case for BI investments.
§ Describe BI architecture, data mining, predictive analytics, dashboards, scorecards, and
other reporting and visualization tools.
§ Understand the value of data, text, and Web mining. Understand managerial decision-
making processes.
§ Describe decision support systems (DSSs), benefits, and structure.
§ Take a forward look at the future of BI in the form of mobile intelligence (MI).
arianto.muditomo@2018
3
§ What is Business Intelligence System?
§ What is major components of BI?
§ What is Decision Support System?
§ What is major components of DSS?
arianto.muditomo@2018
WHAT IS BUSINESS INTELLIGENCE SYSTEM?
4
arianto.muditomo@2018
TYPE OF BUSINESS INTELLIGENCE
5
Book [3] p.328
arianto.muditomo@2018
6
Competing and conflicting versions of the truth
Lagging reports
Can’t perform in-depth analysis
Difficulty finding crucial data
Need simple-to-use production reporting technology
Delay and difficulty consolidating data
Not able to comply with government and regulatory reporting mandates
arianto.muditomo@2018
7
Credit: Gartner Methodology
Source: https://financesonline.com/15-best-business-intelligence-tools-small-big-business/
arianto.muditomo@2018
8
§ Getting information too late
§ Getting data at the wrong level of detail—either too
detailed or too summarized
§ Getting too many directionless data
§ Not being able to coordinate with other departments
across the enterprise
§ Not being able to share data in a timely manner
arianto.muditomo@2018
9
Data W/H; Data Mining;
OLAP; Dashboards;WEB,
Social Media
Search; Data Visualization;
Scorecards
Components
Query;
Reporting;
Analytics
Core Functions
• BI capabilities depend on an integration of several ITs, BI incorporates data warehousing, data mining,
online analytical processing (OLAP), dashboards, the use of the Web, and, increasingly, social media.
• Three core functions of BI are query, reporting, and analytics. Queries are one way to access a particular
view of the data or to analyze what is happening or has happened.
• Data mining and predictive analytic tools are used to find relationships that are hid- den or not obvious, or
to predict what is going to happen.
• BI also includes processes and tools to accurately and consistently consolidate data from multiple sources
and to ensure data quality.
arianto.muditomo@2018
BI technology evolved beyond being primarily a reporting system when the following
features were added: (1) sophisticated predictive analytics, (2) event-driven
(real-time) alerts, and (3) operational decision support.
10
Predictive Analytics
Event-Driven Alerts
Decision Support
is the branch of data mining that focuses on forecasting
trends (e.g., regression analysis) and estimating
probabilities of future events.
are real-time alerts or warnings that are broadcast
when a predefined event, or unusual event, occurs.
arianto.muditomo@2018
11
v Flaw #1. Believing That “IfYou Build It, They Will Come.”
v Flaw #2. Being Locked into an “Excel Culture.”
v Flaw #3. Ignoring Data Quality and Relevance Issues.
v Flaw #4.Treating BI as a Static System.
v Flaw #5. Pressing BI Developers to Buy or Build
Dashboards Quickly and with a Small Budget.
v Flaw #6.Trying to Create a “SingleVersion of the Truth”
When One Doesn’t Exist.
v Flaw #7. Lack of a BI Strategy.
arianto.muditomo@2018
12
Customer
Segmentation
What market segments do my customers
fall into and what are their characteristics?
Propensity to buy
Which customers are most likely to respond to
my promotion?
Customer
profitability
What is the lifetime profitability of my
customers?
Fraud detection
How can I detect which transactions are likely
to be fraudulent?
Customer attrition
Which customers are at risk of leaving?
Channel
optimization
What is the best channel to reach my
customers in each segment?
Personalize customer relationships for
higher customer satisfaction and
retention.
Target customers based on their need
to increase their loyalty to your product
line. Also, increase campaign
profitability by focusing on those most
likely to buy.
Make business interaction decisions
based on the overall profitability of
customers or customer segments.
Quickly detect fraud and take
immediate action to minimize cost.
Prevent loss of high-value customers
and let go of lower-value customers.
Interact with customers based on their
preference and your need to manage
cost.
Analytical App. Business Question Business Value
arianto.muditomo@2018
13
arianto.muditomo@2018
§ Decision Support Systems (DSS) are a specific class of computerized
information system that supports business and organizational decision-making
activities .
§ A properly designed DSS is an interactive software- based system intended to
help decision makers compile useful information from raw data, documents,
personal knowledge, and/or business models to identify and solve problems
and make decisions.
14
Because decision-making involves a complex
sequence of activities over time, it implies there
are at least three functions that should be assigned
to DSSs:
1) The capability of capturing and saving
information from previous activities;
2) Data processing capability;
3) Data retrieval capability.
arianto.muditomo@2018
15
DSS
Data
Knowledge
Database
In-House
Proprietary
DB
Processing
Model
(Financial;
Accounting;
Economical)
External &
Internal
Environment
Qualified Decision
arianto.muditomo@2018
§ Database.
A DSS database system, like any database, contains data from multiple
sources. Some DSSs do not have a separate database; data are entered
into the DSS model as needed (e.g., as soon as they are collected by
sensors).
§ Model Base.
A model base contains completed models and sets of rules, which are
the building blocks necessary to develop DSS applications.Types of
models include financial, statistical, management science, or
economic. Model-building software, such as Excel, has built-in
mathematical and statistical functions.These models provide the
system’s analytical capabilities.
§ User Interface.
The user interface covers all aspects of the communications between a
user and the DSS. A well-designed user interface can greatly improve
the productivity of the user and reduce errors.
§ Users.
A DSS is a tool for the user, the decision maker.The user is considered
to be a part of the highly interactive DSS system. A DSS has two broad
classes of users: managers and staff specialists, such as financial
analysts, production planners, and market researchers.
§ Knowledge Base.
Many unstructured and semi structured problems are so complex that
they require expertise for their solutions. Such expertise can be
provided by a knowledge-based system, such as an expert system.
16
arianto.muditomo@2018
17
Figure 11.15
Conceptual model of
DSS and its
components.
[3] p. 351
arianto.muditomo@2018
18
Figure 11.14
Phases in the decision-
making process.
[3] P. 348
Decision makers go
through four systematic
phases:
• intelligence,
• design,
• choice, and
• implementation
arianto.muditomo@2018
19
Intelligence
Design
Choice
Implementation
Monitoring
Problem
Solving
Decision
Making
arianto.muditomo@2018
§ What is Business Intelligence System?
Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis,
and presentation of business information.. Business Intelligence systems are data-driven Decision Support Systems
(DSS)
§ What is major components of BI?
The major components of BI are data warehouses and/or marts, predictive analytics, data mining, data visualization
soft- ware, and a business performance management system.
§ What is Decision Support System?
A DSS is an approach that can improve the effectiveness of decision making, decrease the need for training, improve
management control, facilitate communication, reduce costs, and allow for more objective decision making.
§ What is major components of DSS?
The major components of a DSS are a database and its management, the model base and its management, and the
user- friendly interface.
20
arianto.muditomo@2018
§ You can find a good reference from
https://blog.marketresearch.com/10-ways-business-
intelligence-can-improve-your-organization
§ Please find a real example how BI can help manager to make
decisions
21
© 2018 Arianto Muditomo All Rights Reserved

More Related Content

What's hot

Management Information System
Management Information SystemManagement Information System
Management Information SystemTinku Kumar
 
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATADATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
 
Big Data is Here for Financial Services White Paper
Big Data is Here for Financial Services White PaperBig Data is Here for Financial Services White Paper
Big Data is Here for Financial Services White PaperExperian
 
Role of MIS in Business Management (Supermarket Case Study)
Role of MIS in Business Management  (Supermarket Case Study)Role of MIS in Business Management  (Supermarket Case Study)
Role of MIS in Business Management (Supermarket Case Study)Loise Maina
 
Boosting Cybersecurity with Data Governance (peer reviewed)
Boosting Cybersecurity with Data Governance (peer reviewed)Boosting Cybersecurity with Data Governance (peer reviewed)
Boosting Cybersecurity with Data Governance (peer reviewed)Guy Pearce
 
Information Governance: Reducing Costs and Increasing Customer Satisfaction
Information Governance: Reducing Costs and Increasing Customer SatisfactionInformation Governance: Reducing Costs and Increasing Customer Satisfaction
Information Governance: Reducing Costs and Increasing Customer SatisfactionCapgemini
 
Advantages of an integrated governance, risk and compliance environment
Advantages of an integrated governance, risk and compliance environmentAdvantages of an integrated governance, risk and compliance environment
Advantages of an integrated governance, risk and compliance environmentIBM Analytics
 
Knowledge Management Megat
Knowledge Management MegatKnowledge Management Megat
Knowledge Management Megatomzairul
 
Executing on Information Governance (Learning From Law Firms)
Executing on Information Governance (Learning From Law Firms)Executing on Information Governance (Learning From Law Firms)
Executing on Information Governance (Learning From Law Firms)Nick Inglis
 
Impact of business intelligence on
Impact of business intelligence onImpact of business intelligence on
Impact of business intelligence onijejournal
 
G11.2011 magic quadrant for mdm of product data solutions
G11.2011   magic quadrant for mdm of product data solutionsG11.2011   magic quadrant for mdm of product data solutions
G11.2011 magic quadrant for mdm of product data solutionsSatya Harish
 
Unstructured Data into EHR Systems: Challenges and Solutions
Unstructured Data into EHR Systems: Challenges and SolutionsUnstructured Data into EHR Systems: Challenges and Solutions
Unstructured Data into EHR Systems: Challenges and SolutionsDATAMARK
 
[MU-630] 001. Information System In Business
[MU-630] 001. Information System In Business[MU-630] 001. Information System In Business
[MU-630] 001. Information System In BusinessArianto Muditomo
 
Enterprise Information management
Enterprise Information managementEnterprise Information management
Enterprise Information managementThe Open Group SA
 
Information governance presentation
Information governance   presentationInformation governance   presentation
Information governance presentationIgor Swann
 
Evtm 281 07_bi2015_infographic_r2h
Evtm 281 07_bi2015_infographic_r2hEvtm 281 07_bi2015_infographic_r2h
Evtm 281 07_bi2015_infographic_r2hNadia Smith
 
Challenges & Benefits In Creating An Information Governance Program
Challenges & Benefits In Creating An Information Governance ProgramChallenges & Benefits In Creating An Information Governance Program
Challenges & Benefits In Creating An Information Governance ProgramKevin Nugent
 

What's hot (20)

Management Information System
Management Information SystemManagement Information System
Management Information System
 
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATADATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATA
 
Big data assignment
Big data assignmentBig data assignment
Big data assignment
 
Big Data is Here for Financial Services White Paper
Big Data is Here for Financial Services White PaperBig Data is Here for Financial Services White Paper
Big Data is Here for Financial Services White Paper
 
Role of MIS in Business Management (Supermarket Case Study)
Role of MIS in Business Management  (Supermarket Case Study)Role of MIS in Business Management  (Supermarket Case Study)
Role of MIS in Business Management (Supermarket Case Study)
 
Boosting Cybersecurity with Data Governance (peer reviewed)
Boosting Cybersecurity with Data Governance (peer reviewed)Boosting Cybersecurity with Data Governance (peer reviewed)
Boosting Cybersecurity with Data Governance (peer reviewed)
 
Information Governance: Reducing Costs and Increasing Customer Satisfaction
Information Governance: Reducing Costs and Increasing Customer SatisfactionInformation Governance: Reducing Costs and Increasing Customer Satisfaction
Information Governance: Reducing Costs and Increasing Customer Satisfaction
 
Advantages of an integrated governance, risk and compliance environment
Advantages of an integrated governance, risk and compliance environmentAdvantages of an integrated governance, risk and compliance environment
Advantages of an integrated governance, risk and compliance environment
 
Offers bank dss
Offers bank dssOffers bank dss
Offers bank dss
 
Knowledge Management Megat
Knowledge Management MegatKnowledge Management Megat
Knowledge Management Megat
 
Executing on Information Governance (Learning From Law Firms)
Executing on Information Governance (Learning From Law Firms)Executing on Information Governance (Learning From Law Firms)
Executing on Information Governance (Learning From Law Firms)
 
Impact of business intelligence on
Impact of business intelligence onImpact of business intelligence on
Impact of business intelligence on
 
G11.2011 magic quadrant for mdm of product data solutions
G11.2011   magic quadrant for mdm of product data solutionsG11.2011   magic quadrant for mdm of product data solutions
G11.2011 magic quadrant for mdm of product data solutions
 
Unstructured Data into EHR Systems: Challenges and Solutions
Unstructured Data into EHR Systems: Challenges and SolutionsUnstructured Data into EHR Systems: Challenges and Solutions
Unstructured Data into EHR Systems: Challenges and Solutions
 
Big data research
Big data researchBig data research
Big data research
 
[MU-630] 001. Information System In Business
[MU-630] 001. Information System In Business[MU-630] 001. Information System In Business
[MU-630] 001. Information System In Business
 
Enterprise Information management
Enterprise Information managementEnterprise Information management
Enterprise Information management
 
Information governance presentation
Information governance   presentationInformation governance   presentation
Information governance presentation
 
Evtm 281 07_bi2015_infographic_r2h
Evtm 281 07_bi2015_infographic_r2hEvtm 281 07_bi2015_infographic_r2h
Evtm 281 07_bi2015_infographic_r2h
 
Challenges & Benefits In Creating An Information Governance Program
Challenges & Benefits In Creating An Information Governance ProgramChallenges & Benefits In Creating An Information Governance Program
Challenges & Benefits In Creating An Information Governance Program
 

Similar to [MU630] 004. Business Intelligence & Decision Support

003. Business Information System
003. Business Information System003. Business Information System
003. Business Information SystemArianto Muditomo
 
Data Science - Part I - Sustaining Predictive Analytics Capabilities
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesData Science - Part I - Sustaining Predictive Analytics Capabilities
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
 
Board matters quarterly – volume 3
Board matters quarterly – volume 3Board matters quarterly – volume 3
Board matters quarterly – volume 3elithomas202
 
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET Journal
 
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET Journal
 
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
 
Unveiling the Power of Data Analytics Transforming Insights into Action.pdf
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfUnveiling the Power of Data Analytics Transforming Insights into Action.pdf
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
 
006. e -Business & e-Commerce
006. e -Business & e-Commerce006. e -Business & e-Commerce
006. e -Business & e-CommerceArianto Muditomo
 
Sit717 enterprise business intelligence 2019 t2 copy1
Sit717 enterprise business intelligence 2019 t2 copy1Sit717 enterprise business intelligence 2019 t2 copy1
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
 
Article mis, hapzi ali, nur rizqiana, nanda suharti, nurul, anisa dwi, vin...
Article mis, hapzi ali, nur    rizqiana, nanda suharti, nurul, anisa dwi, vin...Article mis, hapzi ali, nur    rizqiana, nanda suharti, nurul, anisa dwi, vin...
Article mis, hapzi ali, nur rizqiana, nanda suharti, nurul, anisa dwi, vin...Heru Ramadhon
 
008. Enterprise Information System
008. Enterprise Information System008. Enterprise Information System
008. Enterprise Information SystemArianto Muditomo
 
001. Information System in Business
001. Information System in Business001. Information System in Business
001. Information System in BusinessArianto Muditomo
 
Evolution of Records Management in Law Firms
Evolution of Records Management in Law FirmsEvolution of Records Management in Law Firms
Evolution of Records Management in Law FirmsJim Merrifield, IGP, CIP
 

Similar to [MU630] 004. Business Intelligence & Decision Support (20)

003. Business Information System
003. Business Information System003. Business Information System
003. Business Information System
 
Data Science - Part I - Sustaining Predictive Analytics Capabilities
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesData Science - Part I - Sustaining Predictive Analytics Capabilities
Data Science - Part I - Sustaining Predictive Analytics Capabilities
 
Board matters quarterly – volume 3
Board matters quarterly – volume 3Board matters quarterly – volume 3
Board matters quarterly – volume 3
 
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...
 
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...
 
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
 
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...
 
Unveiling the Power of Data Analytics Transforming Insights into Action.pdf
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfUnveiling the Power of Data Analytics Transforming Insights into Action.pdf
Unveiling the Power of Data Analytics Transforming Insights into Action.pdf
 
006. e -Business & e-Commerce
006. e -Business & e-Commerce006. e -Business & e-Commerce
006. e -Business & e-Commerce
 
Sit717 enterprise business intelligence 2019 t2 copy1
Sit717 enterprise business intelligence 2019 t2 copy1Sit717 enterprise business intelligence 2019 t2 copy1
Sit717 enterprise business intelligence 2019 t2 copy1
 
Article mis, hapzi ali, nur rizqiana, nanda suharti, nurul, anisa dwi, vin...
Article mis, hapzi ali, nur    rizqiana, nanda suharti, nurul, anisa dwi, vin...Article mis, hapzi ali, nur    rizqiana, nanda suharti, nurul, anisa dwi, vin...
Article mis, hapzi ali, nur rizqiana, nanda suharti, nurul, anisa dwi, vin...
 
008. Enterprise Information System
008. Enterprise Information System008. Enterprise Information System
008. Enterprise Information System
 
001. Information System in Business
001. Information System in Business001. Information System in Business
001. Information System in Business
 
Evolution of Records Management in Law Firms
Evolution of Records Management in Law FirmsEvolution of Records Management in Law Firms
Evolution of Records Management in Law Firms
 
Big data Analytics
Big data AnalyticsBig data Analytics
Big data Analytics
 

More from AriantoMuditomo

[HR601] 008. Enterpreneurship & Innovative Leadership
[HR601] 008. Enterpreneurship & Innovative Leadership[HR601] 008. Enterpreneurship & Innovative Leadership
[HR601] 008. Enterpreneurship & Innovative LeadershipAriantoMuditomo
 
[HR601] 007. Creativity & Innovation
[HR601] 007. Creativity & Innovation[HR601] 007. Creativity & Innovation
[HR601] 007. Creativity & InnovationAriantoMuditomo
 
[HR601] 006. Introduction of Innovation & Entrepreneurship
[HR601] 006. Introduction of Innovation & Entrepreneurship[HR601] 006. Introduction of Innovation & Entrepreneurship
[HR601] 006. Introduction of Innovation & EntrepreneurshipAriantoMuditomo
 
[HR601] 005. Organization Change Management & Culture Change management
[HR601] 005. Organization Change Management & Culture Change management[HR601] 005. Organization Change Management & Culture Change management
[HR601] 005. Organization Change Management & Culture Change managementAriantoMuditomo
 
[HR601] 004. Introduction to Change Management
[HR601] 004. Introduction to Change Management[HR601] 004. Introduction to Change Management
[HR601] 004. Introduction to Change ManagementAriantoMuditomo
 
[HR601] 003. KM Strategy & Implementation
[HR601] 003. KM Strategy & Implementation[HR601] 003. KM Strategy & Implementation
[HR601] 003. KM Strategy & ImplementationAriantoMuditomo
 
[HR601] 002. KM & Organizational Learning
[HR601] 002. KM & Organizational Learning[HR601] 002. KM & Organizational Learning
[HR601] 002. KM & Organizational LearningAriantoMuditomo
 
[HR601] 001. Introduction of Knowledge Management
[HR601] 001. Introduction of Knowledge Management [HR601] 001. Introduction of Knowledge Management
[HR601] 001. Introduction of Knowledge Management AriantoMuditomo
 
[MU630] 008. Enterprise Information System
[MU630] 008. Enterprise Information System[MU630] 008. Enterprise Information System
[MU630] 008. Enterprise Information SystemAriantoMuditomo
 
[MU630] 007. Knowledge Management
[MU630] 007. Knowledge Management[MU630] 007. Knowledge Management
[MU630] 007. Knowledge ManagementAriantoMuditomo
 
[MU630] 006. e-Business & e-Commerce
[MU630] 006. e-Business & e-Commerce[MU630] 006. e-Business & e-Commerce
[MU630] 006. e-Business & e-CommerceAriantoMuditomo
 
[MU630] 005. Ethics, Privacy and Security
[MU630] 005. Ethics, Privacy and Security[MU630] 005. Ethics, Privacy and Security
[MU630] 005. Ethics, Privacy and SecurityAriantoMuditomo
 
[MU630] 002. IT Strategic Planning
[MU630] 002. IT Strategic Planning[MU630] 002. IT Strategic Planning
[MU630] 002. IT Strategic PlanningAriantoMuditomo
 

More from AriantoMuditomo (13)

[HR601] 008. Enterpreneurship & Innovative Leadership
[HR601] 008. Enterpreneurship & Innovative Leadership[HR601] 008. Enterpreneurship & Innovative Leadership
[HR601] 008. Enterpreneurship & Innovative Leadership
 
[HR601] 007. Creativity & Innovation
[HR601] 007. Creativity & Innovation[HR601] 007. Creativity & Innovation
[HR601] 007. Creativity & Innovation
 
[HR601] 006. Introduction of Innovation & Entrepreneurship
[HR601] 006. Introduction of Innovation & Entrepreneurship[HR601] 006. Introduction of Innovation & Entrepreneurship
[HR601] 006. Introduction of Innovation & Entrepreneurship
 
[HR601] 005. Organization Change Management & Culture Change management
[HR601] 005. Organization Change Management & Culture Change management[HR601] 005. Organization Change Management & Culture Change management
[HR601] 005. Organization Change Management & Culture Change management
 
[HR601] 004. Introduction to Change Management
[HR601] 004. Introduction to Change Management[HR601] 004. Introduction to Change Management
[HR601] 004. Introduction to Change Management
 
[HR601] 003. KM Strategy & Implementation
[HR601] 003. KM Strategy & Implementation[HR601] 003. KM Strategy & Implementation
[HR601] 003. KM Strategy & Implementation
 
[HR601] 002. KM & Organizational Learning
[HR601] 002. KM & Organizational Learning[HR601] 002. KM & Organizational Learning
[HR601] 002. KM & Organizational Learning
 
[HR601] 001. Introduction of Knowledge Management
[HR601] 001. Introduction of Knowledge Management [HR601] 001. Introduction of Knowledge Management
[HR601] 001. Introduction of Knowledge Management
 
[MU630] 008. Enterprise Information System
[MU630] 008. Enterprise Information System[MU630] 008. Enterprise Information System
[MU630] 008. Enterprise Information System
 
[MU630] 007. Knowledge Management
[MU630] 007. Knowledge Management[MU630] 007. Knowledge Management
[MU630] 007. Knowledge Management
 
[MU630] 006. e-Business & e-Commerce
[MU630] 006. e-Business & e-Commerce[MU630] 006. e-Business & e-Commerce
[MU630] 006. e-Business & e-Commerce
 
[MU630] 005. Ethics, Privacy and Security
[MU630] 005. Ethics, Privacy and Security[MU630] 005. Ethics, Privacy and Security
[MU630] 005. Ethics, Privacy and Security
 
[MU630] 002. IT Strategic Planning
[MU630] 002. IT Strategic Planning[MU630] 002. IT Strategic Planning
[MU630] 002. IT Strategic Planning
 

Recently uploaded

VIP Call Girls Thane Sia 8617697112 Independent Escort Service Thane
VIP Call Girls Thane Sia 8617697112 Independent Escort Service ThaneVIP Call Girls Thane Sia 8617697112 Independent Escort Service Thane
VIP Call Girls Thane Sia 8617697112 Independent Escort Service ThaneCall girls in Ahmedabad High profile
 
Quarter 4- Module 3 Principles of Marketing
Quarter 4- Module 3 Principles of MarketingQuarter 4- Module 3 Principles of Marketing
Quarter 4- Module 3 Principles of MarketingMaristelaRamos12
 
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptxOAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptxhiddenlevers
 
(DIYA) Bhumkar Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(DIYA) Bhumkar Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...(DIYA) Bhumkar Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(DIYA) Bhumkar Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...ranjana rawat
 
Call US 📞 9892124323 ✅ Kurla Call Girls In Kurla ( Mumbai ) secure service
Call US 📞 9892124323 ✅ Kurla Call Girls In Kurla ( Mumbai ) secure serviceCall US 📞 9892124323 ✅ Kurla Call Girls In Kurla ( Mumbai ) secure service
Call US 📞 9892124323 ✅ Kurla Call Girls In Kurla ( Mumbai ) secure servicePooja Nehwal
 
Andheri Call Girls In 9825968104 Mumbai Hot Models
Andheri Call Girls In 9825968104 Mumbai Hot ModelsAndheri Call Girls In 9825968104 Mumbai Hot Models
Andheri Call Girls In 9825968104 Mumbai Hot Modelshematsharma006
 
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...ssifa0344
 
How Automation is Driving Efficiency Through the Last Mile of Reporting
How Automation is Driving Efficiency Through the Last Mile of ReportingHow Automation is Driving Efficiency Through the Last Mile of Reporting
How Automation is Driving Efficiency Through the Last Mile of ReportingAggregage
 
Dividend Policy and Dividend Decision Theories.pptx
Dividend Policy and Dividend Decision Theories.pptxDividend Policy and Dividend Decision Theories.pptx
Dividend Policy and Dividend Decision Theories.pptxanshikagoel52
 
letter-from-the-chair-to-the-fca-relating-to-british-steel-pensions-scheme-15...
letter-from-the-chair-to-the-fca-relating-to-british-steel-pensions-scheme-15...letter-from-the-chair-to-the-fca-relating-to-british-steel-pensions-scheme-15...
letter-from-the-chair-to-the-fca-relating-to-british-steel-pensions-scheme-15...Henry Tapper
 
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptxFinTech Belgium
 
High Class Call Girls Nagpur Grishma Call 7001035870 Meet With Nagpur Escorts
High Class Call Girls Nagpur Grishma Call 7001035870 Meet With Nagpur EscortsHigh Class Call Girls Nagpur Grishma Call 7001035870 Meet With Nagpur Escorts
High Class Call Girls Nagpur Grishma Call 7001035870 Meet With Nagpur Escortsranjana rawat
 
06_Joeri Van Speybroek_Dell_MeetupDora&Cybersecurity.pdf
06_Joeri Van Speybroek_Dell_MeetupDora&Cybersecurity.pdf06_Joeri Van Speybroek_Dell_MeetupDora&Cybersecurity.pdf
06_Joeri Van Speybroek_Dell_MeetupDora&Cybersecurity.pdfFinTech Belgium
 
Log your LOA pain with Pension Lab's brilliant campaign
Log your LOA pain with Pension Lab's brilliant campaignLog your LOA pain with Pension Lab's brilliant campaign
Log your LOA pain with Pension Lab's brilliant campaignHenry Tapper
 
Bladex Earnings Call Presentation 1Q2024
Bladex Earnings Call Presentation 1Q2024Bladex Earnings Call Presentation 1Q2024
Bladex Earnings Call Presentation 1Q2024Bladex
 
The Economic History of the U.S. Lecture 17.pdf
The Economic History of the U.S. Lecture 17.pdfThe Economic History of the U.S. Lecture 17.pdf
The Economic History of the U.S. Lecture 17.pdfGale Pooley
 
Vip B Aizawl Call Girls #9907093804 Contact Number Escorts Service Aizawl
Vip B Aizawl Call Girls #9907093804 Contact Number Escorts Service AizawlVip B Aizawl Call Girls #9907093804 Contact Number Escorts Service Aizawl
Vip B Aizawl Call Girls #9907093804 Contact Number Escorts Service Aizawlmakika9823
 
Independent Lucknow Call Girls 8923113531WhatsApp Lucknow Call Girls make you...
Independent Lucknow Call Girls 8923113531WhatsApp Lucknow Call Girls make you...Independent Lucknow Call Girls 8923113531WhatsApp Lucknow Call Girls make you...
Independent Lucknow Call Girls 8923113531WhatsApp Lucknow Call Girls make you...makika9823
 

Recently uploaded (20)

VIP Call Girls Thane Sia 8617697112 Independent Escort Service Thane
VIP Call Girls Thane Sia 8617697112 Independent Escort Service ThaneVIP Call Girls Thane Sia 8617697112 Independent Escort Service Thane
VIP Call Girls Thane Sia 8617697112 Independent Escort Service Thane
 
Commercial Bank Economic Capsule - April 2024
Commercial Bank Economic Capsule - April 2024Commercial Bank Economic Capsule - April 2024
Commercial Bank Economic Capsule - April 2024
 
Quarter 4- Module 3 Principles of Marketing
Quarter 4- Module 3 Principles of MarketingQuarter 4- Module 3 Principles of Marketing
Quarter 4- Module 3 Principles of Marketing
 
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptxOAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
OAT_RI_Ep19 WeighingTheRisks_Apr24_TheYellowMetal.pptx
 
(DIYA) Bhumkar Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(DIYA) Bhumkar Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...(DIYA) Bhumkar Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(DIYA) Bhumkar Chowk Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
 
Call US 📞 9892124323 ✅ Kurla Call Girls In Kurla ( Mumbai ) secure service
Call US 📞 9892124323 ✅ Kurla Call Girls In Kurla ( Mumbai ) secure serviceCall US 📞 9892124323 ✅ Kurla Call Girls In Kurla ( Mumbai ) secure service
Call US 📞 9892124323 ✅ Kurla Call Girls In Kurla ( Mumbai ) secure service
 
Andheri Call Girls In 9825968104 Mumbai Hot Models
Andheri Call Girls In 9825968104 Mumbai Hot ModelsAndheri Call Girls In 9825968104 Mumbai Hot Models
Andheri Call Girls In 9825968104 Mumbai Hot Models
 
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
Solution Manual for Financial Accounting, 11th Edition by Robert Libby, Patri...
 
How Automation is Driving Efficiency Through the Last Mile of Reporting
How Automation is Driving Efficiency Through the Last Mile of ReportingHow Automation is Driving Efficiency Through the Last Mile of Reporting
How Automation is Driving Efficiency Through the Last Mile of Reporting
 
Dividend Policy and Dividend Decision Theories.pptx
Dividend Policy and Dividend Decision Theories.pptxDividend Policy and Dividend Decision Theories.pptx
Dividend Policy and Dividend Decision Theories.pptx
 
letter-from-the-chair-to-the-fca-relating-to-british-steel-pensions-scheme-15...
letter-from-the-chair-to-the-fca-relating-to-british-steel-pensions-scheme-15...letter-from-the-chair-to-the-fca-relating-to-british-steel-pensions-scheme-15...
letter-from-the-chair-to-the-fca-relating-to-british-steel-pensions-scheme-15...
 
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
05_Annelore Lenoir_Docbyte_MeetupDora&Cybersecurity.pptx
 
High Class Call Girls Nagpur Grishma Call 7001035870 Meet With Nagpur Escorts
High Class Call Girls Nagpur Grishma Call 7001035870 Meet With Nagpur EscortsHigh Class Call Girls Nagpur Grishma Call 7001035870 Meet With Nagpur Escorts
High Class Call Girls Nagpur Grishma Call 7001035870 Meet With Nagpur Escorts
 
06_Joeri Van Speybroek_Dell_MeetupDora&Cybersecurity.pdf
06_Joeri Van Speybroek_Dell_MeetupDora&Cybersecurity.pdf06_Joeri Van Speybroek_Dell_MeetupDora&Cybersecurity.pdf
06_Joeri Van Speybroek_Dell_MeetupDora&Cybersecurity.pdf
 
Log your LOA pain with Pension Lab's brilliant campaign
Log your LOA pain with Pension Lab's brilliant campaignLog your LOA pain with Pension Lab's brilliant campaign
Log your LOA pain with Pension Lab's brilliant campaign
 
Bladex Earnings Call Presentation 1Q2024
Bladex Earnings Call Presentation 1Q2024Bladex Earnings Call Presentation 1Q2024
Bladex Earnings Call Presentation 1Q2024
 
Veritas Interim Report 1 January–31 March 2024
Veritas Interim Report 1 January–31 March 2024Veritas Interim Report 1 January–31 March 2024
Veritas Interim Report 1 January–31 March 2024
 
The Economic History of the U.S. Lecture 17.pdf
The Economic History of the U.S. Lecture 17.pdfThe Economic History of the U.S. Lecture 17.pdf
The Economic History of the U.S. Lecture 17.pdf
 
Vip B Aizawl Call Girls #9907093804 Contact Number Escorts Service Aizawl
Vip B Aizawl Call Girls #9907093804 Contact Number Escorts Service AizawlVip B Aizawl Call Girls #9907093804 Contact Number Escorts Service Aizawl
Vip B Aizawl Call Girls #9907093804 Contact Number Escorts Service Aizawl
 
Independent Lucknow Call Girls 8923113531WhatsApp Lucknow Call Girls make you...
Independent Lucknow Call Girls 8923113531WhatsApp Lucknow Call Girls make you...Independent Lucknow Call Girls 8923113531WhatsApp Lucknow Call Girls make you...
Independent Lucknow Call Girls 8923113531WhatsApp Lucknow Call Girls make you...
 

[MU630] 004. Business Intelligence & Decision Support

  • 1. © 2018 Arianto Muditomo All Rights Reserved Copyright Notice: This presentation is prepared by Author for Perbanas Institute as a part of Author Lecture Series. It is to be used for educational and non- commercial purposes only and is not to be changed, altered, or used for any commercial endeavor without the express written permission from Author and/or Perbanas Institute. Appropriate legal action may be taken against any person, organization, or entity attempting to misrepresent, charge, or profit from the educational materials contained here. Authors are allowed to use their own articles without seeking permission from any person, organization, or entity.
  • 2. arianto.muditomo@2018 Referrences: 1) Baltzan, Paige 2014. Business Driven Information Systems. 4th Edition. New York: McGraw-Hill. 2) Pearlson, Keri E. And Saunders Carol S. 2013. Managing and Using Information Systems: A Strategic Approach. 5th Ed. Danvers: John Wiley & Sons. 3) Turban, Efraim, Volonino, Linda, and Wood, Gregory 2013. Information Technology for Management. 9th Edition. Hoboken: John Wiley & Sons. (Chapter 11) 4) Turban, Efraim, Strauss, Judy, and Lai, Linda 2016. Social Commerce: Marketing and Technology Management. Hidelberg: Springer. 5) Xu, Jun and Quaddus, Mohammed 2013. Managing Information Systems: Ten Essential Topics. Amsterdam: Atlantis Press. 6) Turban, Rainer: Introduction to Information Systems Enablig and Transforming Business 2nd Ed., John Wiley & Sons.2009 7) Kenneth C. Laudon and Jane P. Laudon, Management Information Systems, Managing The Digital Firm, Pearson: Prentice Hall 2006 8) Business information systems : technology, development and management for the e-business / Paul Bocij, Andrew Greasley and Simon Hickie. – Fifth edition., © Pearson Education Limited 2015 1 • Session #1: Information System in Business • Session #2: IT Strategic Planning • Session #3: Business Information System • Session #4: Business Intelligence & Decision Support • Session #5: Ethics, Privacy and Security • Session #6: e-Business and e-Commerce • Session #7: Knowledge Management • Session #8: Enterprise Information System
  • 3. arianto.muditomo@2018 2 § Understand organizations’ need for business intelligence (BI), BI technologies, and how to make a business case for BI investments. § Describe BI architecture, data mining, predictive analytics, dashboards, scorecards, and other reporting and visualization tools. § Understand the value of data, text, and Web mining. Understand managerial decision- making processes. § Describe decision support systems (DSSs), benefits, and structure. § Take a forward look at the future of BI in the form of mobile intelligence (MI).
  • 4. arianto.muditomo@2018 3 § What is Business Intelligence System? § What is major components of BI? § What is Decision Support System? § What is major components of DSS?
  • 5. arianto.muditomo@2018 WHAT IS BUSINESS INTELLIGENCE SYSTEM? 4
  • 6. arianto.muditomo@2018 TYPE OF BUSINESS INTELLIGENCE 5 Book [3] p.328
  • 7. arianto.muditomo@2018 6 Competing and conflicting versions of the truth Lagging reports Can’t perform in-depth analysis Difficulty finding crucial data Need simple-to-use production reporting technology Delay and difficulty consolidating data Not able to comply with government and regulatory reporting mandates
  • 8. arianto.muditomo@2018 7 Credit: Gartner Methodology Source: https://financesonline.com/15-best-business-intelligence-tools-small-big-business/
  • 9. arianto.muditomo@2018 8 § Getting information too late § Getting data at the wrong level of detail—either too detailed or too summarized § Getting too many directionless data § Not being able to coordinate with other departments across the enterprise § Not being able to share data in a timely manner
  • 10. arianto.muditomo@2018 9 Data W/H; Data Mining; OLAP; Dashboards;WEB, Social Media Search; Data Visualization; Scorecards Components Query; Reporting; Analytics Core Functions • BI capabilities depend on an integration of several ITs, BI incorporates data warehousing, data mining, online analytical processing (OLAP), dashboards, the use of the Web, and, increasingly, social media. • Three core functions of BI are query, reporting, and analytics. Queries are one way to access a particular view of the data or to analyze what is happening or has happened. • Data mining and predictive analytic tools are used to find relationships that are hid- den or not obvious, or to predict what is going to happen. • BI also includes processes and tools to accurately and consistently consolidate data from multiple sources and to ensure data quality.
  • 11. arianto.muditomo@2018 BI technology evolved beyond being primarily a reporting system when the following features were added: (1) sophisticated predictive analytics, (2) event-driven (real-time) alerts, and (3) operational decision support. 10 Predictive Analytics Event-Driven Alerts Decision Support is the branch of data mining that focuses on forecasting trends (e.g., regression analysis) and estimating probabilities of future events. are real-time alerts or warnings that are broadcast when a predefined event, or unusual event, occurs.
  • 12. arianto.muditomo@2018 11 v Flaw #1. Believing That “IfYou Build It, They Will Come.” v Flaw #2. Being Locked into an “Excel Culture.” v Flaw #3. Ignoring Data Quality and Relevance Issues. v Flaw #4.Treating BI as a Static System. v Flaw #5. Pressing BI Developers to Buy or Build Dashboards Quickly and with a Small Budget. v Flaw #6.Trying to Create a “SingleVersion of the Truth” When One Doesn’t Exist. v Flaw #7. Lack of a BI Strategy.
  • 13. arianto.muditomo@2018 12 Customer Segmentation What market segments do my customers fall into and what are their characteristics? Propensity to buy Which customers are most likely to respond to my promotion? Customer profitability What is the lifetime profitability of my customers? Fraud detection How can I detect which transactions are likely to be fraudulent? Customer attrition Which customers are at risk of leaving? Channel optimization What is the best channel to reach my customers in each segment? Personalize customer relationships for higher customer satisfaction and retention. Target customers based on their need to increase their loyalty to your product line. Also, increase campaign profitability by focusing on those most likely to buy. Make business interaction decisions based on the overall profitability of customers or customer segments. Quickly detect fraud and take immediate action to minimize cost. Prevent loss of high-value customers and let go of lower-value customers. Interact with customers based on their preference and your need to manage cost. Analytical App. Business Question Business Value
  • 15. arianto.muditomo@2018 § Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decision-making activities . § A properly designed DSS is an interactive software- based system intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions. 14 Because decision-making involves a complex sequence of activities over time, it implies there are at least three functions that should be assigned to DSSs: 1) The capability of capturing and saving information from previous activities; 2) Data processing capability; 3) Data retrieval capability.
  • 17. arianto.muditomo@2018 § Database. A DSS database system, like any database, contains data from multiple sources. Some DSSs do not have a separate database; data are entered into the DSS model as needed (e.g., as soon as they are collected by sensors). § Model Base. A model base contains completed models and sets of rules, which are the building blocks necessary to develop DSS applications.Types of models include financial, statistical, management science, or economic. Model-building software, such as Excel, has built-in mathematical and statistical functions.These models provide the system’s analytical capabilities. § User Interface. The user interface covers all aspects of the communications between a user and the DSS. A well-designed user interface can greatly improve the productivity of the user and reduce errors. § Users. A DSS is a tool for the user, the decision maker.The user is considered to be a part of the highly interactive DSS system. A DSS has two broad classes of users: managers and staff specialists, such as financial analysts, production planners, and market researchers. § Knowledge Base. Many unstructured and semi structured problems are so complex that they require expertise for their solutions. Such expertise can be provided by a knowledge-based system, such as an expert system. 16
  • 18. arianto.muditomo@2018 17 Figure 11.15 Conceptual model of DSS and its components. [3] p. 351
  • 19. arianto.muditomo@2018 18 Figure 11.14 Phases in the decision- making process. [3] P. 348 Decision makers go through four systematic phases: • intelligence, • design, • choice, and • implementation
  • 21. arianto.muditomo@2018 § What is Business Intelligence System? Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information.. Business Intelligence systems are data-driven Decision Support Systems (DSS) § What is major components of BI? The major components of BI are data warehouses and/or marts, predictive analytics, data mining, data visualization soft- ware, and a business performance management system. § What is Decision Support System? A DSS is an approach that can improve the effectiveness of decision making, decrease the need for training, improve management control, facilitate communication, reduce costs, and allow for more objective decision making. § What is major components of DSS? The major components of a DSS are a database and its management, the model base and its management, and the user- friendly interface. 20
  • 22. arianto.muditomo@2018 § You can find a good reference from https://blog.marketresearch.com/10-ways-business- intelligence-can-improve-your-organization § Please find a real example how BI can help manager to make decisions 21
  • 23. © 2018 Arianto Muditomo All Rights Reserved