A clear and elaborate summary of the Business Intelligence: A Managerial Perspective on Analytics by Ramesh Sharda. Extra chapters include Process Mining, De-anonymization, Big Data and EU regulations regarding the General Data Protection Regulation (GDPR). Chapters included are: 1, 2, 3, 6 and 7. ...
Unfortunately we did not have to study chapters 4 and 5 for our course, therefore it is not included. I always mention this in the description of my summaries.
By: annisofia • 6 year ago
By: martijnpaulussen • 6 year ago
Dear Annisofia, thank you for the review! I hope the summary will serve you well :)
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Business Intelligence Summary
A Managerial Perspective on Analytics
Ramesh Sharda, Dursun Delen & Efraim Turban
Author: Martijn C. Paulussen
University: Maastricht University School of Business and Economics
Master: MSc Business Intelligence & Smart Services
Course: [EBC4221] Business Intelligence for Smart Services
School of Business and Economics
MSc Business Intelligence & Smart Services
Nothing in this publication may be reproduced and/or made public by means of printing, offset, photocopy or
microfilm or in any digital, electronic, optical or any other form without the prior written permission of the
owner of the copyright.
,Table of Contents
Chapter 1 – An Overview of Business Intelligence, Analytics, and Decision Support ..................................3
1.1. Opening Vignette: ..........................................................................................................................3
1.2. Business Environments and Computerized Decision Support .......................................................3
1.3. Framework for Business Intelligence (BI) .....................................................................................4
1.4. Intelligence creation, use and BI Governance ................................................................................5
1.5. Transaction Processing (OLTP) versus Analytical Processing (OLAP) ........................................6
1.6. BI Implementation ..........................................................................................................................6
1.7. Analytics overview .........................................................................................................................6
1.8. Introduction: Big Data Analytics....................................................................................................7
1.9. Chapter Highlights..........................................................................................................................7
Chapter 2 – Data Warehousing.......................................................................................................................8
2.1. Data Warehousing concepts ...........................................................................................................8
2.2. Data Warehouse Process overview.................................................................................................9
2.3. Data Warehouse Architectures .......................................................................................................9
2.4. Data integration, extraction, transformation and loading processes .............................................10
2.5. Data Warehouse Development .....................................................................................................11
2.6. Data Warehouse Implementation Issues.......................................................................................12
2.7. Real-Time Data Warehousing (RDW) / Active Data Warehousing (ADW)................................13
2.8. Data Warehouse Administration, Security and Trends ................................................................14
2.9. Chapter Highlights........................................................................................................................14
Chapter 3 – Business Reporting, Visual Analytics, and Business Performance Management.....................15
3.1. Business Reporting Definitions and Concepts ..................................................................................15
3.2. Data and Information Visualization ..................................................................................................16
3.3. Types of Charts and Graphs ..............................................................................................................16
3.4. Emergence of Data Visualization and Visual Analytics....................................................................17
3.5. Performance Dashboards ...................................................................................................................17
3.6. Business Performance Management ..................................................................................................18
3.7. Performance Measurement ................................................................................................................19
3.8. Balanced Scorecards ..........................................................................................................................19
3.9. Six Sigma ..........................................................................................................................................20
Chapter 6 – Big Data and Analytics .............................................................................................................21
6.1. Big Data .............................................................................................................................................21
6.2. Data Analytics Fundamentals ............................................................................................................21
6.3. Big Data Technologies ......................................................................................................................22
6.4. Data Scientist .....................................................................................................................................23
6.5. Big Data and Data Warehousing .......................................................................................................23
6.6. Big Data Vendors ..............................................................................................................................24
1
, 6.7. Big Data and Stream Analytics .........................................................................................................24
6.8. Stream Analytics Applications ..........................................................................................................25
Chapter 7 – Business Analytics: Emerging Trends and Future Impacts ......................................................26
7.1. Location-Based Analytics for Organizations ....................................................................................26
7.2. Analytics Applications for Consumers ..............................................................................................27
7.3. Recommendation Engines .................................................................................................................27
7.4. Web 2.0 Revolution and Online Social Networking .........................................................................28
7.5. Cloud Computing and BI...................................................................................................................28
7.7. Analytics impact on Organizations....................................................................................................29
7.8. Legal, Privacy and Ethics Issues .......................................................................................................30
7.9. Analytical Ecosystem overview ........................................................................................................31
Chapter Extra – Paper Summaries ................................................................................................................32
E.1. Four Strategies for the Age of Smart Services ..................................................................................32
E.2. Process Mining Discovering Workflow Models from Event-Based Data ........................................32
E.3. Process Mining Manifesto ................................................................................................................33
E.4. A Study on Big Data Integration with Data Warehouse ...................................................................35
E.5. Critical Questions for Big Data: Provocations for a Cultural Technological, and Scholarly
Phenomenon .............................................................................................................................................36
E.6. De-anonymization attack on geo-located data ..................................................................................36
E.7. EU regulations on algorithmic decision-making and a “right to explanation” .................................36
2
, Chapter 1 – An Overview of Business Intelligence, Analytics, and Decision
Support
Learning objectives:
Understand business environment and survival of organizations.
Need for computerized support of Managerial Decision making.
Business Intelligence methodology and concepts.
Various types of analytics.
1.1. Opening Vignette:
Reporting analytics: The graphical analysis of the data which allows users to get an insight in the
situation.
Predictive analytics: Analysis using data mining techniques to estimate what future behavior would be.
1.2. Business Environments and Computerized Decision Support
Business Pressures-Responses-Support Model: Responses (actions) taken by companies to counter the
pressures (or take advantage of opportunities) by computerized support that facilitates the monitoring of
the environment.
Business Environmental Factors:
Markets: Competition, global market, outsourcing IT support, real-time transactions.
Consumer demands: Customization, quality, speed of delivery, less loyal.
Technology: Innovations, information overload, Social networking.
Societal: Government regulation, terrorist attacks, sustainability.
Organization responses: Reactive, anticipative, adaptive and proactive. A few examples:
Strategic planning
Innovative business models
Restructure Business Processes
Business alliances
E-commerce
Improve data access
Employ analytics for decision making
Decision support: Analyses, predictions and decisions based on Business Intelligence and data.
3
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