This is a summary of the Business Intelligence course taught at Utrecht University. Its contents are from the book Business Intelligence: A Managerial Approach by Sharda, Delen, and Turban (4th edition). Since the 2018 edition of the course features an open book exam, there is a keyword section in ...
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Universiteit Utrecht (UU)
Master Business Informatics
Business Intelligence (INFOMBIN)
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BUSINESS INTELLIGENCE
BUSINESS INTELLIGENCE, ANALYTICS, AND DATA
SCIENCE – A MANAGERIAL PERSPECTIVE
Ramesh Sharda, Dursun Delen, and Efraim Turban
Fourth Edition
Summary of Business Intelligence 2018 course (INFOMBIN) at Utrecht University
Koen Niemeijer
,Contents
1. An Overview of Business Intelligence, Analytics, and Data Science ............... 4
1.1 OPENING VIGNETTE: Sports Analytics—An Exciting Frontier for Learning
and Understanding Applications of Analytics ..................................................... 4
1.2 Changing Business Environments and Evolving Needs for Decision Support
and Analytics ............................................................................................................ 4
1.3 Evolution of Computerized Decision Support to Analytics/Data Science 4
1.4 A Framework for Business Intelligence ............................................................ 6
1.5 Analytics Overview ............................................................................................ 7
1.6 Analytics Examples in Selected Domains ....................................................... 8
1.7 A Brief Introduction to Big Data Analytics ...................................................... 9
1.8 An Overview of the Analytics Ecosystem ....................................................... 9
2. Descriptive Analytics I: Nature of Data, Statistical Modelling, and Visualisation
...................................................................................................................................... 11
2.2 The Nature of Data .......................................................................................... 11
2.3 A Simple Taxonomy of Data........................................................................... 12
2.4 The Art and Science of Data Preprocessing ................................................ 14
2.5 Statistical Modelling for Business Analytics ................................................... 16
2.6 Regression Modelling for Inferential Statistics .............................................. 19
2.7 Business Reporting............................................................................................ 20
2.8 Data Visualisation ............................................................................................ 21
2.9 Different Types of Charts and Graphs .......................................................... 22
2.10 The Emergence of Visual Analytics ............................................................. 23
2.11 Information Dashboards ............................................................................... 23
3. Descriptive Analytics II: Business Intelligence and Data Warehousing .......... 24
3.2 Business Intelligence and Data Warehousing.............................................. 24
3.3 Data Warehousing Process ............................................................................ 26
3.4 Data Warehousing Architectures .................................................................. 27
3.5 Data Integration and the Extraction, Transformation, and Load (ETL)
Processes ................................................................................................................. 29
3.6 Data Warehouse Development .................................................................... 30
3.7 Data Warehousing Implementation Issues................................................... 35
3.8 Data Warehouse Administration, Security Issues, and Future Trends ....... 36
3.9 Business Performance Management ............................................................ 38
3.10 Performance Measurement ......................................................................... 39
3.11 Balanced Scorecards ................................................................................... 39
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, 3.12 Six Sigma as a Performance Measurement System .................................. 40
4. Predictive Analytics I: Data Mining Process, Methods, and Algorithms......... 41
4.2 Data Mining Concepts and Applications .................................................... 41
4.3 Data Mining Applications ............................................................................... 43
4.4 Data Mining Process ........................................................................................ 44
4.5 Data Mining Methods ..................................................................................... 45
Classification ....................................................................................................... 45
Estimating the True Accuracy .......................................................................... 45
Cluster Analysis for Data Mining ....................................................................... 49
Association Rule Mining ..................................................................................... 50
4.6 Data Mining Software Tools ............................................................................ 51
4.7 Data Mining Privacy Issues, Myths, and Blunders ........................................ 51
6. Prescriptive Analytics: Optimisation and Simulation ......................................... 52
6.2 Model-Based Decision Making ...................................................................... 52
6.3 Structure of Mathematical Models for Decision Support ........................... 53
6.4 Certainty, Uncertainty, and Risk .................................................................... 54
6.6 Mathematical Programming Optimisation .................................................. 54
6.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking .. 55
6.8 Decision Analysis with Decision Tables and Decision Trees ....................... 56
7. Big Data Concepts and Tools .............................................................................. 56
7.2 Definition of Big Data ...................................................................................... 56
7.3 Fundamentals of Big Data Analytics ............................................................. 57
7.7 Big Data and Stream Analytics ...................................................................... 57
8. Future Trends, Privacy and Managerial Considerations in Analytics ............. 58
8.5 Issues of Legality, Privacy, and Ethics............................................................ 58
8.6 Impacts of Analytics in Organisations: An Overview .................................. 59
Keywords..................................................................................................................... 60
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,1. An Overview of Business Intelligence, Analytics, and
Data Science
1.1 OPENING VIGNETTE: Sports Analytics—An Exciting Frontier
for Learning and Understanding Applications of Analytics
Examples illustrating possible use of business intelligence (BI). Interesting read,
but not much to learn from. This applies to the rest of the opening vignettes as
well and will henceforth be skipped.
1.2 Changing Business Environments and Evolving Needs for
Decision Support and Analytics
Because of fast-paced changing business environments, information
technology is vital to support decision making. Some developments have
clearly contributed to facilitating growth of decision support and analytics in a
number of ways, including the following:
• Group communication and collaboration: Information systems can
improve the collaboration process of a group and enable its members
to be at different locations (saving travel costs) so that decisions can be
made together.
• Improved data management: Data for these can be stored in different
databases anywhere in the organisation and even possibly outside the
organisation and systems can search, store, and transmit needed data
quickly, economically, securely, and transparently.
• Managing giant data warehouses and Big Data: Data warehouses (DWs)
contain enormous amounts of data. Big Data has enabled massive data
coming from a variety of sources and in many different forms, which
allows a very different view into organisational performance that was
not possible in the past.
• Analytical support: Analysis technologies allow for quicker and better
execution of processes at a reduced cost. Expertise can even be
derived directly from analytical systems.
• Overcoming cognitive limits in processing and storing information:
People can only store and processes so much. This is known as their
cognitive limits. Computerised systems enable people to overcome their
cognitive limits by quickly accessing and processing vast amounts of
stored information.
• Knowledge management: Gathered formal and informal sources
support decision making in organisations.
• Anytime, anywhere support: Ubiquitous support increases speed at
which information needs to be processed and converted into decisions.
1.3 Evolution of Computerized Decision Support to
Analytics/Data Science
Data analytics started simple with management information systems (MIS) and
decision support systems (DSS). As systems became more advanced, mature
operations research (OR) models and rule-based expert systems (ES) became
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,prevalent. The 1980s was all about structuring data into centralised solutions
such as enterprise resource planning (ERP) systems and relational database
management (RDBM) systems. In the 1990s, the need for more versatile
reporting led to the development of executive information systems (EISs). To
make this highly versatile reporting possible while keeping the transactional
integrity of the business information systems intact, it was necessary to create
a middle data tier known as a data warehouse (DW) as a repository to
specifically support business reporting and decision making. In the 2000s, these
were called business intelligence (BI) systems. Because of the globalised
competitive marketplace, decision makers needed to acquire data more
quickly. This led to the development of right-time data warehousing. Nowadays,
Big Data drives decision making with social networking/social media as an
interesting data source.
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,1.4 A Framework for Business Intelligence
Business intelligence (BI) is an
umbrella term that combines
architectures, tools, databases,
analytical tools, applications, and
methodologies. BI’s major
objective is to enable interactive
access (sometimes in real time) to
data, to enable manipulation of
data, and to give business
managers and analysts the ability
to conduct appropriate analyses.
The process of BI is based on the transformation of data to information, then to
decisions, and finally to actions.
A BI system has four major components: a DW, with its source data; business
analytics, a collection of tools for manipulating, mining, and analysing the data
in the DW; Business Performance Management (BPM) for monitoring and
analysing performance; and a user interface (e.g., a dashboard).
BI is not transaction processing. That is, processing transactions such as
payments, withdrawals., or calculations of sales. These are handled by online
transaction processing (OLTP). DW contain data used in analysis. The results of
these analyses can then be used for decision making. DWs are intended to
work with informational data used for online analytical processing (OLAP)
systems. Most ERP and supply chain management (SCM) systems are stored in
OTLP where each request is considered to be a transaction.
In order to implement BI in an organisation, it must align with some business goal.
A framework developed by Gartner (2004) decomposes planning and
execution into business, organisation, functionality, and infrastructure
components. That is, strategic and operational objectives must be defined
while considering the available organisational skills to achieve those objectives,
plans and assessments need to be made to make the change, a company
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, must be amenable to change, and various systems must be integrated for
successful implementation. When everything is in place, BI should be started
and a BI Competency Centre should be established within the company.
Another important success factor of BI is its ability to facilitate a real-time, on-
demand agile environment. Whereas traditional DWs use static data, real-time,
on-demand BI uses near-real-time data. One approach is the DW model of
traditional BI systems. Products from innovative BI platform providers provide a
service-oriented, near-real-time solution that populates the DW much faster
than the typical nightly extract/ transfer/load batch update does. Another
approach, business activity management (BAM), bypasses the DW entirely and
uses Web services or other monitoring means to discover key business events.
These software monitors (or intelligent agents) can be placed on server or
application to trigger events themselves.
1.5 Analytics Overview
Analytics is the process of
developing actionable
decisions or recommendations
for actions based on insights
generated from historical data.
This idea of looking at all the
data to understand what is
happening, what will happen,
and how to make the best of it
has also been encapsulated by
the Institute for Operations
Research and Management
Science (INFORMS). These three
levels are identified as
descriptive, predictive, and
prescriptive. in proposing three
levels of analytics.
Descriptive (or reporting) analytics refers to knowing what is happening in the
organisation and understanding some underlying trends and causes of such
occurrences. This involves data consolidation for developing reports and
queries, for example, but also data visualisation.
Predictive analytics aims to determine what is likely to happen in the future. This
analysis is based on statistical techniques as well as other more recently
developed techniques that fall under the general category of data mining. The
goal of these techniques is to be able to predict if the customer is likely to switch
to a competitor (“churn”) or to predict other behavioural patterns using, for
example, logistic regression, clustering algorithms, or association mining
techniques.
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