A 30-paged summary for the course Decision Support Systems (Tilburg University, year 3). It contains the slides of every lecture, as well as a summary of the four articles. The book is optional, and therefore not included, nor is there any explanation about Python, since that really depends on your...
Decision Support
Systems (346242-B-6)
SUMMARY – ARTICLES - SLIDES
,INHOUD
Lesson 1 31-8 .......................................................................................................................................................... 1
Literature I: Data warehousing ........................................................................................................................... 3
Lesson 2 7-9 ............................................................................................................................................................ 9
Literature II OLAP database technology ........................................................................................................... 11
Lesson 3 14-9 ........................................................................................................................................................ 11
Literature III Performance dashboards ............................................................................................................. 13
Lesson 4 28-9 ........................................................................................................................................................ 15
Literature IV Knowledge-based systems........................................................................................................... 16
Lesson 5 12-10 ...................................................................................................................................................... 19
Lesson 6 2-11 ........................................................................................................................................................ 24
Lesson 7 16-11 ...................................................................................................................................................... 27
Lecture 8 23-11 ..................................................................................................................................................... 29
Lecture 9 30-11 ..................................................................................................................................................... 30
Tips for the exam:
• Book is only for background reading (Not necessary)
• Go through the slides (Included in this summary)
• Read the four articles (Include in this summary)
• Only one question from lab 1 and lab 2 about Python will be asked.
• Sometimes you have to write algorithms, but not the code itself (Just explain how it would look like).
Criteria are based on creativity
• The code is written on paper, so not Anaconda. Any imports will be given beforehand
• No questions about Corvid will be asked
• Draw.io is allowed. The drawings can be saved as a JPEG file, and upload to testvision
• Proctorio will be used
• Always show the calculation behind a numerical answer
• If you have any questions, don’t hesitate to contact me via Stuvia!
LESSON 1 31-8
Decision support system: An umbrella term to describe any computerized system that supports decision-
making in an organization. It’s a set of expandable, interactive IT techniques and tools designed for processing
and analysing data and for supporting managers in decision making. To do this, the system matches individual
resources of managers with computer resources to improve the quality of the decision made. It’s mainly
evolved from two research fields: decision making processes for organizations and technical research on
interactive IT systems
Business intelligence: Focuses mainly on data warehousing and descriptive analytics.
• It is an umbrella term that combines the processes, technologies, and tools needed to transform data
into information, information into knowledge, and knowledge into plants that drive profitable
business action.
1
, • Information and knowledge that enables business decision-making
Business analytics: Predictive and prescriptive analytics (But is sometimes viewed as the same as BI)
Data warehouse: A collection of data that supports decision-making processes, operating in a read-only mode
(Data cannot be changed and updates are offline). It’s primary goal is to properly define a process to data into
information. A database that is maintained separately from the organization’s operational databases for the
purpose of managerial decision-making. Data warehousing means that you construct/use a warehouse. A
datamart is a is a subset of a data warehouse. It’s subject-oriented, integrated, time-variant and non-volatile
collection of data in support of management’s decision-making process:
• Subject-oriented: Focusing on the analysis of data for decision makers, not on daily
operations/transaction processing. Provides a simple view around particular subjects by excluding
data that are not useful in the decision support process
2
, • Integrated: Constructed by integrating multiple, heterogeneous data sources. Data cleaning and data
integration techniques are applied. Ensures consistency and is therefore converted
• Time-variant: Time horizon for the data warehous is significantly longer than that of operational
systems. The operational database is only current value data, whereas data warehouse data is as long
as the existence of the firm. The data always contains a time-element
• Non-volatile: Operational updates of data do not occur in the data warehouse environment and is
therefore stable. Requires only 2 operations in data processing:
o Initial loading of data (SQL: Insert)
o Access of data (SQL: Select)
A database is separate because of three reasons:
• Historic data: Decision support requires historical data which operational databases do not typically
maintain
• Data integration & consolidation: It requires aggregation & summarization of data from
heterogeneous sources
• Data quality: Different sources typically use inconsistent data representations, codes and formats
which have to be reconciled for decision-making
Meta/master data: Data about data: location, meaning, origin, applied transformations, quality, update
frequency, access right etc.
• Internal meta-data: Relevant for system administrators. It defines data sources, transformation
processes, population policies, and user profiles
• External meta-data: Relevant to end-users. It’s about definitions, quality standards, unit of measure
and relevant aggregation
LITERATURE I: DATA WAREHOUSING
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