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Summary Business Intelligence for Data Science Lecture 2 | Master Data Science & Society $3.21
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Summary Business Intelligence for Data Science Lecture 2 | Master Data Science & Society

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Summary of Business Intelligence for Data Science, based on the lectures from the Master’s program in Data Science and Society at Tilburg University.

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  • December 20, 2024
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  • 2024/2025
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Business Intelligence Lecture 2
Development of Data Warehouse Systems
Data Warehouse is a copy of transaction data
specifically structured for query and analysis.




The differences in design and structure:

Operational database Data Warehouse
Structure: highly integrated (more complex than Structure: collection of star schemas (each
any star schema) representing independent entity)
Purpose: both for queries and updating Purpose: for queries only

What is important at the beginning of data warehouse design?

 Understanding:
1. The problems the client faces in their business
2. The question the client wants to answer, related to their business (E.g. Which product
is the best-selling? What profit can I expect from the sale of individual products?)
 Identification of Business Processes:
Gather user requirements by interviewing clients about their business
Most achieved by conducting surveys and interviewing clients.
 Questions like ‘Describe this specific part of your business’ rather than ‘What do you
want?’

Mapping business processes to dimensions
Map business processes to dimension tables, prioritizing based on importance.




Example:

 Topic/
Process

, Monitoring the efficiency of studying at the faculty or studying fees income
of the faculty.
 Dimensions
Time, Teacher, Course etc.
 Facts
Total number of points a student has in the course
Number of points from pre-exam assignments for the course etc.




Logical and Dimensional Model of DW System
Most common levels of schema design in Data Warehouse

1. Conceptual schema
Represents business needs at a high level using ERD or UML.
 Design techniques: Entity-Relationships Diagram (ERD) or UML Class diagrams
2. Logical schema
Between the conceptual schema and the physical implementation of the database, specifying
details like data types, keys and constraints.
 Design techniques: Entity-Relationships Diagram (ERD) of UML Class diagrams
3. Physical schema
Real implementation of database onto specific DBMS, includes table indexing, storage
optimization, table structure etc.
 Design techniques, DBMS-dependent.
4. Dimensional schema

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