BI's architecture includes a data warehouse, business analytics, and performance and strategy components. Business analytics involves applying models directly to business data using management support systems and decision support. Online analytical processing (OLAP) creates new business information through calculations on existing data using graphical tools that provide multidimensional views of data and allow for simple analysis. OLAP differs from OLTP in examining complex relationships across many data items.

2.  BI’s Architecture and Components
 Data Warehouse
 Business Analytics
▪ Automated decision systems
 Performance and Strategy
2

3. 3

4. The Business Analytics (BA) Field: An
Overview
 The Essentials of BA
 Analytics
The science of analysis.
 Business analytics (BA)
The application of models directly to business data.
▪ BA involves using MSS (Management Support System)
tools, especially models, in assisting decision makers;
essentially a form of OLAP decision support

5. The Business Analytics (BA) Field: An
Overview

6. Online Analytical Processing (OLAP)
 Online analytical processing (OLAP)
Online analytical processing technology that
creates new business information through a
robust set of business transformation and
calculations executed upon existing data

7. Online Analytical Processing (OLAP)
 Online analytical processing (OLAP)
The use of a set of graphical tools that
provides users with multidimensional views
of their data and allows them to analyze the
data using simple windowing techniques

8. Online Analytical Processing (OLAP)
 OLAP versus OLTP
 OLTP concentrates on processing repetitive
transactions in large quantities and conducting
simple manipulations
 OLAP involves examining many data items
complex relationships
 OLAP may analyze relationships and look for
patterns, trends, and exceptions
 OLAP is a direct decision support method

9. Codd’s 12 Rules for OLAP
1. Multidimensional 7. Dynamic sparse matrix
conceptual view for handling
formulating queries 8. Multiuser support rather than
2. Transparency to the user support for only a single user
3. Easy accessibility: batch 9. Unrestricted cross-
and online access dimensional operations
4. Consistent reporting 10. Intuitive data manipulation
performance 11. Flexible reporting
5. Client/server architecture: 12. Unlimited dimensions and
the use of distributed aggregation level
resources
6. Generic dimensionality

10.  Reports
 Routine reports
 Ad hoc (or on-demand) reports
 Multilingual support
 Scorecards and dashboards
 Report delivery and alerting
▪ Report distribution through any touchpoint
▪ Self-subscription as well as administrator-based
distribution
▪ Delivery on-demand, on-schedule, or on-event
▪ Automatic content personalization

11.  Ad hoc query
A query that cannot be determined prior to
the moment the query is issued
 Structured Query Language (SQL)
A data definition and management language
for relational databases. SQL front ends most
relational DBMS

12.  Multidimensionality
The ability to organize, present, and analyze
data by several dimensions, such as sales by
region, by product, by salesperson, and by
time (four dimensions)
 Multidimensional presentation
 Dimensions
 Measures
 Time

15.  Multidimensional tools and vendors
 Tools with multidimensional capabilities often
work in conjunction with database query systems
and other OLAP tools

16.  Limitations of dimensionality
 The multidimensional database can take up significantly
more computer storage room than a summarized
relational database
 Multidimensional products cost significantly more than
standard relational products
 Database loading consumes significant system resources
and time, depending on data volume and the number of
dimensions
 Interfaces and maintenance are more complex in
multidimensional databases than in relational databases

17.  Efraim Turban. Business Intelligence. Prentice
Hall.2008.