comprehensive, cohesive and integrated set of applications used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information to support decision making
main goal: improved decision making
1. integrating architecture
2. common user interface for data reporting and analysis
3. common data repository fosters single version of company data
4. improved organizational performance
1. integrating architecture
2. common user interface for data reporting and analysis
3. common data repository fosters single version of company data
4. improved organizational performance
data storage improvements
business intelligence appliances
business intelligence as a service
big data analytics
personal analytics
business intelligence appliances
business intelligence as a service
big data analytics
personal analytics
data storage improvements
SSD and SATA increase performance and allow larger capacity; more affordable and faster
business intelligence appliances
plug and play app optimized for data warehouse and BI apps; improved price performance ratios, fast installation and integration
cloud, no need for hardwar or softwars; pay as you go
social media as competitive advantage
OLAP gave analytics to desktop of any user and now it is mobile –> even closer to customers
mostly stored in relational database, optimized to support transactions representing daily operations
operational differs from decision support data
time span, granularity, dimensionality
decision support database
specialized DBMS tailored to provide fast answers to complex queries
decision support database requirements
database schema, data extraction an dloading, database size
DSD requirement
must support complex data representations
aggregated and summarized data
queries extract multidimensional time slices
must support complex data representations
aggregated and summarized data
queries extract multidimensional time slices
data extraction and filtering
DSD requirement
supports different data sources
support batch scheduled data extraction
checks for consistency and advanced data integration/classification
supports different data sources
support batch scheduled data extraction
checks for consistency and advanced data integration/classification
integrated, subject oriented, time variant, and nonvolatile collection of data
[usually read only DB optimized for data analysis and query processing
[requires time $$ and managerial effort]
[usually read only DB optimized for data analysis and query processing
[requires time $$ and managerial effort]
extract, transform, load
process of extracting operational data, transforming it, and loading it into the data warehouse
process of extracting operational data, transforming it, and loading it into the data warehouse
data modeling technique
-maps multidimensional devision support data into relational database (almost equivelant, easily implemented)
-maps multidimensional devision support data into relational database (almost equivelant, easily implemented)
4 components of star schemas
facts, dimensions, attributes, attribute hierarchies
numeric measurements that represent specific business aspect of activity (center)
facts computed at run time
qualifying characteristics that provide additional perspectives to a given fact
[stored in dimension tables]
[stored in dimension tables]
use to search, filter, and classify facts
provide descriptions of facts
no mathematical limit
provide descriptions of facts
no mathematical limit
top-down data organization with the purpose of aggregation and drill down/roll up analysis
determine how the data are extracted and represented [stored in data dictionary]
determine how the data are extracted and represented [stored in data dictionary]
master data management (MDM)
In business intelligence, a collection of concepts, techniques, and processes for the proper identification, definition, and management of data elements within an organization.
;
