Chapter 13: Business Intelligence and Data Warehouses

What are organizations always looking for

competitive advantage

comprehensive and integrated decision support framework within organizations

Business intelligence

how do organizations grow and prosper

They gain a better understanding of their environment. Tracking daily transactions and analyzing company data

How do orgs address multilevel decisions support needs

creating autonomous applications for particular groups of users

What is growing that makes us need in depth data analysis

data

how do we define business intelligence

an umbrella term that includes many things

• applications

• Infrastructure

• Tools

• Best practices

Business intelligence

enables access to an analysis of information to improve and optimize decisions nad performance

business intelligence offers a framework that allows a business to transform

• data to information

• Information to knowledge

• Knowledge to wisdom

B.I. Architectural components

• ETL tools

• Data store

• Query and reporting

• Data visualization

• Data monitoring and alerting

• Data analytics

how is data generated

from every action taken on every internet enabled device, its only growing

how old is 90% of our data

2 years old

what is there a growing demand of

fluency in areas like data analytics across entire companies and industries

forms of unstructured data

• documents

• Audio files

• Image files

• Online and offline

how is the effectiveness of BI determined

the quality of data gathered at the operational level

what level is BI used in orgs?

strategic and tactical managerial levels

is operational data used to support decision tasks?

seldomly

operational data is used for

capturing daily business and transactions

Decision support data gives

tactical and strategic business meaning to the operational data

how does decision support data differ from operational data

• time span

• Granularity (level of aggregation)

• Dimensionality

database scheme

• must support complex nonnormalized data representations

• Data must be aggregated and summarized

• Queries must be able to extract multidimensional time slices

data extraction and filtering

• allow batch and scheduled data extraction

• Support different data sources and check for inconsistent data or data validation rules

• Encourage advanced integration, aggregation, and classification

database size

• very large databases

• Advanced storage technologies

• Multiple processor technologies

decision support database requirements

• database schema

• Data extraction and filtering

• Database size

data mart difference from data warehouse

• small single subject data warehouse subset

• Provides decision support to a small group of people

what do relational databases use to map decision support data

star schema

star schemas representation

facts and dimensions represented by physical tables in data warehouse database

what is the technology behind many BI applications

online analytical processing (OLAP)

what can olap do

Multidimensional analysis of business data, provides the ability forComplex calculations, trend analysis, and sophisticated data modeling

OLAP characteristics

• Multidimensional data analysis techniques

• Advanced database support

• Easy-to-use end-user interfaces

LAP Architecture has 3 main components

• GUI

• Analytical processing

• Data processing

SQL OLAP Extensions

• ROLLUO

• CUBE

• GROUPING

• RANKING

• WINDOWING

• STATISTICAL

ROLLUP

used with group by clauses to generate aggregates by different dimensions

How does data analytics component perform data analysis and store data

using the data in the data store

data store

encompasses a wide range of mathematician statistical and modeling techniques with the purpose of extracting knowledge from data

Explanation analytics

focus on discovering and explaining data characteristics and relationships based on existing data

Predictive analytics

focuses on predicting future data outcomes with high degree of Accuracy

data mining

focuses on analyzing massive amounts of data to uncover hidden trends pattterns and relationship

data visualization component

presents data to the end users in a variety of meaningful and innovative ways

roots in cognitive science

how the human brain eceives, interprets, organizes, and processes information - Pattern recognition, Spatial awareness, Aesthetics