What is Business Intelligence and application of business intelligence ppt and adaptive business intelligence by zbigniew michalewicz ppt
Topics in Business Intelligence
Lecture 1: Introduction to BI & case study
Econometric Institute, Erasmus University RotterdamWhat is Business Intelligence (BI)?
BI refers to computer-based techniques used in spotting,
digging-out, and analyzing business data, such as sales
revenue by products and/or departments or associated costs
BI technologies provide historical, current, and predictive
views of business operations.
Business Intelligence often aims to support better business
wikipedia.org/wiki/Business_intelligenceExamples of BIExamples of BIExamples of BIExamples of BIBI framework
Watson & Wixom, 2007Main components in BIKnowledge discovery processWhy data mining?
Tremendous amount of data
Walmart Customer buying patterns a data warehouse 7.5
Terabytes large in 1995
VISA Detecting credit card interoperability issues 6800
payment transactions per second
High dimensionality of data
Many dimensions to be combined together
High complexity of data
Time-series data, temporal data, sequence data
Spatial, spatiotemporal, multimedia, text and Web dataData mining
Text mining: mining of patterns from text
Web mining: discovering patterns from the webData mining: predictive analysis types
Classication of observations to (possibly ordered) classes,
e.g. credit card transactions to normal or fraudulent ones.
Prediction is similar, but instead of assignment to classes, we
try to predict the value of a numerical variable, e.g. amount
of credit card purchase.
Association rules or anity analysis tells what is associated
with the observations. Recommender systems (e.g.
amazon.com) use association rules.Data mining: pre-analyses
Data visualization allows \easy" overview of the data.
Data exploration often needs to be done with large data sets
to answer more vague questions. Similar variables and
observations can be aggregated to get a better picture of the
Data reduction consolidates a large number of variables or
cases into a smaller set. Correlation & principal component
analyses.What is 'data'?
Data can essentially be:
1 Continuous ordered values with a scale. E.g. client monthly
spending (e), speed of car (km/h)
2 Categorical discrete, possibly ordered values. E.g. car class
(small family car, large family car, executive, ...), bank
customer credit class (A, B, C, D)
Often data is categorical due to form of reporting (e.g. from
questionnaires: monthly salary)Data mining methods for BI
Statistical methods for analysis of continuous variables
Machine learning for analysis of categorical variables
Variables are divided into predictors and responsesData nature & methods
Continuous Categorical No
response response response
Continuous Linear regression Logistic regression Principal components
predictors Neural nets Neural nets Cluster analysis
k-nearest neighbors Discriminant analysis
Categorical Linear regression Neural nets Association rules
predictors Neural nets Classication trees
Regression trees Logistic regression
Ordered categorical variables (e.g. 1, 2, 3) can often be converted to
Continuous variables can always be converted to categorical ones through
frequency analysis (binning)Data mining processLearning modes
learning, no outcome
variable is predicted.Learning modes
learning the model is
trained to predict a
The data needs to be
split into training
and test sets.Supervised learning with linear regression
x = 200, y =?