Business intelligence

M. M. JUNAIDAIMS

Business Intelligence

Introduction

Business intelligence (BI) is defined as the ability for an organization to take all its capabilities and convert them

into knowledge, ultimately, getting the right information to the right people, at the right time, via the right channel.

This produces large amounts of information which can lead to the development of new opportunities for the

organization. When these opportunities have been identified and a strategy has been effectively implemented, they can provide an organization with a competitive advantage in the market, and stability in the long run (within its industry).

What is Business Intelligence

Collecting and refining information from many sources

Analyzing and presenting the information in useful ways

So people can make better business decisions

Business Intelligence

BI technologies provide historical, current and predictive views of business operations.

Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.

Business Intelligence

BI technologies provide historical, current and predictive views of business operations.

Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.

BI is a umbrella term that include architectures, tools, databases, analytical tools, applications and methodologies.

Components of BI

Data Repository (i.e. Data Warehouse)Business Analytics (Querying, Reporting,

Analyis & Visualization Tools etc)Data MinningBusiness Performance Measur

Data Mining

Data Mining is a process that uses statistical, mathematical, artificial intelligence, Machine learningtechniques to extract and identify useful information and subsequent knowledge from large databases.

Data mining is the process of finding mathematical patterns from usually large set of data.These patterns can be rules, affinity, correlations, trends and prediction models.

Machine LearningStatistics

Data Mining

Database systems

Steps in data Mining

Steps in Data Mining

Data Mining Tasks

•Association•Sequence •Classification•Clustering•Forecasting•Regression

• Types of information obtainable from data mining

• Associations: Occurrences linked to single event

• Sequences: Events linked over time

• Classifications: Patterns describing a group an item belongs to

• Clusters: Discovering as yet unclassified groupings

• Forecasting: Uses series of values to forecast future values

• Regression : Predict a value of a given continuous valued variable based on the values of other variables

Direct Marketingidentify which prospects should be included in a mailing list Market segmentation identify common characteristics of customers who buy same

products Customer churn Predict which customers are likely to leave your company for

competitor Market Basket Analysis Identify what products are likely to be bought together Insurance Claims Analysisdiscover patterns of fraudulent transactionscompare current transactions against those patterns

Association

Given a set of records each of which contain some number of items from a given collection Produce dependency rules which will predict

occurrence of an item based on occurrences of other items

TID ITems

1 Pencil, Eraser, Sharpener

2 Scale, Pencil,

3 Scale, Eraser, pouch, Sharpener

4 Scale, Pencil, pouch, sharpener

5 Eraser, scale , sharpner

Rules discover {sharpener} --> {eraser} {pouch ,sharpener} --> {scale)

Classification

Given a collection of records (training set ) Each record contains a set of attributes, one of the

attributes is the class.

Find a model for class attribute as a function of the values of other attributes.

Goal: previously unseen records should be assigned a class as accurately as possible.

Classification Example

Direct Marketing Goal: Reduce cost of mailing by targeting a set of

consumers likely to buy a new cell-phone product.

Approach: Use the data for a similar product introduced before. We know which customers decided to buy and which

decided otherwise. This {buy, don’t buy} decision forms the class attribute.

Collect various demographic, lifestyle, and company-interaction related information about all such customers. Type of business, where they stay, how much they earn, etc.

Use this information as input attributes to learn a classifier model.

Clustering

Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that Data points in one cluster are more similar to one

another. Data points in separate clusters are less similar to one

another.

Applications: Marketing: finding groups of customers with similar

buying pattern

Regression

Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency.

Greatly studied in statisticsExamples:

Predicting sales amounts of new product based on advertising expenditure.

Predicting wind velocities as a function of temperature, humidity, air pressure, etc.

Regression Example

Height(CM)

Weight(KG)

160 55162 57165 60168 64170 72171 75175 78178 80182 82

Weight=1.41 * Height - 175.3