Business Intelligence

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Business Intelligence

Definitions
• Data mining (knowledge discovery in databases):
– Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases

• Data mining helps end users extract useful business information from large databases • Data mining is the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules. • The goal of data mining may be to allow a corporation to improve its marketing, sales, and customer support operations through a better understanding of its customers.

Lecture 8

2

Intro to Data Mining

Definitions cont’d
• The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. – – – – Extremely large datasets Discovery of the non-obvious Useful knowledge that can improve processes Can not be done manually

• Technology to enable data exploration, data analysis, and data visualisation of very large databases at a high level of abstraction, without a specific hypothesis in mind. Lecture 8 3 Intro to Data Mining

What is Data Mining and its purpose?
• Search for relationships and global patterns that exist in large databases but are hidden in the vast amounts of data. • Analyst combines knowledge of data and machine learning technologies to discover nuggets of knowledge hidden in the data. • Serendipity to science. • Easier and more effective when the organization has accumulated as much data as possible, such as with a data warehouse • A data warehouse is not a prerequisite to data mining

Lecture 8

4

Intro to Data Mining

Data Mining and Other Disciplines

Lecture 8

5

Intro to Data Mining

Sample Data Mining Applications
• Commercial : – Fraud detection: Identify Fraudulent transaction – Loan approval: Establish the credit worthiness of a customer requesting a loan – Investment analysis : Predict a portfolio's return on investment – Marketing and sales data analysis: Identify potential customers; establishing the effectiveness of a sales campaign Medical: – Drug effect analysis : from patient records to learn drug effects – Disease causality analysis Manufacturing: – Manufacturing process analysis: identify the causes of manufacturing problems – Experiment result analysis : Summarise experiment results and create predictive models





Lecture 8

6

Intro to Data Mining

Market Analysis and Management (1)
• Where are the data sources for analysis? – Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies • Target marketing – Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc. • • Determine customer purchasing patterns over time – Conversion of single to a joint bank account: marriage, etc. Cross-market analysis – Associations/co-relations between product sales – Prediction based on the association information

Lecture 8

7

Intro to Data Mining

Market Analysis and Management (2)
• Customer profiling – data mining can tell you what types of customers buy what products (clustering or classification) • Identifying customer requirements – identifying the best products for different customers – use prediction to find what factors will attract new customers • Provides summary information – various multidimensional summary reports – statistical summary information (data central tendency and variation)

Lecture 8

8

Intro to Data Mining

Fraud Detection and Management (1)
• • Applications – widely used in health care, retail, credit card services, telecommunications (phone card fraud), etc. Approach – use historical data to build models of fraudulent behavior and use data mining to help identify similar instances Examples – auto insurance: detect a group of people who stage accidents to collect on insurance – money laundering: detect suspicious...
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