INTRODUCTION TO BUSINESS INTELLIGENCE and DATA MINING
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This document summarizes key topics from a lecture on business intelligence (BI) and data mining. It discusses what data mining is, the types of activities it can perform like classification, prediction, clustering. It provides examples of how data mining can be used for marketing, customer relationship management, and as a research tool. It also defines BI and discusses its applications and goal of providing information to support business decision making.
INTRODUCTION TO BUSINESS INTELLIGENCE and DATA MINING
- 1. Rajesh Math 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 111 BI I Lecture 1
- 2. Syllabus/Course 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 122 Same group for communication Syllabus Books Tools/Software Title of Course is BI
- 3. Data Mining 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 133 Data Mining is process of exploration and analysis by automatic or semiautomatic means of large quantities of data in order to discover meaningful patterns and rules. Data Miners are the people who apply this potent mixture of massive computing power, clever algorithms, business knowledge and human intuition.
- 4. What Data Mining Can Do ? 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 144 Activities that are to extract meaningful new information from the data. The activities are : 1. Classification 2. Estimation 3. Predication 4. Affinity grouping or association rules. 5. Clustering 6. Description and visualization
- 5. Classification 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 155 e.g. i. Classifying credit applicants as low, medium or high risk. ii. Assigning customers to predefined customer segments. iii. Assigning key-words to articles as they come off the news-wire.
- 6. Estimation/Prediction 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 166 Use modeling to estimate the probability e.g. estimating the value of a real estate.(MLS in US) Prediction: It can be thought as combination of classification and estimation but the difference is in emphasis i.e. we wait to see if our prediction is correct. E.g. Predicting if customer will move away
- 7. Affinity Grouping or Association Rules 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 177 To determine what things go together. e.g. Retail Chains can arrange items together that are purchased together. Used to cross sell different opportunities.
- 8. Clustering 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 188 Task of segmenting a diverse group into a number of more similar subgroups or clusters Different from Classification is absence of predefined classes rather records are grouped together on the basis of self- similarity Normally a first step before doing something else e.g. Before ‘What type of promotion works best for customer ?’ divide people into clusters of similar buying habits.
- 9. Data Mining Discovering Knowledge based on Data 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 199 Descriptive Models Discovery of patterns & relationships in the underlying data e.g. A customer who purchases diapers is 3 times more likely to buy beer. e.g. There is a cluster of households w $60-80K incomes and 2 cars (more than ($60-$80k and 1 or 3 cars, or 2 cars w $40-60K or $80-100k) who have recently bought life insurance. Predictive Models & Anomaly Detection Predictions of trends & behaviors; Noticing deviations from those predictions e.g. How much profit will this customer generate? Is this credit card stolen? Uses Sales & Marketing, Diagnosis, Fraud Detection, …
- 10. Data mining 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11010 Simply describe what is going on complicated database to better understanding of people products or process that produced the data. Data visualization : one powerful form of descriptive data mining. i.e. one picture is worth many thousand association In business context knowledge out of data-mining can be used to lower costs or raise revenue.
- 11. Data mining as Research Tool 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11111 Pharmaceutical Industry to develop leads on binding molecules. Bioinformatics is based on data mining which interprets data being generated by high throughput screening and mapping of the entire human genetic sequence.
- 12. Data Mining for Marketing 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11212 Used to do target marketing for right customers. Prospective targets of a marketing campaign. Identify trends/events that correlate
- 13. Data Mining For CRM 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11313 To make the customer data into meaningful information Good CRM means providing a good image of company across multiple channels.
- 14. 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11414 BI Definition Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
- 15. BI Applications 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11515 Business intelligence applications can be: Mission-critical and integral to an enterprise's operations or occasional to meet a special requirement Enterprise-wide or local to one division, department, or project Centrally initiated or driven by user demand
- 16. BI Goal 09/25/1309/25/13BI - I Lecture 1BI - I Lecture 11616 Main business intelligence goal is to provide sufficient information for making business decisions. Depending on the aim of the business decision, business intelligence methods can provide information about company customers, market trends, effectiveness of marketing campaigns, companies competitors, or even predict future activities.
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