1. Business Intelligence, Data Mining and Data Analytics/Predictive Analytics By: Asela Thomason IS 495 Summer 2015

2. What is Business Intelligence Basic Definition :Information that people use to support their decision making efforts. Data Mining and Data Analytics/predictive Analytics falls within this field.

3. What is Data Mining Data Mining (The analysis step of Knowledge Discovery in Databases” Process or KDD), an interdisciplinary subfield of computer Science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database management systems.

4. Basic-Definitions of Data Mining The discovery of new, non-obvious, valuable information from a large collection of raw data Data Mining (DM) is the core of the KDD [Knowledge Discovery in Databases] process, involving the inferring of algorithms that explore the data, develop the model and discover previously unknown patterns. The set of activities used to find new, hidden or unexpected patterns in data

5. Data Mining -continued The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use: (Predictive analytics) Discovering meaningful new corrections, patterns, trends. Example : Forecasting

6. Data Analytics/Predictive Analytics Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories

7. Data analytics is distinguished from Data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.data set

8. Predictive analytics - Focus Uses lower level of Granularity, meaning it looks at the individual level. Instead of looking at which candidate will win the Presidential election in the state of Ohio, which is forecasting. It looks at the individual level. Which person is voting for or against. Predicts which individuals can be persuaded, which ones will not change, etc. Now with this information we ca change the outcome of the race. Obama used this technique very well.

9. Data Mining: Business What is it? Decision making Marketing Detecting Fraud This technology is popular with many businesses because it allows them to learn more about their customers, prevent frauds and identity theft, and also make smart marketing decisions

10. Keys to a Successful Data Mining Project Credible source of data Knowledgeable personnel Appropriate algorithms

11. Classificationclassify a data item into one of several predefined classes Regressionmap a data item to a real-value prediction variable Clusteringidentify a finite set of categories or clusters to describe the data Summarizationfind a compact description for a set (or subset) of data Dependency Modeling describe significant dependencies between variables or between the values of a feature Change and Deviation Detection Discover the most significant changes Primary Tasks of Data Mining

12. Some of the commonly used data mining methods are: Statistical Data Analysis Cluster Analysis Decision Trees and Decision Rules Association Rules Artificial Neural Networks Genetic Algorithms Fuzzy Sets and Fuzzy Logic

13. Data Mining Applications In direct marketing a company saves much time by marketing to prospects that would have the highest reply rate. Instead of random selection on which customers to pick for their surveys, a company could use direct marketing from data mining to find the “correct” customers to ask.

14. Direct Marketing-Example 1 million mailers- cost $.40 to ship letter=400,000 cost Conversion is 1percent without data mining

15. Direct Marketing using data mining, gives us 3% Conversion Identifies smaller group, example ¼ of population and gets a higher conversion, 3%,

16. Data Mining Applications Market segmentation is used in data mining in order to identify the common characteristics of customers who buy the products from one’s company. With market segmentation, you will be able to find behaviors that are common among your customers. As a company seeks customer’s trends, it helps them find necessities in order to help them improve their business.

17. Data Mining Applications Customer churn predicts which customers will have a change of heart towards your company and join another company (competitor). Although customer churns are negative to one’s business, it allows the corporation to seek out the problem they are facing and create solutions.

18. Customer Churn Example: Magazine subscriber Ideas to keep customer: Discount, coupons, etc.

19. Data Mining Applications Market basket analysis- involves researching customer characteristics in respect to their purchase patterns Example: Ralphs Club Card Cereal and Milk

20. Market Basket Beer and diapers merchandising

21. Prediction based on Data mining/Predictive analysis Examples of real life. Target – can predict which customers will be pregnant Hospitals can predict which payments may need to be admitted Credit card – can predict which customers may miss their payment based upon where card is used. Example Bar-alcohol=missed payments

22. Class Identification Mathematical taxonomy Concept clustering

23. Data Mining Applications Class identification, which consists of mathematical taxonomy and concept clustering. Mathematical taxonomy focuses on what makes the members of a certain class similar, as opposed to differentiating one class from another. For example, Ralphs can classify its customers based on their income or past purchases

24. Data Mining Applications Concept clustering - determines clusters according to attribute similarity. Consider the pattern a purchase of toys for age group 3–5 years, is followed by purchase of kid’s bicycle within 6 months about 90% of the time by high income customers, which was discovered by data mining. The Company can identify the prospective customers for kid’s bicycle based on toy purchase details and adjust the mail catalog accordingly.

25. Data mining Applications Deviation analysis, A deviation can be fraud or a change. In the past, such deviations were difficult to detect in time to take corrective action. Data mining tools help identify such deviations. For example, a higher than normal credit purchase on a credit card can be a fraud, or a genuine purchase by the customer. Once a deviation has been discovered as a fraud, the company takes steps to prevent such frauds and initiates corrective action

26. NetFlix Example covered in class.