1. Chapter 3 Building Business Intelligence Chapter 3 DATABASES AND DATA WAREHOUSES Building Business Intelligence 6/22/2016 1Management Information Systems

2. Student Learning Outcomes 1.List and describe the key characteristics of a data warehouse. 2.Define the 4 major types of data-mining tools. 3.Describe the role of business intelligence. 4.List key considerations in information ownership. 6/22/20162 Management Information Systems

3. Introduction Businesses use many IT tools to manage and organize information for many reasons Online transaction processing (OLTP) – gathering and processing information and updating existing information to reflect the processed information Online analytical processing (OLAP) – manipulation of information to support decision making 6/22/20163 Management Information Systems

4. Introduction OLTP – Online Transaction Processing – Supports operational processing – Sales orders, accounts receivable, etc – Supported by operational databases & Database Management Systems (DBMS) OLAP – Online Analytical Processing – Helps build business intelligence – Supported by data warehouses and data-mining tools – Receives data from OLTP 6/22/20164 Management Information Systems

5. OLTP, OLAP, and Business Intelligence 6/22/20165 Management Information Systems

6. Data Warehouses and Data Mining Help you build and work with business intelligence and some forms of knowledge Data warehouse – collection of information (from many places) that supports business analysis activities and decision making 6/22/20166 Management Information Systems

7. Data Warehouse Characteristics Multidimensional – Rows, columns, and layers Support decision making, not transaction processing – Contain summaries of information – Not every detail 6/22/20167 Management Information Systems

8. Data-Mining Tools Data-mining tools – software tools you use to query information in a data warehouse 6/22/20168 Management Information Systems

9. Data-Mining Tools Query-and-reporting tools – similar to QBE tools, SQL, and report generators Intelligent agents – utilize AI tools to help you “discover” information and trends Multidimensional analysis (MDA tools) – slice-and- dice techniques for viewing multidimensional information Statistical tools – for applying mathematical models to data warehouse information 6/22/20169 Management Information Systems

10. Data Mining: A KDD Process 6/22/201610 Management Information Systems – Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation

11. Data Marts Data mart – subset of a data warehouse in which only a focused portion of the data warehouse information is kept 6/22/201611 Management Information Systems

12. Data Warehouse Considerations Do you really need one, or does your database environment support all your functions? Do all employees need a big data warehouse or a smaller data mart? How up-to-date must the information be? What data-mining tools do you need? 6/22/201612 Management Information Systems

13. Business Intelligence Revisited Business intelligence (BI) – collective information about customers, competitors, business partners, competitive environment, and your internal operations for making important, effective, and strategic business decisions Hot topic in business today Current market is $50 billion and double-digit annual growth 6/22/201613 Management Information Systems

14. BI Objectives Help people understand – Capabilities of the organization – State of the art trends and future directions of the market – Technological, demographic, economic, political, social, and regulatory environments in which the organization competes – Actions of competitors 6/22/201614 Management Information Systems

15. Data Mining and Business Intelligence 6/22/201615 Management Information Systems Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP

16. Viewing Business Intelligence Digital dashboard – displays key information gathered from several sources in a format tailored to the needs and wants of an individual 6/22/201616 Management Information Systems

17. Information Ownership Information is a resource you must manage and organize to help the organization meet its goals and objectives You need to consider – Strategic management support – Sharing information with responsibility – Information cleanliness 6/22/201617 Management Information Systems

18. Sharing Information Everyone can share – while not consuming – information But someone must “own” it by accepting responsibility for its quality and accuracy 6/22/201618 Management Information Systems

19. Information Cleanliness Related to ownership and responsibility for quality and accuracy No duplicate information No redundant records with slightly different data, such as the spelling of a customer name GIGO – if you have garbage information you get garbage information for decision making 6/22/201619 Management Information Systems

20. Why Data Mining? — Potential Applications Database analysis and decision support – Market analysis and management customer relation management, market basket analysis – Risk analysis and management Forecasting, quality control, competitive analysis Fraud detection and management Other Applications – Text mining (news group, email, documents) and Web analysis. – Intelligent query answering 6/22/201620 Management Information Systems

21. Market analysis and management Where are the data sources for analysis? – Credit card transactions, clickstreams, customer forms, shopping baskets Target marketing – Clusters of “model” customers with same characteristics: interest, income level Determine customer purchasing patterns over time Cross-market analysis – Identify associations between product sales, use to predict purchases Customer profiling – what types of customers buy what products? (clustering or classification) Identifying customer requirements – identify best products for different customers, predict factors to attract new customers 6/22/201621 Management Information Systems

22. Amazon recommender systems Rich set of recommendations using multiple techniques – Books like this, authors like this: straight descriptive statistics – individual recommendations, based on purchases and ratings – Editorial Filtering: Lists provided by users – Best Seller lists, current rank of books Access recommender system directly – I own it – Rate it – Not interested – exclude this item – why was this recommended? 6/22/201622 Management Information Systems