A Practical Guide to Data Mining for Business and Industry

3 All about data

All about Data

1. 3.1 Some Basics
   1. 3.1.1 Data, Information, Knowledge and Wisdom
   2. 3.1.2 Sources and Quality of Data
   3. 3.1.3 Measurement Level and Types of Data
   4. 3.1.4 Measures of Magnitude and Dispersion
   5. 3.1.5 Data Distributions
2. 3.2 Data Partition: Random Samples for Training, Testing and Validation
3. 3.3 Types of Business Information Systems
   1. 3.3.1 Operational Systems Supporting Business Processes
   2. 3.3.2 Analysis-Based Information Systems
   3. 3.3.3 Importance of Information
4. 3.4 Data Warehouses
   1. 3.4.1 Topic Orientation
   2. 3.4.2 Logical Integration and Homogenisation
   3. 3.4.3 Reference Period
   4. 3.4.4 Low Volatility
   5. 3.4.5 Using the Data Warehouse
5. 3.5 Three Components of a Data Warehouse: DBMS, DB and DBCS
   1. 3.5.1 Database Management System (DBMS)
   2. 3.5.2 Database (DB)
   3. 3.5.3 Database Communication Systems (DBCS)
6. 3.6 Data Marts
   1. 3.6.1 Regularly Filled Data Marts
   2. 3.6.2 Comparison between Data Marts and Data Warehouses
7. 3.7 A Typical Example from the Online Marketing Area
8. 3.8 Unique Data Marts
   1. 3.8.1 Permanent Data Marts
   2. 3.8.2 Data Marts Resulting from Complex Analysis
9. 3.9 Data Mart: Do’s and Don’ts
   1. 3.9.1 Do’s and Don’ts for Processes
   2. 3.9.2 Do’s and Don’ts for Handling
   3. 3.9.3 Do’s and Don’ts for Coding/Programming

3.1 Some Basics

In most companies, marketing, sales and process control are major drivers for promoting data quality and producing comparable numbers and facts about the business. But, even production and Research and Development (R&D) departments need reliable data ...