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Chapter 12 Sections Q1-Q4: Business Intelligence Chapter 13 Sections Q2-Q4: Knowledge Mgmt Chapter 14 All Sections: Database Marketing Chapter 15 Sections.

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Presentation on theme: "Chapter 12 Sections Q1-Q4: Business Intelligence Chapter 13 Sections Q2-Q4: Knowledge Mgmt Chapter 14 All Sections: Database Marketing Chapter 15 Sections."— Presentation transcript:

1 Chapter 12 Sections Q1-Q4: Business Intelligence Chapter 13 Sections Q2-Q4: Knowledge Mgmt Chapter 14 All Sections: Database Marketing Chapter 15 Sections Q1 & Q4: OLAP

2 Reporting Business Intelligence Data Mining Knowledge Management Expert Systems

3  Define business intelligence systems.  Know the characteristics of reporting systems.  Know the purpose and role of data warehouses and data marts.  Understand fundamental data-mining techniques.  Know the purpose, features, and functions of knowledge management systems.

4  Q1: How Big is an Exatype?  Q3: What Problems do Operational Data Pose?

5  software that searches vast amounts of data to derive information for improved decision making

6  Reporting  Data Mining  Knowledge Management  Expert Systems

7  software that reads data  processes data by: ◦ filtering ◦ sorting ◦ grouping ◦ simple calculations  produces formatted summaries of data

8  On-Line Analytical Processing ◦ Drill-Down ◦ Consolidation ◦ Slicing and Dicing

9

10

11 Report A Report B

12  Large Database  Subject-Oriented  Integrated  Time-Variant  Nonvolatile  User-Friendly Interface

13 Oper- ational DB Other DB External DB Data Ware- house Reporting Data Mining KM Expert

14  Mini data warehouses  Hold subsets of data from the data warehouse  Data focuses on a specific aspect of the company

15  software that searches through data  uses complex statistical calculations  outputs ◦ Trends ◦ Patterns ◦ Correlations ◦ Exceptions

16  Nestle processes Social Media ?videoId=

17  Process  Creating Value from Intellectual Property  Sharing Knowledge with Others

18  Knowledge captured as rules.

19  Poor data quality  Context  User resistance


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