Management Information Systems Competitive Advantage with Information Systems for Decision Making Chapter 9

2 This Could Happen to You How can information systems improve decision making?  Business processes and decision making are closely allied  IS facilitate competitive strategy by adding value to or reducing costs of processes  IS adds value or reduces costs by improving quality of decisions Can an information system assist in the selection of a vendor based on past performance?

3 Study Questions Q1. How big is an exabyte, and why does it matter? Q2. How do business intelligence systems provide competitive advantages? Q3. What problems do operational data pose for BI systems? Q4. What are the purpose and components of a data warehouse? Q5. What is a data mart, and how does it differ from a data warehouse? Q6. What are the characteristics of data-mining systems?

4 Q1. How Big Is an Exabyte? Figure 9-1

5 Why Does It Matter? Storage capacity is increasing as cost decreases  Nearly unlimited Over 2.5 exabytes of data have been created  Exponential growth both inside and outside of organizations  Can be used to improve decision making

6 硬碟儲存空間

7 Q2. Business Intelligence (BI) Systems Provide information for improving decision making Primary systems:  Reporting systems  Data-mining systems  Knowledge management systems  Expert systems

8 Reporting Systems Integrate data from multiple sources Process data by sorting, grouping, summing, averaging, and comparing Results formatted into reports Improve decision making by providing right information to right user at right time

9 Data-Mining Systems Process data using statistical techniques  Regression analysis  Decision tree analysis Look for patterns and relationships to anticipate events or predict outcomes  Market-basket analysis  Predict donations

10 Knowledge-Management Systems Create value from intellectual capital Collects and shares human knowledge Supported by the five components of the information system Fosters innovation Increases organizational responsiveness

11 Expert Systems Encapsulate experts’ knowledge Produce If/Then rules Improve diagnosis and decision making in non- experts

12 Q3. Problems with Operational Data Raw data usually unsuitable for sophisticated reporting or data mining Dirty data Values may be missing Inconsistent data Data can be too fine or too coarse Too much data  Curse of dimensionality  Too many rows

13 對 BI 系統而言，使用作業系統會有的問題

14 Guide: Counting and Counting and Counting Product managers wanted data miners to analyze customer clicks on Web page  Determine preferences for product lines  Data miners wanted to sample; product managers wanted all data  Would take days to calculate Sampling is acceptable  Must be appropriate  Saves time and money

15 Q4. Data Warehouse Used to extract and clean data from operational systems Prepares data for BI processing Data-warehouse DBMS  Stores data  May also include data from external sources  Metadata concerning data stored in data-warehouse meta database  Extracts and provides data to BI tools

16 資料倉儲的元件資料

17 從資料商可購買到的顧客資料

18 Q5. Data Mart Data collection  Created to address particular needs  Business function  Problem  Opportunity  Smaller than data warehouse  Users may not have data management expertise  Knowledgeable analysts for specific function

19 資料市集範例

20 Q6. Data Mining Application of statistical techniques to find patterns and relationships among data Knowledge discovery in databases (KDD) Take advantage of developments in data management Two categories:  Unsupervised  Supervised

21 資料探勘結合許多領域

22 Unsupervised Data Mining Analysts do not create model before running analysis Apply data-mining technique and observe results Hypotheses created after analysis as explanation for results Example: cluster analysis

23 Supervised Data Mining Model developed before analysis Statistical techniques used to estimate parameters Examples:  Regression analysis  Neural networks

24 Ethics Guide: Data Mining Real World Data mining is different from the way it is shown in textbooks  Data is dirty  Values are missing or outside of ranges  Time value make no sense  You add parameters as you gain knowledge, forcing reprocessing  Overfitting  Based on probabilities, not certainty  Seasonality problem

25 Using This Knowledge to Close the Gap Reporting system could process supplier information to rank quality Data-mining system could search for patterns to predict delivery delays or quality problems Knowledge management system could rank suppliers or share experiences Expert system could contain rules for supplier selection Data mart could maintain information on inbound logistics and manufacturing