IS5740:  Management Support Systems   Data Management: Data Warehousing ,  Access and Visualization (Source: Turban & Aron...
Overview <ul><li>Data warehousing   </li></ul><ul><li>Data mining   </li></ul><ul><li>Online analytical processing   </li>...
Data Warehousing and OLAP
Data , Information and Knowledge <ul><li>Data : Raw  </li></ul><ul><li>Information: Data organized to convey meaning  </li...
The Nature and  Sources of Data   <ul><li>DSS Data Items:  </li></ul><ul><ul><li>Documents, Pictures, Maps, Sound, Animati...
Database Management Systems in DSS   <ul><li>A DBMS combined with a modeling language is a typical system development pair...
Data Warehousing <ul><li>Physical  separation  of operational and decision support environments   </li></ul><ul><li>Purpos...
Data Warehousing (contd.) <ul><li>Data are  transformed  and  integrated  into a consistent structure   </li></ul><ul><li>...
Data Warehousing Benefits <ul><li>Increase in knowledge worker productivity   </li></ul><ul><li>Supports all decision make...
Data Warehousing Benefits <ul><li>Data Warehousing results in   </li></ul><ul><ul><li>Improved business knowledge   </li><...
Data Warehouse Architecture and Process
Data Warehouse Suitability <ul><li>For organizations where   </li></ul><ul><li>Data are in different systems   </li></ul><...
Characteristics of Data Warehousing <ul><li>Data organized by detailed subject with information relevant for decision supp...
Online Analytical processing (OLAP)   <ul><li>OLAP computing done by end-users versus online transaction processing (OLTP)...
Online Analytical processing (OLAP) <ul><li>OLAP uses the data warehouse and a  set of tools , usually with multidimension...
Data Mining <ul><li>Data Mining is used for   </li></ul><ul><ul><li>Knowledge discovery in databases   </li></ul></ul><ul>...
Major Data Mining Characteristics and Objectives <ul><li>Data are often buried deep   </li></ul><ul><li>Client/server arch...
Data Mining  Application Areas <ul><li>Marketing   </li></ul><ul><li>Banking </li></ul><ul><li>Retailing and sales   </li>...
Data Visualization Technologies <ul><li>Digital images   </li></ul><ul><li>Geographic information systems   </li></ul><ul>...
Multidimensionality <ul><li>3-D + Spreadsheets   </li></ul><ul><li>Data can be organized the way managers like to see them...
Multidimensionality <ul><li>Measures: money, sales volume, head count, inventory profit, actual versus forecasted   </li><...
Multidimensionality Limitations <ul><li>Extra storage requirements   </li></ul><ul><li>Higher cost   </li></ul><ul><li>Ext...
Intelligent Databases <ul><li>Developing MSS applications requires access to databases   </li></ul><ul><li>AI technologies...
Intelligent Data Mining <ul><li>Use intelligent search to discover information within data warehouses that queries and rep...
Main Tools Used in  Intelligent Data Mining <ul><li>Case-based Reasoning   </li></ul><ul><li>Neural Computing   </li></ul>...
Upcoming SlideShare
Loading in...5
×

Data Management: Warehousing, Access and Visualization

840

Published on

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
840
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
48
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Data Management: Warehousing, Access and Visualization

  1. 1. IS5740: Management Support Systems Data Management: Data Warehousing , Access and Visualization (Source: Turban & Aronson 1998, Chap. 4)
  2. 2. Overview <ul><li>Data warehousing </li></ul><ul><li>Data mining </li></ul><ul><li>Online analytical processing </li></ul><ul><li>Multidimensionality </li></ul><ul><li>Intelligent databases </li></ul>
  3. 3. Data Warehousing and OLAP
  4. 4. Data , Information and Knowledge <ul><li>Data : Raw </li></ul><ul><li>Information: Data organized to convey meaning </li></ul><ul><li>Knowledge: Data items organized and processed to convey understanding, experience, accumulated learning, and expertise </li></ul>
  5. 5. The Nature and Sources of Data <ul><li>DSS Data Items: </li></ul><ul><ul><li>Documents, Pictures, Maps, Sound, Animation, Video, Can be hard or soft </li></ul></ul><ul><li>Data Sources: </li></ul><ul><ul><li>Internal, External, Personal </li></ul></ul>
  6. 6. Database Management Systems in DSS <ul><li>A DBMS combined with a modeling language is a typical system development pair, used in constructing DSS </li></ul><ul><li>DBMS are designed to handle large amounts of information /data </li></ul>
  7. 7. Data Warehousing <ul><li>Physical separation of operational and decision support environments </li></ul><ul><li>Purpose: to establish a data repository making operational data accessible </li></ul><ul><li>Transforms operational data to relational form </li></ul><ul><li>Only data needed for decision support come from the TPS </li></ul>
  8. 8. Data Warehousing (contd.) <ul><li>Data are transformed and integrated into a consistent structure </li></ul><ul><li>Data warehousing (or information warehousing): a solution to the data access problem </li></ul><ul><li>End users perform ad hoc query, reporting analysis and visualization </li></ul>
  9. 9. Data Warehousing Benefits <ul><li>Increase in knowledge worker productivity </li></ul><ul><li>Supports all decision makers’ data requirements </li></ul><ul><li>Provide ready access to critical data </li></ul><ul><li>Insulates operation databases from ad hoc processing </li></ul><ul><li>Provides high-level summary information </li></ul><ul><li>Provides drill down capabilities </li></ul>
  10. 10. Data Warehousing Benefits <ul><li>Data Warehousing results in </li></ul><ul><ul><li>Improved business knowledge </li></ul></ul><ul><ul><li>Competitive advantage </li></ul></ul><ul><ul><li>Enhances customer service and satisfaction </li></ul></ul><ul><ul><li>Facilitates decision making </li></ul></ul><ul><ul><li>Help streamline business processes </li></ul></ul>
  11. 11. Data Warehouse Architecture and Process
  12. 12. Data Warehouse Suitability <ul><li>For organizations where </li></ul><ul><li>Data are in different systems </li></ul><ul><li>Information-based approach to management in use </li></ul><ul><li>Large, diverse customer base </li></ul><ul><li>Same data have different representations in different systems </li></ul><ul><li>Highly technical, messy data formats </li></ul>Top 10 Data Warehousing Mistakes http://www.dw-institute.com/mistakes/dw-consultant.html
  13. 13. Characteristics of Data Warehousing <ul><li>Data organized by detailed subject with information relevant for decision support </li></ul><ul><li>Integrated data </li></ul><ul><li>Time-variant data </li></ul><ul><li>Non-volatile data </li></ul>
  14. 14. Online Analytical processing (OLAP) <ul><li>OLAP computing done by end-users versus online transaction processing (OLTP) </li></ul><ul><li>OLAP Activities </li></ul><ul><ul><li>Generating queries </li></ul></ul><ul><ul><li>Requesting ad hoc reports </li></ul></ul><ul><ul><li>Conducting statistical analyses </li></ul></ul><ul><ul><li>Building multimedia applications </li></ul></ul>
  15. 15. Online Analytical processing (OLAP) <ul><li>OLAP uses the data warehouse and a set of tools , usually with multidimensional capabilities </li></ul><ul><ul><li>Query tools </li></ul></ul><ul><ul><li>Spreadsheets </li></ul></ul><ul><ul><li>Data mining tools </li></ul></ul><ul><ul><li>Data visualization tools </li></ul></ul>
  16. 16. Data Mining <ul><li>Data Mining is used for </li></ul><ul><ul><li>Knowledge discovery in databases </li></ul></ul><ul><ul><li>Knowledge extraction </li></ul></ul><ul><ul><li>Data archeology </li></ul></ul><ul><ul><li>Data exploration </li></ul></ul><ul><ul><li>Data pattern processing </li></ul></ul><ul><ul><li>Data dredging </li></ul></ul><ul><ul><li>Information harvesting </li></ul></ul>
  17. 17. Major Data Mining Characteristics and Objectives <ul><li>Data are often buried deep </li></ul><ul><li>Client/server architecture </li></ul><ul><li>Sophisticated new tools--including advanced visualization tools--help to remove the information &quot;ore&quot; </li></ul><ul><li>Massaging and synchronizing data </li></ul><ul><li>Usefulness of &quot;soft&quot; data </li></ul><ul><li>End-user minor is empowered by &quot;data drills&quot; and other power query tools with little or no programming skills </li></ul><ul><li>Often involves finding unexpected results </li></ul><ul><li>Tools are easily combined with spreadsheets etc. </li></ul><ul><li>Parallel processing for data mining </li></ul>
  18. 18. Data Mining Application Areas <ul><li>Marketing </li></ul><ul><li>Banking </li></ul><ul><li>Retailing and sales </li></ul><ul><li>Manufacturing and production </li></ul><ul><li>Brokerage and securities trading </li></ul><ul><li>Insurance </li></ul><ul><li>Computer hardware and software </li></ul><ul><li>Government and defense </li></ul><ul><li>Airlines </li></ul><ul><li>Health care </li></ul><ul><li>Broadcasting </li></ul><ul><li>Law Enforcement </li></ul>
  19. 19. Data Visualization Technologies <ul><li>Digital images </li></ul><ul><li>Geographic information systems </li></ul><ul><li>Graphical user interfaces </li></ul><ul><li>Multidimensions </li></ul><ul><li>Tables and graphs </li></ul><ul><li>Virtual reality </li></ul><ul><li>Presentations </li></ul><ul><li>Animation </li></ul>See Risk analysis at ABN AMRO Bank
  20. 20. Multidimensionality <ul><li>3-D + Spreadsheets </li></ul><ul><li>Data can be organized the way managers like to see them, rather than the way that the system analysts do </li></ul><ul><li>Different presentations of the same data can be arranged easily and quickly </li></ul><ul><li>Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or industry </li></ul>
  21. 21. Multidimensionality <ul><li>Measures: money, sales volume, head count, inventory profit, actual versus forecasted </li></ul><ul><li>Time: daily, weekly, monthly, quarterly, or yearly </li></ul><ul><li>Multidimensionality is especially popular in executive information and support systems </li></ul>
  22. 22. Multidimensionality Limitations <ul><li>Extra storage requirements </li></ul><ul><li>Higher cost </li></ul><ul><li>Extra system resource and time consumption </li></ul><ul><li>More complex interfaces and maintenance </li></ul>
  23. 23. Intelligent Databases <ul><li>Developing MSS applications requires access to databases </li></ul><ul><li>AI technologies (ES, ANN) to assist database management </li></ul><ul><li>Link ES to large databases </li></ul><ul><li>Natural language interfaces </li></ul>
  24. 24. Intelligent Data Mining <ul><li>Use intelligent search to discover information within data warehouses that queries and reports cannot effectively reveal </li></ul><ul><li>Find patterns in the data and infer rules from them </li></ul><ul><li>Use patterns and rules to guide decision-making and forecasting </li></ul><ul><li>Five common types of information that can be yielded by data mining: 1) association, 2) sequences, 3) classifications, 4) clusters, and 5) forecasting </li></ul>
  25. 25. Main Tools Used in Intelligent Data Mining <ul><li>Case-based Reasoning </li></ul><ul><li>Neural Computing </li></ul><ul><li>Intelligent Agents </li></ul><ul><li>Other Tools </li></ul><ul><ul><li>decision trees </li></ul></ul><ul><ul><li>rule induction </li></ul></ul><ul><ul><li>data visualization </li></ul></ul>Knowledge discovery in databases – http://www.kdnuggets.com/
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×