1. DECISION SUPPORT SYSTEM ARCHITECTURE: The data management component

2. Data Collection SOURCE: Primary / Secondary or External / Internal / Personal TYPE: ‘Hard’ / ‘Soft’ LEVEL: Strategic / Tactical / Operational What are the problems with data collection? What gives information quality?  ACCURACY  TIMELINESS  RELIABILITY  RELEVANCE  COMPLETENESS  CURRENCY  INTERPRETABILITY  PRESENTATION  ACCESSIBILITY

3. The Data Management sub system of a DSS  Extracts information from internal company databases (specialised integrated database or data warehouse)  Has links to external data sources (Web access)  Interfaces with modelling capabilities, user interface design.  May have a knowledge component (AI capabilities)

5. Database Management Systems  A DBMS enables greater integration of data, complex file structure, user query facilities. e.g. The university’s DBMS is Oracle. The query facility is through the language SQL  The main type of DSS database organisation is relational.

6. Data Warehouses  The combination of many data sources into one store, specifically for end user access. This store is separate from the organisation’s records of operations (transaction processing system files) but partly derived from them.  Appropriate in large organisations with different systems which may store the same data for different needs and in different formats.  Data warehousing provides a means for integrating the data from the various systems.  Useful for static (usually historical) data

7. Data Mining a.k.a. data exploration or data pattern processing  The need for tools to help with data access is due to the complexity and size of many organisation’s databases (data warehouses)  The query can be conducted quickly, and the miner does not need programming skills to explore the database (end user support)  A focus on discovery vs verification  On line Analytical Processing – multidimensional databases  Problems with data warehouses/ data mining may be Data Noise, Missing information, Security, Reliability

8. Data Visualisation Incorporates any technology that allows the user to picture the information in a more meaningful way.  GUI (windows and icons applications  graphical facilities  GIS (geographical information systems)  3D presentations/ animation

9. Continuing Research and Development Progress over time……………………. DATA INFORMATIONKNOWLEDGE sources sourcessources tables/ lists documentsexpertise, experience facts/ figures concepts, opinions, best practice cases verbal reportsshared practice “hard data” “soft data” intelligence

10. Continuing Research…..  Intelligent component Intelligent agents (‘detect and alert’ capabilities) on the Internet Case based reasoning and neural networks (pattern recognition capabilities)  Web integrated database systems