Components of DSS Data Management Subsystem Model Management Subsystem User Interface (Dialog) Subsystem Knowledge-based Management Subsys-tem User

Components of DSS

DSS Components Data Management Subsystem DSS database DBMS Data directory Query facility

Data Management Subsystem The DSS Database Internal Data come mainly from the organization’s Transaction Processing System (TPS) Private Data can include guidelines used by some decision makers assessments of specific data and/or situations External Data includes industry data market research data census data regional employment data government regulations tax rate schedules national economic data

Data Management Subsystem Data Organization Data for DSS can be entered directly into models extracted directly from larger databases e.g. Data Warehouse Can include multimedia objects OODBs in XML used in m-commerce

Data Management Subsystem Data Extraction (ETL) The process of capturing data from several sources synthesizing, summarizing determining which of them are relevant and organizing them resulting in their effective integration

Data Management Subsystem Database Management System A database is created, accessed and updated by a DBMS Software for establishing, updating, and querying e.g. managing a database record navigation data relationships report generation

Data Management Subsystem Query Facility The (database) mechanism that accepts requests for data accesses manipulates and queries data Includes a query language e.g. SQL

Data Management Subsystem Data Directory A catalog of all the data in a database or all the models in a model base Contains data definitions data source data meaning Supports addition and deletion of new entries

Data Management Subsystem Key DB & DBMS Issues Data quality “Garbage in/garbage out" (GIGO) Managers feel they do not get the data they need – 54% satisfied Poor quality data leads to poor quality information waste lost opportunities unhappy customers

Data Management Subsystem Key DB & DBMS Issues Data integration For DSS to work, data must be integrated from disparate sources “Creating a single version of the truth” Scalability Volume of data increases dramatically e.g. from 2001 – 2003, size of largest TPS DB increase two-fold (11 – 20 terabytes) Needs new storage and search technologies

Data Management Subsystem Key DB & DBMS Issues Data security data must be protected from unauthorized access through security measures tools to monitor database activities audit trail

10 Key Ingredients of Data (Information) Quality Management Data quality is a business problem, not only a systems problem Focus on information about customers and suppliers, not just data Focus on all components of data: definition, content, and presentation Implement data/information quality management processes, not just software to handle them Measure data accuracy as well as validity

10 Key Ingredients of Data (Information) Quality Management Measure real costs (not just the percentage) of poor quality data/information Emphasize process improvement/preventive maintenance, not just data cleansing Improve processes (and hence data quality) at the source Educate managers about the impacts of poor data quality and how to improve it Actively transform the culture to one that values data quality

DSS Components Model Management Subsystem Model base MBMS Modeling language Model directory Model execution, integration, and command processor Note: MBMS – model base management systems – software for establishing, updating, combining A DSS model base

DSS Components Model Management Subsystem The four (4) functions Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks Generation of new routines and reports Model updating and changing Model data manipulation

Model Management Subsystem Categories of Models Strategic Models – 5- 10 years planning Models that represent problems for the strategic level E.g Southwest Airlines – used its system to create accurate financial forecasts to identify strategic opportunities. Can plan large, expensive equipment needed in future i.e. executive level of management developing corporate objectives forecasting sales target Tactical Models 1 month – 4 years Models that represent problems for the tactical level i.e. mid-level management allocates and controls resources labour requirement planning sales promotion planning

Model Management Subsystem Categories of Models Operational Model Models that represent problems for the operational level of management Supports day-to-day working activities manufacturing targets e-commerce transaction acceptance approval of personal loans Analytical Models – perform analysis of data – Mathematical models into which data are loaded for analysis Statistical models management science models data mining algorithms Financial models Integrated with other models, e.g. strategic planning model

Model Management Subsystem Model Directory Similar to database directory A catalog of all models and other soft-ware in the model base model definitions functions availability and capability

Model Management Subsystem Model Execution, Integration the process of controlling the actual running of the model Model integration involves combining the operations of several models when needed e.G use a DSS that contains six integrated models: planning and scheduling models, forecasting models

Model Management Subsystem Model Command Processor A model command processor accepts and interpret modeling instruc-tions from the user interface component and route them to the MBMS model execution or integration functions

Model Management Subsystem Some DSS Questions Which models should be used for what situations? Cannot be done by MBMS What method should be used to solve a problem in a specific model class? highly dependent on the knowledge com-ponent

DSS Components User Interface (Dialog) Subsystem Application interface User Interface Graphical User Interface (GUI) DSS User Interface Portal Graphical icons Dashboard Color coding Interfacing with PDAs, cell phones, etc.

DSS Components Knowledgebase Management System Incorporation of intelligence and expertise Knowledge components: Expert systems, Knowledge management systems, Neural networks, Intelligent agents, Fuzzy logic, Case-based reasoning systems, and so on Often used to better manage the other DSS components

DSS User One faced with a decision that an MSS is designed to support Manager, decision maker, problem solver, … The users differ greatly from each other Different organizational positions they occupy; cognitive preferences/abilities; the ways of arriving at a decision (i.e., decision styles) User = Individual versus Group Managers versus Staff Specialists [staff assistants, expert tool users, business (system) analysts, facilitators (in a GSS)]

DSS Components Future/current DSS Developments Hardware enhancements Smaller, faster, cheaper, … Software/hardware advancements data warehousing, data mining, OLAP, Web technologies, integration and dissemination technologies (XML, Web services, SOA, grid computing, cloud computing, …) Integration of AI -> smart systems

DSS Hardware Typically, MSS run on standard hardware Can be composed of mainframe computers with legacy DBMS, workstations, personal computers, or client/server systems Nowadays, usually implemented as a distributed/integrated, loosely-coupled Web-based systems Can be acquired from A single vendor Many vendors (best-of-breed)

End of the Chapter Questions / Comments…

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