Decision Support and Business Intelligence Systems (9th Ed Decision Support and Business Intelligence Systems (9th Ed., Prentice Hall) Chapter 3: Decision Support Systems Concepts, Methodologies, and Technologies: An Overview

Learning Objectives Understand possible decision support system (DSS) configurations Understand the key differences and similarities between DSS and BI systems Describe DSS characteristics and capabilities Understand the essential definition of DSS Understand important DSS classifications Understand DSS components and how they integrate

Learning Objectives Describe the components and structure of each DSS component Explain Internet impacts on DSS (and vice versa) Explain the unique role of the user in DSS versus management information systems Describe DSS hardware and software platforms Become familiar with a DSS development language Understand current DSS issues

Opening Vignette: “Decision Support System Cures for Health Care” Company background Problem Proposed solution Results Answer and discuss the case questions

DSS Configurations Many configurations exist; based on management-decision situation specific technologies used for support DSS have three basic components Data Model User interface (+ optional) Knowledge

DSS Configurations Each component has several variations Managed by a commercial of custom software Typical types: Model-oriented DSS Data-oriented DSS

DSS Description An early definition of DSS A system intended to support managerial decision makers in semistructured and unstructured decision situations meant to be adjuncts to decision makers (extending their capabilities but not replacing their judgment) aimed at decisions that required judgment or at decisions that could not be completely supported by algorithms would be computer based; operate interactively; and would have graphical output capabilities…

Overall Capabilities of DSS A DSS is typically built to support the solution of a certain problem (or to evaluate a specific opportunity). DSS is an approach (or methodology) for supporting decision making uses an interactive, flexible, adaptable computer-based information system (CBIS) developed (by end user) for supporting the solution to a specific nonstructured management problem uses data, model and knowledge along with a friendly (often graphical; Web-based) user interface incorporate the decision maker's own insights supports all phases of decision making can be used by a single user or by many people

Overall Capabilities of DSS Easy access to data/models/knowledge Proper management of organizational experiences and knowledge Easy to use, adaptive and flexible GUI Timely, correct, concise, consistent support for decision making Support for all who needs it, where and when he/she needs it - See Table 3.2 for a complete list...

A Web-Based DSS Architecture

DSS Characteristics and Capabilities DSS is not quite synonymous with BI DSS are generally built to solve a specific problem and include their own database(s) BI applications focus on reporting and identifying problems by scanning data stored in data warehouses Both systems generally include analytical tools (BI called business analytics systems) Although some may run locally as a spreadsheet, both DSS and BI uses Web

DSS Characteristics and Capabilities

DSS Characteristics and Capabilities Business analytics Analytics is the science of analysis, Its refers to analysis data. Business analytics (BA) is abroad category of applications and techniques for gathering ,storing , analyzing , and providing access to data to help enterprise users make better business and strategic decisions. See Ch6 Business analytics implies the use of models and data to improve an organization's performance and/or competitive posture Example: An analytic application used for credit scoring for a loan application might: Automatically accept or deny the loan application

DSS Characteristics and Capabilities Web analytics implies using business analytics on real-time Web information to assist in decision making; often related to e-Commerce Predictive analytics describes the business analytics method of forecasting problems and opportunities rather than simply reporting them as they occur

Components of DSS

Components of DSS Data Management Subsystem Model Management Subsystem User Interface Subsystem Knowledgebase Management Subsystem

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

Database Management Subsystem The Database Is a collection of interrelated data, organized to meet the needs and structure of an organization. The data are extracted from internal and external data sources. Data Organization A standalone database? Data Extraction (ETL) Importing of files, summarizing, standardization, filtration, and condensation of data. Managed by DBMS The Database Management System A database is created , accessed and updated by a DBMS (technology insights 3.3) Most DSS built with a standard commercial relational DBMS Major vendors are (Oracle, IBM, Microsoft) The Query Facility

Database Management Subsystem Key Data Issues The Directory Is a catalog of all the data in a database It contains data definitions Its main functions is to answer questions about the availability of data items, their source and their exact meaning. Key Data Issues Data quality A key issue in data management Poor quality data, leads to poor quality information leads to waste. “Garbage in/garbage out" (GIGO) E.g., given inaccurate data in a CRM system, customers may be contacted many times. Data quality clean up (e.g., validation and verification) tools for data cleansing

Database Management Subsystem Key Data Issues Data integration “Creating a single version of the truth” Scalability Is ability of a system , network, or process to handle a growing a mount of work in a capable manner E.g., increased size of database Data Security Data must be protected from unauthorized access through security measures such as ID and password Data can be encrypted, in the case of unauthorized access New tools developed to monitor database activity and provide audit trail E.g., SQL Guard from Guardium,Inc.

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

DSS Components Model Management Subsystem The Model base Contains routine and special statistical, financial, forecasting ,management science and other quantitative models that provide the analysis capabilities in a DSS Model Types Strategic Used to support top managers’ strategic planning responsibilities Potential applications include an e-commerce venture, developing corporate objectives, selecting a plant location, creating a noun routine capital budget, .. Tactical Used mainly by middle managers to assist in allocating and controlling the organization’s resources E.g., sales promotion planning, web server selection routine capital budgeting.

DSS Components Model Management Subsystem Operational Used to support day to day working activates of the organization E.g., e-commerce transaction acceptance (e.g., purchases), approval of personal loans, production scheduling, enventory control, maintenance planning Analytic models Used to perform analysis on data They include statistical models, management science models, data mining algorithms, financial models and more They can integrated with others They can be classified by functional areas or by discipline Model building blocks and Routines The model base can contain model building blocks and routines such as the functions and add- ins in excel.

DSS Components Model Management Subsystem Modeling Tools For semistructured and unstructured problems : Programming tools and languages used to customize models such as .Net Framwork, C++,Java For samll and meduim sized DSS or less complex ones: Excel usually used. The MBMS has four main 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 directory Model execution, integration and command

DSS Components Model Management Subsystem Model directory Its role is similar to that of database It’s a catalog of all the models and other software in the model base Contains definitions and its main function to answer questions about the availability and capabilities of the models. Model execution, integration and command Model execution is the process of controlling the actual running of the model Model integration involves combining the operations of several models when needed or integrating the DSS with other applications A model command processor is used to accept and interpret modeling instructions from the user interface component and route them to the model execution or model integration functions

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 User Interface (Dialog) Subsystem The term User Interface covers all aspects of communication between a user and the DSS or any MSS It includes H\W and S\W and all factors that deal with ease to use , accessibility, and human machine interaction. User Interface Management System (UIMS) Software(Composed of several programs) that managed the user interface subsystem The user Interface Process See figure 3.7 DSS User Interfaces Access provided through Web browsers including ( Voice input, portable devices) Web browsers provides a portal or dashboard to access the system.

DSS Components Knowledgebase Management System Incorporation of intelligence and expertise Because many semistructured and unstructured problems are so complex Knowledge components: Expert systems, Knowledge management systems, Neural networks, Intelligent agents, Fuzzy logic, Case-based reasoning systems, and so on

DSS User One faced with a decision that an MSS is designed to support is called : 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 Specialists such as Staff assistants, Business analysts, Expert tool users, Facilitators (in a GSS )use computers more than managers They tend to be more detail oriented, more willing to use complex system in their work They are intermediaries between managers and MSS

Intermediary: allows a manager to benefit from a DSS without actually having to use the keyboard Staff assistants: have a specialized knowledge about management problems and some experience with decision support technology Expert tool users: are skilled in the application of one or more types of specialized problem solver tools Business (system) analysts: have a general knowledge of the application area and considerable skill in using DSS tools Facilitators (GSS) :support the work of people working in groups

DSS Components Future/current DSS Developments DSS have evolved with advances in Computer H\W and S\W Hardware enhancements Smaller, faster, cheaper, … 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

DSS Classifications The Association for information System Special Interest Group on Decision Support Systems (AIS SIGDSS) Classification for DSS: Communications-driven and group DSS (GSS) Includes how computer, collaboration, and communication technology support groups in tasks Meetings, design collaboration E.g., SCM Data-Driven DSS Rely on data and their processing into information E.g., OLAP Strong |Report and Query capabilities Document-Driven DSS Rely on knowledge coding, analysis, search and retrieval for decision support Include text based

DSS Classifications Model-Driven DSS Compound DSS Knowledge-Driven DSS, Data mining, and Management Expert Systems Applications Involve the application of knowledge technologies to address specific decision support needs All Artificial Intelligence-based DSS fall into this category Model-Driven DSS The major emphases of DSS that are primarily developed around one or more optimization or simulation model E.g., Excel includes dozens of statistical packages, Solver, and many financial and management models. Compound DSS Or Hybrid DSS includes two or more of the major categories of DSS Custom-Made Systems Versus Ready-Made Systems

A DSS Modeling Language Planners Lab (plannerslab.com) Generating Assumptions

A DSS Modeling Language Planners Lab (plannerslab.com) Creating a new model

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

A DSS Modeling Language Planners Lab (plannerslab.com)

End of the Chapter Questions / Comments…

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