Decision Support Systems and Executive Information Systems

1. Decision Support Systems and Executive Information Systems Chapter 12 Information Systems Management In Practice 5E McNurlin & Sprague

2. What are Decision Support Systems? • Systems that support, not replace, managers in their decision-making activities • Decision modeling, decision theory, and decision analysis, attempt to make models from which the “best decision can be derived, by computation • DSS is defined as:Computer-based systems • That help decision makers • Confront ill-structured problems • Through direct interaction • With data and analysis models

3. The Architecture for DSS: Components • DDM Paradigm is the interaction of • Dialog (D) between the user and the system • Data (D) that support the system • Models (M) that provide the analysis capabilities

4. The Architecture for DSS: Components • A good DSS should have a balance among the three capabilities: • Easy to use to support interaction with non-tech users • Access to wide variety (4 types) of information sources • Provide analysis and modeling in a variety of ways

5. The Architecture for DSS:The Dialog Component • The attributes of the dialog components can be called a “dialog style” • Reference card • Mouse to access pull-down menus

6. The Architecture for DSS:The Data Component • Data sources • Data warehousing • Data mining

7. The Architecture for DSS:The Model Component • Models provide the analysis capabilities for a DSS. Using a mathematical representation of the problem, algorithmic processes are employed to generate information to support decision making.

8. Types of DSS: Institutional • Institutional DSS: Intended for organizational support on a continuing basis,written using a DS language. Typically mainframe, now PC-based • For Marketing Analysis (e.g., Ore-Ida): Support three main tasks in decision-making process: • Data retrieval - help manager answer “What has happened?” • Market analysis - answer question “Why did it happen?” • Modeling - helps manager answer “What will happen if…?”

9. Types of DSS: Institutional • Institutional DSS: (cont.) • For Sales Forecasting (e.g., Sara-Lee): • Previously sales forecasts came from sales force - were too optimistic, inventories were excessive. • Then time-series analysis of historical data was used, did not handle impact of sales promotions well. • Now companies use multiple regression models in order to inject “explanatory variables” into analysis of historical data - and therefore into the forecasts

10. Types of DSS: Quick Hit DSS • Quick hit DSS: Means a system that is quite limited in scope, is developed and put into use quickly, and helps a manager come to a decision fast. Can be useful for: • Getting managers started in using DSS • Providing DS for certain types of management decisions on an ad hoc or recurring basis • Providing a basis for deciding whether or not to build a full DSS • For supporting decision situations where the executives cannot wait for a full DSS to be built

11. Types of DSS: Quick Hit DSS • Quick hit DSS (cont.) - types: • Reporting DSS: Select, summarize, and list data from existing data files to meet manager's specific info needs • Short Analysis program: Analyze data as well as print or display the data. Generally use a small amount of data, which can be entered manually, e.g., impact of ESOP • DSS generators: Provide a way to develop quick,high - payoff DSS. Include languages, interfaces, and other facilities that aid in setting up specific DSS within a class of decision support applications

12. Important Developments in DSS • PC-based DSS has continued to grow. Spreadsheets encompass some of the functions previously performed by DSS generators. • Group DSS to support interdependent group decisions • Focused versions targeted at specific users • DSS groups as support teams for variety of other types of user support • User friendly capabilities • DSS refers mostly to systems for analysis of complex situations, having absorbed most of the work of management science and operations research in business organizations

13. Data Warehousing and Data Mining • Data warehouse: Houses data used to make decisions. This data is obtained periodically from transaction databases. The warehouse provides a snapshot of a situation at a specific time. Data warehouses differ from operational databases in that they do not house data used to process daily transactions. Operational databases have the latest data.

14. Data Warehousing and Data Mining • Key Concepts: • Metadata: The part of the warehouse that defines the data. Metadata means “data about data.” Metadata explains the meaning of each data element, how each element relates to each other, etc. • Quality data: Is the cleaning process • Data marts: Is a subset of data pulled off the warehouse for a specific group of users

15. Data Warehousing and Data Mining Give people new insights into data • Uncover unknown similarities, correlations that exist within one customer group that differentiates them from other groups • Is an advanced use of data warehouses, and it requires huge amounts of detailed data

16. Executive Information Systems (EIS) & Executive Support Systems (ESS) ESS: • Company performance data: sales, production, earnings, budgets, and forecasts • Internal communications: personal correspondence, reports, and meetings • Environmental scanning: for news on government regulations, competition, financial and economics developments, and scientific subjects

17. EIS and ESS Cont. • EIS is a DSS that provides access to (mostly) summary performance data, • using sophisticated graphics to display and visualize that data, • in a very easy to use fashion, • and with a minimum of analysis for modeling beyond the capability to “drill down” in summary data to examine components. • ESS adds communications and environmental scanning.

18. Pitfalls in ESS Development • Lack of executive support - executives must provide the funding, but are the principal users and supply the needed continuity • Undefined system objectives - the technology, the convenience, and the power of EIS are impressive, but the underlying objectives and business values of an EIS must be carefully thought through

19. Pitfalls in ESS Development (cont) • Poorly defined information requirements: EIS typically need non - traditional information sources - judgments, opinion, external text-based documents - in addition to traditional financial and operating data. • Inadequate support staff: support staff must have technical competence, understand the business, and ability to relate executives, and be a permanent team to manage evolution of systems

20. Pitfalls in ESS Development (cont.) • Poorly planned evolution: highly competent system professionals using the wrong development process will fail with EIS; EIS are not developed, delivered, and then maintained. They should evolve over a period of time under the leadership of a team that includes the executive sponsor, the operating sponsor, executive users, the EIS support staff manager, and the IS technical staff

21. What is a strong reason to install an EIS? • Attack a critical business need: EIS can be viewed as an aid to dealing with important needs that involve the future health of the organization • A strong personal desire by the executive: The executive sponsoring the project may want to get information faster than he/she is now getting it, or have a quicker access to a broader range of information, or have the ability to select and display only desired information and to probe for supporting detail, or to see information in graphical form

22. A weak reason to install an EIS • “The thing to do”: An EIS is seen as something that modern management must have, in order to be current in management practices. The rationale given is that the EIS will increase executive performance and reduce time that is wasted by such things as telephone tag.

23. The Main Role of EIS • A Status Access System: Filter, extract, and compress a broad range of up-to-date internal and external information. It should call attention to variances from plan. It should also monitor and highlight the critical success factors of the individual executive user. EIS is a structured reporting system for executive management, providing the executive with the data and information of choice and desired form.

24. The Main Role of EIS • Human Communications Support: This viewpoint sees an EIS in terms of human communications support that it provides. Manager can call on “network of help” (peers, subordinates, clients, customers, suppliers, etc). Manager makes requests, gives instructions, asks questions to selected members of this network, and acts through communications. EIS supports these communications.

25. DSS Trends • Personal computer based DSS: Newer packages • For the institutional DSS that support sequential interdependent decision making: Distributed DSS • For interdependent decision support: Group DSS • Decision support system products are incorporating tools and techniques from artificial intelligence

26. DSS Trends • Continued efforts to leverage the usefulness of DSS: EIS • DSS development groups have become less like special project commando teams and more a part of the end user support team • Cutting across all the preceding trends is the continued development of user friendly capabilities: Dialog support, speech recognition

27. Future of DSS • Application of technologies to improve the performance of information workers in organizations, specially dealing with ill-structured problems. • Challenges: • Integrated architecture: a common interface at the desktop as common dialog interface to access all IS • Connectivity: an integrated part of IS • Document in addition to data • More intelligence