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Are Executive Information Systems (EIS) needed?

by Dan Power

Editor, DSSResources.com

At least 30 years ago, Chief Executive Officers (CEOs) began going online for information(cf., Rockhart, 1979; Rockhart and Treacy, 1982; Houdeshel and Watson, 1987). SpecializedExecutive Information Systems (EIS) were developed to support senior management. By themid-1990s, these systems were losing favor in IT departments and in corporate board rooms.Some perceived having a system only for executives was elitist, others saw EIS briefing

books as hard to maintain, or under used and redundant with other systems, and somemanagers felt EIS had low quality data. Some IT managers saw web-based, enterprise-wide

business intelligence systems as a replacement. So what do we need today? Modern EIS?Decision Intelligence Systems(Imhoff and White, 8/27/2008)? Portals?or Executive user views to the enterprise-wide data warehouse?

Let's look back. Jack Rockarts (1979, 1982) field research stimulated the development of executive information systems (EIS) and executive support systems (ESS). These systemsevolved from single user, model-driven decision support systems and from the developmentof new relational database products. The first EIS generally used pre-defined informationdisplays maintained by analysts for senior executives. For example, in the Fall of 1978,Lockheed-Georgia began development of an EIS called Management Information andDecision Support (MIDS) system (cf., Houdeshel and Watson, 1987).

An Executive Information System (EIS) is a computerized system intended to provide currentand appropriate information to support executive decision-making. The emphasis has been ongraphical displays and an easy-to-use interface that presents information from the corporatedatabase. Also, EIS often provide canned reports or briefing books to top-level executives.An EIS should offer strong reporting and drill-down capabilities. The goal was to haveexecutives as "hands-on" users of the EIS for email, calendar, reading reports, findinginformation and monitoring key performance indicators.

Executive Information Systems differed from traditional information systems in a number of ways (cf., Kelley, 1994):

1. EIS were specifically tailored to an executive's information needs. So there was a targeteduser group.

2. Managers using EIS were able to access data about specific issues and problems as well asread aggregated reports.

3. EIS provided extensive on-line analysis tools including trend analysis, exception reportingand "drill-down" capability.

4. EIS accessed a broad range of internal and external data.

In my opinion, we still need targeted systems like EIS. Certainly BI, DSS, Group DSS andEIS applications are overlapping. The features, intended audience, and development


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technology used are often common between these applications. In addition, many decisionsupport technologies have related and overlapping purposes. Differentiating the concept of anexecutive information system (EIS) may help IS/IT analysts understand senior executivedecision support needs. Some specific information system capability should focus on thedirect information needs for decision making of senior managers. EIS were intended to helpsenior executives find problems, identify opportunities, forecast trends and make "fact based"

decisions. These remain important goals.

Executive Information Systems, business intelligence and data warehousing technologies areconverging in the marketplace. Twenty years ago, EIS used proprietary databases thatrequired many staff people to update, maintain and create. This approach was very expensiveand remains hard to justify. Organizing external data may however be best done in adedicated database. Today executives need both structured and unstructured external data.Realistically external data becomes obsolete quickly and IS/IT staff aren't the appropriatemaintainers for such data. Today data warehouses, business intelligence technologies, theWeb and OLAP have made Executive Information Systems potentially more powerful andmore practical.

Modern EIS should report key results to managers. Performance measures in an EIS must beeasy to understand and collect. Wherever possible, data should be collected as part of routinework processes. An EIS should not add substantially to the workload of managers or staff.EIS should create value.

So a modern EIS should be an enterprise-wide, data-driven DSS that helps senior managersanalyze, compare, and highlight trends in key internal and external variables, a store of reports and briefings, and a tool to monitor performance and identify opportunities and

problems. Effective EIS should increase the ability of senior executives to monitor manydiverse activities and may help reengineer decision tasks and increase managerial

productivity by reducing the number of management levels in an organization.

An Executive Information System (EIS) was intended as a type of management informationsystem to facilitate and support the information and decision making needs of senior executives. According to Wikipedia, an EIS is commonly considered as a specialized form of a Decision Support System (DSS).

EIS, portals, strategic business intelligence and data warehousing technologies have beenconverging in the marketplace. Modern EIS are needed.

We need information systems that are easy for senior executives to use! Modern EIS should provide timely delivery of secure, sensitive decision relevant company information; presentinformation in a context that helps executives understand what is important and what is

happening; provide filters and drill-down to reduce data overload; assist in tracking events,finding reports and monitoring results; and finally, a modern EIS should increase theefficiency and effectiveness of executive decision makers.

The truth is that it does not matter what we call information systems targeted to senior executives. In reality, executives should be an important targeted user group for corporateinformation. Some would say the most important user group! So let's commit resources and

build modern EIS, or create decision intelligence systems, or create an executive portal withlinks to appropriate decision support applications.


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References

Houdeshel, G. and H. Watson, "The Management Information and Decision Support (MIDS)System at Lockheed-Georgia", MIS Quarterly, 11, 1, March 1987, 127-140.

Imhoff, C. and C. White, "Full Circle: Decision Intelligence (DSS 2.0)," B-Eye-Network,Published: August 27, 2008, URL http://www.b-eye-network.com/view/8385 .

Kelly, F., "Implementing an Executive Information System (EIS)", DSSResources.COM,11/07/2002, HTML File. This is a review paper from 1994 that was featured atceoreview.com.

Power, D. J., Decision Support Systems Hyperbook, Cedar Falls, IA: DSSResources.COM,HTML version, Fall 2000, URL http://dssresources.com/dssbook/.

Power, D.J.A Brief History of Decision Support Systems.DSSResources.COM, World WideWeb, http://DSSResources.COM/history/dsshistory.html, version 4.0, March 10, 2007.

Rockart, J. F. "Chief Executives Define Their Own Data Needs," Harvard Business Review,67, 2 March-April 1979, 81-93.

Rockart, J.F. and M.E. Treacy, The CEO Goes On-Line, Harvard Business Review,January-February, 1982, 82-88.

Watson, Hugh J. and Frolick, Mark (1992). Executive information systems: Determininginformation requirements. Information Systems Management, Spring 1992, pp. 37-43.

Watson, Hugh J., and Rainer, R. Kelly Jr. (1991).A manager's guide to executive supportsystems. Business Horizons, March-April 1991, pp. 44-50.

Watson, Hugh J., Rainer, R. Kelly, and Houdeshel, George (1992). Executive InformationSystems: Emergence, Development, Impact. (New York: John Wiley & Sons Inc.)

Watson, H., G., Houdeshel and R. K. Rainer, Jr., Building Executive Information Systemsand other Decision Support Applications, New York: John Wiley, 1997.

Wikipedia, "Executive Information Systems," URLhttp://en.wikipedia.org/wiki/Executive_Information_System .

Last update: 2008-09-17 10:19Author: Daniel Power


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provides generalizations and directions for building more effective DSS (cf., Baskerville & Myers, 2002; Keen,1980).

The next section describes the origins of the field of decision support systems. Section 3 discusses the decision

support systems theory development that occurred in the late 1970s and early 1980s. Section 4 discussesimportant developments to communications-driven , data-driven, document driven, knowledge-driven and

model-driven DSS (cf., Power, 2002). The final section briefly discusses how DSS practice, research andtechnology is continuing to evolve.

II. Decision Support Systems Origins

In the 1960s, researchers began systematically studying the use of computerized quantitative models to assistin decision making and planning (Raymond, 1966; T urban, 1967; Urban, 1967, Holt and Huber, 1969). Ferguson

and Jones (1969) reported the first experimental study using a computer aided decision system. Theyinvestigated a production scheduling application running on an IBM 7094. In retrospect, a major historical

turning point was Michael S. Scott Morton's (1967) dissertation field research at Harvard University.

Scott Morton s study involved building, implementing and then testing an interactive, model-drivenmanagement decision system. Fellow Harvard Ph.D. student Andrew McCosh asserts that the concept of decision support systems was first articulated by Scott Morton in February 1964 in a basement office inSherman Hall, Harvard Business School (McCosh email, 2002) in a discussion they had about Scott Morton sdissertation. During 1966, Scott Morton (1971) studied how computers and analytical models could help

managers make a recurring key business planning decision. He conducted an experiment in which managersactually used a Management Decision System (MDS). Marketing and production managers used an MDS tocoordinate production planning for laundry equipment. The MDS ran on an IDI 21 inch CRT with a light pen

connected using a 2400 bps modem to a pair of Univac 494 systems.

The pioneering work of George Dantzig, Douglas Engelbart and Jay Forrester likely influenced the feasibility of building computerized decision support systems. In 1952, Dantzig became a research mathematician at the

Rand Corporation, where he began implementing linear programming on its experimental computers. In themid-1960s, Engelbart and colleagues developed the first hypermedia groupware system called NLS (oNLineSystem). NLS facilitated the creation of digital libraries and the storage and retrieval of electronic documents

using hypertext. NLS also provided for on-screen video teleconferencing and was a forerunner to groupdecision support systems. Forrester was involved in building the SAGE (Semi-Automatic Ground Environment)air defense system for North America completed in 1962. SAGE is probably the first computerized data-drivenDSS. Also, Professor Forrester started the System Dynamics Group at the Massachusetts Institute of

Technology Sloan School. His work on corporate modeling led to programming DYNAMO, a general simulationcompiler.

In 1960, J.C.R. Licklider published his ideas about the future role of multiaccess interactive computing in a

paper titled Man-Computer Symbiosis. He saw man-computer interaction as enhancing both the quality andefficiency of human problem solving and his paper provided a guide for decades of computer research tofollow. Licklider was the architect of Project MAC at MIT that furthered t he study of interactive computing.

By April 1964, the development of the IBM System 360 and other more powerful mainframe systems made it

practical and cost-effective to develop Management Information Systems (MIS) for large companies (cf., Davis,1974). These early MIS focused on providing managers with structured, periodic reports and the information

was primarily from accounting and transaction processing systems, but the systems did not provide interactivesupport to assist managers in decision making.


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Around 1970 business journals started to publish articles on management decision systems, strategic planningsystems and decision support systems (cf., Sprague and Watson 1979).. For example, Scott Morton andcolleagues McCosh and Stephens published decision support related articles in 1968. The first use of the termdecision support system was in Gorry and Scott-Morton s (1971) Sloan Management Review article. They

argued that Management Information Systems primarily focused on structured decisions and suggested thatthe supporting information systems for semi-structured and unstructured decisions should be termed

Decision Support Systems .

T.P. Gerrity, Jr. focused on Decision Support Systems design issues in his 1971 Sloan Management Reviewarticle titled "The Design of Man-Machine Decision Systems: An Application to Portfolio Management". Thearticle was based on his MIT Ph.D. dissertation. His system was designed to support investment managers in

their daily administration of a clients' stock portfolio.

John D.C. Little, also at Massachusetts Institute of Technology, was studying DSS for marketing. Little andLodish (1969) reported research on MEDIAC, a media planning support system. Also, Little (1970) identified

criteria for designing models and systems to support management decision-making. His four criteria included:robustness, ease of control, simplicity, and completeness of relevant detail. All four criteria remain relevant inevaluating modern Decision Support Systems. By 1975, Little was expanding the frontiers of computer-supported modeling. His DSS called Brandaid was designed to support product, promotion, pricing andadvertising decisions. Little also helped develop the financial and marketing modeling language known as

EXPRESS.

In 1974, Gordon Davis, a Professor at the University of Minnesota, published his influential text onManagement Information Systems. He defined a Management Information System as "an integrated,man/machine system for providing information to support the operations, management, and decision-making

functions in an organization. (p. 5)." Davis's Chapter 12 was titled "Information System Support for DecisionMaking" and Chapter 13 was titled "Information System Support for Planning and Control". Davis s frameworkincorporated computerized decision support systems into the emerging field of management informationsystems.

Peter Keen and Charles Stabell claim the concept of decision support systems evolved from "the theoreticalstudies of organizational decisionmaking done at the Carnegie Institute of Technology during the late 1950s

and early '60s and the technical work on interactive computer systems, mainly carried out at theMassachusetts Institute of Technology in the 1960s. (Keen and Scott Morton, 1978)". Herbert Simon s books(1947, 1960) and articles provide a context for understanding and supporting decision making.

In 1995, Hans Klein and Leif Methlie noted A study of the origin of DSS has still to be written. It seems that thefirst DSS papers were published by PhD students or professors in business schools, who had access to the first

time-sharing computer system: Project MAC at the Sloan School, the Dartmouth Time Sharing Systems at theTuck School. In France, HEC was the first French business school to have a time-sharing system (installed in

1967), and the first DSS papers were published by professors of the School in 1970. (p. 112).

III. Theory Development

In the mid- to late 1970s, both practice and theory issues related to DSS were discussed at academic

conferences including the American Institute for Decision Sciences meetings and the ACM SIGBDP Co nferenceon Decision Support Systems in San Jose, CA in January 1977 (the proceeding were included in the journal

Database). The first International Conference on Decision Support Systems was held in Atlanta, Georgia in1981. Academic conferences provided forums for idea sharing, theory discussions and information exchange.


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At about this same time, Keen and Scott Morton s DSS textbook (1978) provided the first broad behavioralorientation to decision support system analysis, design, implementation, evaluation and development. Thisinfluential text provided a framework for teaching DSS in business schools. McCosh and Scott-Morton s (1978)DSS book was more influential in Europe.

In 1980, Steven Alter published his MIT doctoral dissertation results in an influential book. Alter's research and

papers (1975; 1977) expanded the framework for thinking about business and management DSS. Also, his casestudies provided a firm descriptive foundation of decision support system examples. A number of other MIT

dissertations completed in the late 1970s also dealt with issues related to using models for decision support.

Alter concluded from his research (1980) that decision support systems could be categorized in terms of the

generic operations that can be performed by such systems. These generic operations extend along a singledimension, ranging from extremely data-oriented to extremely model-oriented. Alter conducted a field studyof 56 DSS that he categorized into seven distinct types of DSS. His seven types include:

y File drawer systems that provide access to data items.

y D ata analysis systems that support the manipulation of data by computerized tools tailored to a

specific task and setting or by more general tools and operators.

y A nalysis information systems that provide access to a series of decision-oriented databases andsmall models.

y Acc ounting and finan c ial models that calculate the consequences of possible actions.

y R epresentational models that estimate the consequences of actions on the basis of simulation

models.

y O ptimization models that provide guidelines for action by generating an optimal solution

consistent with a series of constraints.

y S uggestion models that perform the logical processing leading to a specific suggested decisionfor a fairly structured or well-understood task.

Donovan and Madnick (1977) classified DSS as institutional or ad hoc. Institutional DSS support decisions thatare recurring. An ad hoc DSS supports querying data for one time requests. Hackathorn and Keen (1981)

identified DSS in three distinct yet interrelated categories: Personal DSS, Group DSS and Organizational DSS.

In 1979, John Rockart of the Harvard Business School published a ground breaking article that led to thedevelopment of executive information systems (EISs) or executive support systems (ESS). Rockart developed

the concept of using information systems to display critical success metrics for managers.

Robert Bonczek, Clyde Holsapple, and Andrew Whinston (1981) explained a theoretical framework for

understanding the issues associated with designing knowledge-oriented Decision Support Systems. Theyidentified four essential "aspects" or general components that were common to all DSS: 1. A language system(LS) that specifies all messages a specific DSS can accept; 2. A presentation system (PS) for all messages a DSScan emit; 3. A knowledge system (KS) for all knowledge a DSS has; and 4. A problem-processing system (PPS)

that is the "software engine" that tries to recognize and solve problems during the use of a specific DSS. Theirbook explained how Artificial Intelligence and Expert Systems technologies were relevant to developing DSS.


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Finally, Ralph Sprague and Eric Carlson s (1982) book Building Effective Decision Support Systems was animportant milestone. Much of the book further explained the Sprague (1980) DSS framework of data base,model base and dialog generation and management software. Also, it provided a practical, andunderstandable overview of how organizations could and should build DSS. Sprague and Carlson (1982)

defined DSS as "a class of information system that draws on transaction processing systems and interacts withthe other parts of the overall information system to support the decision-making activities of managers and

other knowledge workers in organizations (p. 9).

IV. DSS Applications Development

Beginning in about 1980 many activities associated with building and studying DSS occurred in universities and

organizations that resulted in expanding the scope of DSS applications. These actions also expanded the fieldof decision support systems beyond the initial business and management application domain. These diverse

systems were all called Decision Support Systems. From those early days, it was recognized that DSS could bedesigned to support decision-makers at any level in an organization. Also, DSS could support operations

decision making, financial management and strategic decision-making.

A literature survey and citation studies (Alavi&Joachimsthaler, 1990, Eom& Lee, 1990a, Eom, 2002,Arnott&Pervan, 2005) suggest the major applications for DSS emphasized manipulating quantitative models,

accessing and analyzing large data bases, and supporting group decision making. Much of the model-drivenDSS research emphasized use of the systems by individuals, i.e., personal DSS, while data-driven DSS were

usually institutional, ad hoc or organizational DSS. Group DSS research emphasized impacts on decisionprocess structuring and especially brainstorming.

The discussion in this section follows the broad historical progression of DSS research. The first subsection

examines model-driven DSS, then the focus turns to data-driven DSS and executive information systems andnotes the growing prominence of such systems beginning in the late 1980s. The origins of communications-driven DSS are then briefly explored and the bifurcation into two types of group DSS, model-driven andcommunications-driven. Developments in document storage technologies and search engines then made

document-driven DSS more widely available as web-based systems. The last subsection summarizes majordevelopments in Artificial Intelligence (AI) and expert systems that made suggestion or knowledge-driven DSSpractical.

IV .1 Model-driven DSS

Scott-Morton s (1971) production planning management decision system was the first widely discussed model-driven DSS, but Ferguson and Jones (1969) production scheduling application was also a model-driven DSS.Many of the early decision systems mentioned in section 2, e.g., Sprinter, MEDIAC and Brandaid, are probablymodel-driven DSS.

A model-driven DSS emphasizes access to and manipulation of financial, optimization and/or simulationmodels. Simple quantitative models provide the most elementary level of functionality. Model-driven DSS use

limited data and parameters provided by decision makers to aid decision makers in analyzing a situation, but ingeneral large data bases are not needed for model-driven DSS (Power, 2002). Early versions of model-driven

DSS were called model-oriented DSS by Alter (1980), computationally oriented DSS by Bonczek, Holsapple andWhinston (1981) and later spreadsheet-oriented and solver-oriented DSS by Holsapple and Whinston (1996).

The first commercial tool for building model-driven DSS using financial and quantitative models was calledIFPS, an acronym for interactive financial planning system. It was developed in the late 1970's by Gerald R.


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Wagner and his students at the University of Texas. Wagner s company, EXECUCOM Systems, marketed IFPSuntil the mid 1990s. Gray s Guide to IFPS (1983) promoted the use of the system in business schools. AnotherDSS generator for building specific systems based upon the Analytic Hierarchy Process (Saaty, 1982), calledExpert Choice, was released in 1983. Expert Choice supports personal or group decision making. Ernest Forman

worked closely with Thomas Saaty to design Expert Choice.

In 1978, Dan Bricklin and Bob Frankston co-invented the software program VisiCalc (Visible Calculator). VisiCalcprovided managers the opportunity for hands-on computer-based analysis and decision support at a

reasonably low cost. VisiCalc was the first "killer" application for personal computers and made possibledevelopment of many model-oriented, personal DSS for use by managers. The history of microcomputerspreadsheets is described in Power (2000). In 1987, Frontline Systems founded by Dan Fylstra marketed the

first optimization solver add-in for Microsoft Excel.

In a 1988 paper, Sharda, Barr, and McDonnell reviewed the first 15 years of model-driven DSS research. Theyconcluded that research related to using models and financial planning systems for decision support was

encouraging but certainly not uniformly positive. As computerized models became more numerous, researchfocused on model management and on enhancing more diverse types of models for use in DSS such asmulticriteria, optimization and simulation models.

The idea of model-driven spatial decision support system (SDSS) evolved in the late 1980 s (Armstrong,Densham, and Rushton., 1986) and by 1995 the SDSS concept had become firmly established in the literature

(Crossland, Wynne, and Perkins, 1995). Data-driven spatial DSS are also common.

IV .2 Data-driven DSS

In general, a data-driven DSS emphasizes access to and manipulation of a time-series of internal company data

and sometimes external and real-time data. Simple file systems accessed by query and retrieval tools providethe most elementary level of functionality. Data warehouse systems that allow the manipulation of data by

computerized tools tailored to a specific task and setting or by more general tools and operators provide

additional functionality. Data-Driven DSS with On-line Analytical Processing (cf., Codd et al., 1993) provide thehighest level of functionality and decision support that is linked to analysis of large collections of historical

data. Executive Information Systems are examples of data-driven DSS (Power, 2002). Initial examples of thesesystems were called data-oriented DSS, Analysis Information Systems (Alter, 1980) and retrieval-only DSS byBonczek, Holsapple and Whinston (1981).

One of the first data-driven DSS was built using an APL-based software package called AAIMS, An AnalyticalInformation Management System. It was developed from 1970-1974 by Richard Klaas and Charles Weiss atAmerican Airlines (cf. Alter, 1980).

As noted previously, in 1979 John Rockart s research stimulated the development of executive information

systems (EIS) and executive support systems (ESS). These systems evolved from single user model-drivendecision support systems and from the development of relational database products. The first EIS used pre-

defined information screens maintained by analysts for senior executives. For example, in the Fall of 1978,development of an EIS called Management Information and Decision Support (MIDS) system began at

Lockheed-Georgia (cf., Houdeshel and Watson, 1987).

The first EIS were developed in the late 1970s by Northwest Industries and Lockheed who risked being on thebleeding edge of technology . Few even knew about the existence of EIS until John Rockart and Michael

Treacy s article, The CEO Goes On-line, appeared in the January-February 1982 issue of the Ha rv a rd Business


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Review . (Watson, Houdeshel and Rainer, 1997, p. 6) Watson and colleagues.further note A major contributorto the growth of EIS was the appearance of vendor-supplied EIS software in the mid-1980s. Pilot Software sCommand Center and Comshare s Commander EIS made it much easier for firms to develop an EIS byproviding capabilities for (relatively) easy screen design, data importation, user-friendly front ends, and access

to news services. (p. 6) In a related development in 1984, Teradata s parallel processing relational database

management system shipped to customers Wells Fargo and AT&T .

In about 1990, data warehousing and On-Line Analytical Processing (OLAP) began broadening the realm of EIS

and defined a broader category of data-driven DSS (cf., Dhar and Stein, 1997). Nigel Pendse (1997), author of the OLAP Report, claims both multidimensional analysis and OLAP had origins in the APL programming

language and in systems like Express and Comshare s System W. Nylund (1999) traces the developmentsassociated with Business Intelligence (BI) to Procter & Gamble s efforts in 1985 to build a DSS that linked sales

information and retail scanner data. Metaphor Computer Systems, founded by researchers like Ralph Kimballfrom Xerox s Palo Alto Research Center (PARC), built the early P&G data-driven DSS. Staff from Metaphor later

founded many of the Business Intelligence vendors: The term BI is a popularized, umbrella term coined andpromoted by Howard Dresner of the Gartner Group in 1989. It describes a set of concepts and methods to

improve business decision making by using fact-based support systems. BI is sometimes used interchangeablywith briefing books, report and query tools and executive information systems. In general, business

intelligence systems are data-driven DSS.

Bill Inmon and Ralph Kimball actively promoted decision support systems built using relational databasetechnologies. For many Information Systems practitioners, DSS built using Oracle or DB2 were the first decisionsupport systems they read about in the popular computing literature. Ralph Kimball was "The Doctor of DSS"

and Bill Inmon was the "father of the data warehouse . By 1995, Wal-Mart s data-driven DSS had more than 5terabytes of on-line storage from Teradata that expanded to more than 24 terabytes in 1997.In more recentyears, vendors added tools to create web-based dashboards and scorecards.

IV .3 Communications-driven DSS

Communications-driven DSS use network and communications technologies to facilitate decision-relevantcollaboration and communication. In these systems, communication technologies are the dominant

architectural component. Tools used include groupware, video conferencing and computer-based bulletinboards (Power, 2002).

Engelbart's 1962 paper "Augmenting Human Intellect: A Conceptual Framework" is the anchor for much of the

later work related to comm unications-driven DSS. In 1969, he demonstrated the first hypermedia/groupwaresystem NLS (oNLine System) at the Fall Joint Computer Conference in San Francisco. Engelbart invented the

both the computer mouse and groupware.

Joyner and Tunstall s article (1970) reporting testing of their Conference Coordinator computer software is thefirst empirical study in this research area. Murray Turoff s (1970) article introduced the concept of Computerized Conferencing. He developed and implemented the first Computer Mediated CommunicationsSystem (EMISARI) tailored to facilitate group communications.

In the early 1980s, academic researchers developed a new category of software to support group decision-making called Group Decision Support Systems abbreviated GDSS (cf., Gray, 1981; Huber, 1982; Turoff and

Hiltz, 1982). Mindsight from Execucom Systems, GroupSystems developed at the University of Arizona and theSAMM system developed by University of Minnesota researchers were early Group DSS.


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Eventually GroupSystems matured into a commercial product. Jay Nunamaker, Jr. and his colleagues wrote in1992 that the underlying concept for GroupSystems had its beginning in 1965 with the development of Problem Statement Language/Problem Statement Analyzer at Case Institute of Technology. In 1984, theforerunner to GroupSystems called PLEXSYS was completed and a computer-assisted group meeting facility

was constructed at the University of Arizona. The first Arizona facility, called the PlexCenter, housed a large U-shaped conference table with 16 computer workstations.

On the origins of SAMM, Dickson, Poole and DeSanctis (1992) report that Brent Gallupe, a Ph.D. student at the

University of Minnesota, decided in 1984 "to program his own small GDSS system in BASIC and run it on hisuniversity s VAX computer".

DeSanctis and Gallup (1987) defined two types of GDSS. Basic or level 1 GDSS are systems with tools to reducecommunication barriers, such as large screens for display of ideas, voting mechanisms, and anonymous input of

ideas and preferences. These are communications-driven DSS. Advanced or level 2 GDSS provide problem-structuring techniques, such as planning and modeling tools. These are model-driven group DSS. Since the mid-1980s, many research studies have examined the impacts and consequences of both types of group DSS. Also,

companies have commercialized model-driven group DSS and groupware.

Kersten (1985) developed NEGO, a computerized group tool to support negotiations. Bui and Jarke (1986)

reported developing Co-op, a system for cooperative multiple criteria group decision support. Kraemer andKing (1988) introduced the concept of Collaborative Decision Support Systems (CDSSs). They defined them as

interactive computer-based systems to facilitate the solution of ill-structured problems by a set of decisionmakers working together as a team.

In 1989, Lotus introduced a groupware product called Notes and broadened the focus of GDSS to include

enhancing communication, collaboration and coordination among groups of people. Notes had its roots in aproduct called PLATO Notes, written at the Computer-based Education Research Laboratory (CERL) at the

University of Illinois in 1973 by David R. Woolley.

In general, groupware, bulletin boards, audio and videoconferencing are the primary technologies forcommunications-driven decision support. In the past few years, voice and video delivered using the Internet

protocol have greatly expanded the possibilities for synchronous communications-driven DSS.

IV .4 Document-driven DSS

A document-driven DSS uses computer storage and processing technologies to provide document retrieval and

analysis. Large document databases may include scanned documents, hypertext documents, images, soundsand video. Examples of documents that might be accessed by a document-driven DSS are policies and

procedures, product specifications, catalogs, and corporate historical documents, including minutes of meetings and correspondence. A search engine is a primary decision-aiding tool associated with a document-

driven DSS (Power, 2002). These systems have also been called text-oriented DSS (Holsapple andWhinston,1996).

The precursor for this type of DSS is Vannevar Bush s (1945) article titled "As We May Think". Bush wrote

"Consider a future device for individual use, which is a sort of mechanized private file and library. It needs aname, and to coin one at random, memex will do . Bush s memex is a much broader vision than that of

today s document-driven DSS.


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Text and document management emerged in the 1970s and 1980s as an important, widely used computerizedmeans for representing and processing pieces of text (Holsapple and Whinston, 1996). The first scholarlyarticle for this category of DSS was written by Swanson and Culnan (1978). They reviewed document-basedsystems for management planning and control. Until the mid-1990s little progress was made in helping

managers find documents to support their decision making. Fedorowicz (1993, 1996) helped define the needfor such systems. She estimated in her 1996 article that only 5 to 10 percent of stored business documents are

available to managers for use in decision making. The World-wide web technologies significantly increased theavailability of documents and facilitated the development of document-driven DSS.

IV .5 Knowledge-driven DSS

Knowledge-driven DSS can suggest or recommend actions to managers. These DSS are person-computer

systems with specialized problem-solving expertise. The "expertise" consists of knowledge about a particulardomain, understanding of problems within that domain, and "skill" at solving some of these problems (Power,

2002). These systems have been called suggestion DSS (Alter, 1980) and knowledge-based DSS (Klein&Methlie, 1995). Goul, Henderson, and Tonge (1992) examined Artificial Intelligence (AI) contributions to DSS.

In 1965, a Stanford University research team led by Edward Feigenbaum created the DENDRAL expert system.

DENDRAL led to the development of other rule-based reasoning programs including MYCIN, which helpedphysicians diagnose blood diseases based on sets of clinical symptoms. The MYCIN project resulted in

development of the first expert-system shell (Buchanan and Shortliffe, 1984).

Bonczek, Holsapple and Whinston s (1981) book created interest in using these technologies for DSS. In 1983,Dustin Huntington established EXSYS. That company and product made it practical to use PC based tools to

develop expert systems. By 1992, some 11 shell programs were available for the MacIntosh platform, 29 forIBM-DOS platforms, 4 for Unix platforms, and 12 for dedicated mainframe applications (National ResearchCouncil, 1999). Artificial Intelligence systems have been developed to detect fraud and expedite financialtransactions, many additional medical diagnostic systems have been based on AI, expert systems have been

used for scheduling in manufacturing operation and web-based advisory systems. In recent years, connecting

expert systems technologies to relational databases with web-based front ends has broadened thedeployment and use of knowledge-driven DSS.

V. Web-based DSS

Beginning in approximately 1995, the World-wide Web and global Internet provided a technology platform for

further extending the capabilities and deployment of computerized decision support. The release of the HTML2.0 specifications with form tags and tables was a turning point in the development of web -based DSS. In 1995,a number of papers were presented on using the Web and Internet for decision support at the 3rdInternational Conference of the International Society for Decision Support Systems (ISDSS). In addition to Web-based, model-driven DSS, researchers were reporting Web access to data warehouses. DSS Research

Resources was started as a web-based collection of bookmarks. By 1995, the World-Wide Web (Berners-Lee,1996) was recognized by a number of software developers and academics as a serious platform for

implementing all types of Decision Support Systems (cf., Bhargava& Power, 2001).

In November 1995, Power, Bhargava and Quek submitted the Decision Support Systems Research page forinclusion in ISWorld. The goal was to provide a useful starting point for accessing Web-based material related

to the design, development, evaluation, and implementation of Decision Support Systems. Nine months later,a DSS/WWW Workshop organized by Power and Quek was held as part of the IFIP Working Group 8.3


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Conference on Implementing Systems for Supporting Management Decisions: Concepts, Methods andExperiences , July 21-24, 1996 in London, UK.

In 1996-97, corporate intranets were developed to support information exchange and knowledge

management. The primary decision support tools included ad hoc query and reporting tools, optimization andsimulation models, online analytical processing (OLAP), data mining and data visualization (cf., Powell, 2001).

Enterprise-wide DSS using database technologies were especially popular in Fortune 2000 companies (Power,1997). Bhargava, Krishnan and Mller (1997) continued to discuss and experiment with electronic markets for

decision technologies.

In 1999, vendors introduced new Web-based analytical applications. Many DBMS vendors shifted their focus

to Web-based analytical applications and business intelligence solutions. In 2000, application service providers(ASPs) began hosting the application software and technical infrastructure for decision support capabilities.2000 was also the year of the portal. More sophisticated "enterprise knowledge portals" were introduced byvendors that combined information portals, knowledge management, business intelligence, and

communications-driven DSS in an integrated Web environment (cf., Bhargava and Power, 2001).

Power (1998) defined a Web-based decision support system as a computerized system that delivers decisionsupport information or decision support tools to a manager or business analyst using a "thin-client" Webbrowser like Netscape Navigator or Internet Explorer. The computer server that is hosting the DSS applicationis linked to the user's computer by a network with the TCP/IP protocol.

VI. Conclusions

DSS practice, research and technology continue to evolve. By 1996, Holsapple and Whinton had identified five

specialized types of DSS, including text-oriented DSS, database-oriented DSS, spreadsheet-oriented DSS,solver-oriented DSS, and rule-oriented DSS. These last four types of DSS match up with some of Alter s (1980)categories. Arnott and Pervan (2005) traced the evolution of DSS using seven sub-groupings of research andpractice: personal DSS, group support systems, negotiation support systems, intelligent DSS, knowledge

management-based DSS, executive information systems/business intelligence, and data warehousing. Thesesub-grouping overlap, but reflect the diverse evolution of prior research.

This chapter used an expanded DSS framework (Power, 2001, 2002) to retrospectively discuss the historicalevolution of decision support systems. The Web has had a significant impact on the variety, distribution andsophistication of DSS, but handheld PCs, wireless networks, expanding parallel processing coupled with verylarge data bases and visualization tools are continuing to encourage the development of innovative decision

support applications.

Historians use two approached to apply the past to the future: reasoning by analogy and projection of trends.

In many ways computerized decision support systems are like airplanes, coming in various shapes, sizes and

forms, technologically sophisticated and a very necessary tool in many organizations. Decision support systemsresearch and development will continue to exploit any new technology developments and will benefit fromprogress in very large data bases, artificial intelli gence, human-computer interaction, simulation and

optimization, software engineering, telecommunications and from more basic research on behavioral topicslike organizational decision making, planning, behavioral decision theory and organizational behavior.

Trends suggest that data-driven DSS will use faster, real-time access to larger, better integrated databases.

Model-driven DSS will be more complex, yet understandable, and systems built using simulations and theiraccompanying visual displays will be increasingly realistic. Communications-driven DSS will provide more real-


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time video communications support. Document-driven DSS will access larger repositories of unstructured dataand the systems will present appropriate documents in more useable formats. Finally, knowledge-driven DSSwill likely be more sophisticated and more comprehensive. The advice from knowledge-driven DSS will bebetter and the applications will cover broader domains.

Decision Support Systems pioneers came from a wide variety of backgrounds and faced many challenges that

they successfully overcame to demonstrate the value of using computers, information technologies andspecific decision support software to enhance and in some situations improve decision making. The DSS

pioneers created particular and distinct streams of technology development and research that serve as thefoundation for much of today s interest in building and studying computerized decision support systems. Thelegacy of the pioneers must be preserved. Check the Decision Support Systems Pioneers list at

DSSResources.com/history/pioneers/pioneerslist.html .

The future of decision support systems will certainly be different than the opportunistic and incrementalinnovations seen in the recent past. Decision support systems as an academic discipline is likely to follow a

path similar to computer architecture and software engineering and become more rigorous and more clearlydelineated. DSS consulting, teaching and research can be mutually supportive and each task can help establisha niche for those interested in building and studying DSS whether in Colleges of Information, Business orEngineering.

The history of Decision Support Systems covers a relatively brief span of years, and the concepts and

technologies are still evolving. Today it is still possible to reconstruct the history of Decision Support Systems(DSS) from retrospective accounts from key participants as well as from published and unpublished materials.Many of the early innovators and early developers are retiring but their insights and actions can be captured toguide future innovation in this field. It is hoped this paper leads to email and retrospective accounts that can

help us understand the "real" history of DSS. The Internet and Web have speeded-up developments in decisionsupport and have provided a new means of capturing and documenting the development of knowledge in thisresearch area. Decision support pioneers include many academic researchers from programs at MIT, Universityof Arizona, University of Hawaii, University of Minnesota and Purdue University. The DSS pioneers created

particular and distinct streams of technology development and research that serve as the foundation for muchof today s work in DSS.

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Appendix I. DSS Timeline

Year Major Milestones 1945 Bush proposed Memex 1947 Simon book titled Administrative Behavior 1952 Dantzig joined RAND and continued research on linear programming 1955 Semiautomatic Ground Environment (SAGE) project at M.I.T. Lincoln Lab uses

first light pen; SAGE completed 1962, first data-driven DSS 1956 Forrester started System Dynamics Group at the M.I.T. Sloan School 1960 Simon book The New Science of Management Decision; Licklider article on

Man-Computer Symbiosis 1962 Licklider architect of Project MAC program at M.I.T.; Iverson s book A

Programming Language (APL); Engelbart's paper "Augmenting HumanIntellect: A Conceptual Framework"

1963 Englebart established Augmentation Research Center at SRI 1965 Stanford team led by Feigenbaum created DENDRAL expert system; Problem

Statement Language/Problem Statement Analyzer (PSL/PSA) developed at

Case Institute of Technology 1966 UNIVAC 494 introduced; Tymshare founded and Raymond article on computer

time-sharing for business planning and budgeting 1967 Scott Morton s dissertation completed on impact of computer-driven visual

display devices on management decision-making process; Turban reportsnational survey on use of mathematical models in plant maintenance decision

making 1968 Scott Morton and McCosh article; Scott Morton and Stephens article;

Englebart demonstrated hypermedia groupware system NLS (oNLineSystem) at Fall Joint Computer Conference in San Francisco

1969 Ferguson and Jones article on lab study of a production scheduling computer-aided decision system running on an IBM 7094; Little and Lodish MEDIAC,media planning model; Urban new product model-based system called

SPRINTER 1970 Little article on decision calculus support system; Joyner and Tunstall article

on Conference Coordinator computer software; IRI Express, a


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multidimensional analytic tool for time-sharing systems, becomes available;Turoff conferencing system

1971 Gorry and Scott Morton SMR article first published use of term DecisionSupport System; Scott Morton book Management Decision Systems; Gerrity

article Man-Machine decision systems; Klein and Tixier article on SCARABEE 1973 PLATO Notes, written at the Computer-based Education Research Laboratory

(CERL) at the University of Illinois by David R. Woolley 1974 Davis s book Management Information Systems; Meador and Ness article DSS

application to corporate planning 1975 Alter completed M.I.T. Ph.D. dissertation "A Study of Computer Aided Decision

Making in Organizations"; Keen SMR article on evaluating computer-baseddecision aids; Boulden book on computer-assisted planning systems

1976 Sprague and Watson article "A Decision Support System for Banks"; Gracepaper on Geodata Analysis and Display System

1977 Alter article "A Taxonomy of Decision Support Systems", Klein article onFinsim; Carlson and Scott Morton chair ACM SIGBDP Conference DSS

Conference

1978 Development began on Management Information and Decision Support(MIDS) at Lockheed-Georgia; Keen and Scott Morton book; McCosh and Scott

Morton book; Holsapple dissertation completed; Wagner founded Execucomto market IFPS; Bricklin and Frankston created Visicalc (Visible Calculator)microcomputer spreadsheet; Carlson from IBM, San Jose plenary speaker atHICSS-11; Swanson and Culnan article document-based systems for

management planning

1979 Rockart HBR article on CEO data needs 1980 Sprague MISQ article on a DSS Framework; Alter book; Hackathorn founded

MicroDecisionware 1981 First International Conference on DSS, Atlanta, Georgia; Bonczek, Holsapple,

and Whinston book; Gray paper on SMU decision rooms and GDSS 1982 Computer named the Man of the Year by Time Magazine; Rockart and

Treacy article The CEO Goes On-Line HBR; Sprague and Carlson book;Metaphor Computer Systems founded by Kimball and others from XeroxPARC; ESRI launched its first commercial GIS software called ARC/INFO; IFIP

Working Group 8.3 on Decision Support Systems established 1983 Inmon Computerworld article on relational DBMS; IBM DB2 Decision Support

database released; Student Guide to IFPS by Gray; Huntington established

Exsys; Expert Choice software released 1984 PLEXSYS, Mindsight and SAMM GDSS; first Teradata computer with relational

database management system shipped to customers Wells Fargo and AT&T;MYCIN expert system shell explained

1985 Procter & Gamble use first data mart from Metaphor to analyze data fromcheckout-counter scanners; Whinston founded Decision Support Systems journal; Kersten developed NEGO

1987 Houdeshel and Watson article on MIDS; DeSanctis and Gallupe article onGDSS; Frontline Systems founded by Fylstra, marketed solver add-in for Excel

1988 Turban DSS textbook; Pilot Software EIS for Balanced Scorecard deployed atAnalog Devices

1989 Gartner analyst Dresner coins term business intelligence; release of Lotus


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Notes; International Society for Decision Support Systems (ISDSS) founded byHolsapple and Whinston

1990 Inmon book Using Oracle to Build Decision Support Systems; Eom and Lee co-citation analysis of DSS research 1971 1988

1991 Inmon books Building the Data Warehouse and Database Machines andDecision Support Systems; Berners-Lee s World Wide Web server and

browser, become publicly available 1993 Codd et al. paper defines online analytical processing (OLAP) 1994 HTML 2.0 with form tags and tables; Pendse s OLAP Report project began 1995 The Data Warehousing Institute (TDWI) established; DSS journal issue on Next

Generation of Decision Support; Crossland, Wynne, and Perkins article onSpatial DSS; ISWorld DSS Research pages and DSS Research Resources

1996 InterNeg negotiation software renamed Inspire; OLAPReport.com established; 1997 Wal-Mart and Teradata created then world s largest production data

warehouse at 24 Terabytes (TB) 1998 ACM First International Workshop on Data Warehousing and OLAP 1999 DSSResources.com domain name registered 2000 First AIS Americas Conference mini-track on Decision Support Systems 2001 Association for Information Systems (AIS) Special Interest Group on Decision

Support, Knowledge and Data Management Systems (SIG DSS) founded 2003 International Society for Decision Support Systems (ISDSS) merged with AIS

SIG DSS

Author Profile

Daniel J. Power is a Professor of Information Systems and Management at the College of Business

Administration at the University of Northern Iowa, Cedar Falls, Iowa and the editor of DSSResources.COM, theWeb-based knowledge repository about computerized systems that support decision making, the editor of PlanningSkills.COM, and the editor of DSS News, a bi-weekly e-newsletter. Dan writes the column "Ask Dan!"

in DSS News.

Dr. Power's research interests include the design and development of Decision Support Systems and how DSSimpact individual and organizational decision behavior. Since 1982, Power has published more than 40

articles, book chapters and proceedings papers. He was founding Chair of the Association for InformationSystems Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIG DSS).

Thanks for visiting. If you have any suggestions for improving this brief history of DSS, I'dlike to hear from you. I'm trying to collect retrospective reports for my "Brief History of Decision Support Systems" hypertext document at DSSResources.COM. I'm includingrecollections, reflections and comments of those involved in the various DSS "threads" andI'm trying to correct any errors of omission or misinterpretation.

How to citeA Brief History of Decision Support Systems should be cited as:

Power, D.J. A Brief History of Decision Support Systems. DSSResources.COM, World WideWeb, http://DSSResources.COM/history/dsshistory.html, version 4.0, March 10, 2007.


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Executive I nformation System OverviewDashboards derive from their older ancestors- executive information systems (EIS) which

were capable of similar functions but with less emphasis on visual representation of information. An

executive information system is defined as general term for a type of management information

system program used by top executives to aid and support in decision making (Singh, 2002). EIS

emerged in the mid 1980 s before dashboards and helped organizations monitor organization

performance, reach decisions, and consolidate and connect information between departments and

employees (Hwang, 2007 and Singh, 2002). The goal of these systems was to provide managers

easy access to variables critical to the business success (Singh, 2002, p. 71). These factors came

from both internal and external sources (Singh, 2002). EIS were meant to help executives reach

decisions by providing analyses that showed relationships hidden in the data through slicing and

dicing (Singh, 2002, p.71).

While not the same exact system, a dashboard is specific case or application of an executive

information system (Lamont, 2007). These older EIS placed less importance on visual components

than dashboards. Dashboards take advantage of modern improvements in graphics and data

streaming that were not available when executive information systems were first implemented.

HR Dashboards OverviewSpecifically, Human Resource dashboards are visual representations of relevant external and

internal data meant to improve decision outcomes in HR. Campos identifies six Human Resource

management activities in which dashboards can help the firm gain a competitive advantage:

organization, working environment, knowledge management, Human Resource development,

reward management, and workers relationships (2008). HR dashboards have the potential to

improve Human Resource functioning by standardizing policies and processes of staff through all

organization, facilitating the development of an integrated and coherent system of staff


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management while lessening the load of work of Human Resource functions eliminating low value

tasks (Campos, 2008, p.258). They also allow for supplying of efficient administrative services

and enhancing winning strategies and competitive advantages over rivals (Campos, 2008 p. 258).

It should be used to support decision making, remain clear and efficient, be easily adaptable as the

organization changes, maintain maximum visibility of key indicators, and motivate management

(Campos, 2008). Thus dashboards serve to improve operational activities at the transactional, or

granular level in order to present information in a way that facilitates critical analyses of HR

functioning requiring sophisticated managerial thinking. Ideally they should automate or simplify the

dull job of collecting relevant data in order to leave more time to for higher functioning activities

such as critical analysis and decision making.

HR dashboards aid executives in decision making by consolidating internal and external

information graphically in order to make it easier to evaluate. This is mainly accomplished through

taking large sums of data and drilling-down variables to uncover trends and patterns (Boudreau,

2002). If successfully developed and maintained, Human Resource departments can use information

technology to improve decision making and both directly and indirectly lower costs. Direct cost

reductions for example are increased productivity while indirect savings could stem from lower

turnover leading to less training expenses. Although HR dashboards are used mainly by Human

Resource professionals, they must also incorporate data from other departments and sources in

order for users to make well informed decisions. In addition to improving functional specific

performance, they must also benefit the organization overall. Boudreau argues that ideal HR

dashboards should tie HR measures to a compelling business concept and, in principle, can

articulate links between HR measures and strategic or financial outcomes (Boudreau, 2002, p.14).


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D efinition:- Information System is an organized set of components for collecting, processing,transmitting, and storing data in order to deliver information for action.

D ifferent types of Information System

1. Executive Information Systems support the long-term strategic view that directors and

senior executives need to take . 2. D ecision Support Systems support the process of individual and collective decision

making indirectly .

3. M anagement Reporting System provide report to managers for easier for management .

4. T ransaction Processing Systems support the operations of an organization by processingits business transactions .

5 . Professional Support Systems support of professional tasks .

6. O ffice Information Systems support and coordinate knowledge work in an officeenvironment .

Executive Information System (EC-EIS)Purpose

An executive information system (EIS) provides information about all the factors that influencethe business activities of a company. It combines relevant data from external and internalsources and provides the user with important current data which can be analyzed quickly.The EC-Executive Information System (EC-EIS) is a system which is used to collect and evaluateinformation from different areas of a business and its environment. Among others, sources of thisinformation can be the Financial Information System (meaning external accounting and costaccounting), the Human Resources Information System and the Logistics Information System.The information provided serves both management and the employees in Accounting.

Implementation ConsiderationsEC-EIS is the information system for upper management. It is generally suitable for the collectionand evaluation of data from different functional information systems in one uniform view.

IntegrationThe Executive Information System is based on the same data basis, and has the same datacollection facilities as Business Planning (EC-BP) [Page 115] . In EC-EIS you can report on thedata planned in EC-BP.Within this documentation, unless indicated otherwise, the sections Data Basis (EC-EIS/EC-BP)[Page 23] , Data Collection (EC-EIS/EC-BP) [Page 99] , and Tools (EC-EIS/EC-BP) [Page 224]are relevant for both applications. The section Business Planning (EC-BP) is only relevant if youhave the EC-BP component installed.

Features

When customizing your Executive Information System you set up an individual EIS database for your business and have this supplied with data from various sub-information systems (FinancialInformation System, Human Resources Information System, Logistics Information System, costaccounting, etc.) or with external data. Since this data is structured heterogeneously, you canstructure the data basis into separate EIS data areas for different business purposes. These dataareas are called aspects. You can define various aspects for your enterprise containing, for example, information on the financial situation, logistics, human resources, the market situation,and stock prices. For each aspect you can create reports to evaluate the data. You can either carry out your own basic evaluations in the EIS presentation (reporting) system or analyze thedata using certain report groups created specifically for your requirements. To access the EISpresentation functions, choose Information systems EIS.


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In this documentation the application functions are described in detail and the customizingfunctions in brief. It is intended for the EC-EIS user but also those responsible for managing thesystem. To access the EC-EIS application menu, choose Acc ounting Enterprise c ontrol.

Exe c utive InfoSystem.To call up the presentation functions from the application menu, choose Environment Exe c utive menu.N ecessary preliminary tasks and settings are carried out in Customizing. You can find a detailed

description of the customizing functions in the implementation guidelines.

EXECUTIVE INFORMATION SYSTEMSEIS grew out of the development of IS to be used directly by executives and used to augment thesupply of information by subordinates (Srivihok, 1998). An EIS is a computer-based system thatserves the information needs of top executives (Turban et al., 2004). For the purposes of this paper,EIS is defined as a computerized system that provides executives with easy access to internal andexternal information that is relevant to their critical success factors (Watson, Houdeshel and Rainer,1997). EIS are an important element of the information architecture of an organisation. EIS has

become a significant area of business computing and there are increasing amounts of money invested by organisations in EIS development projects (Kaniclides and Kimble, 1995) and the subsequentoperation (use) of these systems (Belcher and Watson, 1993). For example, in October 1997 thelargest water utility in South Africa, Rand Water, took a decision to build an EIS (based onOracle products) and invested R4,5m in revamping its IT infrastructure to support that deployment(Harris, 2000).Web-based technologies are causing a revisit to existing IT implementation models, including EIS.Web-based tools are very much suited to executives key activities of communicating and informing

What is EIS ?EIS is a web-based data-mining tool that uses powerfulgraphics to help understand risk, improve patient safety,and manage business. This system leverages the encoded

analysis database to display both graphic and written clinicalsummaries, diagnosis and procedure-specifi c targets,plaintiff allegations, risk management issues, problems,and medication-related events. A dashboard displayingcurrent information places you in control.How does EIS work?With EIS , you can track, analyze and compare (by specialty,severity, institution, and network):Reserve movementsIndemnity and expense paymentsRisk management issuesLoss detailsCaseloadsDefense fi rm performance

Actuarial dataWhat are the benefits?EIS is an evidence-based management tool that helps drivecompliance with patient safety standards, identifies areas of claims frequency and processes improvement, and focusesuse of limited resources.Built in analysis logic, graphic presentation, and pre-configured

dashboards that eliminate the need for users to have adetailed understanding of statistical representations Ability to tailor reports to your key issues for


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trends monitoringFull security model with the highest level of

encryption available Allows organizations to target problem areas and focusquality improvement efforts, even with scare resources

Implementing an Executive Information System (EIS)

by Floyd Kelly

An EIS is a tool that provides direct on-line access to relevant informationabout aspects of a business that are of particular interest to the senior

manager.

Introduction

Many senior managers find that direct on-line access to organizational data is helpful. For example, Paul Frech, president of Lockheed-Georgia, monitored employee contributions to company-sponsored programs (UnitedWay, blood drives) as a surrogate measure of employee morale (Houdeshel and Watson, 1987). C. RobertKidder, CEO of Duracell, found that productivity problems were due to salespeople in Germany wasting timecalling on small stores and took corrective action (Main, 1989).

Information systems have long been used to gather and store information, to produce specific reports for workers, and to produce aggregate reports for managers. However, senior managers rarely use these systemsdirectly, and often find the aggregate information to be of little use without the ability to explore underlyingdetails (Watson & Rainer, 1991, Crockett, 1992).

An Executive Information System (EIS) is a tool that provides direct on-line access to relevant information in auseful and navigable format. Relevant information is timely, accurate, and actionable information about aspects

of a business that are of particular interest to the senior manager. The useful and navigable format of the systemmeans that it is specifically designed to be used by individuals with limited time, limited keyboarding skills, andlittle direct experience with computers. An EIS is easy to navigate so that managers can identify broad strategicissues, and then explore the information to find the root causes of those issues.

Executive Information Systems differ from traditional information systems in the following ways:

y are specifically tailored to executive's information needsy are able to access data about specific issues and problems as well as aggregate reportsy provide extensive on-line analysis tools including trend analysis, exception reporting & "drill-down"

capabilityy access a broad range of internal and external datay are particularly easy to use (typically mouse or touchscreen driven)

y are used directly by executives without assistancey present information in a graphical form

P urpose of EIS

The primary purpose of an Executive Information System is to support managerial learning about anorganization, its work processes, and its interaction with the external environment. Informed managers can ask

better questions and make better decisions. Vandenbosch and Huff (1992) from the University of WesternOntario found that Canadian firms using an EIS achieved better business results if their EIS p romoted


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managerial learning. Firms with an EIS designed to maintain managers' "mental models" were less effectivethan firms with an EIS designed to build or enhance managers' knowledge.

This distinction is supported by Peter Senge in Th e Fift h Dimension. He illustrates the benefits of learning aboutthe behaviour of systems versus simply learning more about their states. Learning more about the state of asystem leads to reactive management fixes. Typically these reactions feed into the underlying system behaviour and contribute to a downward spiral. Learning more about system behaviour and how various system inputs and

actions interrelate will allow managers to make more proactive changes to create long -term improvement.

A secondary purpose for an EIS is to allow timely access to information. All of the information contained in anEIS can typically be obtained by a manager through traditional methods. However, the resources and timerequired to manually compile information in a wide variety of formats, an d in response to ever changing andever more specific questions usually inhibit managers from obtaining this information. Often, by the time auseful report can be compiled, the strategic issues facing the manager have changed, and the report is never fullyutilized.

Timely access also influences learning. When a manager obtains the answer to a question, that answer typicallysparks other related questions in the manager's mind. If those questions can be posed immediately, and the nextanswer retrieved, the learning cycle continues unbroken. Using traditional methods, by the time the answer is

produced, the context of the question may be lost, and the learning cycle will not continue. An executive inRockart&Treacy's 1982 study noted that:

Your staff really can't h elp you t h ink. Th e problem wit h giving a question to t h e staff is t h at t h ey provide you wit h t h e answer. You learn t h e nature of t h e real question you s h ould h ave asked w h en you muck around in t h e data (p. 9).

A third purpose of an EIS is commonly misperceived. An EIS has a powerful ability to direct managementattention to specific areas of the organization or specific business problems. Some managers see this as anopportunity to discipline subordinates. Some subordinates fear the directive nature of the system and spend agreat deal of time trying to outwit or discredit it. Neither of these behaviours is appropriate or productive.Rather, managers and subordinates can work together to determine the root causes of issues highlighted by theEIS.

The powerful focus of an EIS is due to the maxim "what gets measured gets done." Managers are particularlyattentive to concrete information about their performance when it is available to their superiors. This focus isvery valuable to an organization if the information reported is actually important and represents a balanced viewof the organization's objectives.

Misaligned reporting systems can result in inordinate management attention to things that are not important or tothings which are important but to the exclusion of other equally important things. For example, a productionreporting system might lead managers to emphasize volume of work done rather than quality of work. Worseyet, productivity might have little to do with the organization's overriding customer service objectives.

Contents of EIS

A general answer to the question of what data is appropriate for inclusion in an Executive Information System is"whatever is interesting to executives." While this advice is rather simplistic, it does re flect the variety of systems currently in use. Executive Information Systems in government have been constructed to track dataabout Ministerial correspondence, case management, worker productivity, finances, and human resources to

name only a few. Other sectors use EIS implementations to monitor information about competitors in the newsmedia and databases of public information in addition to the traditional revenue, cost, volume, sales, marketshare and quality applications.

Frequently, EIS implementations begin with just a few measures that are clearly of interest to senior managers,and then expand in response to questions asked by those managers as they use the system. Over time, the

presentation of this information becomes stale, and the information diverges from what is strategically importantfor the organization. A "Critical Success Factors" approach is recommended by many management theorists(Daniel, 1961, Crockett, 1992, Watson and Frolick, 1992). Practitioners such as Vandenbosch (1993) found that:


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