Analytical Processing as Executive Decision Support Copyright 1993 CAUSE From _CAUSE/EFFECT_ Volume 16, Number 2, Summer 1993. Permission to copy or disseminate all or part of this material is granted provided that the copies are not made or distributed for commercial advantage, the CAUSE copyright and its date appear,and notice is given that copying is by permission of CAUSE, the association for managing and using information resources in higher education. To disseminate otherwise, or to republish, requires written permission.For further information, contact CAUSE, 4840 Pearl East Circle, Suite 302E, Boulder, CO 80301, 303-449-4430, e-mail info@CAUSE.colorado.edu ANALYTICAL PROCESSING AS EXECUTIVE DECISION SUPPORT by Steve Chatman ABSTRACT: The 1990s will see the rise of analytical processing based on select data elements warehoused as archival, integrated, and subject- oriented relational records. LAN-based software will support managers and analysts in quickly creating "programs" of linked analytical and reporting tools dynamically exchanging data. Such a system was the focus of a two-year pilot project to implement a University/Integrated Data System (U/IDS) at the four-campus University of Missouri System. This article places analytical processing in the context of executive decision support, reports on the U/IDS project, and discusses advantages of analytical processing. The University of Missouri has reached the end of a two-year pilot project to produce an executive decision support system, supported by an analytical processing environment, for ad hoc queries, decision support, and reporting. There is much that other institutions can learn from the University's experience. Among the lessons to be learned are those owing to differences between collegiate and corporate environments and problems associated with trying to move too far too fast. But more importantly, higher education faces many challenges that should force it to reconsider prior notions of executive information and decision support. At the national level, higher education faces poor public perception, severe budget deficits, ebbing demand, and growing gloom in the industry.[1] Locally, the demands can be far more challenging. In Missouri, the Coordinating Board for Higher Education has adopted a series of goal statements, developed by a task force composed principally of governing board chairs, that include among other standards: * establish institutional categories based on admission criteria, * set the number of hours faculty will teach, * control administrative growth, * require annual reporting of outcomes assessment results, * establish targeted graduation rates, and * define a minimum number of degrees expected by every program with those programs not meeting the standard to be dropped. When faced with these challenges, executives cannot afford slow responses or to rely on inflexible regular reports. Instead, higher education executives need a system designed to respond to specific concerns and policy issues in the short time frame in which they must make decisions. Management information systems are now reaching an evolutionary point where decision support professionals can contribute to the decision-making process in a manner and within a time frame that should improve the quality of decisions made.[2] In general, this type of information handling has been called analytical processing. This article begins with a discussion of analytical processing as a logical next evolutionary step in decision support that was anticipated twenty years ago by leading institutional researchers. Next, the University/Integrated Data System (U/IDS) project representing an agreement between the University of Missouri and IBM is described. Of special emphasis is discussion of the two major obstacles to the development of the analytical processing systems, the political environment, and the problems inherent in integrating the online transaction systems of a university. The following section contrasts the steps required in ad hoc reporting within current systems and within analytical processing, while emphasizing gains in efficiency, quality, and clarity in communication. The article concludes with a discussion of the two-year pilot project, with an emphasis on lessons learned. ANALYTICAL PROCESSING Analytical processing describes an environment where regularly scheduled data extracts are taken from principal transaction databases, integrated as a "warehouse" of data, and addressed through software that integrates retrieval, analysis, and reporting functions.[3] The warehouse is composed of data captured at census points and integrated through unifying organizational structures that link the various administrative structures that can exist in distinct transaction systems. But as important to the analytical system as integrated, warehoused data is the fact that the database is uniquely designed to support standard reporting as well as ad hoc requirements. An analytical processing environment also requires integrated software able to seamlessly transfer data from retrieval tools to reporting tools. The data warehouse As described by Inmon,[4] warehoused data in an analytical processing environment can be identified by three features. First, a warehouse of data is a collection of data extracts, pre-processed conditional values for variables frequently used in reporting and analysis, and pre- computed subtotals for summary counts often used in reporting and analysis. The nature of a warehouse is one of values frozen in time, and as such it is time-invariant instead of current and changing. Second, the time-invariant information is integrated across transaction systems to support linking of data from each system--financial, personnel, and student information--through use of a common organizational structure. Third, the data in a warehouse reside in a relational environment that is designed to support reporting and ad hoc requirements, as opposed to supporting individual record transactions. The second and third features, integrated data and a custom relational design to support reporting and analysis, most clearly distinguish data warehouses from collections of frozen records, whether stored in relational files or as flat files. To help clarify the distinction between a data warehouse and a collection of frozen records, imagine that you want to build a warehouse of previously identified payroll and personnel information and student information. The first challenge would be to identify the informational links that exist between the systems and to clearly establish them through an integrated structure. Three of the links that naturally exist are: (1) that many students are also employees; (2) that faculty teaching courses are also in the personnel records; and (3) that there are organization structures responsible for students, instructional activities, and employees. Therefore, student records may also contain payroll segments, faculty personnel records may contain course segments, and all of this information should be linked to a common organizational unit. There may also be collective information, like number of faculty with tenure in a department or number of students majoring in the disciplines of the department, that are stored as summary values linked to the common organizational unit. The second challenge would be to build a relational system that takes advantage of the common features to guide design. Because the data warehouse need not support online, real-time transactions, warehouse design can be done to exclusively support reporting and analysis. For example, reporting tends to follow a top-down approach, selecting first on organizational unit or time period. The warehouse can be built to facilitate this top-down analysis by treating the department as a key record linked to employee and student records. Structurally, the organizational unit of interest can be used to identify personnel and student information data instead of examining each student or employee record to determine whether it is part of the organizational unit of interest. Once the warehouse is designed, data can be loaded at points in time consistent with official census reporting to ensure reliable results. Another distinction between a collection of frozen files and a data warehouse is the inclusionof elements to specifically support reporting and analysis (pre-processed conditional values) as well as elements that are aggregate values (pre-computed subtotals) to reduce processing demand during analysis and reporting. These new elements are computed from the extracts and entered into the warehouse as it is created. Examples of elements to support reporting and analysis might include nine-month equivalent salary based on definitions of the American Association of University Professors, or first-time freshman status as defined by IPEDS. These elements do not exist in the transaction systems, but are frequently used in analysis and reporting, and require several programming steps to determine. In building a data warehouse, the necessary processing is done once and a new element, "first-time freshman" or "AAUP nine-month adjusted salary," is stored. An example of a frequently used aggregated sub-total is the number of IPEDS first-time freshmen in a college or at a campus. This is an often- reported value and is therefore better stored as a new aggregate element than recomputed with each report. Integrated software In addition to requiring a data warehouse, analytical processing also requires integrated software for retrieval, analysis, and reporting. If the system is to be flexible enough to support the ad hoc and decision support needs of institutional executives, then the steps between retrieval and reporting must be accomplished as smoothly and as flexibly as possible.[5] Follow-up queries often ask for the same analysis to be repeated at different levels of a variable or with different restrictions. Integrated software allows the changes to be made, to the retrieval component in this case, and for the report document to be reproduced with the alterations in a nearly transparent and automatic manner. For example, a recent case at the University involved standards for admission. One of the reports produced to support decision-making was a spreadsheet combining a variety of analyses for two different cohort groups. As the executives asked for the same information using different admission standards, the conditional statements were altered and the report was reproduced with very minor manual changes and with little effort. A logical and necessary evolutionary advancement The necessity of warehoused data and integrated software is made clearer when analytical processing is viewed within historic context as an evolutionary advancement. As the batch processing systems of the 1960s gave way to the transaction systems of the 1970s, one trend remained true throughout: reporting and ad hoc analysis were always afterthoughts.[6] It was recognized that transactional systems could support enumerative reporting and various reports were developed as management information systems or in response to government-mandated requirements. Following the microcomputer revolution of the 1980s and the rise of the use of fourth-generation languages, it is now possible to view decision support activities in a new light. Partly because reporting and ad hoc analysis were viewed as afterthoughts and partly due to the limitations of software and hardware in the past, there have been several information processing trends and developments on campuses that present challenges to building an analytical processing environment. First, transaction systems have developed as essentially "private databases,"[7] where access is controlled by custodians who often behave like greedy owners, but whose motivations are pure: there are issues of data integrity and privacy that must be protected, data elements and their interrelationships may be difficult to understand, and direct access is potentially dangerous and disruptive. In contrast, data warehouses are typically read-only collections of agreed upon and understood elements with English-like values. Second, transaction systems have often been developed independently, following the de facto organizational structures necessary to support the transactions. For example, student information systems often follow academic structures that are different from the administrative structures existing in financial and personnel systems. These organizational differences hinder integrating information for cost studies and other purposes. In addition, the independent transaction systems are often supported by different database software and accessed through different programming languages, rather than being integrated and accessed through a common language. Third, the necessity to produce consistent reports that support historical analysis requires frozen files that are fragile. Occasionally, entire years are lost. The read-only access to warehoused data and the routine maintenance of centrally supported systems help to prevent similar problems in analytical processing. In addition, ad hoc projects often demand that the databases be reestablished from frozen files before processing can occur. This rebuilding of the databases is expensive and the space requirements often restrict normal and ongoing operations. Fourth, there has been a proliferation of databases.[8] Many offices and individuals now maintain databases that bear only an indirect relationship to institution records. They do this because the software they want to use requires it, it is a problem to quickly get the information from computing services, they wish to merge data from different transaction systems, or they want to add data that are not in the institution's records. If the data warehouse has been well constructed, then user data needs have been anticipated. Also, if the software used gives users the control and flexibility they have experienced when working with microcomputer software, or if access to their preferred software is transparent, then the problem of proliferating databases is lessened. Fifth, the institutional research function has become fragmented.[9] While not necessarily a problem or even a concern, inconsistency of reporting produces conflict that is made worse by competition among units for scarce resources. In an analytical processing environment, analysis relies on a shared data source, thereby limiting arguments over who has the right number. Differences of judgement based on the data continue, but the discussion can focus on the issues, not the data. In all these cases, analytical processing helps to alleviate information handling problems. There is, however, another problem that exists in higher education decision support that the ad hoc facilities of an analytical processing system can help to alleviate. That problem results from the myth of executive information systems as decision support tools. It has often been suggested that the information needs of college and university executives can be anticipated, and if anticipated, can be programmed and produced on a regular basis. But, beyond the most rudimentary aggregates, packaged executive information systems are of little use in higher education for three reasons. The first is that campus cycles are typically no shorter than semesters, and an electronic system to convey new information rapidly is superfluous. Second, the information needs that can be confidently anticipated are relatively few in number and require no new reporting capabilities. Third, systems of packaged aggregates are unlikely to help the executive faced with issues like poor public perception, severe budget deficits, and involved and active governing board members, legislators, and coordinating boards. In short, executive information systems, and management information systems in general, are of limited value in higher education because the information contained in them very seldom directly and completely answers any specific need for information as policy is being considered. Also, many executive information systems are not conducive to follow-up questions and seldom contain politically useful comparative information from other institutions, national averages, or other valuable external data. It is time to rethink the management information system and decision support system paradigm--a conclusion anticipated by institutional researchers over twenty years ago: "The concept of an analytical information system is appealing. A system in which accurate, consistent, and complete sets of basic data are produced automatically, stored conveniently, interrelated readily, and available as needed is certainly to be sought by any college or university."[10] The reason an analytical processing system that supports ad hoc activities is a better solution is that there is no value inherent in data. As stated by Sandin, "From the point of view of educational administration, the general requirement of an information system is that it should achieve a flow of information in the forms and at the times needed for responsible decision- making. Decision-making is an event that occurs at a determinate point in time. It is in the moment of decision that information is relevant. If the time schedule for the production of information is not in phase with the time schedule for decision-making, an information system of even the finest design will have no influence on planning."[11] Or, more succinctly stated by McCorkle, "Data become informative when we have specific policy questions that need illumination and resolution."[12] It is the act of decision-making that gives information value, and that value is directly related to the precision with which it addresses the informational needs of the executive. In an analytical processing environment, decision support is now at a position where it can address questions related to issues with true precision and where it can readily support follow-up questions based on the prior response. Through this focused, iterative interaction, the value of information is greatly increased. UNIVERSITY/INTEGRATED DATA SYSTEM (U/IDS) Before they had heard of "analytical processing," institutional researchers at the University of Missouri had begun to define core elements needed on a regular basis for ad hoc inquiry and decision support. When the assistant vice president for information technology arranged a demonstration of an integrated software product that could interface with the data warehouse being built, the institutional research staffs recognized its potential immediately and began to collaborate with the vice president to form a joint agreement. The University/Integrated Data System project resulted. The U/IDS project was a two-year pilot study to build an analytical processing system around a previously identified warehouse of data. The principal objectives of the joint agreement between IBM and the University were to explore the application of Data Interpretive System (DIS)[13] software to higher education administration information needs and to examine the possibility of linking DIS and IBM's Executive Decisions software. Toward this end, IBM contributed software, training, and hardware valued at over $250,000, and the University of Missouri contributed two full-time computer analysts, computing charges, and microcomputer equipment and upgrades. Organizationally, the U/IDS project was composed of three administrative groups: a management group, project team, and data applications group for each of the transaction systems. The management group--composed of vice presidents, respective associate and assistant vice presidents, and chancellors--was responsible for review and evaluation functions. The project team--a key working group reporting to the management group--was composed of System institutional researchers, campus institutional research representatives, and central computing support personnel. To respond to access issues and to ensure that data were comparable, accurate, and complete, a series of data applications groups were formed. Data applications groups were composed of transaction system custodians and a subset of the project team composed of System personnel and one campus institutional research representative. In addition to these administrative groups, an intercampus faculty advisory committee was formed to ensure that data were "properly used" by the administration and to protect the interests of faculty. The first step taken was a review by the project team of data elements that had previously been identified by the institutional research staffs. The selected data were to be regularly extracted from the student information, payroll and personnel, accounting, and financial aid transaction systems. Data element inclusion in U/IDS was based on three factors. First, regular reports were reviewed to ensure that routinely required data were included. Second, the experiences of these data providers and analysts were used to ensure that the analytical challenges they had faced could have been met with the system. Third, the project team attempted to anticipate future requirements. The initial lists created by the project team were discussed with the data applications group members and modified based on their recommendations. The review of these elements began in December of 1989. Almost immediately there was resistance to the project. The standard question was, "What are you going to do with the information?" What was seldom understood, and was difficult to communicate effectively, was that if those involved could anticipate all the specific applications in detail, then the data warehouse would not be needed. There was also resistance from custodians asserting that they could provide the information when it was needed and that the U/IDS users could not understand the nuances of the systems for which they were responsible. There was also resistance from campus institutional researchers who had built information systems and formed relationships with data providers that they felt might be threatened by this new administrative system. Resistance also came from the faculty sparked by knowledge that information linking payroll records and course instructional assignments would be part of the system. Information is a valuable resource and custodians, campus institutional researchers, and faculty all recognized that this resource was being redistributed. The process of gaining support and overcoming resistance for the creation of a data warehouse was one of gaining acceptance and executive sponsorship, and working within the University environment to address the concerns of interested parties. Gaining executive support required an understanding of the management philosophy of the institution and styles of senior administrators. In particular, it was stressed that information was an asset to decision-making and planning and was therefore an institutional resource like the faculty or laboratories. The difference in speed and quality of response to their information needs when extracts existed versus when they had to be created using current procedures, showed senior administrators the potential value of a data warehouse. Response took days when the extract already existed and weeks when it did not. The steps of identifying data elements, reconciling differences by campus in data element use, and gaining support for regular delivery of the elements were required to form the data warehouse. In turn, the data warehouse was a necessary but not sufficient condition for analytical processing. To create an analytical processing environment, integrated software and warehoused data must be brought together in a design that maximizes performance. Metaphor's DIS product, the software used in the U/IDS project, is an example of this kind of integrated software. The two-year pilot ended in May of 1993, and it will not be continued in its present form. In the author's opinion, a number of factors have combined to defeat the project: the resignation of the assistant vice president for information technology and subsequent changes in the organizational structure of central computing; budget constraints that have reduced institutional research staffs across the System campuses; delays in the delivery of database segments; the prohibitive time required for some operations to run; and anticipated future costs for hardware, software, and storage. However, the project will definitely be continued in some form, because the nature of decision-making at the System level has become much more information-based and iterative. Executives have become more accustomed to expecting requests to be answered precisely and quickly and for subsequent requests to be answered even more quickly. ADVANTAGES OF ANALYTICAL PROCESSING Analytical processing offers a much more efficient way of responding to ad hoc queries in an executive decision support environment. Fewer steps are required, fewer people are needed, the skills of the people participating in the response are less computer-specific, and computing costs are greatly reduced. Responding to typical queries about students using previous processes at the University of Missouri required at least eight steps. * First, analysts would determine the data needed to respond to the question and any likely follow-up questions. * Second, a request would be made to the central computing staff for the data needed. * Third, the central computing staff and institutional research analysts discussed data needed with the custodians to determine its validity and availability. * Fourth, central computing staff would write or modify programs to recover data from the transaction systems. * Fifth, registrars and registrar staff members would run the programs and create flat files. * Sixth, the data would be made available to the institutional research staff who would process the data using high-level statistical and reporting languages, correcting differences in data element treatments by campus. * Seventh, the results would be transferred to independent spreadsheets, graphs, and word processor files. * And eighth, a document would be prepared in response to the initial query and returned to the requester. Follow-up questions would require steps six through eight to be repeated. In the analytical processing environment only two steps are required. First, analysts determine the data needed for the question and likely follow-up questions, and directly analyze the data using integrated spreadsheets, graphs, and word processor files. Second, a report document is prepared in response to the initial query and the report is sent to the requester. Any follow-up questions require that these two steps be repeated, although most of the first step will be the same the second time. This fundamental change in procedure reduces response time from a minimum of one or two weeks to one or two days. Thus the first gain is time. The second gain is in better use of the skills of the people participating in the response and a reduction in the number of people required to form a response. Ad hoc response under the existing system requires personnel with three different areas of expertise. First, the institutional research analysts need to be versed in higher-level languages and microcomputer applications, and their formal training is in research design and statistics. Second, the central computing staff are expert in database processes and use lower-level languages. Third, the expertise of institutional personnel in the area or areas related to the inquiry, like the registrar and the registrar's staff, is required. Because the response is made within a system environment of four campuses, a student information system-based query will usually involve a minimum of ten to twelve people and require five to ten person-days. In an analytical processing environment, only one or two people need be involved in the formation of any one response. A third gain is measured by lower computing costs. Day-to-day computing costs are greatly reduced for the campuses in an analytical processing environment. For example, student information system-based queries frequently require a re-creation of prior databases for short periods. The total computing costs can easily reach $1,000 just to re-create the database and extract selected data. In the analytical processing system, distributed computing greatly reduces billable costs. A more complicated query requiring information from more than one operating system quickly expands the number of personnel involved and greatly inflates costs. Efficiency gains in an analytical processing environment are related most strongly to the existence of the data warehouse. In the creation of the data warehouse, discussion of unique element characteristics by campus has already occurred. Those differences have been resolved, often through the creation of elements that exist only in the warehouse. In addition, the data have already been delivered and are ready to support analysis. While the data warehouse is of fundamental importance, integrated, object-oriented software like DIS greatly facilitates analysis and releases the potential of the data to support decision- making. Because DIS software applications are straightforward, object- oriented application tools, minimal special training is required and no training in computer programming is needed. This ease of use means that statisticians, researchers, accounting analysts, and others can directly apply their expertise in response to the question without the need to translate that understanding for a programmer. In addition, the computing resource requirements are distributed, reducing the burden and cost of using central computers and relying instead on microcomputers for much of the intensive processing. It should be noted that although DIS is easy to use, it is probably unrealistic to expect most current institutional executives to form their own queries. Executives will most likely continue to rely upon the most sophisticated decision support system at their disposal, their information processing and analysis staff. The use of an analytical processing system in our four-campus system could produce many gains in the quality of analysis and communication with campuses. First, the data are shared among all participants. There is only one set of data to use, so arguments over whose numbers are best are limited to questions of data treatment and interpretation. Second, there are no black boxes. Data retrieval and analysis is an open process. Users do not need to be programmers to know exactly how data were treated in the analysis. For example, there are occasionally differences between program specifications and the actual program actions that can not be deduced by reading specifications. If you do not see the code then you do not know exactly what is occurring. You only know what is supposed to occur. Concerned others can also follow the treatment of data precisely. For example, campus institutional researchers can see exactly how the central offices manipulated their data. Third, data from various transaction systems have been integrated and the integration supports more complex, useful, and valid analyses. Fourth, costs are supported by central administration instead of by the campuses. While not a direct gain in quality, reduced costs encourage campus participation and interaction with the system, thereby indirectly improving the quality and completeness of the data and of the work done. Fifth, the data can be made available to a wider group of professionals with diverse talents. And sixth, the shared warehouse prevents the need for a proliferation of extract files and small databases. CONCLUSIONS The challenges facing institutional executives require more timely information, treated in a more sophisticated manner, and focused on the specific issues at hand. Executives and external constituencies have a better understanding of the capabilities of computing and are not inclined to wait patiently, and postpone action, while a response is formed over two or three months. They expect the response in a timely fashion. If the response can be made within the decision-making time frame, then the data become information and have value. In fact, the executive may now see reason to make a second request to explore new possibilities. Through this quick interactive exchange, decision-making is improved. A recent example of this interplay was illustrated in institutional discussions about admission policies. The University was nearing a critical Board meeting after six months of discussion and exploration of alternatives during which dozens of policies were examined. One afternoon, the vice president for academic affairs suggested that his case could be strengthened by revising a series of tables to the recently ended term. However, the changes had to be made within the afternoon if they were to be included for consideration at the Board meeting. The vice president did not know the challenge presented by the request, only that nearly all other requests on the project had been answered quickly. The changes, from modifications to programs to preparation of transparencies for overhead projection, were made and the potential contribution of available data was realized. A less integrated system could not have supported the process within the time frame allowed and the decision reached might not have been the same. Decision support for the admission policy considerations was a particularly good application of analytical processing because general models could support numerous variations in student populations and in standards imposed, with minimal change or effort. Analytical processing is only new in the sense that old ideas about analytical systems can now be supported due to hardware and software developments. An analytical processing model of executive decision support was anticipated by Sandin, writing in 1977: But after many disappointing attempts, I myself have come to the conclusion that, at least for the short run, we are not well served by the conventional wisdom about total information system implementation in higher education. believe that development of rational decision-making in colleges and universities might be better served by a reduction of our aspirations for implementation of an information system and by concentration of our efforts on a limited series of steps which fall short of comprehensive design.... What can be achieved in the short run are the following tasks: (1) identification of the data elements that are the necessary basis for a comprehensive information system if such a system could be established; and (2) installation of procedures for collecting, maintaining, storing, and retrieving these raw and unanalyzed data. These are the first steps that must be taken in information system development. Programs for processing basic data and transforming them into usable information should then be created on an ad hoc basis in response to specific information requests from decision-makers. ... The information system that will result from this strategy will not be a system, in the strict sense, at all. It will be a patchwork of reports derived from a unified data bank which can be readily and accurately accessed. But the information that flows from such a processing system, though a compromise of information system theory, will be of direct benefit to the quality of planning and management in higher education, for it will be generated in direct response to management demands.[14] Sandin was exactly right. Executive decision support, specifically, and management information systems, generally, are able to correctly anticipate only the crudest of measures. They can also function well only within closed, uniform processes like budget development. In contrast, the process of decision-making requires more focused, tailored analysis. Canned programs can no more anticipate the precise analysis to support future decision-making than decision-makers can precisely anticipate the need for future support. It is executive decision-making that must become better understood and supported because it is the decision-making process that allows data to become informative. When the data directly apply to the situational demands of the executive and are conveyed within the time frame required of the decision, then data and data handlers become informative and valuable. ======================================================================== Footnotes: 1 B. S. Shafer and L. E. Coate, "Benchmarking in Higher Education," Business Officer, November 1992, p. 28. 2 R.F.E. Weissman, "Toward 2000: Institutional Research and the Next Generation of Campus Computing," in Building Bridges for the Twenty- First Century: General Session Presentations of the 31st Annual Forum (Tallahassee, Fla.: Association for Institutional Research, 1991), p. 13. 3 W. H. Inmon, "Building the Perfect Beast," Information Executive, Spring 1991, p. 33. Also K. L. Miselis, "Organizing for Information Resource Management," in Organizing Effective Institutional Research Offices, New Directions for Institutional Research, 66 (1990), p. 60. 4 Ibid., p. 34. 5 R. H. Glover, "Decision Support/Executive Systems at the University of Hartford," CAUSE/EFFECT, Fall 1989, p. 16. 6 Inmon, p. 34. 7 Weissman, p. 8. 8 Ibid., p. 8. 9 M. W. Peterson, "Institutional Research: An Evolutionary Perspective," in Institutional Research in Transition, New Directions for Institutional Research, 46 (1985), p. 13. 10 J. L. Saupe, "Collecting and Utilizing Basic Data," in P.L. Dressel, ed., Institutional Research in the University: A Handbook (San Francisco: Jossey-Bass, 1971), p. 98. 11 R. T. Sandin, "Information Systems and Educational Judgment," in Appraising Information Needs of Decision Makers, New Directions for Institutional Research, 15 (1977), p. 20. 12 C. O. McCorkle, "Information for Institutional Decision Making," in Appraising Information Needs of Decision Makers, New Directions for Institutional Research, 15 (1977), p. 3. 13 Data Interpretive System (DIS) is an integrated system of data retrieval, analysis, and reporting software for executive decision support by Metaphor. Metaphor is a wholly owned subsidiary of IBM, but will soon have independent operational responsibility and will market DIS under the Metaphor brand name. 14 Sandin, pp. 25-26. ************************************************************************ Steve Chatman is entering his fourth year with the office of Planning and Budget as Director of Institutional Research for the University of Missouri System. Planning and Budget supports decision-making, policy formulation, and planning at the system level by providing support services in management information, policy analysis, and budget planning. ************************************************************************