Business Process Redesign and Information Architecture: Exploring the Relationships

Transcription

1 Business Process Redesign and Information Architecture: Exploring the Relationships James T. C. Teng William J. Kettinger College of Business Administration University of South Carofina Abstract Business process redesign (BPR) and information architecture (IA) have been recognized as high priority agenda items in organizations during the 1990s. This paper explores the relationships between the two concepts. Specifically, this paper focuses on three main concerns: how IA supports BPR, how the lack of IA hinders BPR, and what approach to IA can facilitate BPR? The paper begins with a brief review of the development and implementation of each concept. This is followed by an examination of how IA and BPR are related to business processes, both existing and newly designed processes. This analysis, in conjunction with results from field research, provide the basis for nine propositions regarding the BPR and IA relationship. In support of BPR, IS professionals have the opportunity not only to contribute to a firm's business objectives, but also to begin to achieve IA's promise while avoiding some of IA's past implementation problems. It is hoped that further research based on these propositions will lead to more complete answers and perhaps more specific questions on the BPR/IA relationships. Key Words: information architecture, business process redesign, reengineering, methodology, information systems, strategy. Introduction Organizations are undergoing revolutionary changes in the way they function and compete. Popular business publications are replete with reports on corporations embarking on campaigns to rework fundamentally how they do business (Hammer and Champy, 1993; Davis, 1993). Business process redesign (BPR) or reengineering seeks to redesign business processes to enhance productivity and competitiveness. A 1994 survey of 497 large companies in the United States and 124 firms in Europe revealed that 69% of the American companies and 75% of the Europeans have attempted reengineering, and more than half of the remainder are planning BPR initiatives (CSC Index, 1994). Running in parallel with this recent attention on BPR has been a somewhat quieter, but equally significant organizational initiative focusing on the importance of information as a strategic resource of the firm and the vital need to design a 30 DATA BASE ADVANCES-- February 1995 (Vol. 26, No. 1)

2 corporate "information architecture" (IA) to support dynamic competitive strategies and processes. A 1991 study of senior IS executives identified the "development of an information architecture" as the most critical issue facing IS managers in the 1990's (Niederman, Brancheau, and Wetherbe, 1991). Given the heightened attention on both BPR and IA, it is surprising that little research has been conducted to examine the relationships between these two concepts in depth, and to integrate BPR and IA into existing IS scholarly thinking or practice. As both BPR and IA attain strategic significance in organizations, it becomes imperative for corporate as well as IS management to understand the relationships between these two important organizational initiatives and properly integrate them within their corporate and IS strategies. This paper will discuss how data base technology and information architecture fit with BPR. Specifically, BPR/IA relationships will be explored with respect to three main concerns: how IA supports BPR, how the lack of IA can hinder BPR, and an approach to IA that effectively facilitate BPR. This paper begins with a separate review of the origin and development of both BPR and IA. This brief review will set the stage for subsequent analysis of the relationships between the two concepts leading to a "process linkage model" depicting their common focus on business processes. These linkages are further examined in light of field experience and real case studies. Based on the conceptual possibilities and practical limitations outlined, we proceed to explore the BPR/IA relationships by formulating a set of research propositions in relation to the three research concerns. In the last part of the paper, concluding observations and suggestions for further studies on BPR/IA relationships are discussed. Business Process Redesign: Origins and Progress BPR: Development of the Concept A business process, according to Davenport and Short (1990), is "a set of logically related tasks performed to achieve a defined business outcome." As such, a process can be conceptualized as operating within a traditional function to achieve a narrowly "defined business outcome" or spanning across different, but "logically related" functions to achieve broad, strategically "defined business outcome." The current phenomenon called business process redesign or "reengineering" has its roots in industrial engineering (Davenport and Short, 1990). With the rise of the industrial revolution, a field of engineering emerged concerned with the design of processes to achieve efficient production. One industrial engineering tenet is the division of labor into localized, specialized, and repeatable tasks. As might be expected, the same techniques that proved to be successful with line functions on the factory floor were mirrored in supporting business processes. The value chain of business information tasks were organized as a production line of employees who repeatedly applied their narrowly focused skill to stacks of paperwork. These "scientific management" theories lie behind many of the business processes we have in place today. As time progressed, technological developments were often incrementally applied to business tasks with a narrow efficiency orientation. The idea was that in achieving maximum efficiency at each point in the value chain, the whole process would benefit. However, because today's businesses are changing so rapidly, this localized, incremental approach has created extremely complex process juggernauts which may contribute "clock-wise" efficiency to specific tasks, while allowing overall effectiveness of the process to suffer (Teng, Grover, and Fiedler, 1994). In recent years, heightened attention on "business process" has come in large part from the total quality movement, which has swept through the manufacturing industry and spread to the service sector and non-profit organizations (Harrington, 1991). However, as observed by Davenport (1993) and by Dixon, Arnold, Heineke, Kim, and Mulligan (1994), while quality specialists tend to focus on incremental change and gradual improvement of processes, proponents of reengineering often seek radical redesign and innovative improvements to processes. As BPR projects do vary in the "radicalness" of the change undertaken, it is useful to conceive BPR initiatives as falling along a continuum ranging from moderate improvement of existing processes to radical design of new processes (Davenport, 1993). This will be referred to as (Vol. 26, No. 1) February DATA BASE ADVANCES 31

3 "business process redesign continuum" in the remainder of the paper. Many reengineering success stories have been reported (Hammer and Champy, 1993). CIGNA RE Corp. has completed over 20 reengineering projects since 1989 and achieved $2 to $3 in returned benefits for each $1 invested in these projects (Caron, Jarvenpaa, and Stoddard, 1994). At Zale Corp., the world's largest jewelry retailer, a reengineered jewelry appraisal system has led to better asset management, increased revenues, and higher productivity and morale (Newman and Kozar, 1994). The reengineered claims and price quoting processes has enabled Progressive, a car insurance company, to achieve a 30% increase in premium income (Hammer, 1994), The tremendous growth in the number of firms undertaking such efforts provides insight as to how a typical organization of the late 1990s will be designed. This organization will be networked across functions and designed around business processes rather than functional hierarchies (Drucker, 1988; Norton, Pollock, and Ware, 1989; Rockart and Short, 1989). In essence, reengineering business processes has been suggested as a paradigm of organizational change necessary in achieving needed flexibility and competitiveness (Davenport, 1993; Venkatraman, 1991). Because the breadth and depth of these changes tend to emphasize fundamental structural change, planning at the strategic level by top management appears to be a critical success factor of BPR (CSC Index, 1994; Dixon et al., 1994; Keen, 1991; Hall, Rosenthal, and Wade 1993). Based on this view, Kettinger, Guha, and Teng (1995) defined business process reengineering as "an organizational initiative to accomplish strategy-driven redesign of business processes to achieve competitive breakthroughs in quality, responsiveness, cost, and satisfaction," and "these initiatives may differ in scope from process improvement to radical new process design" (Kettinger et al., p. 213). BPR: Current Concems and Emerging Trends While reports on early BPR successes generated a high level of enthusiasm and expectation among corporate executives, the recent rush to implement the reengineering concept has yielded mixed results. According to a 1993 in- dustry survey conducted by Deloitte & Touche (Hayley, Plewa, and Watts, 1993), CIOs indicate that the actual benefits of BPR projects generally fall short of their expectation. On a scale of 1 to 10, the average ratings on such categories of BPR benefits as quality, cost reduction, and competitiveness are all below 5. The increasing incidence of BPR failures has generated considerable discussion on the rationale of "radical" process redesign (Davenport and Stoddard, 1994). In response to recent criticism of the radical BPR concept, Hammer has reasserted the merits of reengineering and distinguished it from the arbitrary downsizing efforts in many corporations (Lancaster, 1995). He points out that contrary to downsizing, which typically involve mere reduction of workforce without improvement in business processes, BPR entails careful but often daring changes to existing processes aimed at manyfold improvements in productivity (Lancaster, 1995). In the ensuing discussion, a number of researchers have stressed the importance of linking reengineering initiatives to corporate strategy (Teng, Grover, and Fiedler, 1994; Kettinger et al., Letb, 1994; Dixon et al., 1994). In clarifying the nature of reengineering, Hammer (1994) believes that reengineered processes not only help to implement a defined strategy; they can in fact shape strategy in the first place. As the BPR concept takes hold and the number of actual reengineering projects continues to grow, attention is now focused more on the substantive issues in managing the reengineering project (Champy, 1995; Caron et al., 1994) rather than justifying the initiative. Experience has shown that a number of change management issues not directly related to IT are critical to reengineering success. These include securing top management support, communicating reengineering rationale to employees, and reengineering project management, etc. (CSC Index, 1994; Champy, 1995; Schonberger, 1994). In a study involving 20 reengineering cases, Hall et al. (1993) revealed the critical role that top management plays in supporting reengineering initiatives. Based on these current concerns, a trend toward extending the notion of process reengineering to a more general concept of process management can be seen. It has been recognized that the rich experience and knowledge from the more established field 32 DATA BASE ADVANCES--February 1995 (Vol. 26, No. 1)

4 of total quality management cannot only be utilized during the reengineering project, it is especially applicable after project completion where the continuous motoring and improvement of the new process must be carried on indefinitely (Davenport and Stoddard, 1994; Kettinger et al.; Moad, 1994). Information Architecture: Origins and Progress Developments of Data Base Technologies and the IA Concept The development of modern computer-based information systems began with a file-oriented approach that had as its emphasis the "program" rather than "data." The resulting redundancy, inconsistency, and inflexibility led to the development of data base management technology (Bachman, 1969; CODASYL, 1971) based on the 3-schema architecture: external models (subschemas), conceptual model (schema), and internal model (ANSI, 1975). The concept of data independence brought the realization that the center of the data processing universe was the data rather than the application program (Nolan, 1973; Everest, 1974). Through the conceptual model, the data independence concept calls for the design of a stable data base structure that is capable of supporting several related applications each with its own external view. To the extent that the conceptual model is properly designed, this goal of data independence is achievable even when some modifications to the external views becomes necessary. The methods for the logical design of shared data bases have been greatly facilitated by the development of relational data base theodes and techniques such as normalization (Codd, 1971) and the entity-relationship data modeling approach (Chen, 1976). It soon became apparent that, if a particular subject data base could provide a stable data structure for a group of related applications, a set of such data bases could support organization-wide processing requirements. This macro-level architectural approach to structure the entire organization's information resources was the major thrust of the early IA approach, BSP (business systems planning), developed by IBM Corp. in the 1970s. As outlined in BSP, a collection of "data classes," which support vari- ous business processes throughout the entire organization, can be systematically identified in a "top-down" fashion based on strategic goals and objectives (IBM Corp., 1984). Brancheau and Wetherbe define an information architecture as "a high-level map of the information requirements of an organization. It is a personnel-, organization-, and technology- independent profile of the major information categories used within an enterprise" (1986, p. 453). One representation of this high-level map is the traditional process/data class matrix which demonstrates how data classes (i.e., information categories) support information requirements of various business processes in the organization. Following a derivation of the BSP approach, Vogel and Wetherbe (1991, p. 9) reports the successful use of such a matrix in their illustration of the supporting relationship of data classes (e.g., students, courses, faculty) to various business processes (e.g., registration, financial planning, and admission) at the University of Minnesota. Another representation of IA is the enterprise data model, which is typically expressed in the form of high-level E-R models for the organization (Brancheau, Schuster, and March, 1989). It is important to recognize that information architecture as discussed by Wetherbe and Davis (1983), Brancheau and Wetherbe (1986), and Everest and Kim (1989) is one component of a broader concept commonly known as information system architecture (ISA) (Sowa & Zachman, 1992; Targowski, 1988; Wardle, 1984; Zachman, 1987) which also includes architectures for hardware, communications, and business practice. IA Methodologies and Implementations Due to its wide organizational scope, the development of IA typically calls for a comprehensive planning and design exercise generally known as strategic data planning (SDP). According to Goodhue, Kirsch, Quillard, and Wybo (1992), SDP is "a formalized, top-down, data-oriented planning approach" that seeks to define "a data architecture to guide future systems development efforts" by building "a model of the enterprise, its functions, its processes, and its underlying data" (Goodhue et al., 1992, p. 12). The BSP method was the first such SDP methodology. However, experience with this methodol- (Vol. 26, No. 1) February DATA BASE ADVANCES 33

5 ogy over the last 20 years has not always been positive. The methodology has as many as 13 formal steps. It has been pointed out that the method requires too many interviews, takes too long to complete, and often produces voluminous output without visible impacts (Goodhue, Quillard, and Rockart, 1988; Lederer & Sethi, 1988; Rockart, 1979). By retaining the important elements of BSP, a number of newer methods, such as PC Prism (Index Technology, 1988) and the information engineering (IE) methods developed by James Martin (1982) (1990), attempted to simplify its complicated procedures and incorporate additional techniques such as critical success factors and the entity-relationship approach to enterprise data modeling. In practice, Martin's (1990) IE methodology has been particularly influential. It should be noted that, while both BSP and IE focus on business processes, the concept of business process is defined differently in the two methodologies. In BSP, "business processes are defined as a group of logically related decisions and activities required to manage the resources of the business" and "they are studied and identified without regard to the organization responsible for them" (IBM, 1984, p.29). In contrast to this broad definition of a process independent of organizational structure, the identification of processes within the IE methodology takes place within "business area analysis" as components of an enterprise function. Thus, it is likely that processes identified with the IE approach tend to be quite narrow and operate within the confines of a single function (Davenport, 1991). Even with later modifications to the IA concept, research has shown a variety of implementation obstacles in its adoption and use. Limited management support, user resistance, inadequate resources, and lack of alignment with corporate goals and strategies have been found to contribute to IA implementation difficulties (Goodhue et al., 1988; Goodhue et al., 1992; Lederer and Sethi, 1988; Lederer and Sethi, 1991). Goodhue et al. (1988) identified five cases of SDP efforts, and found none of them were completely successful. In fact, three of the five were never implemented. Additional studies by the researchers suggests that SDPs difficulties are rooted in its attempt to model the entire en- terprise which results in a massive amount of details and unacceptable time requirements. Consequently, the methodology can quickly lose its strategic focus and draw low-level or unqualified personnel into the project team. Many of these problems in IA implementation have been observed in connection to "technocratic utopianism" (Davenport, Eccles, and Prusak 1992). In their study of "information politics," these three researchers found many instances where a few IS professionals in the organization propagate the belief that problems of information management can best be solved by enterprise data models and other technological solutions alone. These "technocratic utopians" assume that politics are an aberrant form of behavior, and that information can flow freely in the organization once the planned IA is fully implemented. This utopian view fans a penchant for comprehensive, very detailed modeling and categorization of data resources (i.e., an attempt at precision "information engineering" for the entire organization). Unfortunately, such excessive scope and detail can eventually bury IA's strategic focus and drive away much need management involvement and resource support. IA, BPR, and Business Processes We have separately reviewed the development and implementation problems associated with business process redesign and IA. Business processes were identified as central to each concept. In this section, we will explore the relationships between BPR and IA through their intended and potential effects on existing as well as new business processes. IA and BPR in Relation to Existing Processes In the past, the major thrust of IA initiatives has been centered on supporting the information requirements of existing business processes. To illustrate IA support for existing processes, let us consider a typical accounts payable procedure as represented by a data flow diagram (see Figure 1). Notice that the data flow diagram covers only the accounts payable function, but two other related functions, purchasing and inventory receiving, are also involved in the process of paying suppliers. In a well-executed enterprise-wide IA initiative, the purchasing and 34 DATA BASE ADVANCES--February 1995 (Vol. 26, No. 1)

6 Supplier I Purchase I Supplier I Accounts orders accounts payable Purchase Account I Payable I order J balance I balance ~ Payment Batched, / Accounts f ~ ~ i ~ Supplier~ Processes I Check T I Account pa~y]ble - amountch!c~kinl alance I,ovoices I l account 1 Figure 1: Data Flow Diagram foratypicalaccounts Payable System inventory receiving functions would be identified as parts of a common business process calling for a shared data base. A high level data model for this data base can be expressed in the form of an entity-relationship diagram. As shown in Figure 2, two entities: supplier and part are involved in all three functions. Through data independence, this global data model serves a variety of different user views including those required by the existing procedures in the purchasing, payable, and the inventory receiving functions. Suppher~ Just as IA can support existing processes, BPR may also focus on existing business processes. As discussed earlier, BPR initiatives can range in radicalness from modest improvements of existing business processes to fundamental rethinking and design of new processes along a "business process redesign continuum." As depicted in Figure 3, while IA is intended to support existing processes (Link Y), a less radical approach to BPR may only attempt modest refinements in the I I (,~1~pplier~ Figure 2: High-Level E-R Diagram for an Accounts Payable Process Improves existing (Link X). processes IA and BPR in Relation to New While typically not its primary intent, IA Can result in the rethinking and redesign of business processes. For instance, Martin (1982) reports cases of information architects engaged in streamlining business procedures while designing shared data bases. This typically occurred when analysts involved in the development of IA for different functional areas sharing a common data resource have recognized duplication and inefficiencies of procedures among these functions. In another example, Moad (1989) reported that Syntex Corp., a pharmaceutical manufacturer, started to build a cross-functional IS using relational data base technology to track the full life cycle of its products, but encountered severe user resistance from the affected functions. Eventually, the project team began to use "structured data flow and organizational analysis to determine exactly how work got done Existing business process Supports Figure 3: BPR and IA in Relation to Existing Business Process (Vol. 26, No. 1) February DATA BASE ADVANCES 35

7 in the business unit" and "discovered that between 30% and 50% of the tasks people were doing were redundant" (Moad, 1989, p.74). With top management endorsement, the team began to redesign certain aspects of the process to take full advantage of IA and shared data bases. In this way, if the cross functional process calls for shared data, IA can be applied to facilitate the BPR effort. This relationship between BPR and IA can be illustrated by a well-cited BPR case at Ford Motor Corp. (Hammer, 1990). At Ford, the BPR team targeted the accounts payable process for redesign. Instead of automating the existing process within the confine of the payable function, the team conceptualized accounts payable as a business process which spans across three function: accounts payable, purchasing, and inventory receiving. Attempts to reengineer the process led to the design of an IA to guide the development of a common data base shared by all three functions. The BPR team designed a new process that no longer sends purchase orders to accounts payable; instead, the order is simply entered into the central data base. When the goods arrive, the receiving clerk checks the data base to see if an outstanding order corresponds to the arriving materials; if not, the materials are sent back to the supplier. Unlike the old system, which dictated that accounts payable clerks check 14 data items when matching the purchase order, receipt record, and the invoice, the reengineered process requires the matching of only 3 data items: part number, supplier code, and the unit of measurement. The new process, besides being faster and more reliable, results in a 75% reduction in workforce requirement, from 500 to 125 employees. IA and BPR: Lessons from the Field A summative model of the relationship between BPR and IA is presented in Figure 5, the "process linkage model." This model centers on the "degree of change in business process redesign" (i.e., the business process redesign continuum) and depicts where shared data bases and IA facilitate a variety of BPR endeavors ranging from incremental improvement of existing processes to radical reengineering for new process design. It is important to realize, however, that Figure 5 represents an "ideal" model. Just as BPR and IA can separately lead to problems as discussed earlier in this paper, one may expect problems to occur when BPR and IA are used together. Indeed, some problems have been reported from early field research on the BPR/IA relationship. In Figure 5, Link B represents potential opportunities leading from IA to new process designs. The possibility that IA initiative may induce the redesign of business processes, as alluded by anecdotal evidence described earlier, has recently received research attention. In a comprehensive field study, Goodhue et al. (1992) utilized the case study method on nine firms that adopted the strategic data planning (SDP) method in their organizations. The researchers identified "rethinking business processes" as one of five possible outcomes of SDP, but found that none of the nine companies indicated any success in rethinking business processes. In another case reported by Davenport (1991), IA techniques were applied in BPR at Rank Xerox U.K. (RXUK). With considerable impetus from top management, the company undertook As can be seen in Figure 4, BPR can radically engineer a new process design (Link A), which can be facilitated by IA and shared data bases (Link B). In the case of the Ford accounts payable BPR project, the new process design may be based on the same data model, as outlined in Figure 2, which could also be used in a traditional system development projects seeking automation of the existing process. Figure 4: BPRand Engineers q Facilitates New business process design 1 lain Relation to New Business Process 36 DATA BASE ADVANCES-- February 1995 (Vol. 26, No. 1)

8 Improving Existing B u ~ I Developing New Business Process I I ~ Process Figure 5: The BPR and IA Relationship: A Process Linkage Model a strategic reexamination of its environment and mission aimed at reshaping the company's business processes. Convinced that the process orientation of Martin's information engineering (IE) was consistent with BPR, the company adopted the IEF (information engineering facility) system from Texas Instrument to conduct process redesign. A total of 145 business processes and 80 distinct data entities were identified to form a 145-by-80 planning matrix. The initial effects of this IA application in BPR was evident in speedy systems development with data bases and applications emerging at a fast rate. However, actual process improvement was minor. As one key member of the BPR team noted early in the BPR project, the processes identified with IEM were typically within a single business function. Two years into its implementation, other BPR team members also began to realize that the single-function boundaries of the IE approach had led the BPR effort astray. This lack of cross-functional perspective inhibited the ability of the firm to achieve significant process improvement as demanded by its strategic objectives. This realization pushed the company to refocus its attention on process definitions consistent with its cross-functional strategic goals and objectives. In this reexamination of business processes, only 10 cross-functional processes were identified, as compared with the 145 single-function processes determined previously through the IE approach. To achieve quick results of strategic significance, three of these processes were selected for immediate improvement. One of the three processes, the customer order life cycle, was studied. Drastic process improvements were deemed possible. For example, the study team discovered many unnecessary approval steps in the customer billing procedure and determined that the time to issue a bill may be reduced from 112 days to a day and a half. Interestingly, it was determined that these process improvements could be accomplished by using cross-functional work teams, workflow modifications, and, possibly, without adding new information systems. Exploring the BPR/IA Relationship: Research Propositions We have attempted to develop a conceptual basis and gathered relevant research results to begin to address the three research concerns: - How IA supports BPR; - How the lack of IA hinders BPR; and - What approach to IA facilitates BPR. Given a lack of systematic empirical studies on the BPR/IA relationship, it is felt that observations and conclusions regarding these research concerns can most appropriately be stated in the form of research propositions. These tentative statements represent the "maximum likeli- (Vol. 26, No. 1) February DATA BASE ADVANCES 37

9 hood estimates" of the answers given the available knowledge and limited information about the BPR/IA relationships. For each research concern, three propositions are developed. This set of nine propositions, however, are not meant to be all inclusive. It is hoped that further research based on these propositions will generate additional propositions and provide more definitive answers to the three research concems. How IA Supports BPR To address this first research concern, the following propositions are put forth to explore contingencies under which IA can support BPR: Proposition 1: To the extent that the process to be redesigned requires data sharing, IA can support the BPR effort regardless of the degree of radicalness attempted in the process change. Proposition 2: IA can provide a greater degree of support for cross-functional BPR projects than those involving a single function. Proposition 3: The degree of IA support to a firm's overall BPR initiatives are pro portional to the extent to which processes to be redesigned are related to each other in terms of data sharing. Proposition 1 is a general statement based on the principle of data independence and the discussion presented earlier leading to the process linkage model (see Figure 5). Since crossfunctional processes are more likely to require data sharing as compared to single- function processes, as was seen in the Ford BPR example, we might expect Proposition 2 to be valid. Given that "early" research suggests that cross-functional BPR makes a greater contribution to business performance, IA dedicated to cross-functional reengineering efforts should make important contributions to business performance. For a group of processes involved in a firm's overall BPR initiatives, IA representation such as the process/data class matrix can be valuable. If the processes included in these BPR initiatives are related to each other in terms of data sharing, such matrices make the relationships explicit. Using an IA representation, data bases that are needed the most by processes targeted for redesign can be developed first, hence Proposition 3. How the Lack of IA Hinders BPR To explore how the lack of IA hinders BPR, the following propositions warrant consideration: Proposition4: To the extent that the process to be redesigned can be improved through data sharing, the lack of an IA will hinder the BPR effort regardless of how radical the degree of process change is attempted. Proposition5: To the extent that the process to be redesigned is cross-functional, the lack of an IA will hinder the BPR project. Proposition 6: To the extent the processes to be redesigned in a firm's overall BPR initiatives are related to each other in terms of data sharing, lack of an IA will increase the likelihood that the resulting "ad hoc data architecture" will hinder future BPR efforts. These three propositions represent the reverse of the first three propositions. In explicitly stating these propositions, attention is focused on the impact of the lack of IA on BPR success. Proposition 4 applies when the process to be redesigned can benefit from data sharing. A lack of IA in this situation can either slow down the redesign process because an IA must be developed, or the designer may choose not to include needed data base applications in the process design. Proposition 5 assumes that the need for data sharing increases when several different functions must coordinate with each other in achieving common process outcomes. Thus, lack of IA will handicap BPR if a crossfunctional process is involved. In a case where a series of BPR projects are undertaken and no IA exists, the data base(s) (or file systems) that emerges are typically based on the ad hoc needs of the first reengineering project. These "ad hoc data bases" often do not correspond to methodically configured data base(s) in an information architecture. For example, if process and data class relationships are clearly delineated, it becomes clear that a business process may access a number 38 DATA BASE ADVANCES--February 1995 (Vol. 26, No. 1)

10 of separate data bases which are also used by other processes. If these data sources are structured to optimize only the performance of a particular reengineering project without considering the access requirements of related processes, there will be higher risk that future BPR projects will find these "ad hoc data bases" unaccommodating or even incompatible. What Approach to IA Facilitates BPR We have elaborated on how an IA can support BPR and how the lack of it can hinder BPR. It is also proposed that this IA/BPR relationship operates under the following contingencies: Proposition 7: IA can facilitate BPR to the extent that process definitions in IA are based on the firm's strategic goals and objectives. Proposition 8: IA can facilitate BPR to the extent that it is used to assist in process definition. Proposition 9: IA can facilitate BPR to the extent that it is used to assist in BPR project prioritization. According to the research results reported by Hall et al. (1993), the performance improvement of BPR projects tends to be greater for broadly-defined cross-functional processes with such multidimensional improvement objectives as cost, time, quality, satisfaction, etc. that are tied to the firm's strategic goals. However, as demonstrated in the RXUK case, users of some IA methods, such as Martin's (1982) information engineering methodology, may have the tendency to conceptualize a process within a function, which may not be consistent with strategydriven, cross-functional process reengineering seeking significant performance impact. However, Proposition 7 suggests that process definition in IA is not necessarily technically bound. As discussed earlier, the definition of business processes in IA should highlight their independence from existing organizational structure. For example, the BSP approach specifically emphasizes the identification of business processes that support a firm's strategic goals and objectives in a "top-down" design of IA. This "structural-independent" IA tradition, which has often been lost in practice under mountains of massive paper work and single function approaches, is consistent with reengineering's strategy-driven objectives. To the extent that the processes to be redesigned require data sharing, are cross-functional, or target a set of processes that are interrelated in term of data sharing, neglecting IA will lead to an "ad hoc data architecture" which is dysfunctional for future reengineering projects. After initially defining a firm's business processes as part of a BPR effort, it is important to cross check these BPR process definitions with the enterprise-wide information architecture. As a result of this cross check, if it is found that some adjustments to the BPR process definitions can be made to accommodate an enterprise IA without compromising BPR performance objectives, these process definition modifications should be undertaken. This assistance in process definition, as indicated in Proposition 8, is an important avenue for IA to facilitate BPR effectively. In addition to process identification, IA can assist in prioritizing BPR projects as indicated in Proposition 9. By referring to the information architecture, some adjustment to the priority schedule can be attempted. A simple scenario can illustrate this. Let us assume that the initial schedule in an organization's BPR plan begin with process A, followed by process B and C. However, the enterprise IA indicates that the data resource associated with process C are also needed by a number of other processes, including process B, and that data for process B is not related to other processes in the BPR plan. In this circumstance, assigning higher priority to process C can significantly facilitate the overall BPR initiatives. In sum, propositions 7, 8, and 9 advocate a form of IA that can greatly facilitate BPR when: IA is aligned with BPR's strategy-driven business performance objectives (Proposition 7); and IA supports important reengineering tasks such as process definition and project prioritization, and assists, rather than drives, the BPR effort (Propositions 8 and 9). Thus, IA can contribute significantly to business process reengineering. IA/BPR collaboration calls for the inclusion of IA analysts as members of BPR teams. Field experience, however, suggests that allowing IA to drive the reengineering endeavor may bias the implementation in favor of IT solutions when organizational solutions would have been more appropriate. Further- (Vol. 26, No. 1) February DATA BASE ADVANCES 39

11 more, the resulting process definitions may be too narrow, hindering a cross-functional approach that is needed for more significant process improvements. All too often in the past, implementation problems of IA have blurred its strategic focus. In putting forth these propositions, we recognize that seeking major improvements in business outcome requires a broader perspective than IA alone offers. With BPR driving the overall initiative, many implementation problems of IA such as lack of management involvement and "technocratic utopianism" may be abated. This would allow BPR to provide a high profile business justification to the IA efforts that should help in overcoming some of its past implementation "stumbling blocks." Conclusion Business process redesign and information architecture have received considerable attention in recent years. In this paper, relationships between these two concepts were explored and examined in light of field research in IA and BPR. Some significant similarities between BPR and IA were observed. Both concepts are centered on business processes and are driven by organizational goals and objectives. Each concept can be related to both existing processes and new process designs. Based on these observations and results from recent field research, a set of propositions were put forth to address three important research concerns: how IA supports BPR, how the lack of IA can hinder BPR and what approach to IA can facilitate BPR. As suggested in these propositions, IA can contribute significantly to process reengineering. The enormous organizational change unleashed by business process reengineering can be wrenching (Champy, 1995; CSC Index, 1994; Lancaster, 1995). In finding their proper places in this tidal wave of business transformation, IA professionals can provide significant assistance to process reengineering initiatives. In collaborating with BPR, as suggested in the propositions, IA can in fact begin moving toward fulfilling its promises. 1 Additional research based on the propositions developed in this paper may further clarify the role of IA in process reengineering and provide more detailed guidelines to practicing professionals. References ANSI X3 SPARC/DBMS (1975). Study Group Report. American National Standards Institute, Washington, DC. Bachman, C.W. (1969). "Data Base Management: The Keystone of Applied Systems Architecture." In Critical Factors in Data Management (Edited by Gruenberger, F.) Prentice-Hall, Englewood Cliffs, NJ. Brancheau, J.C., and Wetherbe, J.C. (1986). "Information Architecture: Methods and Practice." Information Processing and Management, Vol. 22, No. 6, pp Brancheau, J.C., Schuster, L., and March, S.T. (1989). "Building and Implementing an Information Architecture." Data Base, Vol. 19 (Summer), pp Caron, J.R., Jarvenpaa, S.L., and Stoddard, D.B. (1994). "Business Reengineering at CIGNA Corporation: Experiences and Lessons Learned From the First Five Years." MIS Quarterly, Vol. 18, No. 3, pp Champy, J.A. (1993). "Grand Designs." ClO, Vol. 6, No. 6, pp Champy, J.A. (1995). Reengineering Management: The Mandate for New Leadership. Harper Collins Publishers, Inc. Chen, P.P.S. (1976). "The Entity-Relationship Model -- Toward a Unified View of Data." ACM Transactions on Database Systems, Vol. 1 (March), pp CODASYL Systems Committee Technical Report (1971). Association for Computing Machinery, New York, London, and Amsterdam. Codd, E.F. (1971). "A Relational Model of Data for Large Shared Data Banks." Communications of the ACM, Vol. 13, No. 6, pp CSC Index (1994). State of Reengineering Report. CSC Index. Davenport, T.H. (1991). "Rank Xerox U.K." Harvard Business School Case (Part A) and (Part B). Davenport, T.H. (1993). Process Innovation: Reengineering Work Through Information 1As reported by Davenport (1993), the consuiting firm of Ernst and Young is in the process of modifying its information engineering methodology to accommodate cross-functional process innovation. 40 DATA BASE ADVANCES-- February 1995 (Vol. 26, No. 1)

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