The Business Intelligence Value Chain: Data-Driven Decision Support ...

Proceedings of the 33rd Hawaii International Conference on System Sciences - 2000

The Business Intelligence Value Chain: Data-Driven Decision Support in a Data Warehouse Environment: An Exploratory Study1 M. Kathryn Brohman Terry College of Business, University of Georgia [email protected] Michael Parent, Michael R. Pearce and Michael Wade Richard Ivey School of Business, University of Western Ontario [email protected] [email protected] [email protected]

Abstract The recent introduction of a spate of data access applications, such as OLAP and data mining tools, has led to an increased interest on the part of both scholars and practitioners on how best to use and benefit from these tools. This paper reports on six exploratory case studies involving eight decision-makers and seven endusers. A process model based on the Value Chain is proposed and explained. Results show that database usage and information processing practices have indeed grown more sophisticated. Implications for practice and future research aimed at testing the Value Chain model are proposed.

1. Introduction In today’s competitive marketplace, organizations are evolving toward a greater dependence on data to drive the development of better products and services that will help them outsell their competitors [31,35]. Investment in expensive centralized, analytical database solutions, such as data warehouses and data marts, has demonstrated this dependence. The purpose of this paper is to present the findings of an exploratory study to explain how organizations are processing information in this new environment, and ways they are gaining from the usage of analytical database solutions. Fifteen semi-structured interviews in 6 organizations were completed to explore how organizations use data warehouse applications to support decision-making and generate value for the business. The results of these interviews were summarized in a model named the 1

“Business Intelligence Value Chain”. This model explains the newly evolved process of data-driven decision support through data warehouse usage. Details of how this model was created and a description of stages involved are provided in this paper.

2. Literature review A review of the literature was completed to develop an understanding of how organizations process information to support decision-making through the use analytical database infrastructures. The processing of information refers to the gathering and interpretation of data, as well as the synthesis of that data into information. The study of decision-making in IS research has been grounded in the working assumptions of information processing theory [43]. Information processing theory assumes that organizations design their structure, strategy, processes, people, rewards and information systems to cope with both external and internal sources of uncertainty [16,50,47]. Grounded in information processing theory, Simon [39] stated that organizations use quantitative tools and techniques to understand complex relationships among organizational and environmental variables. Simon’s decision-making model was developed based on Norbert Weiner’s classic model of an organization as an adaptive system [48]. In this model, inputs are processed into outputs which feed back to influence inputs and enable adaptation to external uncertainty. To date, Simon’s [39] perspective of decision-making has been the most popular approach adopted in information systems research [3,36].

This research was funded by the Direct Selling Education Foundation of Canada.

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In order to explore the applicability of Simon’s [39] model to explain decision support in a data warehouse environment, literature was reviewed to define data warehouse usage, factors that impact usage, and finally how usage impacts decision-making and the organization. A data warehouse is the newest form of decision support system [21,12]. Prior to data warehouse introduction, decision support literature defined database usage in two levels: reporting and analysis [38,15,40]. A third level of usage has been introduced in the data warehouse literature: data mining. Data mining was defined as one of the hottest technologies in decision support applications to date [7,13,44]. Participants in this research study criticized the term data mining. They categorized it as a “buzzword” that has been used to describe multiple techniques in data analysis. The common definition of data mining is a bottom-up, discovery-driven process that uses reporting and analytic tools to uncover patterns and trends, make novel linkages and associations, and generate new knowledge from data that other analytical methods might miss [13]. Data warehouse literature has not clearly explained how data mining differs from reporting and analysis. In 1998 Wayne Eckerson [12] coined the term “The Decision Support Sweet Spot”. He defined the sweet spot as the intersection between reporting and analysis where data warehouse usage needs to be if organizations want to reap maximum benefit. Tools that exist today in the “sweet spot” include executive information systems (EIS) applications, drillable reports, desktop OLAP, and spreadsheets. More research needs to be completed to clarify the understanding of data warehouse usage. Data warehouse research has made progress in differentiating between types of data warehouse users [22]. The first type has been defined as a “data warehouse user”. This individual was often a member of the information systems group and was responsible for the maintenance of applications that utilize data warehouse data. The second type was a “data warehouse end-user”. This individual used specific applications of the data warehouse and was most part of the finance or marketing departments [35]. End-user computing has been a popular topic in information systems literature. Researchers have conducted several studies focused on identifying success factors for end-user computing [4,25,33]. One factor that has been found consistently important to end-user computing success was ongoing support. Components of support have included training, development, assistance with use of software, research of new products, data extraction, consultation, and diffusion of information [6,25,32]. In a data warehouse context, the influence of support in end-user computing success has not been studied. In fact, most literature has focused on the role of the user.

The role of the end-user in the data-driven decision support process has received little attention to date. Information systems implementation literature was also reviewed to identify factors that influence information systems usage. Important variables identified include task characteristics [18], individual characteristics [1,23], organizational characteristics [5], system factors [29,49], and user attitudes [10,19]. Haley [22] studied the influence of these factors on the implementation of a data warehouse infrastructure. She defined implementation success as information quality, system quality, extent of implementation, and business value. The influence of these factors on usage as a success measure has not yet been tested. Finally, information systems researchers have identified a conceptual link between information systems usage and organizational performance [26,41]. In decision support literature, usage has been most commonly defined as either frequency of usage or extent of use [11]. Only few studies have defined usage by levels [17,46]. More specifically, Fuerst and Cheney [15] defined decision support usage by two levels previously defined by Schewe [37]. General usage referred to routinely generated reports and specific usage referred to personally initiated requests for information. They explored factors that influenced perceived utilization of a decision support system. Vandenbosch [45] defined two modes for information retrieval in an executive support system, scanning and focused search. She explained how these different modes influenced learning. Practitioner journals state that usage at the data mining level will allow organizations to develop better products and services and help them outsell their competitors [7,31,34]. To date, this has not been empirically validated.

3. Research methodology A case study methodology was undertaken to explore the phenomenon of data warehouse usage and how it influences the organization. Based on the case study perspective defined by Benbasat, Goldstein and Mead [2], there are three reasons why this methodology was appropriate for this research. First, the process of datadriven decision support has evolved and research on this phenomenon is in the formative stages. Second, the appropriateness of general systems theory [39] to explain data warehouse usage has not yet been determined. Lastly, data warehouse usage is unique from other types of information systems usage therefore factors other than those identified in prior research may be important. The unit of analysis for the case study was the task. The research method was to gather information about a task from beginning to end and identify actors and processes involved. To gather a rich understanding of this

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process, semi-structured interviews were completed with eight managers and seven analysts from six organizations. As data warehousing was a very popular topic of discussion at the time of the interviews, appendix A was used as a guide to focus the interview on the research questions [52]. The guide was developed based on Yin’s [53] approach to case study design. Possible answers to the questions were not predetermined and the interviewer used discretion in asking questions to encourage participants to explore concepts that were unanticipated. All six sites were large Canadian organizations that had implemented a data warehouse or data mart infrastructure. The six organizations represented three industries, three from retail, two from finance, and one from telecommunications. These industries were chosen as they represented those most advanced in data mining usage and understanding [22,30,42]. In an attempt to triangulate results, systematic observation was used to develop a better understanding of the data analysis process. Consistent with Todd and Benbasat’s [51] approach to observation, end-users were asked to “think aloud” during the usage process and all comments were recorded on tape. As the average task took more than one week to complete, only two analysts agreed to be observed. Further, the two analysts only agreed to be observed part of the time, so some of the processes were described rather than observed. In the end, these limitations motivated the researchers to transcribe the observation notes and analyze them as interview data. All interviews and observation sessions were taped and transcribed verbatim. Transcriptions were sent to participants for verification. Verified documents were then analyzed based on Miles and Huberman's [28]

qualitative data analysis model. Categories for analysis were identified based on results from the literature review as well as general concepts identified from a preliminary review of the qualitative data. To validate the categories, a second independent researcher was asked to identify the key concepts in the decision support model based on a preliminary review of qualitative data. He identified 14 out of the 19 categories/relationships that were included in the final pattern-coding key. Each category was described by a number of items (n). These items were identified based on interview notes and literature. A coding key was created that included all categories and items down the left column and interview numbers along the top. Each interview was coded by checking first whether or not the participant mentioned each category. Once categories were defined, the interview notes were reexamined to determine which items the participant mentioned within each category. To ensure reliability of results, the same independent researcher coded a secondary interpretation of qualitative results. There results were used to verify conclusions and minimize the influence of primary researcher bias.

4. Results All participants were asked to explain the evolution of database usage within their company. Preliminary analysis of exploratory data focused specifically on the description of their data-driven decision-making process. Figure 1 illustrates the results of analysis in a model named the “Business Intelligence Value Chain”.

Figure 1: Business intelligence value chain ANALYSIS (100%) Clarify Business Problem (67%)

BUSINESS PROBLEM (92%)

(75%)

Identify Data Analysis Needs (92%)

TASK DEFINITION (92%)

BUSINESS INTELLIGENCE (100%) Strengthen Business Case (58%)

Exploratory Data Analysis (EDA) (75%)

(100%) (92%)

Drill Down (83%)

Explanation (Reporting) (75%)

(92%)

DECISION MAKING (100%)

(92%)

Structured Data Analysis (SDA) (83%)

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(67%)

Prediction (Recommendation and Model Building) (100%)

(92%)

BUSINESS VALUE (92%)

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The value chain concept implies that all of the stages and relationships in the model will add value to the decision support process. Two concepts are at the core of this model: business intelligence and business value. Business Intelligence was borrowed from software vendors who market their newest data analysis products as business intelligence tools. Business value was chosen as it has been defined as the appropriate outcome for warehouse development and usage [20,22,30]. Results from qualitative analysis provided strong support for the stages and relationships in the value chain model. Percentages illustrated in Figure 1 represent the percentage of subjects who made reference to that particular stage or relationship in the model. To clarify, 91% of respondents identified business problem as initiating the decision support process. Sixty-seven percent made reference to the feedback process that clarified the business problem.

To assess interrater reliability between the primary and secondary analysis of qualitative data, kappa coefficients were calculated for all stages in the model [8]. Relationships in the model were captured by the categories in which they influenced. For example, the “identify data analysis needs” relationship was captured in the “task definition” category. Kappa is a correlation coefficient that represents the degree of interrater reliability. This statistic has a built in adjustment that corrects for the correlation between raters that occurred by chance. Values exceeding .60 typically represent acceptable agreement with those over .75 indicating excellent agreement beyond chance [14,24]. As shown in Table 1, all kappa coefficients exceed .70 with the exception of one, business intelligence. Column 3 identifies the mean number of times raters agreed (m) out of a total of 12 interviews.

Table 1: Results of interrater reliability assessment Stage in Model

Number of Items (n)

Average Number of Agreements (m)

Business Problem Task Definition Analysis Business Intelligence Decision Making Business Value

16 18 33 12 14 13

9.19 9.06 8.75 8.00 8.93 9.15

Kappa = (m/n) (0.5n) / 1 - (0.5n) 0.77 0.75 0.73 0.67 0.74 0.76

4.1 Data warehouse usage example The following usage example provides details of an exemplary decision support process described by a subject from a large Canadian retail store. The purpose of this example is to more clearly explain the value chain model. In the spring of 1999, store dealers were concerned that they had too little inventory space to store all the products in the promotional plan for the fall 1999 season. After voicing this concern at a meeting with the VP finance, the VP finance approached the senior database manager to discuss analytical requirements to address this problem. Together they decided that a report needed to be generated that identified the average store requirements for all deals. This report would be used by the VP finance to decide whether changes to the fall 1999 promotional plan were required or not. With this task, the senior database manager assigned an analyst to “go into the data warehouse and calculate the volume of products expected in the plan, and compare these with 1998 volumes. When complete, do further analysis on the top 25 products that are causing problems.” In a 5-minute discussion, the analyst asked a

few questions such as, "what would you like to see in the report", "should I use the forecast given or do you want me to make some assumptions", and "should I use any other sources of data?" The senior database manager suggested the forecast be knocked down by 20% because this was mid-way between the low and high estimate. He also suggested that the data warehouse would be a sufficient data source. To begin, the analyst did a series of ad hoc queries to pull out product volume sales data for 1998. He analyzed the data to identify the top 25 products with the highest cube (the amount of physical space required to store a product on deal). Cube is calculated by multiplying the volume of the product by the quantity shipped for a specific deal. He compared these results with the 1999 fall promotional plan and calculated total cube for each promotional period. During the analysis, he also found a few high cube deals that were running together during the same promotional period. To further understand, he extracted data related to customer baskets and analyzed baskets to determine if products were being purchased together.

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Finally, the analyst prepared and delivered a 10-15page report that included background, analysis results, and recommendations. The background section included the average size of a deal based on how much cube could fit in a tractor-trailer. He presented the information this way as he felt senior managers could relate to the size of a tractor-trailer and this would provide them with a clearer perspective on the size of the deal. He summarized analysis results in graphs, which were broken down into departments. On the graph he labeled how much each department was contributing to the cube. He presented information on a per flyer basis to see if page number had an impact on sales volume of a promotional item. He also included results from the basket analysis. The report made recommendations that were relevant to both logistic and marketing decisions. This whole process took approximately two weeks to complete.

5. Discussion Results from this research show that the usage of database systems has evolved over the last 10 years, and so have information processing practices. The majority of data warehouse pioneers involved in this study categorized themselves in either the second or third stage of database usage evolution. The first stage was list pulling from legacy systems where decisions related to criteria for lists generation were typically based on intuition. Due to data availability limitations, most lists included customer names and addresses and were used for mass mail campaigns. Stage two was direct database marketing where organizations needed to access more information related to customer preference and choice. Marketing campaigns and product development strategies were designed based on customer data. Specific products and promotions could be targeted at specific customer groups who were most likely to reap benefit. Stage three represented where some organizations have evolved to, and others aspire to reach. This stage is differentiated by the ability to make individual customer knowledge-based decisions. Specifically, through the use of predictive models, organizations could assess an individual customer’s needs based on their past behavior. This stage required data that was clean and defined. It also required data warehouse applications that enabled more innovative data analysis. The evolution of database usage has encouraged the development of more complex database infrastructures to support growing dependence on data in organizations. The results of this research provide evidence that users and information processing processes have changed as a result of data warehouse implementation. It is important to note

that this research is exploratory, the following discussion is not meant to be conclusive.

5.1 Data Warehouse Users The primary user identified in the results was the enduser. This individual often held the title of business analyst or information analyst within the marketing, advertising or finance departments. The background education and experience of these individuals were mixed. Some came from analytical (mathematical/statistical) backgrounds, some came from computer science backgrounds, and others came from business backgrounds. This individual was responsible for completing the data analysis for a task, generating a report to summarize findings, and often make recommendations based on analysis. Results from this study introduced another important actor in the data-driven decision support process that was not recognized in data warehouse literature. This actor was defined as the decision-maker. Their role was to define the business problem and then use the results of the analysis to support a business decision. Decision-makers are indirect users of the data warehouse and often hold the title of marketing, advertising, or finance managers. Decision-makers in a data warehouse environment depend on end-users to extract data to support their decisions. Traditionally, end-users depended on the information center to provide this service [32]. Information center employees were generally from the information systems department. These individuals have been defined as users in data warehouse literature [22]. To avoid confusion, this paper will refer to technical users as system users from this point forward. Beyond the development and implementation of the data warehouse, the end-users interviewed in this research made little reference to system user support. System users supported data design questions, chose analytical tools for the desktop, and researched new technologies and products. End-users often turned to each other for assistance related to complex analysis. In some cases, one individual would take the role of both the decision-maker and the end-user. In other cases, a database manager was also involved in defining the analysis task. Common factors that influenced these cases were the complexity of the task and the experience of the end-user. These results show evidence that many factors influence the complex process of information processing in a data warehouse environment.

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5.2 Information Processing in a Data Warehouse Environment Information processing is defined as the gathering and interpretation of data, as well as the synthesis of that data into information [43]. Results from this research provide a more detailed description of information processing in a data warehouse environment. The business intelligence value chain supports the importance of input, processing, and outputs in the decision-making process introduced by Simon [39]. There was also evidence of feedback as several participants stated that business intelligence would often expose new business problems. However, the interpretation that occurs between phases of the business intelligence value chain was not well represented in Weiner’s [48] view of the organization as an adaptive system. Perhaps an updated view of the organization as an interpretive system may further explain the more complex data-driven decision support process in a data warehouse environment. Daft and Weick [9] define the process by which managers translate data into knowledge as interpretation and identify organizational rules/regulations and environmental perceptions as factors influencing information processing capability. There was significant evidence of interpretation in the data-driven decision support process defined by the participants in this study. This interpretation is illustrated in the business intelligence value chain as the feedback loops between individual stages of the model. For example, the feedback loop from task definition to business problem illustrates the process the analyst goes through in interpreting the business problem defined by the decision-maker. Another example is the feedback loop between business intelligence and analysis that illustrates the process a decision-maker goes through in interpreting the report completed by the analyst. In most cases the analyst and decision-maker will communicate during these stages to make sure their interpretations are consistent. This process often resulted in the clarification of results or generation of different approaches or ideas. The influence of interpretation on the decision-support process is a complex but interesting field of research that has received limited attention. Exploring how interpretation influences data warehouse usage success represents an opportunity for future research. The following discussion describes in detail the datadriven decision support process defined in the business intelligence value chain. There are multiple categories and relationships imperative to the effectiveness of this process in supporting decision-making and generating value. Again, these results represent a high-level view of how a business problem translates into business value through data warehouse usage. There is an opportunity for

information systems researchers to drill down into each phase and explore more deeply how information processing in a data warehouse differs from other information systems, and how it impacts the organization. 5.2.1 The business problem and defining the task. To initiate the process, a business problem is defined by the decision-maker. Business problems were described as both strategic and operational and were influenced mostly by industry competition and the company's strategic orientation. The business problem was clarified through interaction between the decision-maker and the analyst (end-user). This process helped the decision-maker think about what data was available and relevant to the problem at hand. During this phase, decision-makers depended on the analysts to “ask the right questions”. Right questions were defined as those that related to restructuring the business problem to align with data requirements, breaking the problem down into workable task(s), and looking at the problem from multiple perspectives. Decision-makers and analysts were asked in the interview to comment on how the interaction during this phase influenced business value. The majority of participants recognized that the business knowledge of the decision-maker and the data knowledge of the analyst would allow the task to be approached in different directions and generate more business value. However, a few participants raised the concern that identifying data analysis requirements may add too much structure to the task and actually stifle the opportunity for the analyst to be innovative and creative during analysis. 5.2.2 Data analysis. In the Business Intelligence Value Chain, analysis is defined by three main components: exploratory data analysis, structured data analysis, and drill down. Exploratory data analysis (EDA) is the process of extracting data needed for structured data analysis (SDA). For routine tasks, it was likely that the extraction path had already been determined. For more unique task requests, the analyst used a series of ad hoc queries or packaged inductive tools (e.g. Enterprise Miner or Intelligent Miner) to extract data. Ad hoc queries were generally built on logical assumptions. Packaged inductive tools had built-in intelligence and ran heuristics on the data to identify correlations. This bottom-up approach to finding associations in data is more commonly referred to as data mining [13]. This research defined the bottom up approach as inductive data analysis (IDA) to stay away from the ambiguity that surrounds the data mining concept. Exploratory results suggested that the more queries generated to explore data, the more insight the analyst

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would generate about the task at hand. Participants also identified the effectiveness of knowledge sharing and networking with other analysts and managers to; (1) clarify data analysis requirements, and (2) gain further insight into the data and its relationships. One organization matched each of their analysts with an analyst from an affiliated company and encouraged them communicate with one another to gain further insight into exploring the data. Once the analyst had completed exploratory data analysis, they “did something with it” during structured data analysis (SDA). Participants described three types of SDA: trend analysis, mathematical analysis, and statistical analysis. Trend analysis was the identification of patterns in data without mathematical or statistical assistance. Examples of trend analysis included comparisons of products in a basket, mapping product sales against geographic regions, or graphing sales volume over time. Mathematical analysis included calculations such as sales margins and growth percentages. Statistical analysis tested the significance of patterns in the data using statistics such as regression and chi-square. Drill down was defined as the process of viewing deeper levels of information in order to capture more detail relevant to the result. Drill down involved further exploration of a finding by extraction and analyses of more data. To capture this process, drill down is illustrated as a feedback relationship between SDA and EDA in the business intelligence value chain model. This type of analysis fits in with the list of applications Eckerson [12] defined in the decision support sweet spot. Therefore, there is preliminary support in data warehouse literature that drill down will have a positive impact on business value. One participant described the following example that clearly illustrated the different phases of analysis. The task was to help a marketing manager decide what products to put on deal for a new store opening. The goal was to promote products that would drive traffic into the new store. Based on the logical assumption that greater than 10,000 units represented high traffic products, the analyst extracted all products that sold over 10,000 units when first put on deal. The analyst compared the results on a graph and chose the ten products with the highest number of units. It wasn't until the analyst drilled down into the number of units purchased per customer that they realized the average person bought 10 videotapes at a time when videotapes were on deal. This deeper understanding that videotapes would only drive 1000 customers (instead of 10,000) caused the analyst to remove videotapes and replace them with a higher traffic product. 5.2.3 Business intelligence. Continuing the deal example above, the analyst prepared a report for the marketing

manager that explained why 10,000 units was chosen as a cut-off point, what products sold more that 10,000 when first put on deal, and how many product units each customer purchased on average. In the report, the analyst also recommended which products should be put on deal for the new store opening. Both the explanation and recommendations generated new insight for the marketing manager. This is defined as business intelligence in the value chain model. Participants described recommendations as a form of prediction. Another form of prediction described by participants was model building. Model building was most common in finance organizations that had evolved to the third phase of database evolution. They build models to predict behavior in order to support individual customer knowledge-based decisions. Once analysis results had been reported into business intelligence, participants identified two ways analysts could strengthen the business case. The first was to find further support by bringing in additional internal and/or external information. The second way was to explore alternative explanations. It was common that decisionmakers would request that analysts go back and strengthen the business case after they had interpreted the results from analysis. To clarify, below is an example of an analyst who strengthened the business case and added value to the analysis. The task was to explain why sales of a particular product were down. Results from initial analysis did not address the problem. The analyst then brought in additional information to explore the influence of promotions and competitors. After analyzing several alternatives, he found that the product was also sold by a competitor who almost doubled their number of site locations over the past 5 years. The analyst included the results from the initial request as well as alternative explanation in his report to the manager. 5.2.4 Decision-making. The influence of new insight on decision-making performance was consistently found in the exploratory study. Key data-driven decisions were described as both strategic and operational. Strategic decisions related mostly to site location, product/category management, promotional vehicles, and store merchandising. Operational decisions were more specific to business operations and concentrated on up-sell/crosssell campaigns and targeted promotions. The majority of participants talked about the impact of new insights on decision-making effectiveness. Business intelligence would generate new ideas that enabled more informed decisions. Several decision-makers mentioned that the analysis performed for a task had only a partial impact on the decision. Often there were other factors that needed to be considered that could not be evaluated as

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part of the analysis (i.e. environmental and political factors). Some evidence was also found of a negative relationship between new insight gained and decisionmaking efficiency. These results are consistent with literature related to the impact of group support systems on decision-making performance. Both McLeod [27] and Benbasat and Nault [3] found that the usage of group support systems increased the quality of the decision but also increased the time to make a decision. 5.2.5 Business Value. Finally, if this whole process did not create value for the company, the investment would be questionable. Data warehouse research identified business value as the appropriate measure for success [20,22,30]. Compared to other measures of organizational performance [11], results from the exploratory study identified the most common business value outcomes as better quality decision-making and profitability (e.g. ROI, lift, cost savings, and profit). Better quality decisions were defined as those that created an advantage in the market. One organization described how they used their data warehouse to decide on a new store layout. The results of the analysis, as well as other factors such as traffic flow and store appeal, impacted the final layout. A reliable source informed this company that one of their key competitors had walked through the store and were terrified as they could not make sense of the company’s behavior. Specifically, they were making decisions that the competitor could not understand, which gave them a competitive advantage.

6. Limitations The main limitation of this paper is that the majority of the data collected was through interviews with decisionmakers and analysts. Limited structure around the interview questions meant that no two interviews followed the same path. Therefore, some steps critical to the process may have been overlooked. The planned research methodology attempted to overcome this limitation by observing a series of data-driven decision support processes. However, a low number of systematic observations were completed due to the length of the average task. Researchers plan to further track the decision processes within several companies to test the validity and generalizability of the Business Intelligence Value Chain.

7. Conclusion This research has provided early evidence that the data-driven decision support process is evolving with the

implementation of more complex analytical database architectures (data warehouses and data marts). It raises some interesting questions about the roles and procedures related to information processing practice of tomorrow. Specifically, what will be the role of the information center in a data warehouse environment? What kind of questions do decision-makers need to be asking to generate new insights? What competencies will make tomorrow’s analysts most effective at generating new insights? How important is interpretation in explaining data warehouse usage behavior? The most significant contribution of this paper is the introduction of the business intelligence value chain model. It has provided a new look at the phases, processes, and players involved in a data warehouse decision-making environment. As this study was only exploratory, there is a wide opportunity for future research to validate and generalize this model. Specifically, there is a critical assumption behind the business intelligence value chain that all stages in the model will generate business value. Two stages in particular may negatively impact value. First, by altering problems to accommodate available data, the analyst may lose site of the business problem and generate less value in the end. The incentive to get wrapped up in the data will become more intense as more data and advanced analytical tools become available. Second, the incentive to get wrapped up in the data may negatively impact decision-making efficiency to a point where little value is gained. There is opportunity for future researchers to empirically test segments of the value chain to determine how each stage influences business value. A second opportunity for future research is to empirically test the influence of both the decision-maker and the analyst on the generation of business value through decision-support. Participants in this study had different predictions related to the next step in the evolution of decision support. Some predicted that as database-marketing groups mature, the decision-making process would no longer have to wait for the business to present the problems. The database-marketing group would have an advanced knowledge about the business from the data and it would be their vision that would take the organization down a new road. Others predicted that the decision-makers would acquire advanced technical and analytical skills enabling them to complete their own decision-support. In both of these predictions, one player became obsolete. As an alternative, there may be benefit to adopting the business intelligence value chain model due to the degree of interpretation built into the process. As Daft and Weick [9] have provided evidence that interpretation enhances learning and decision-making, organizations may want to encourage interpretation between individuals rather than remove critical individuals from the process. There is an opportunity for

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researchers to operationalize the concept of interpretation and empirically test the influence it has on generation of new insight, decision-making performance, and business value. Building on the results of this study, an empirical study is currently being completed that tests the influence of interpretation on data warehouse application usage from the analysts’ perspective. It will also test whether usage positively influences business intelligence, decisionmaking performance, and business value. This research will make an initial attempt to empirically validate the business intelligence value chain model.

8. References [1] M. Alavi and E.A. Joachimsthaler, “Revisiting DSS implementation research: A meta-analysis of the literature and suggestions for researchers”, MIS Quarterly, March, 1992, 95116. [2] I. Benbasat, D.K. Goldstein, and M. Mead, “The Case Research Strategy in Studies of Information Systems”, MIS Quarterly, 11:3, 1987, 368-386. [3] I. Benbasat and B.R. Nault, “An evaluation of empirical research in management support systems”, Decision Support Systems, 6, 1990, 203-226. [4] F. Bergeron and C. Bérubé, “The management of the enduser environment: An empirical investigation”, Information and Management, 14:3, 1988, 107-113. [5] F. Bergeron and L. Raymond, “Evaluation of EIS from a managerial perspective”, Journal of Information Systems, 2, 1992, 45-60. [6] F. Bergeron, S. Rivard, and S. De Serre, “Investigating the support role of the information center”, MIS Quarterly, September, 1990, 247-260. [7] M. Brown, “Data mining as an extension to the data warehouse”, Proceedings from the Fourth Annual Leadership Conference, Data Warehousing Institute, 1998, 75-98. [8] H. J. Cohen, J. “A Coefficient of Agreement for Nominal Scales”, Education and Psychology Measurement, 220:1, 1960, 37-46. [9] R.L. Daft and K.F. Weick, “Toward a model of organizations as interpretation systems”, Academy of Management Review, 9:2, 1984, 284-295. [10] F.D. Davis, R.P. Bagozzi and P.R. Warsaw, “User acceptance of computer technology: A comparison of two theoretical models”, Management Science, 35:8, 1989, 9831003. [11] W.H. DeLone and E.R. McLean, “Information Systems Success: The Quest for the Dependent Variable”, Information Systems Research, 3:1, 1992, 60-95. [12] W.W. Eckerson, “The decision support sweet spot”, Journal of Data Warehousing, 3:2, Summer, 1998, 2-7. [13] H.A. Edelstein, “Data mining: The state of practice”, Proceedings from the Fourth Annual Leadership Conference, The Data Warehousing Institute, Thursday November 5, 1998, 63-97. [14] Fleiss, J.L. , Statistical Methods for Rates and Proportions. Wiley, New York, 1981.

[15] W.L. Fuerst and P.H. Cheney, “Factors affecting the perceived utilization of computer-based decision support systems”, Decision Sciences, 13:4, 1982, 554-569. [16] Galbraith, J., Designing organizations: An executive briefing on strategy, structure, and process. Joessy-Bass Publishers, San Francisco, CA., 1995. [17] M.J. Gintzberg, “Finding an adequate measure of OR/MS effectiveness”, Interfaces, 8:4, August, 1978, 59-62. [18] D.L. Goodhue, “Understanding user evaluations of information systems”, Management Science, 41(12), 1995, 1827-1844. [19] D.L. Goodhue and R.L. Thompson, “Task-technology fit and individual performance”, MIS Quarterly, June, 1995, 213236. [20] Graham, S., The Foundations of Wisdom: A Study of Financial Impact of Data Warehousing, International Data Corporation, Toronto, 1996. [21] Gray, P. and H.J. Watson, Decision support in the data warehouse, Prentice Hall, Upper Saddle River, N.J., 1998. [22] Haley, B. Implementing the Decision Support Infrastructure: Key Success Factors in Data Warehousing, Unpublished doctoral dissertation, University of Georgia, 1998. [23] J. Hartwick and H. Barki, “Hypothesis testing and hypothesis generating research: An example from the user participation literature”, Information Systems Research, 5:4, 1994, 446-450. [24] J.R. Landis and G.G. Koch, “The measurement of observer agreement for categorical data”, Biometrics, 33, 1977, 671-679. [25] R.L. Leitheiser and J.C. Wetherbe, “The successful information center: What does it take”, Proceedings of the 21st Annual ACM Computer Personnel Conference, Minneapolis, MN, May, 1985, 56-65. [26] H.C. Lucas Jr., “The Use of an Accounting Information System: Action and Organizational Performance”, The Accounting Review, 50:4, 1975, 735-746. [27] P.L. McLeod, “An assessment of the experimental literature on electronic support of group work: Results of a meta-analysis”, Human Computer Interaction, 7, 1992, 257280. [28] Miles, M.B. and A.M. Huberman, Qualitative data analysis: An expanded sourcebook, Sage Publications, Thousand Oaks, CA, 1994. [29] L. Mohan, W.K. Holstein and R.B. Adams, R.B. “EIS: It can work in the public sector”, MIS Quarterly, December, 1990, 434-448. [30] Y. Park, “Strategic uses of data warehouses: An organization's suitability for data warehousing”, Journal of Data Warehousing, 2:1, 1997, 24-33. [31] S. Reda, “Retailers respond to growing privacy debate”, Stores, December, 1996, 20-25. [32] S. Rivard, “Successful implementation of end-user computing”, Interfaces, 17:3, May-June, 1987, 25-33. [33] S. Rivard and S.L. Huff, “Factors of success for end-user computing”, Communications of the ACM, 31:5, 1988, 552-561. [34] J.R. Ross, “Data warehousing surges as retailers of all sizes fuel growth”, Stores, April, 72, 1997, 74-76. [35] T. Sakaguchi and M.N. Frolick, “A Review of the Data Warehousing Literature”, Journal of Data Warehousing, 2(1), 1997, 34-54.

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Proceedings of the 33rd Hawaii International Conference on System Sciences - 2000

[36] G.L. Sanders and J.R. Courtney, “A field study of organizational factors influencing DSS success”, MIS Quarterly, March, 1985, 77-93. [37] C.D. Schewe, “The management of information system users: An exploratory behavioral analysis”, Academy of Management Journal, 19:4, December 1976, 577-590. [38] M.S. Silver, Systems that support decision-makers: Description and analysis. John Wiley & Sons, New York, 1991. [39] Simon, H.A., The new science of management decisions, Prentice Hall, Englewood Cliffs, N.J., 1960. [40] R.H. Sprague, “A framework for the development of decision support systems”, MIS Quarterly, 4(4), 1980, 7-32. [41] A.W. Trice and M.E. Treacy, “Utilization as a Dependent Variable in MIS Research”, Data Base, Fall/Winter, 1988, 3341. [42] Two Crows. Introduction to data mining and knowledge discovery. 2nd edition, Two Crows Corporation, 1998. [43] M.L. Tushman and D.A. Nadler, “Information processing as an integrating concept in organization design”, Academy of Management Review, 3, 1978, 613-624. [44] J. van den Hoven, “Data warehousing: Bringing it all together”, Information Systems Management, Spring, 1998. [45] Vandenbosch, B., ESS impact viewed from a learning perspective, Unpublished doctoral dissertation, The University of Western Ontario, 1993. [46] E. Vanlommel and B. DeBrabander, “The organization of electronic data processing (EDP) activities and computer use”, Journal of Business, 48:3, July 1975, 391-410. [47] Weick, K., The social psychology of organizing, AddisonWesley, Reading, Mass., 1969. [48] Weiner, J.C., Cybernetics, MIT Press, Cambridge, Ma., 1948. [49] J.C. Wetherbe, “Executive information requirements: Getting it right”, MIS Quarterly, March, 1991, 51-65. [50] Zaltman, G., R. Duncan, and J. Holbek, Innovation and Organizations, Wiley Co., New York, 1973. [51] P. Todd and I. Benbasat. “Process tracing methods in decision support systems research: Exploring the black box”, MIS Quarterly, 11(4): 1987, 493-512. [52] Merton, R.K., M. Fiske & P.L. Kendall, The focused interview, Free Press, New York, 1956. [53] Yin, R.K., Case study research: Design and methods, Sage Publications, Beverly Hills, 1988.

Research Question #2: What factors influence variation in data warehouse usage? Interview Questions 5. How are data warehouse applications used? 6. How do you (the analyst) decide which application(s) to use for a specific task? 7a.Analyst: Do you use applications differently than other analysts? Please explain. 7b.Manager: What factors differentiate a good analyst from an average analyst in terms of their analytical capability? Research Question #3: How does variation in data warehouse usage influence the organization? Interview Questions 8. At the task level, how does database application usage impact the organization? 9. Do different levels of analysis have different impacts on the organization? 10.How does data warehouse usage relate to decisionmaking?

Appendix A: Data Warehouse User Interview Guide (Revised) Research Question #1: What does the data-driven decision support process look like in a data warehousing environment? Interview Questions 1. Please explain how the use of data and database technology has evolved in your organization over the last 10 years. 2. Where do you see usage evolving over the next 5 years? 3. How do you use the data warehouse? Do other individuals within the organization use it differently? 4. Can you describe a real example of how you used the data warehouse over the last 6 months?

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