The Business Intelligence Value Chain: Data-Driven ...€¦ · The Business Intelligence Value Chain: Data-Driven Decision Support in a Data Warehouse Environment: An Exploratory

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 Study 1

M. Kathryn BrohmanTerry College of Business, University of Georgia

[email protected]

Michael Parent, Michael R. Pearce and Michael WadeRichard Ivey School of Business, University of Western Ontario

[email protected] [email protected] [email protected]

AbstractThe recent introduction of a spate of data accessapplications, such as OLAP and data mining tools, hasled to an increased interest on the part of both scholarsand practitioners on how best to use and benefit fromthese tools. This paper reports on six exploratory casestudies involving eight decision-makers and seven end-users. A process model based on the Value Chain isproposed and explained. Results show that databaseusage and information processing practices have indeedgrown more sophisticated. Implications for practice andfuture research aimed at testing the Value Chain modelare proposed.

1. Introduction

In today’s competitive marketplace, organizations areevolving toward a greater dependence on data to drive thedevelopment of better products and services that will helpthem outsell their competitors [31,35]. Investment inexpensive centralized, analytical database solutions, suchas data warehouses and data marts, has demonstrated thdependence. The purpose of this paper is to present thefindings of an exploratory study to explain howorganizations are processing information in this newenvironment, and ways they are gaining from the usage ofanalytical database solutions.

Fifteen semi-structured interviews in 6 organizationswere completed to explore how organizations use datawarehouse applications to support decision-making andgenerate value for the business. The results of theseinterviews were summarized in a model named the

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“Business Intelligence Value Chain”. This model explainsthe newly evolved process of data-driven decision supportthrough data warehouse usage. Details of how this modelwas created and a description of stages involved areprovided in this paper.

2. Literature review

A review of the literature was completed to develop anunderstanding of how organizations process informationto support decision-making through the use analyticaldatabase infrastructures. The processing of informationrefers to the gathering and interpretation of data, as wellas the synthesis of that data into information. The study ofdecision-making in IS research has been grounded in theworking assumptions of information processing theory[43]. Information processing theory assumes thatorganizations design their structure, strategy, processes,people, rewards and information systems to cope withboth external and internal sources of uncertainty[16,50,47]. Grounded in information processing theory,Simon [39] stated that organizations use quantitative toolsand techniques to understand complex relationshipsamong organizational and environmental variables.Simon’s decision-making model was developed based onNorbert Weiner’s classic model of an organization as anadaptive system [48]. In this model, inputs are processedinto outputs which feed back to influence inputs andenable adaptation to external uncertainty. To date,Simon’s [39] perspective of decision-making has been themost popular approach adopted in information systemsresearch [3,36].

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1 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 warehouseenvironment, literature was reviewed to define datawarehouse usage, factors that impact usage, and finallyhow usage impacts decision-making and the organization.

A data warehouse is the newest form of decisionsupport system [21,12]. Prior to data warehouseintroduction, decision support literature defined databaseusage in two levels: reporting and analysis [38,15,40]. Athird level of usage has been introduced in the datawarehouse literature: data mining. Data mining wasdefined as one of the hottest technologies in decisionsupport applications to date [7,13,44]. Participants in thisresearch study criticized the term data mining. Theycategorized it as a “buzzword” that has been used todescribe multiple techniques in data analysis. Thecommon definition of data mining is a bottom-up,discovery-driven process that uses reporting and analytictools to uncover patterns and trends, make novel linkagesand associations, and generate new knowledge from datathat other analytical methods might miss [13]. Datawarehouse literature has not clearly explained how datamining differs from reporting and analysis.

In 1998 Wayne Eckerson [12] coined the term “TheDecision Support Sweet Spot”. He defined the sweet spotas the intersection between reporting and analysis wheredata warehouse usage needs to be if organizations want toreap maximum benefit. Tools that exist today in the“sweet spot” include executive information systems (EIS)applications, drillable reports, desktop OLAP, andspreadsheets. More research needs to be completed toclarify the understanding of data warehouse usage.

Data warehouse research has made progress indifferentiating between types of data warehouse users[22]. The first type has been defined as a “data warehouseuser”. This individual was often a member of theinformation systems group and was responsible for themaintenance of applications that utilize data warehousedata. The second type was a “data warehouse end-user”.This individual used specific applications of the datawarehouse and was most part of the finance or marketingdepartments [35]. End-user computing has been a populartopic in information systems literature. Researchers haveconducted several studies focused on identifying successfactors for end-user computing [4,25,33]. One factor thathas been found consistently important to end-usercomputing success was ongoing support. Components ofsupport have included training, development, assistancewith use of software, research of new products, dataextraction, consultation, and diffusion of information[6,25,32].

In a data warehouse context, the influence of supportin end-user computing success has not been studied. Infact, most literature has focused on the role of the user.

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The role of the end-user in the data-driven decisionsupport process has received little attention to date.

Information systems implementation literature wasalso reviewed to identify factors that influenceinformation systems usage. Important variables identifiedinclude task characteristics [18], individual characteristics[1,23], organizational characteristics [5], system factors[29,49], and user attitudes [10,19]. Haley [22] studied theinfluence of these factors on the implementation of a datawarehouse infrastructure. She defined implementationsuccess as information quality, system quality, extent ofimplementation, and business value. The influence ofthese factors on usage as a success measure has yet been tested. Finally, information systems researchers haveidentified a conceptual link between information systemsusage and organizational performance [26,41]. In decisionsupport literature, usage has been most commonly definedas either frequency of usage or extent of use [11]. Onlyfew studies have defined usage by levels [17,46]. Morespecifically, Fuerst and Cheney [15] defined decisionsupport usage by two levels previously defined by Schewe[37]. General usage referred to routinely generated reportsand specific usage referred to personally initiated requestsfor information. They explored factors that influencedperceived utilization of a decision support system.Vandenbosch [45] defined two modes for informationretrieval in an executive support system, scanning andfocused search. She explained how these different modeinfluenced learning. Practitioner journals state that usageat the data mining level will allow organizations todevelop better products and services and help them outsetheir competitors [7,31,34]. To date, this has not beenempirically validated.

3. Research methodology

A case study methodology was undertaken to explorethe phenomenon of data warehouse usage and how iinfluences the organization. Based on the case studyperspective defined by Benbasat, Goldstein and Mead [2],there are three reasons why this methodology wasappropriate for this research. First, the process of data-driven decision support has evolved and research on thisphenomenon is in the formative stages. Second, theappropriateness of general systems theory [39] to explaindata warehouse usage has not yet been determined. Lastldata warehouse usage is unique from other types ofinformation systems usage therefore factors other thanthose 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 atask from beginning to end and identify actors andprocesses involved. To gather a rich understanding of this

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process, semi-structured interviews were completed witheight managers and seven analysts from six organizationsAs data warehousing was a very popular topic ofdiscussion at the time of the interviews, appendix A wasused as a guide to focus the interview on the researchquestions [52]. The guide was developed based on Yin’s[53] approach to case study design. Possible answers tothe questions were not predetermined and the interviewerused discretion in asking questions to encourageparticipants to explore concepts that were unanticipated.

All six sites were large Canadian organizations thathad implemented a data warehouse or data martinfrastructure. The six organizations represented threeindustries, three from retail, two from finance, and onefrom telecommunications. These industries were chosenas they represented those most advanced in data miningusage and understanding [22,30,42].

In an attempt to triangulate results, systematicobservation was used to develop a better understanding othe data analysis process. Consistent with Todd andBenbasat’s [51] approach to observation, end-users wereasked to “think aloud” during the usage process and allcomments were recorded on tape. As the average tasktook more than one week to complete, only two analystsagreed to be observed. Further, the two analysts onlyagreed to be observed part of the time, so some of theprocesses were described rather than observed. In the endthese limitations motivated the researchers to transcribethe observation notes and analyze them as interview data.

All interviews and observation sessions were taped andtranscribed verbatim. Transcriptions were sent toparticipants for verification. Verified documents werethen analyzed based on Miles and Huberman's [28]

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qualitative data analysis model. Categories for analysiswere identified based on results from the literature reviewas well as general concepts identified from a preliminaryreview of the qualitative data. To validate the categories, asecond independent researcher was asked to identify thekey concepts in the decision support model based on apreliminary review of qualitative data. He identified 14out of the 19 categories/relationships that were included inthe final pattern-coding key.

Each category was described by a number of items (n).These items were identified based on interview notes andliterature. A coding key was created that included allcategories and items down the left column and interviewnumbers along the top. Each interview was coded bychecking first whether or not the participant mentionedeach category. Once categories were defined, theinterview notes were reexamined to determine whichitems the participant mentioned within each category. Toensure reliability of results, the same independentresearcher coded a secondary interpretation of qualitativeresults. There results were used to verify conclusions andminimize the influence of primary researcher bias.

4. Results

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

Figure 1: Business intelligence value chain

ANALYSIS(100%)

BUSINESSINTELLIGENCE

(100%)Clarify

BusinessProblem(67%)

Identify DataAnalysis Needs

(92%)Exploratory

DataAnalysis(EDA)(75%)

StrengthenBusiness

Case(58%)

Explanation(Reporting)

(75%)

(92%) DECISIONMAKING(100%)

BUSINESSPROBLEM

(92%)

(75%) TASKDEFINITION

(92%)

(100%)(92%)

DrillDown(83%)

(92%)(67%) (92%)

StructuredData

Analysis(SDA)(83%)

Prediction(Recommendation

and ModelBuilding)(100%)

BUSINESSVALUE(92%)

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The value chain concept implies that all of the stagesand relationships in the model will add value to thedecision support process. Two concepts are at the core ofthis model: business intelligence and business value.Business Intelligence was borrowed from softwarevendors who market their newest data analysis products asbusiness intelligence tools. Business value was chosen asit has been defined as the appropriate outcome forwarehouse development and usage [20,22,30].

Results from qualitative analysis provided strongsupport for the stages and relationships in the value chainmodel. Percentages illustrated in Figure 1 represent thepercentage of subjects who made reference to thatparticular stage or relationship in the model. To clarify,91% of respondents identified business problem asinitiating the decision support process. Sixty-sevenpercent made reference to the feedback process thaclarified the business problem.

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To assess interrater reliability between the primary andsecondary analysis of qualitative data, kappa coefficientswere calculated for all stages in the model [8].Relationships in the model were captured by thecategories in which they influenced. For example, the“identify data analysis needs” relationship was captured inthe “task definition” category. Kappa is a correlationcoefficient that represents the degree of interraterreliability. This statistic has a built in adjustment thatcorrects for the correlation between raters that occurred bychance. Values exceeding .60 typically representacceptable agreement with those over .75 indicatingexcellent agreement beyond chance [14,24]. As shown inTable 1, all kappa coefficients exceed .70 with theexception of one, business intelligence. Column 3identifies the mean number of times raters agreed (m) outof a total of 12 interviews.

Table 1: Results of interrater reliability assessment

Stage in Model Number of Items (n) Average Number ofAgreements (m)

Kappa = (m/n) -

(0.5n) / 1 - (0.5n)Business Problem 16 9.19 0.77Task Definition 18 9.06 0.75

Analysis 33 8.75 0.73Business Intelligence 12 8.00 0.67

Decision Making 14 8.93 0.74Business Value 13 9.15 0.76

4.1 Data warehouse usage example

The following usage example provides details of anexemplary decision support process described by a subjecfrom a large Canadian retail store. The purpose of thisexample is to more clearly explain the value chain model.

In the spring of 1999, store dealers were concernedthat they had too little inventory space to store all theproducts in the promotional plan for the fall 1999 season.After voicing this concern at a meeting with the VPfinance, the VP finance approached the senior databasemanager to discuss analytical requirements to address thisproblem. Together they decided that a report needed to begenerated that identified the average store requirementsfor all deals. This report would be used by the VP financeto decide whether changes to the fall 1999 promotionalplan were required or not.

With this task, the senior database manager assignedan analyst to “go into the data warehouse and calculate thevolume of products expected in the plan, and comparethese with 1998 volumes. When complete, do furtheranalysis on the top 25 products that are causingproblems.” In a 5-minute discussion, the analyst asked a

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few questions such as, "what would you like to see in thereport", "should I use the forecast given or do you wantme to make some assumptions", and "should I use anyother sources of data?" The senior database managersuggested the forecast be knocked down by 20% becausethis was mid-way between the low and high estimate. Healso suggested that the data warehouse would be asufficient data source.

To begin, the analyst did a series of ad hoc queries topull out product volume sales data for 1998. He analyzedthe data to identify the top 25 products with the highestcube (the amount of physical space required to store aproduct on deal). Cube is calculated by multiplying thevolume of the product by the quantity shipped for aspecific deal. He compared these results with the 1999fall promotional plan and calculated total cube for eachpromotional period. During the analysis, he also found afew high cube deals that were running together during thesame promotional period. To further understand, heextracted data related to customer baskets and analyzedbaskets to determine if products were being purchasedtogether.

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Finally, the analyst prepared and delivered a 10-15-page report that included background, analysis results, andrecommendations. The background section included theaverage size of a deal based on how much cube could fitin a tractor-trailer. He presented the information this wayas he felt senior managers could relate to the size of atractor-trailer and this would provide them with a clearerperspective on the size of the deal. He summarizedanalysis results in graphs, which were broken down intodepartments. On the graph he labeled how much eachdepartment was contributing to the cube. He presentedinformation on a per flyer basis to see if page number hadan impact on sales volume of a promotional item. He alsoincluded results from the basket analysis. The report maderecommendations that were relevant to both logistic andmarketing decisions. This whole process tookapproximately two weeks to complete.

5. Discussion

Results from this research show that the usage of databassystems has evolved over the last 10 years, and so haveinformation processing practices. The majority of datawarehouse pioneers involved in this study categorizedthemselves in either the second or third stage of databaseusage evolution. The first stage was list pulling fromlegacy systems where decisions related to criteria for listsgeneration were typically based on intuition. Due to dataavailability limitations, most lists included customernames and addresses and were used for mass maicampaigns.

Stage two was direct database marketing whereorganizations needed to access more information relatedto customer preference and choice. Marketing campaignsand product development strategies were designed basedon customer data. Specific products and promotions couldbe targeted at specific customer groups who were mostlikely to reap benefit.

Stage three represented where some organizationshave evolved to, and others aspire to reach. This stage isdifferentiated by the ability to make individual customerknowledge-based decisions. Specifically, through the useof predictive models, organizations could assess anindividual customer’s needs based on their past behavior.This stage required data that was clean and defined. It alsorequired data warehouse applications that enabled moreinnovative data analysis.

The evolution of database usage has encouraged thedevelopment of more complex database infrastructures tosupport growing dependence on data in organizations. Theresults of this research provide evidence that users andinformation processing processes have changed as a resuof data warehouse implementation. It is important to note

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

5.1 Data Warehouse Users

The primary user identified in the results was the end-user. This individual often held the title of businessanalyst or information analyst within the marketing,advertising or finance departments. The backgroundeducation and experience of these individuals were mixed.Some came from analytical (mathematical/statistical)backgrounds, some came from computer sciencebackgrounds, and others came from businessbackgrounds. This individual was responsible forcompleting the data analysis for a task, generating a reportto summarize findings, and often make recommendationsbased on analysis.

Results from this study introduced another importantactor in the data-driven decision support process that wasnot recognized in data warehouse literature. This actorwas defined as the decision-maker. Their role was todefine the business problem and then use the results of theanalysis to support a business decision. Decision-makersare indirect users of the data warehouse and often hold thetitle of marketing, advertising, or finance managers.

Decision-makers in a data warehouse environmentdepend on end-users to extract data to support theirdecisions. Traditionally, end-users depended on theinformation center to provide this service [32].Information center employees were generally from theinformation systems department. These individuals havebeen defined as users in data warehouse literature [22]. Toavoid confusion, this paper will refer to technical users assystem users from this point forward. Beyond thedevelopment and implementation of the data warehouse,the end-users interviewed in this research made littlereference to system user support. System users supporteddata design questions, chose analytical tools for thedesktop, and researched new technologies and products.End-users often turned to each other for assistance relatedto complex analysis.

In some cases, one individual would take the role ofboth the decision-maker and the end-user. In other cases, adatabase manager was also involved in defining theanalysis task. Common factors that influenced these caseswere the complexity of the task and the experience of theend-user. These results show evidence that many factorsinfluence the complex process of information processingin a data warehouse environment.

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5.2 Information Processing in a Data WarehouseEnvironment

Information processing is defined as the gathering andinterpretation of data, as well as the synthesis of that datainto information [43]. Results from this research provide amore detailed description of information processing in adata warehouse environment. The business intelligencevalue chain supports the importance of input, processing,and outputs in the decision-making process introduced bySimon [39]. There was also evidence of feedback asseveral participants stated that business intelligence wouldoften expose new business problems. However, theinterpretation that occurs between phases of the businessintelligence value chain was not well represented inWeiner’s [48] view of the organization as an adaptivesystem. Perhaps an updated view of the organization as aninterpretive system may further explain the more complexdata-driven decision support process in a data warehouseenvironment.

Daft and Weick [9] define the process by whichmanagers translate data into knowledge as interpretationand identify organizational rules/regulations andenvironmental perceptions as factors influencinginformation processing capability. There was significantevidence of interpretation in the data-driven decisionsupport process defined by the participants in this study.This interpretation is illustrated in the businessintelligence value chain as the feedback loops betweenindividual stages of the model. For example, the feedbackloop from task definition to business problem illustratesthe process the analyst goes through in interpreting thebusiness problem defined by the decision-maker. Anotherexample is the feedback loop between businessintelligence and analysis that illustrates the process adecision-maker goes through in interpreting the reportcompleted by the analyst. In most cases the analyst anddecision-maker will communicate during these stages tomake sure their interpretations are consistent. This processoften resulted in the clarification of results or generationof different approaches or ideas. The influence ofinterpretation on the decision-support process is acomplex but interesting field of research that has receivedlimited attention. Exploring how interpretation influencesdata warehouse usage success represents an opportunifor future research.

The following discussion describes in detail the data-driven decision support process defined in the businessintelligence value chain. There are multiple categories andrelationships imperative to the effectiveness of thisprocess in supporting decision-making and generatingvalue. Again, these results represent a high-level view ofhow a business problem translates into business valuethrough data warehouse usage. There is an opportunity for

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information systems researchers to drill down into eachphase and explore more deeply how informationprocessing in a data warehouse differs from otherinformation systems, and how it impacts the organization.

5.2.1 The business problem and defining the task. Toinitiate the process, a business problem is defined by thedecision-maker. Business problems were described asboth strategic and operational and were influenced mostlyby industry competition and the company's strategicorientation.

The business problem was clarified through interactionbetween the decision-maker and the analyst (end-user)This process helped the decision-maker think about whatdata was available and relevant to the problem at hand.During this phase, decision-makers depended on theanalysts to “ask the right questions”. Right questions weredefined as those that related to restructuring the businessproblem to align with data requirements, breaking theproblem down into workable task(s), and looking at theproblem from multiple perspectives.

Decision-makers and analysts were asked in theinterview to comment on how the interaction during thisphase influenced business value. The majority ofparticipants recognized that the business knowledge of thedecision-maker and the data knowledge of the analystwould allow the task to be approached in differentdirections and generate more business value. However, afew participants raised the concern that identifying dataanalysis requirements may add too much structure to thetask and actually stifle the opportunity for the analyst tobe innovative and creative during analysis.

5.2.2 Data analysis. In the Business Intelligence ValueChain, analysis is defined by three main components:exploratory data analysis, structured data analysis, anddrill down.

Exploratory data analysis (EDA) is the process ofextracting data needed for structured data analysis (SDA).For routine tasks, it was likely that the extraction path hadalready been determined. For more unique task requeststhe analyst used a series of ad hoc queries or packageinductive tools (e.g. Enterprise Miner or IntelligentMiner) to extract data. Ad hoc queries were generallybuilt on logical assumptions. Packaged inductive tools hadbuilt-in intelligence and ran heuristics on the data toidentify correlations. This bottom-up approach to findingassociations in data is more commonly referred to as datamining [13]. This research defined the bottom upapproach as inductive data analysis (IDA) to stay awayfrom the ambiguity that surrounds the data miningconcept.

Exploratory results suggested that the more queriesgenerated to explore data, the more insight the analyst

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would generate about the task at hand. Participants alsoidentified the effectiveness of knowledge sharing andnetworking with other analysts and managers to; (1)clarify data analysis requirements, and (2) gain furtherinsight into the data and its relationships. Oneorganization matched each of their analysts with ananalyst from an affiliated company and encouraged themcommunicate with one another to gain further insight intoexploring the data.

Once the analyst had completed exploratory dataanalysis, they “did something with it” during structureddata analysis (SDA). Participants described three types ofSDA: trend analysis, mathematical analysis, and statisticalanalysis. Trend analysis was the identification of patternsin data without mathematical or statistical assistance.Examples of trend analysis included comparisons ofproducts in a basket, mapping product sales againstgeographic regions, or graphing sales volume over time.Mathematical analysis included calculations such as salesmargins and growth percentages. Statistical analysis testedthe significance of patterns in the data using statistics suchas regression and chi-square.

Drill down was defined as the process of viewingdeeper levels of information in order to capture moredetail relevant to the result. Drill down involved furtherexploration of a finding by extraction and analyses ofmore data. To capture this process, drill down isillustrated as a feedback relationship between SDA andEDA in the business intelligence value chain model. Thistype of analysis fits in with the list of applicationsEckerson [12] defined in the decision support sweet spot.Therefore, there is preliminary support in data warehouseliterature that drill down will have a positive impact onbusiness value.

One participant described the following example thatclearly illustrated the different phases of analysis. The taskwas to help a marketing manager decide what products toput on deal for a new store opening. The goal was topromote products that would drive traffic into the newstore. Based on the logical assumption that greater than10,000 units represented high traffic products, the analystextracted all products that sold over 10,000 units whenfirst put on deal. The analyst compared the results on agraph and chose the ten products with the highest numberof units. It wasn't until the analyst drilled down into thenumber of units purchased per customer that they realizedthe average person bought 10 videotapes at a time whenvideotapes were on deal. This deeper understanding thatvideotapes would only drive 1000 customers (instead of10,000) caused the analyst to remove videotapes andreplace them with a higher traffic product.

5.2.3 Business intelligence. Continuing the deal exampleabove, the analyst prepared a report for the marketing

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manager that explained why 10,000 units was chosen as acut-off point, what products sold more that 10,000 whenfirst put on deal, and how many product units eachcustomer purchased on average. In the report, the analystalso recommended which products should be put on dealfor the new store opening. Both the explanation andrecommendations generated new insight for the marketingmanager. This is defined as business intelligence in thevalue chain model. Participants describedrecommendations as a form of prediction. Another form ofprediction described by participants was model building.Model building was most common in financeorganizations that had evolved to the third phase ofdatabase evolution. They build models to predict behaviorin order to support individual customer knowledge-baseddecisions.

Once analysis results had been reported into businessintelligence, participants identified two ways analystscould strengthen the business case. The first was to findfurther support by bringing in additional internal and/orexternal information. The second way was to explorealternative explanations. It was common that decision-makers would request that analysts go back and strengthenthe business case after they had interpreted the resultsfrom analysis.

To clarify, below is an example of an analyst whostrengthened the business case and added value to theanalysis. The task was to explain why sales of a particularproduct were down. Results from initial analysis did notaddress the problem. The analyst then brought inadditional information to explore the influence ofpromotions and competitors. After analyzing severalalternatives, he found that the product was also sold by acompetitor who almost doubled their number of sitelocations over the past 5 years. The analyst included theresults from the initial request as well as alternativeexplanation in his report to the manager.

5.2.4 Decision-making. The influence of new insight ondecision-making performance was consistently found inthe exploratory study. Key data-driven decisions weredescribed as both strategic and operational. Strategicdecisions related mostly to site location, product/categorymanagement, promotional vehicles, and storemerchandising. Operational decisions were more specificto business operations and concentrated on up-sell/cross-sell campaigns and targeted promotions.

The majority of participants talked about the impact ofnew insights on decision-making effectiveness. Businessintelligence would generate new ideas that enabled moreinformed decisions. Several decision-makers mentionedthat the analysis performed for a task had only a partialimpact on the decision. Often there were other factors thatneeded to be considered that could not be evaluated as

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part of the analysis (i.e. environmental and politicalfactors). Some evidence was also found of a negativerelationship between new insight gained and decision-making efficiency. These results are consistent withliterature related to the impact of group support systemson decision-making performance. Both McLeod [27] andBenbasat and Nault [3] found that the usage of groupsupport systems increased the quality of the decision butalso increased the time to make a decision.

5.2.5 Business Value. Finally, if this whole processdid not create value for the company, the investmentwould be questionable. Data warehouse researchidentified business value as the appropriate measure forsuccess [20,22,30]. Compared to other measures oforganizational performance [11], results from theexploratory study identified the most common businessvalue outcomes as better quality decision-making andprofitability (e.g. ROI, lift, cost savings, and profit).Better quality decisions were defined as those that createdan advantage in the market. One organization describedhow they used their data warehouse to decide on a newstore layout. The results of the analysis, as well as otherfactors such as traffic flow and store appeal, impacted thefinal layout. A reliable source informed this company thatone of their key competitors had walked through the storeand were terrified as they could not make sense of thecompany’s behavior. Specifically, they were makingdecisions that the competitor could not understand, whichgave them a competitive advantage.

6. Limitations

The main limitation of this paper is that the majority of thedata collected was through interviews with decision-makers and analysts. Limited structure around theinterview questions meant that no two interviews followedthe same path. Therefore, some steps critical to theprocess may have been overlooked. The planned researcmethodology attempted to overcome this limitation byobserving a series of data-driven decision supportprocesses. However, a low number of systematicobservations were completed due to the length of theaverage task. Researchers plan to further track thedecision processes within several companies to test thevalidity and generalizability of the Business IntelligenceValue Chain.

7. Conclusion

This research has provided early evidence that thedata-driven decision support process is evolving with the

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implementation of more complex analytical databasearchitectures (data warehouses and data marts). It raisessome interesting questions about the roles and proceduresrelated to information processing practice of tomorrow.Specifically, what will be the role of the informationcenter in a data warehouse environment? What kind ofquestions do decision-makers need to be asking togenerate new insights? What competencies will maketomorrow’s analysts most effective at generating newinsights? How important is interpretation in explainingdata warehouse usage behavior? The most significantcontribution of this paper is the introduction of thebusiness intelligence value chain model. It has provided anew look at the phases, processes, and players involved ina data warehouse decision-making environment. As thisstudy was only exploratory, there is a wide opportunity forfuture research to validate and generalize this model.

Specifically, there is a critical assumption behind thebusiness intelligence value chain that all stages in themodel will generate business value. Two stages inparticular may negatively impact value. First, by alteringproblems to accommodate available data, the analyst maylose site of the business problem and generate less valuein the end. The incentive to get wrapped up in the datawill become more intense as more data and advancedanalytical tools become available. Second, the incentive toget wrapped up in the data may negatively impactdecision-making efficiency to a point where little value isgained. There is opportunity for future researchers toempirically test segments of the value chain to determinehow each stage influences business value.

A second opportunity for future research is toempirically test the influence of both the decision-makerand the analyst on the generation of business valuethrough decision-support. Participants in this study haddifferent predictions related to the next step in theevolution of decision support. Some predicted that asdatabase-marketing groups mature, the decision-makingprocess would no longer have to wait for the business topresent the problems. The database-marketing groupwould have an advanced knowledge about the businessfrom the data and it would be their vision that would takethe organization down a new road. Others predicted thatthe decision-makers would acquire advanced technicaland analytical skills enabling them to complete their owndecision-support. In both of these predictions, one playerbecame obsolete. As an alternative, there may be benefitto adopting the business intelligence value chain modeldue to the degree of interpretation built into the process.As Daft and Weick [9] have provided evidence thatinterpretation enhances learning and decision-making,organizations may want to encourage interpretationbetween individuals rather than remove criticalindividuals from the process. There is an opportunity for

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researchers to operationalize the concept of interpretationand empirically test the influence it has on generation ofnew insight, decision-making performance, and businessvalue.

Building on the results of this study, an empirical studyis currently being completed that tests the influence ofinterpretation on data warehouse application usage fromthe analysts’ perspective. It will also test whether usagepositively influences business intelligence, decision-making performance, and business value. This researchwill make an initial attempt to empirically validate thebusiness intelligence value chain model.

8. References

[1] M. Alavi and E.A. Joachimsthaler, “Revisiting DSSimplementation research: A meta-analysis of the literature andsuggestions for researchers”, MIS Quarterly, March, 1992, 95-116.[2] I. Benbasat, D.K. Goldstein, and M. Mead, “The CaseResearch Strategy in Studies of Information Systems”, MISQuarterly, 11:3, 1987, 368-386.[3] I. Benbasat and B.R. Nault, “An evaluation of empiricalresearch in management support systems”, Decision SupportSystems, 6, 1990, 203-226.[4] F. Bergeron and C. Bérubé, “The management of the end-user environment: An empirical investigation”, Information andManagement, 14:3, 1988, 107-113.[5] F. Bergeron and L. Raymond, “Evaluation of EIS from amanagerial perspective”, Journal of Information Systems, 2,1992, 45-60.[6] F. Bergeron, S. Rivard, and S. De Serre, “Investigating thesupport role of the information center”, MIS Quarterly,September, 1990, 247-260.[7] M. Brown, “Data mining as an extension to the datawarehouse”, Proceedings from the Fourth Annual LeadershipConference, Data Warehousing Institute, 1998, 75-98.[8] H. J. Cohen, J. “A Coefficient of Agreement for NominalScales”, Education and Psychology Measurement, 220:1, 1960,37-46.[9] R.L. Daft and K.F. Weick, “Toward a model oforganizations as interpretation systems”, Academy ofManagement Review, 9:2, 1984, 284-295.[10] F.D. Davis, R.P. Bagozzi and P.R. Warsaw, “Useracceptance of computer technology: A comparison of twotheoretical models”, Management Science, 35:8, 1989, 983-1003.[11] W.H. DeLone and E.R. McLean, “Information SystemsSuccess: The Quest for the Dependent Variable”, InformationSystems 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 andProportions. Wiley, New York, 1981.

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[15] W.L. Fuerst and P.H. Cheney, “Factors affecting theperceived utilization of computer-based decision supportsystems”, Decision Sciences, 13:4, 1982, 554-569.[16] Galbraith, J., Designing organizations: An executivebriefing on strategy, structure, and process. Joessy-BassPublishers, San Francisco, CA., 1995.[17] M.J. Gintzberg, “Finding an adequate measure of OR/MSeffectiveness”, Interfaces, 8:4, August, 1978, 59-62.[18] D.L. Goodhue, “Understanding user evaluations ofinformation systems”, Management Science, 41(12), 1995,1827-1844.[19] D.L. Goodhue and R.L. Thompson, “Task-technology fitand individual performance”, MIS Quarterly, June, 1995, 213-236.[20] Graham, S., The Foundations of Wisdom: A Study ofFinancial Impact of Data Warehousing, International DataCorporation, Toronto, 1996.[21] Gray, P. and H.J. Watson, Decision support in the datawarehouse, Prentice Hall, Upper Saddle River, N.J., 1998.[22] Haley, B. Implementing the Decision SupportInfrastructure: Key Success Factors in Data Warehousing,Unpublished doctoral dissertation, University of Georgia, 1998.[23] J. Hartwick and H. Barki, “Hypothesis testing andhypothesis generating research: An example from the userparticipation literature”, Information Systems Research, 5:4,1994, 446-450.[24] J.R. Landis and G.G. Koch, “The measurement ofobserver agreement for categorical data”, Biometrics, 33, 1977,671-679.[25] R.L. Leitheiser and J.C. Wetherbe, “The successfulinformation 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 InformationSystem: Action and Organizational Performance”, TheAccounting Review, 50:4, 1975, 735-746.[27] P.L. McLeod, “An assessment of the experimentalliterature on electronic support of group work: Results of ameta-analysis”, Human Computer Interaction, 7, 1992, 257-280.[28] Miles, M.B. and A.M. Huberman, Qualitative dataanalysis: An expanded sourcebook, Sage Publications,Thousand Oaks, CA, 1994.[29] L. Mohan, W.K. Holstein and R.B. Adams, R.B. “EIS: Itcan work in the public sector”, MIS Quarterly, December, 1990,434-448.[30] Y. Park, “Strategic uses of data warehouses: Anorganization's suitability for data warehousing”, Journal of DataWarehousing, 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-usercomputing”, Interfaces, 17:3, May-June, 1987, 25-33.[33] S. Rivard and S.L. Huff, “Factors of success for end-usercomputing”, Communications of the ACM, 31:5, 1988, 552-561.[34] J.R. Ross, “Data warehousing surges as retailers of allsizes fuel growth”, Stores, April, 72, 1997, 74-76.[35] T. Sakaguchi and M.N. Frolick, “A Review of the DataWarehousing Literature”, Journal of Data Warehousing, 2(1),1997, 34-54.

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[36] G.L. Sanders and J.R. Courtney, “A field study oforganizational factors influencing DSS success”, MIS Quarterly,March, 1985, 77-93.[37] C.D. Schewe, “The management of information systemusers: An exploratory behavioral analysis”, Academy ofManagement 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 ofdecision support systems”, MIS Quarterly, 4(4), 1980, 7-32.[41] A.W. Trice and M.E. Treacy, “Utilization as a DependentVariable in MIS Research”, Data Base, Fall/Winter, 1988, 33-41.[42] Two Crows. Introduction to data mining and knowledgediscovery. 2nd edition, Two Crows Corporation, 1998.[43] M.L. Tushman and D.A. Nadler, “Information processingas an integrating concept in organization design”, Academy ofManagement Review, 3, 1978, 613-624.[44] J. van den Hoven, “Data warehousing: Bringing it alltogether”, Information Systems Management, Spring, 1998.[45] Vandenbosch, B., ESS impact viewed from a learningperspective, Unpublished doctoral dissertation, The Universityof Western Ontario, 1993.[46] E. Vanlommel and B. DeBrabander, “The organization ofelectronic data processing (EDP) activities and computer use”,Journal of Business, 48:3, July 1975, 391-410.[47] Weick, K., The social psychology of organizing, Addison-Wesley, 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 andOrganizations, Wiley Co., New York, 1973.[51] P. Todd and I. Benbasat. “Process tracing methods indecision support systems research: Exploring the black box”,MIS Quarterly, 11(4): 1987, 493-512.[52] Merton, R.K., M. Fiske & P.L. Kendall, The focusedinterview, Free Press, New York, 1956.[53] Yin, R.K., Case study research: Design and methods,Sage Publications, Beverly Hills, 1988.

Appendix A: Data Warehouse User InterviewGuide (Revised)

Research Question #1: What does the data-driven decisionsupport process look like in a data warehousing environment?

Interview Questions1. 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?

Rw

Ru

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esearch Question #2: What factors influence variation in dataarehouse usage?

Interview Questions5. 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?

esearch Question #3: How does variation in data warehousesage influence the organization?

Interview Questions8. 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 decision-

making?

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The Business Intelligence Value Chain: Data-Driven ...€¦ · The Business Intelligence Value Chain: Data-Driven Decision Support in a Data Warehouse Environment: An Exploratory