Business Intelligence & Organisational Change
Research Proposal on Business Intelligence Diffusion in Organizations
Business intelligence (BI) systems comprise one of the largest and fastest growing areas of it expenditure in companies today. Companies? experiences with deriving benefits from these systems are still mixed. One of the differences between BI and other types of information systems is that how BI systems are used, not just whether they are used, can have a major impact on the benefits derived. Therefore the characteristics of BI users and the organizations within which they work can have a disproportionate impact on the benefits derived from investments in BI. Organizational competence is one way to evaluate the characteristics of individuals and organizations relative to their ability to achieve organizational goals. This dissertation examines the characteristics of BI users and their organizations within the framework of organizational competences. Models representing those competences at both the individual and organizational level are presented. A combined competency model and resulting emerging competences are proposed that, if adopted, can improve the likelihood of organizations realizing benefits from their BI investments.
Table of Contents
2 ABSTRACT
4 INTRODUCTION
Background
Research Objectives
Structure of the thesis
7 LITERATURE REVIEW
Developing an Assessment Tool
12 PROPOSED REFERENCES
Research Proposal on Business Intelligence Diffusion in Organizations
INTRODUCTION
Background
Companies spend billions of dollars annually on implementation and maintenance of information systems (IS). Estimates are that IS expenses constitute the largest portion of organizational expenditures (Carr 2004; Nash 2008). Given the size of these expenditures one would hope that companies were gaining benefits commensurate with the money being spent. Unfortunately recent figures estimated that nearly half of IS projects did not result in the anticipated benefits (Nash 2008). It is therefore important to understand what can help companies gain benefits from the investments in these systems.
Early information systems were used to automate otherwise manual processes, such as maintaining accounting ledgers or processing financial transactions. The benefits from these types of systems resulted from increases in efficiency or effectiveness of the underlying processes resulting in measurable cost savings or revenue increases (Zuboff 1988). BI systems provide benefits by supporting analytical processes that provide recommendations for changing products or processes in ways that improve their competitiveness or operational efficiency (Scheps 2008). These benefits are therefore dependent on the ability of the individuals using BI to do so effectively and the organizational ability to support the implementation of the resulting recommendations. Another way to describe organizational abilities to perform tasks or functions effectively is competence (Javidan 1998). This dissertation will develop a model to help understand how an organization can gain benefits via BI systems by understanding the competencies necessary for effective BI use and the relationship between those competencies and realizing BI benefits.
Research Objectives
The main objective of this research will be to understand the characteristics of an organization that allow it to be successful in deriving benefits from business intelligence systems. This research poses the proposition that beneficial use of BI requires certain competences to be present in an organization. There are business books that discuss organizational factors for successful BI. Williams and Williams (2007) identified seven factors defining “business intelligence readiness” as being “Strategic Alignment, Continuous Process Improvement Culture, Culture Around the Use of Information and Analytics, BI Portfolio Management, Decision Process Engineering Culture, BI & DW Technical Readiness, and Business/it Partnership” (Williams et al. 2007, pg 202). They suggested that only when an organization has this BI readiness would they be able to realize the benefits of BI.
Davenport and Harris in their book “Competing on Analytics,” looked at the impact of BI systems on organizations. They identified something they called an analytical capability, which was their conception of the ability of an organization to use BI and as consisting of organizational acumen and technology factors (Davenport et al. 2007). They suggest that for an organization to benefit from an analytical capability that both organizational and technology factors must exist in that organization. They provide a high level view of the organizational factors, but they haven’t defined the detailed competencies that an organization must possess in order to exploit these capabilities. Research in information systems is generally focused on either developing theories that explain related phenomena or on verifying existing theories (Hevner et al. 2004).
This research will be directed towards developing a theoretical model of BI success. Competence has been shown to be an important element in the success of information systems, and appears to have the potential to be of particular value in explaining the attainment of benefits from BI. However, a framework that explains competence for successful BI does not exist. This research will therefore seek to develop a framework to help explain the organizational competencies that would support the attainment of business value from BI.
Structure of the thesis
This dissertation will be divided into seven chapters. The first chapter will introduce some key concepts that provide the motivation for this research and introduces the basic objective of this work. The second chapter will contain a review of the literature that informs the research and provides a foundation for the remainder of the work. Chapter three will provide the theoretical underpinnings and research methodology taken in studying the key research questions and provides a summary of the key questions to be addressed. Chapter four will provide an exposition of the evidence collected during the research and presents the initial models that relate the key concepts that emerge from the evidence. In chapter five a framework for evaluating the fundamental research question will be presented and evaluated. Chapter six will provide a synthesis of all of the findings in this research, and chapter seven summarizes the entire document and outlines limitations and potential future directions.
Research Questions
The specific questions that describe this proposition based on the model with which we are starting would therefore be:
Question one: What are the characteristics of individual know-how and skill for BI?
Question two: What are the characteristics of purposeful and heedful interactions for BI?
Question three: What is the relationship between individual know-how and skills and BI success?
Question four: What is the relationship between purposeful heedful interactions and BI success? Given the researcher-s ontological and epistemological perspective, a retroductive methodology will be used as realized though a case study method.
LITERATURE REVIEW
Early information systems were focused on automating routine computational tasks. Computers were viewed as tools to help perform routine tasks done faster than was previously possible. However, as computers grew more capable, and in particular data storage became more accessible and flexible, the use of information technology expanded from purely an automation perspective to something that has been called “informating” (Zuboff 1988). Zuboff suggested that technology can “informate, empowering ordinary working people with overall knowledge, making them capable of critical and collaborative judgments” (Zuboff 1988, pg 243). The term that is used for systems of this type is Decision Support Systems (DSS) (Barki et al. 1985). Early DSS were typically single function (Arnott et al. 2008). They supported a particular decision making process for a particular part of an organization. The underlying data was specific to the application and the user interfaces were often customized for a particular purpose. This changed with the emergence of data warehousing (Inmon 1992). As organizations began to build data warehouses they often started by trying to create a large, centralized, analytic repository for all of their historical data. These early data warehouses were often built without clear objectives as to how this data was to be used. Organizations began to recognize that even when cleansed and centralized, a large scale data warehouse would not provide organizational benefits without clearly defined business needs for the data (Inmon 1992; Kimball et al. 1998). The term that was coined in 1989 for the class of applications designed to take advantage of these data warehouses was Business Intelligence (BI) (Rajesh 2008).
The emergence of BI as a concept caused organizations to begin to see these types of systems as part of a larger framework of analytical capabilities enabled by technology. Several definitions of BI were given in the preceding chapter. BI has also been defined as “an active, model-based, and prospective approach to discover and explain hidden, decision-relevant aspects in large amounts of business data to better inform business decision processes” (Liebowitz 2006). There are probably as many different definitions of business intelligence as there are authors, but consistent among the definitions is the use of an analytic data store coupled with analysis software and reporting/visualization tools to solve business problems (Golfarelli et al. 2004; Negash et al. 2003; Rajesh 2008). The problems that BI has been applied to vary and include most aspects of a company.s operations and marketing (Davenport et al. 2006). An important part of any BI implementation is how the system will be used by people to achieve its goals (Jourdan et al. 2008; Rajesh 2008). Put another way, “BI converts data into useful information and, through human analysis, into knowledge” [emphasis added] (Negash et al. 2003, pg. 3191). While the human analysis component of this definition is important, very little research has looked at it in any level of detail. Combining the various BI definitions we will use the following definition for BI in this research: Business Intelligence consists of the use of analytical technologies and data stores by people in an organization to analyze business problems and produce related business recommendations to improve business performance. The key technologies that make up the technological components of BI are data warehousing, and related extraction transformation and load (ETL) tools; analysis tools, including statistical analysis and online analytical processing (OLAP) tools; and reporting/visualization tools. Based on this definition it becomes clear that the people/organizational component of BI is as important as the technological.
Since BI is a relatively new topic, research specifically referring to BI is still sparse. However, the volume of DSS research is much larger. BI is considered a subset of DSS research by some (Arnott et al. 2005; Arnott et al. 2008), while others have suggested that DSS is a component of BI (Negash et al. 2003; Rajesh 2008). However you look at it, BI related research is still one of the least studied areas of DSS. BI related research accounted for only 7% of all of the DSS articles published between 1990 and 2004 (Arnott et al. 2008). We will examine the key research relative to DSS success and the major BI research that does exist. Research in DSS and BI can be categorized into four main areas; effectiveness, tools and technologies, algorithms and data mining, and organizational impacts. The next sections will examine research in each of these areas.
RESEARCH METHODOLOGY
This research will use a retroductive methodology. Retroduction “refers to the process of building hypothetical models of structures and mechanisms that are assumed to produce empirical phenomena” (Blaikie 2007, pg 83). Bhaskar (1998) suggests that retroduction is the appropriate method to use when exploring social phenomena from a realist perspective. Retroduction starts with a model of the constructs to be studied. These models are then tested to determine if they represent the reality that the researcher is attempting to uncover. These tests are then used to modify the model based on understanding developed through testing. Finally a new model is postulated based on the revisions suggested by the evidence discovered during testing (Blaikie 2007).
This research will seek to understand the specific competencies that would allow an organization to gain business value from business intelligence systems. The question being studied is concerned with “how” and “why” questions regarding the relationship between competencies and BI. These types of questions can most effectively be addressed, especially in the early phases of a stream of research, via a case study approach (Benbasat et al. 1987; Yin 2003). A case study approach also allows one to examine a real world phenomenon in detail in a real life setting. Such an examination can lead to insights that could subsequently be used to develop generalized theories about the phenomenon in question.
Data Analysis Approach
In order to ensure validity of the data collected in this case study it will be necessary to follow a structured approach to data analysis. While the data collection will be done from the perspective of existing theory, it will still be important to structure analysis that ensures a deep understanding of underlying meanings. This will be accomplished by using the techniques of open and axial coding (Strauss et al. 1998). Open coding is the process by which “concepts are identified and their properties and dimensions are discovered in data” (Strauss et al. 1998, pg 102). Through this process underlying themes embodied in the interviews should emerge. These themes will be evaluated within the theoretical constructs of individual know-how and skills and purposeful heedful interactions. Once themes have emerged from the data, axial coding will be performed to align emergent themes within the overall theoretical model. Axial coding relates the categories identified through open coding “to subcategories along the lines of their properties and dimensions” (Strauss et al. 1998, pg 124). From these coding steps should emerge a model of competencies that will begin to explain successful BI.
As the coding of data will be performed new concepts may emerge that are not necessarily represented in the original theoretical model. These concepts will be explored by referring back to the literature to examine whether they have been studied. This iterative review of the research data and associated literature should provide insights that can be used to develop a theoretical model (Strauss et al. 1998).
Developing an Assessment Tool
An additional goal of this research will be to develop a preliminary assessment tool that can be used to determine an organization-s level of competence for BI. The tool will be created using the individual elements of competence that emerge from the coding of the interview data. These elements should represent the various items that will be identified as antecedents to BI competence in our evaluation. Respondents will be asked to evaluate each of the elements in terms of the status of their organization on each item and the importance of each item. This process will allow the evaluation of the potential for this tool to be used by an organization to determine their current likelihood of attaining BI benefits and provide direction for steps to take to increase their BI success. Evaluating how respondents use this tool can provide insight into how it can be used and its potential impact.
PROPOSED REFERENCES
Ahituv, N. “A Systematic Approach Toward Assessing the Value of an Information System,” MIS Quarterly (4:4) 1980, pp 61-75.
Ahn, H., and Kim, K.-j. “Using genetic algorithms to optimize nearest neighbors for data mining,” Annals of Operations Research (163:1) 2008, pp 5-18.
Alavi, M., and Joachimsthaler, E.A. “REVISITING DSS IMPLEMENTATION RESEARCH – a METAANALYSIS of the LITERATURE and SUGGESTIONS for RESEARCHERS,” MIS Quarterly (16:1), Mar 1992, pp 95-116.
Alavi, M., and Leidner, D.E. “Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations ans Research Issues,” MIS Quarterly (25:1), March 2001, pp 107-136.
Allport, G.W. “The psychology of participation,” Psychological Review (52:3) 1945, pp 117-132. Alter, S.L. “A Study of Computer Aided Decision Making in Organizations,” MIT, Cambridge, MA, 1975.
Alvesson, M. “ORGANIZATIONS as RHETORIC: KNOWLEDGE-INTENSIVE FIRMS and the STRUGGLE WITH AMBIGUITY,” Journal of Management Studies (30:6) 1993, pp 997-1015.
Amidon, S.R. “WRITING the LEARNING ORGANIZATION,” Business Communication Quarterly (68:4) 2005, pp 406-428.
Anderson, B. “G explained,” Medical Hypotheses (45:6) 1995, pp 602-604. Andreu, R., and Ciborra, C. “Organisational learning and core capabilities development: the role of it,” the Journal of Strategic Information Systems (5:2) 1996, pp 111-127.
Apte, C., Bing, L., Pednault, E.P.D., and Smyth, P. “Business APPLICATIONS of DATA MINING,” in: Communications of the ACM, Association for Computing Machinery, 2002, p. 49.
Argyris, C. Strategy, Change and Defensive Routines Pitman Publishing Limited, Marshfield, MA, 1985. Argyris, C. “Teaching Smart People How to Learn,” Harvard Business Review Reflections (4:2) 1991, pp 4-15.
Argyris, C. On Organizational Learning Blackwell Business, Cambridge, MA, 1992. Argyris, C. “Learning and Teaching: A theory of Action Perspective,” Journal of Management Education (21:1) 1997, pp 9-26.
Argyris, C., and Schon, D. Organizational Learning: A Theory of Action Perspective Addison-Wesley Publishing Company, Reading, MA, 1978.
Argyris, C., and Schon, D. Organizational Learning II: Theory, Method, and Practice Addison-Wesley Publishing Company, Reading, MA, 1996.
Arnold, V., Clark, N., Collier, P.A., Leech, S.A., and Sutton, S.G. “THE DIFFERENTIAL USE and EFFECT of KNOWLEDGE-BASED SYSTEM EXPLANATIONS in NOVICE and EXPERT JUDGMENT DECISIONS,” MIS Quarterly (30:1) 2006, pp 79-97.
Arnott, D., and Pervan, G. “A critical analysis of decision support systems research,” Journal of Information Technology (20:2) 2005, pp 67-87.
Arnott, D., and Pervan, G. “Eight key issues for the decision support systems discipline,” Decision Support Systems (44:3) 2008, pp 657-672.
Azvine, B., Cui, Z., and Nauck, D.D. “Towards real-time business intelligence,” BT Technology Journal • Vol 23 No 3 • July 2005 (23:3) 2005, pp 214-225.
Bajema, C.J. “A Note on the Interrelations among Intellectual Ability, Educational Attainment, and Occupational Achievement: A Follow-up Study of a Male Kalamazoo Public School Population,” Sociology of Education (41:3), Summer68 1968, pp 317-319.
Ballou, D.P., and Tayi, G.K. “Enhancing data quality in data warehouse environments,” Communications of the ACM (42:1), January 1999, pp 73-78.
Barki, H., and Huff, S.L. “Change, attitude to change, and decision
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