thesis business intelligence

35 years of research on business intelligence process: a synthesis of a fragmented literature

Management Research Review

ISSN : 2040-8269

Article publication date: 7 December 2020

Issue publication date: 7 May 2021

The business intelligence (BI) research witnessed a proliferation of contributions during the past three decades, yet the knowledge about the interdependencies between the BI process and organizational context is scant. This has resulted in a proliferation of fragmented literature duplicating identical endeavors. Although such pluralism expands the understanding of the idiosyncrasies of BI conceptualizations, attributes and characteristics, it cannot cumulate existing contributions to better advance the BI body of knowledge. In response, this study aims to provide an integrative framework that integrates the interrelationships across the BI process and its organizational context and outlines the covered research areas and the underexplored ones.

Design/methodology/approach

This paper reviews 120 articles spanning the course of 35 years of research on BI process, antecedents and outcomes published in top tier ABS ranked journals.

Building on a process framework, this review identifies major patterns and contradictions across eight dimensions, namely, environmental antecedents; organizational antecedents; managerial and individual antecedents; BI process; strategic outcomes; firm performance outcomes; decision-making; and organizational intelligence. Finally, the review pinpoints to gaps in linkages across the BI process, its antecedents and outcomes for future researchers to build upon.

Practical implications

This review carries some implications for practitioners and particularly the role they ought to play should they seek actionable intelligence as an outcome of the BI process. Across the studies this review examined, managerial reluctance to open their intelligence practices to close examination was omnipresent. Although their apathy is understandable, due to their frustration regarding the lack of measurability of intelligence constructs, managers manifestly share a significant amount of responsibility in turning out explorative and descriptive studies partly due to their defensive managerial participation. Interestingly, managers would rather keep an ineffective BI unit confidential than open it for assessment in fear of competition or bad publicity. Therefore, this review highlights the value open participation of managers in longitudinal studies could bring to the BI research and by extent the new open intelligence culture across their organizations where knowledge is overt, intelligence is participative, not selective and where double loop learning alongside scholars is continuous. Their commitment to open participation and longitudinal studies will help generate new research that better integrates the BI process within its context and fosters new measures for intelligence performance.

Originality/value

This study provides an integrative framework that integrates the interrelationships across the BI process and its organizational context and outlines the covered research areas and the underexplored ones. By so doing, the developed framework sets the ground for scholars to further develop insights within each dimension and across their interrelationships.

• Business intelligence
• Literature review
• Antecedents

Talaoui, Y. and Kohtamäki, M. (2021), "35 years of research on business intelligence process: a synthesis of a fragmented literature", Management Research Review , Vol. 44 No. 5, pp. 677-717. https://doi.org/10.1108/MRR-07-2020-0386

Emerald Publishing Limited

Copyright © 2020, Yassine Talaoui and Marko Kohtamaki.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

The business intelligence (BI) process research has grown exponentially during the past three decades into a fragmented state drawing from a diverse set of studies with widely different contributions ( Talaoui and Kohtamäki, 2020 ). Although this pluralism is necessary for the BI process research to generate momentum from insightful findings, it can yield a disjointed theoretical progress if it lacks proper literature reviews that uncover what is already known and set a direction for the way ahead (Hart, 1998 ; Rowe, 2014). Unfortunately, extant reviews of the BI process research still focus on the scheme that BI follows to provide actionable intelligence for organizations to act upon (Jourdan et al. , 2008 ) rather than the context where this process occurs and guide organizations (Bingham and Eisenhardt, 2011 ; Loock and Hinnen, 2015 ). For instance, the stock of previous reviews on BI research focused on its attributes and conceptualization (Ekbia et al. , 2015 ), its methodologies and research strategies (Jourdan et al. , 2008 ), its application to operations models (Roden et al. , 2017 ), its contribution to business value (Trieu, 2017 ) or decision-making (Mora et al. , 2005 ), its dimensions and taxonomy (Holsapple et al. , 2014 ), its usage (Watson and Wixom, 2007 ), its field development (Arnott and Pervan , 2005, 2014 ; Toit, 2015 ), its attitudes (Rouach and Santi, 2001 ), its characteristics and applications (Chen et al. , 2012 ; Eom and Kim, 2006 ; Moro et al. , 2015 ), its technologies and challenges (Shim et al. , 2002 ; Sivarajah et al. , 2017 ) and its trends (Watson, 2009 ).

To this date, no literature review has examined the BI process and its interrelationships with the organizational context. To address this gap, our paper synthesizes the body of knowledge of the BI process to discern patterns of the interrelated relationships of its characteristics, and its context, i.e. antecedents and outcomes (Hutzschenreuter and Kleindienst, 2006 ; Rajagopalan et al. , 1993 ; Van De Ven, 1992 ). We follow other scholars’ conceptualization of BI process as an integrative sequence that encompasses the collection, transformation and usage (Chen et al. , 2012 ; Davenport and Paul Barth, 2012 ; Trieu, 2017 ) that occurs in an organizational context, exerts an influence upon it and is shaped by its antecedents (Bingham and Eisenhardt, 2011 ; Loock and Hinnen, 2015 ).

To capture the BI process within its context, we follow the process framework of Hutzschenreuter and Kleindienst (2006) , Rajagopalan et al. (1993) and Van De Ven (1992) for it allows to position the BI process within its organizational context and explore their interrelated linkages. In this vein, we purposefully follow Levy and Ellis (2006) and Webster and Watson (2002) ’s “effective methodology” of conducting systematic reviews in cross-disciplinary research such as the BI process body of knowledge and adheres to its processual scheme to select 120 articles published in top tier ABS ranked journals that we synthesize and integrate drawing from the process view framework that emphasizes the role of organizational context (Hutzschenreuter and Kleindienst, 2006 ; Rajagopalan et al. , 1993 ; Fischer et al. , 2016 ; Vaara and Lamberg, 2014 ). By so doing, we seek to synthesize the contributions of prior studies on the BI process and its organizational context and pinpoint to gaps in linkages across the BI process, its antecedents and outcomes for future researchers to build upon. The paper begins with a detailed explanation of our systematic method, then presents our synthetic review and concludes with research gaps for further studies.

Methodology

It addresses the peculiar and cross-disciplinary nature of the IS research in general and the BI body of knowledge in particular.

It follows a process protocol of literature reviews that fits our process perspective of integrating the BI body of knowledge.

Following Levy and Ellis (2006) , a high-quality input yields a high-quality output if it adheres to comprehensiveness, quality and relevance inclusion criteria. To ensure comprehensiveness, we go beyond the IT contributions on BI and extend our search scope beyond one database to capture all fruitful work regardless of its inherent discipline (Levy and Ellis, 2006 ). We, therefore, use four scientific databases, reputable among scholars of management, marketing and information management fields, namely, ABI/Inform, EBSCO academic search elite, EBSCO business premier, Emerald journals (Levy and Ellis, 2006 ; Webster and Watson, 2002 ). We conducted a pilot search of keywords in the aforementioned databases with two keywords, namely, BI and competitive intelligence. The intention of this trial was to gather all keywords related to both concepts. In total, 26 keywords were deemed appropriate for this review. Boolean operators (“AND” and “OR”) and the asterisk “*” wildcard were used to concatenate the keywords set to generate multiple query strings that returned 11,745 hits across the four databases from 1985 through 2020 as Table 1 depicts. We selected 1990 as a starting year of our search as it represents the inception of BI (Chen et al. , 2012 ; Davenport et al. , 2001 ). A first scrutiny of the hits sought the elimination of duplicates shrinking the set of papers to 780 including conference papers, which we excluded because their research rigor is inferior to top journals and are not subjected to a rigorous peer review process (Culnan, 1978 ; Levy and Ellis, 2006 ; Webster and Watson, 2002 ). Besides, the high quality input criterion Levy and Ellis (2006) and Webster and Watson (2002) impose limits our sample to articles published in high quality peer reviewed journals of a reputable ranking because they are likely to contain the major contributions we ought to deal with to ensure rigor and leading theoretical discussions on BI (Levy and Ellis, 2006 ; Vogel, 2012 ; Webster and Watson, 2002 ). Therefore, we chose the ABS journal ranking because it offers an extensive cross-disciplinary list that is corroborated by a documented hybrid and iterative ranking process based upon peer reviews, peers’ consensus and citations (Mingers and Willcocks, 2017 ; Morris et al. , 2009 ), which, in turn, offers us a credible guide that we can gauge papers against with confidence (Levy and Ellis, 2006 ; Morris et al. , 2009 ; Webster and Watson, 2002 ). This high-quality criterion reduced our sample to 290 articles whose abstracts we read and evaluated against our relevance criterion that, based on the research gap and motivation, deems only articles addressing BI process, antecedents or outcomes relevant to the review at hand. This step reduced the sample to 113 articles that contain one or several linkages to the BI process, antecedents or outcomes. To verify the comprehensiveness of our sample and prevent the exclusion of any older and relevant contribution, we conducted a backward search that consists of reviewing the reference lists in our final set of papers to identify any work that our time frame criterion might have excluded and/or that our databases search might not have revealed (Bandara et al. , 2015 ; Levy and Ellis, 2006 ; Müller and Jensen, 2017 ; Thennakoon et al. , 2018 ; Webster and Watson, 2002 ). Our backward search analyzed each title in the reference lists of the 113 articles and identified 7 seminal works published prior to 1990 such as El Sawy (1985) and Ghoshal and Kim (1986) , which, in turn, extended our final sample to 120 articles. We gauged the census of this review complete when no new concepts or relationships were identified in the literature set (Levy and Ellis, 2006 ; Webster and Watson, 2002 ).

A synthetic framework of the business intelligence process

According to Levy and Ellis (2006) and Webster and Watson (2002) , a good literature review offers a complete census of its synthesis and follows an analytical framework to structure the body of knowledge it deals with. As a corollary, we followed the process linkage exploring framework of Hutzschenreuter and Kleindienst (2006) and Rajagopalan et al. (1993) because it emphasizes the role of organizational context (Vaara and Lamberg, 2014 ) and the mediating mechanisms that reveal the causality between antecedents and outcomes (Fischer et al. , 2016 ). We coded all articles using a two-digit key (01–120) that we plotted in Table 2 to provide summaries of the studies. Our thorough review of the 120 articles revealed shared patterns along which three streams were discernable, namely, antecedents, BI process and outcomes. In addition, our analysis revealed that each article focused on different interrelationships across the organizational context of the BI process. For the sake of comprehensiveness and in-depth analysis, we marked each article with a linkage code composed of a letter designating the contextual domain [(1) antecedents; (2) BI process; and (3) outcome] and a number that refers to the factor responsible of the relationship between contextual domains:

Antecedents . Similar to biological organisms, firms’ actions are often constrained by their external environments (Brownlie, 1994 ). This implies that organizations should constantly monitor their respective environments to ensure the detection of plausible alterations susceptible of jeopardizing their competitive advantage. Their BI processes are, hence, influenced by environmental factors (A-I) such as uncertainty ( Hubert and Daft, 1987 ), complexity ( Child, 1972 ), rate of change ( Daft et al. , 1988 ), importance ( Aaker, 1983 ; Pfeffer and Salancik, 1978 ), culture (Leidner et al. , 1999 ) and competitive pressures ( Zhu and Kraemer, 2005 ). Further influence on the BI process can be attributed to the organizational context (A-II). This may include organizational factors such as size (Yasai-Ardekani and Nystrom, 1996 ), institutional isomorphism ( DiMaggio and Powell, 1983 ), core technologies (Thompson, 1967), structural flux (Maltz and Kohli, 1996 ), market orientation ( Narver and Slater, 1990 ) and IT sophistication ( Armstrong and Sambamurthy, 1999 ). Finally, managerial and individual attitudes (A-III) affects the BI process through managerial heterogeneity (Cho, 2006 ), experience ( Thomas et al. , 1991 ), managerial attitude (Qiu, 2008 ; Pryor et al. , 2019 ), absorptive capacity (Elbashir et al. , 2011 ) and decision roles ( Mintzberg, 1973 ).

BI process. While alterations in the aforementioned antecedents are believed to impact the BI process, characteristics of this latter are also crucial for understanding the different patterns of the BI process literature. At the outset, the intelligence collection phase (B-I) is pictured as the first link between a firm and its environment, whereby it can comprehend the happenings and remain vigilant to changes ( Hambrick, 1981 ; Lönnqvist and Pirttimäki, 2006 ; Turban et al. , 2010 ). Traditionally, the collection phase was fed through open and human sources. However, with the advent of the internet, it faced the challenge of information overload (Chen et al. , 2002 ). The abundance of data created a lack of executives’ attention, and called for a more tailored intelligence transformation phase (B-II) to support managerial action ( Fabbe-Costes et al. , 2014 ; Christen et al. , 2009 ). In response, the BI analysts used computerized decision support systems to prepare the requested intelligence for executives (Leidner and Elam, 1993 ). Such decision aids stimulated, eventually, the design of the executive information system with the purpose of retrieving the information related to internal operations and the business environment ( Turban and Schaeffer, 1987 ; Turban et al. , 2010 ). A further scrutiny of the transformation phase (B-II) reveals that both structured and unstructured data are extracted from operational and external sources, then prepared and loaded into the data warehouse, for a later clustering into Data Marts. This process is usually performed through the extract-transform-load (ETL) application. On the one hand, the data warehouse usually deploys a relational database management system (RDBMS) to store data and rapidly execute queries across a wide range of data. On the other hand, the data warehouse is corroborated by an online analytic processing (OLAP) server in charge of filtering, and drawing thorough analysis (slicing and dicing, drill down…) of the data, which, in turn, is communicated to the user interface (dashboards, spreadsheets…) that yields the way to the Usage phase (B-III) (Chaudhuri et al. , 2011 ; Sen and Sinha, 2005 ; Singh et al. , 2002 ). This last phase of the BI process offers the required capability to conduct predictive analysis, streamline intelligence content and ensure an effective practice of the BI process and its alignment across organizational culture, analytical capabilities and the human capital propensity for BI (Holsapple et al. , 2014 ; Viaene and Bunder, 2011 ; Chaudhuri et al. , 2011 ; Sen and Sinha, 2005 ; Singh et al. , 2002 ).

Outcomes . The BI process was found related to certain outcomes (C): of a strategic order (C-I) such as strategic management process (Hofer, 1978 ) and managerial representations of competitive advantage ( Porac and Thomas, 1990 ); at a firm performance level (C-II) such as share of wallet ( Zeithaml, 1988 ), customer perceived value (Hughes et al. , 2013 ), product development (Lynn, 1998) and superior sales growth (Slater and Narver, 2000 ); related to decision-making (C-III) including decision-making speed (Leidner and Elam, 1995 ), problem identification speed (Leidner and Elam, 1995 ) and extent of analysis ( Miller and Friesen, 1980 ); and under the umbrella of organizational intelligence (C-IV) encompassing perceived intelligence quality (Popovič et al. , 2012 ), perceived information availability (Leidner and Elam, 1995 ), intelligence use (Maltz and Kohli, 1996 ), receiver’s trust ( Moorman et al. , 1992 ) and insight generation speed (Heinrichs and Lim, 2003 ).

After plotting the linkages of each study in Table 2 , we sought to allow for a visual display of the linkages explored, and the ones overlooked, therefore we juxtaposed the elements of the BI process (BI-II-III), antecedents (AI-II-III) and outcomes (CI-II-III) in a review matrix, exhibited in Figure 1 , where rows represent the independent variables, and columns represent the dependent variables, and each coded study (01–120) is allocated into its appropriate linkage cell. Finally, we synthesized and depicted the aforementioned interrelationships in the form of an integrative framework we present in Figure 2 . The framework displays three clusters of antecedents (A), namely, environmental factors (A-I), organizational factors (A-II) and managerial and individual attitudes (A-III); three characteristics of the BI process (B), namely, collection (B-I), transformation (B-II), usage (B-III); and four sets of outcomes (C), namely, strategic (C-I), firm performance (C-II), decision-making (C-III) and organizational intelligence (C-IV). Research within the framework falls into four categories, namely, the first one explores the influence of the antecedents on the BI process (A-I-II-III – B-I-II-III); the second explores the BI phases separately, describing the state of affairs and prescribing optimal processes (B-I-II-III); the third set of studies examines the linkages between the BI process and its ensuing outcomes (B-I-II-III – C-I-II-III-IV); and the fourth set of studies examines the moderating role of antecedents on the relationship between the BI process and outcomes (A-I-II-III – B-I-II-III – C-I-II-III-IV).

Literature synthesis

Stream 1: the influence of antecedents on the bi process (links a-i-ii-iii – b-i-ii-iii).

The environmental influence on the BI process motivated multiple studies that shaped the first cluster of this stream, although the nature of this linkage is still equivocal. This is due to inconsistent views of environmental heterogeneity and uncertainty, and the partial accounts of the BI process. These treatments, rooted in management, bifurcate into two strands. First, a constellation of studies that focus on the frequency and scope of BI collection (Boyd and Fulk, 1996 ; Daft et al. , 1988 ; Ebrahimi, 2000 ; Elenkov, 1997 ; Maltz and Kohli, 1996 ; May et al. , 2000 ; Sawyerr, 1993 ). Their findings are at best exploratory and piecemeal as they adopt a “one rule fits all” approach to different environmental layers (e.g. political, customer, direct and remote) let alone country-level contexts (e.g. developed vs developing). By so doing, they overlook the peculiarities of developing economies where other informal pressures and singularities (cultural, institutional and cognitive) moderate the relationship between the environment and BI collection. The second thread of studies examine executives’ goal orientations (Pryor et al. , 2019 ), strategic priorities (Opait et al. , 2016 ) quality of information source (El Sawy, 1985 ; Jones and McLeod, 1986 ; Robinson and Simmons, 2017 ), experience and educational background (Cho, 2006 ), entrepreneurial attitude (Qiu, 2008 ), intuitive judgments (Constantiou et al. , 2019 ) and boundary spanners’ intelligence effort (Le Bon and Merunka, 2006 ; Mariadoss et al. , 2014 ), customer orientation (Hughes et al. , 2013 ). Unfortunately, these studies overlook to consider the collection activity as a formal unit within the organization, and explore the informal BI collection and source selection of boundary spanners and executives despite previous evidence of their bounded rationality (Cyert and March, 1963 ). Besides, we still know little about the upper management’s cognitive and managerial characteristics, which implicitly determine their BI collection, not to mention the need to verify, which leadership approach serves best this activity. Credit is given to Elbashir et al. (2011) , being the only scholars of this stream who examined the influence of the absorptive capacity of managers on BI assimilation. Similar studies must follow this line to explore the influence of absorptive capacity on the entirety of the BI process. To this date, all we know, in this context, is the positive influence of the absorptive capacity of managers on organizations’ BI assimilation (Elbashir et al. , 2011 ). Further, studies examining boundary spanners collecting and gathering of intelligence like their engagement to their desire for upward mobility and recognition. Therefore, boundary spanners’ involvement in BI collection is a variable of managerial stimulation, and hence, more studies are needed to examine the moderating effect of management appraisal on the linkage between BI collection and boundary spanners’ scope and frequency of BI collection.

The significant focus of management scholars on the environment and the managerial and individual factors as the primary antecedents of the BI process came at the expense of overlooking the organizational factors susceptible of influencing the BI process. Conversely, studies, rooted in marketing and decision support, shed light on the ability of the organizational context to alter the BI process, particularly the collection phase and its linkage to decentralized organizational culture (Babbar and Rai, 1993), size and core technologies (Yasai-Ardekani and Nystrom, 1996), inter-functional distance and structural flux (Maltz and Kohli, 1996 ), organizational market orientation (Qiu, 2008 ), resource scarcity (Christen et al. , 2009 ), institutional isomorphism (Ramakrishnan et al. , 2012 ), analytical culture (Holsapple et al. , 2014 ; Popovič et al. , 2012 ); IT infrastructure (Elbashir et al. , 2011 ), organizational culture ( Leidner and Elam, 1995 , 1999 ) and organizational beliefs (Reinmoeller and Ansari, 2016 ). Although harmonious in its uniformity, this line of research was limited to the BI collection phase except for two studies that extended their focus to BI support and its linkage to organizational orientation and culture (Lin and Kunnathur, 2019 ) and organizational tensions (Kowalczyk and Buxmann, 2015 ).

Stream 2: the business intelligence process (links B-I-II-III)

The review of the literature illustrates a shared conceptual meaning, across marketing and management scholars, regarding the nature of BI collection as an activity that seeks to proactively monitor a dynamic environment and that ends once data has been collected (Babbar and Rai, 1993 ; Bernhardt, 1994 ; Calof and Wright, 2008 ; Slater and Narver, 2000 ). Unfortunately, the literature within this stream was considerably explorative of the BI collection activities and practices ( Taylor, 1992 ; Vedder et al. , 1999 ; Dishman and Calof, 2008 ; Wright et al. , 2009 ). While some marketing scholars emphasized the use of Bayes’ theorem to determine when more collection becomes cost (Michaeli and Simon, 2008 ), other explored information sources companies use (Fleisher et al. , 2008 ; Lasserre, 1993 ; Peyrot et al. , 1996 ) or developed indices to evaluate the adaptability of firm capabilities to BI collection of boundary spanners (Hallin et al. , 2017 ) or to collect BI from disaggregated data (Kumar et al. , 2020 ). While a stream of scholars examined trust in BI collection quality (Robinson and Simmons, 2017 ), others investigated the type and source of the collected intelligence (Peyrot et al. , 1996 ) or the capabilities to decode each type of intelligence be it soft (Lasserre, 1993 ) or web-based (Fleisher, 2008 ; Pawar and Sharda, 1997 ). On the other hand, an apparent discussion within this stream involves the collection approach, i.e. the comprehensive vs the project-based model. A priori, the comprehensive mode seems a better fit to broad strategic decisions, while the ad-hoc approach is more project-oriented. The narrowed focus of the project-based approach is believed to generate more accurate intelligence compared to the holistic model (Prescott and Smith, 1987 ). Nonetheless, this paradox shifts the debate to the culture and the core business of organizations. For some scholars, organizations might choose to participate in the environment rather than passively observing it (Brownlie, 1994 ). By so doing, the underpinning motive of such an activity swings from BI collection to sense giving (Gioia and Chittipeddi, 1991 ), from informing to influencing, from a mere passive to proactive BI collection (Brownlie, 1994 ). Other scholars suggest that ambidexterity arises as a reasonable option whereby the firm can develop two cultures, namely, one for sensing peripheral patterns; the other is core business-oriented (Brown, 2004 ; O’Reilley and Tushman, 2002 ; Ghosal and Westney, 1991 ; Gilad et al. , 1993 ).

Conversely, literature with scaffolding in information systems and decision support, fueled by the desire of bridging the gap between the business user and BI transformation and usage, criticized the firms’ focus on collection over analysis despite the challenge of information overload and gave significant attention to testing in-house acquisition techniques of BI collection to curb the exorbitant price of third-party sources by proposing Limited Information NBD/Dirichlet (LIND) models to infer key competitive measures based on site-centric data (Zheng et al. , 2012 ) or two level conditional random fields (CRF) models to extract comparative relation features from entities and words (Xu et al. , 2011 ) or event detection (NEED) applications that perform events detection based on properties extracted from news stories (Wei and Lee, 2004 ) or proposed 80/20 rule-based models for reduction of cycle time (Kohavi et al. , 2002 ; Liu and Wang, 2008 ) or suggested data slicing and dicing technologies, which index and analyze documents collected from websites matching users’ interest (Chen et al. , 2002 ) or grant rapid access displays of data ( Walters et al. , 2003 ). One commonality within this research stream is the evaluation of the proposed tool against the commercial engines (Chen et al. , 2002 ; Zheng et al. , 2012 ; Xu et al. , 2011 ).

The coming of the WEB 2.0, digitization, the internet of things and Big Data further challenged the BI process by technical issues in regard to (a) the time consuming process of transforming and storing structured and unstructured data into the data warehouse, (b) the lack of techniques capable of, simultaneously, alleviating data heterogeneity and integrating slice, dice, roll-up and drill-down dimensions for data evaluation, (c) the multidimensional view of data through OLAP, which needs continuous performance improvement; (d) the rising volume of data, which challenges the capacity of the RDBMSs to query and store data, (e) the pressure on ETL to filter, cluster and integrate current operational data, for real time decision-making support and (d) detect hidden patterns in terabytes of data (Chaudhuri et al. , 2011 ). This ushered most empirical studies in this stream to shift their attention to what Chen et al. (2012) refer to as BI 3.0 or mobile BI and accordingly update BI technologies and develop new applications that can detect patterns in terabytes of data, diminish further information overload, and merge structured with unstructured data (Chen et al. , 2012 ; Srivastava and Cooley, 2003 ; Chung et al. , 2005 ; Chau et al. , 2007 ; Cheng et al. , 2009 ; Lin et al. , 2009 ) or decipher frameworks for evaluation BI process based on users’ feedback ( Brichni et al. , 2017 ) or modeling its best practice approach for less challenges ( Vidgen et al. , 2017 ; Wang et al. , 2018a ; 2018b ). However, this might not be enough to ensure an effective usage of BI as this latter hinges on the alignment across organizational culture, analytical capabilities and the human capital propensity for BI (Holsapple et al. , 2014 ; Viaene and Bunder, 2011 ). No empirical studies have yet to investigate this triadic relationship and its moderating variables for better BI usage.

Stream 3: the influence of the business intelligence process on outcomes (links B-I-II-III – C-I-II-III-IV)

Drawing from marketing research, scholars explored the influence of BI collection and managerial representation of competitive advantage (Qiu, 2008 ), managerial belief in formulating and implementing strategies (Vedder et al. , 1999 ) improvement of marketing strategies (Fleisher et al. , 2008 ). Other scholars suggested that BI collection translates to share of wallet and profit margin (Hughes et al. , 2013 ) and sales performance (Mariadoss et al. , 2014 ), product innovation and competitive pricing strategies (Trim and Lee, 2008 ), price optimization, expanding product lines and service improvements (Peyrot et al. , 1996 ), superior sales growth, customer satisfaction (Slater and Narver, 2000 ), innovation (Tanev and Bailetti, 2008 ) and profitability and revenues increase (Wright et al. , 2009 ). Although these studies might pinpoint to the relationship between BI collection and strategic outcomes, the question of whether or not this step of the BI process contributes to strategy formulation or implementation remains ambiguous.

Furthermore, the available evidence, drawing from management, demonstrates two stocks of research: one that indicates a clear relation between BI support and productivity enhancement, and information distribution cost savings (Belcher and Watson, 1993 ), price competition (Abramson et al. , 2005 ), firm performance (Akter et al. , 2016 ; Gupta and George, 2016 ), business value (Côrte-Real et al. , 2020 ; Seddon et al. , 2016 ; Wang et al. , 2018a ; 2018b ), innovation (Ghasemaghaei and Calic, 2020 ); another that suggests BI support adds value to the organizational intelligence in at least two interrelated ways, namely, workforce learning (Cheung and Li, 2012 ), information access quality (Popovič et al. , 2012 ), data security (Gordon and Loeb, 2001 ; McCrohan, 1998 ; Sheng et al. , 2005 ; Vedder et al. , 1999 ) and intelligence use (Maltz and Kohli, 1996 ) and organizational knowledge management (Côrte-Real et al. , 2017 ; Shollo and Galliers, 2015 ).

The research strand, rooted in information systems, was limited to providing benchmarks of their BI support technologies to which they ascribe a linkage to the decision-making process. Scholars presented their prototypes and evaluated their success for mergers and acquisitions (Lau et al. , 2012 ), and banking and financial decisions (Moro et al. , 2015 ). Besides, information systems scholars had a penchant for solving tactical issues because of their straightforward evaluation or to scholars’ approach to BI, as a set of separate technologies rather than a holistic decisional paradigm. Therefore, their contributions integrate BI technologies such as data warehouse and data mining into BI support and address its ability to improve firm performance indicators. Studies examined and demonstrated the positive impact of BI support on crafting personalized customer strategies (Li et al. , 2008 ), decision-making (Aversa et al. , 2018 ), strengthen innovation capability (Mikalef et al. , 2019 ), business value (Sharma et al. , 2014 ), identify sales ordering patterns (Cheung and Li, 2012 ), business model insight (Heinrichs and Lim, 2003 ). Research, herein, seems obsessed with solving tactical issues because of their straightforward evaluation or to scholars’ approach to BI as a set of separate technologies rather than a holistic decisional paradigm.

Studies rooted in decision support empirically examined the linkage between BI support and the speed of problem identification, decision-making speed and the extent of analysis (Leidner et al. , 1999 ; Leidner and Elam, 1993 ; Leidner and Elam, 1995 ; Belcher and Watson, 1993 ; Arnott et al. , 2017 ). Still little is known about how BI collection influences decision-making. While it is true that explorative studies reveal the utility of BI collection for organizational decision-making (Ghosal and Westney, 1991 ; Vedder et al. , 1999 ), no empirical evidence has yet examined this belief. The outcome of BI collection on decision-making might be, as well negative than positive, at least for competitor analysis blind spots in the case of capacity expansion, new business entry and acquisition (Zajac and Bazerman, 1991 ). One might keep wonder about the contexts and the extent to which BI can bring value to the decision-making if scholars’ attention does not shift from explorative, inductive studies to more cross functional longitudinal ones to further delve into the relation between BI and the decision-making process.

Stream 4: the moderating effects of antecedents on the relationship between the business intelligence process and outcomes (links A-I-II-III – B-I-II-III–C-I-II-III-IV)

This stream of research is threefold, namely, research at the individual level, organizational level and environment level. At the individual level, scholars, with scaffolding in marketing research, investigated the moderating role of boundary spanners adaptive skills on BI collection sales performance outcomes (Hughes et al. , 2013 ; Mariadoss et al. , 2014 ; Ahearne et al. , 2013 ), the moderating role of the relationship between intelligence officers and strategists on boosting product innovation and generating competitive pricing strategies (Trim and Lee, 2008 ), the moderating effect of the relationship between district managers centrality and district BI quality diversity on salespersons’ performance (Ahearne et al. , 2013 ). Unfortunately, studies rooted in management and information systems or decision support overlooked the moderating role of antecedents at the individual level on the relationship between BI process and outcomes.

At the organizational level, management scholars explored the moderating role of the alignment between business strategy and IT on the relationship between BI usage and business value (Côrte-Real et al. , 2019 ; Urbinati et al. , 2019 ), the moderating role of the relationship between the alignment of business strategy and BI analytics on BI usage and firm performance (Akter et al. , 2016 ), the moderating role of deep organizational structure on the relationship between BI usage and strategy outcomes (Audzeyeva and Hudson, 2015 ), the moderating role of organizational learning and ambidextrous organizational culture on the relationship between BI usage and business value (Bordeleau et al. , 2020 ) and BI usage and organizational learning (Fink et al. , 2016 ) and the mediating role of dynamic capabilities on the relationship of BI usage and firm performance (Wamba et al. , 2017 ). In like fashion, marketing scholars investigated the moderating effects of the relationships between organizational antecedents such as structural flux and perceived intelligence quality on BI usage (Maltz and Kohli, 1996 ), the curvilinear relationship between organizational size and BI use, as well as between marketing departments size and BI usage (Peyrot et al. , 2002 ). On the other hand, decision support scholars shed light on the moderating role of decision-making culture on the relation between the BI content quality and the BI usage (Popovič et al. , 2012 ), the moderating role of the relationship between organizational readiness and design factors on the relationship between BI usage and business value (Popovič et al. , 2012 ) and the moderating role of the information system BI infrastructure investment on the relationship between BI usage and value targets (Grover et al. , 2018 ).

At the environmental level, marketing scholars showcased the moderating role of the relationship between perceived competitiveness of the environment and the perceived value of BI quality on BI usage and organizational outcomes (Maltz and Kohli, 1996 ; Peyrot et al. , 2002 ). On the other hand, one study, rooted in information systems, explored the moderating role of the environment dynamism on the influence of the BI usage on value creation (Chen et al. , 2015 ).

Future research

35 years of BI process research seemed fragmented and scattered around similar areas, with scant initiatives to weave strands of lookalike contributions into one unifying paradigm. Research spawned a considerable number of articles partly prescriptive, partly explorative, revealing discrepancies between theory and practice across the BI process, antecedents and outcomes. Figure 3 displays the covered and underexplored areas in each of the aforementioned streams. Antecedents exploring studies focused on the supply side of the market to formulate viable strategies for an existing industry. These contributions unanimously adopted an outside in perspective, examining the external environmental influence on the frequency and mode of BI collection. They adopted the same structuralist approach to different business environments and neglected the influence of cultural factors and institutional pressures on the BI process. Another limitation of this stream is the exclusiveness of collection activity to executives, rather than the organization as a whole, following a top-down approach in an apparent discontinuity from the literature on bounded rationality that grant executives limited capacity to fathom the dynamism of the environment.

The significant focus on the environment as the primary antecedent of BI collection marginalized discussions on organizational factors susceptible of influencing the BI process. For instance, the ramifications of one single event on the BI use of multinational corporations in different settings. In this vein, managerial heterogeneity seems a potential frontier for research through which scholars shall compare heterogeneous teams to homogeneous groups of executives’ vis-a-vis their uncertainty perception and use of the BI process. Additionally, researchers still need to investigate, which structure represents an environment ripe for effective BI use: organic or mechanistic structure. Similarly, the causation link between strategic orientation and BI process is still vague, despite some studies suggest a one-way association from strategic orientation to BI collection. Moreover, contrary to the trend line of recommendation positing the BI process at the outset of the decision-making or the strategic management process, the authors of the article at hand personally encountered situations, in monopolistic economies, where the BI process was regarded more as legitimacy tools that solidify an already taken decisional or strategic choice. As a corollary, it might be crucial to incorporate the singularity of the decision-making process in developing countries, when hypothesizing coming empirical studies. Another trend line across studies examining BI use is the focus on the receiver’s trust in regard to the intelligence sender. Nonetheless, this latter’s willingness to share intelligence was treated as a given, while it is far from being the case. Particularly, in developing countries where information is shared among individuals pertaining to the same interest groups. It becomes, hence, evident to account for the sender’s trust and influence on the BI dissemination and use, in future research.

In addition, cognitive factors of managers and boundary spanners were rarely on the scholars’ agenda. After all, the environmental uncertainty is a matter of interpretation, which, in turn, is framed by intrinsic factors rooted in the person’s background. More studies, in this respect, should incorporate elements such as age, gender and personality traits. Moreover, the rationale behind decision-makers’ BI collection behavior still appears ambiguous, for there seems to be no evidence regarding the value it adds to their mental models. Another overlooked matter by scholars, caught in an everlasting development of new ways of codifying structured and unstructured data, is the ability of the BI process to acquire and communicate tacit knowledge. Another gap worth mentioning is the scarcity of studies comparing BI practices of multinational corporations in the western world to emerging countries, in a world where anything might happen any second, where new technologies disrupt the status quo of businesses, economies and political regimes. The Covid-19 epidemic, political upheavals or data privacy issues present an opportunity for researchers to examine the linkage between the BI process and strategic agility let alone employees’ and organizations’ privacy and readiness for disruption.

Finally, a myriad of research methods was adopted by scholars, to delve into issues related to the BI process phases ranging from bibliometric studies, surveys and case studies. Some were conceptual papers, whereas others field tested their hypotheses or settled for laboratory experiments. Except for qualitative exploration examining linkage between BI transformation to decision-making success, benchmarking data mining or data warehousing applications against commercial products marked most BI transformation studies, let alone the quantitative exploratory and conceptual articles representing a common trend across studies tackling BI collection. The absence of comparative studies urges researchers to invest time and money probing differences across industries, not in an exploratory superficial manner, but more as a longitudinal thorough analysis depicting whether or not the industry type is a contributing factor to the BI process. Longitudinal studies were, surprisingly, absent, notwithstanding their presence in multiple scholars’ future directions. Another advantage longitudinal studies shall have is related to the evaluation of prototypes and technologies in an accurate manner, encompassing the residual value of such applications on the organizational learning. Longitudinal studies might also enable scholars to tap into cognitive changes prior and after BI collection and usage and track front line managers intelligence use as they assume high level positions. With that said, studies shall alter to a more dynamic view of the environment capable of capturing all the various interactions among its constantly shifting elements.

Nowadays, confidential strategies and tactics are swiftly replicated; the sustainability of the competitive advantage is no longer a result of a secret recipe. Managers shall recognize that room for intuition is shrinking as the need for a rational predictability is rising. Therefore, it seems wiser and beneficial for managers to tear down their walls, and engage in double loop learning with scholars, should they want a better real time decision-making and strategic agility. This review carries some implications for practitioners and particularly the role they ought to play should they seek actionable intelligence as an outcome of the BI process. Across the studies this review examined, managerial reluctance to open their intelligence practices to close examination was omnipresent. Although their apathy is understandable, due to their frustration regarding the lack of measurability of intelligence constructs, managers manifestly share a significant amount of responsibility in turning out explorative and descriptive studies partly due to their defensive managerial participation. Interestingly, managers would rather keep an ineffective BI unit confidential than open it for assessment in fear of competition or bad publicity. Therefore, this review highlights the value open participation of managers in longitudinal studies could bring to the BI research and by extent the new open intelligence culture across their organizations where knowledge is overt, intelligence is participative, not selective and where double loop learning alongside scholars is continuous. Their commitment to open participation and longitudinal studies will help generate new research that better integrates the BI process within its context and fosters new measures for intelligence performance.

Although far from completeness, this systematic review strived to synthesize the BI process body of knowledge via an integrative process framework that pinpoints to areas of redundancies and research gaps where scholars’ attention should be directed. It is hoped that this article will encourage researchers to change perspective and adopt a more comprehensive view of the BI process aimed at contributing to its organizational context and focus its attention on the interrelationships across the BI process, antecedents and outcomes. Drawing from Levy and Ellis (2006) and Webster and Watson (2002) , we sought comprehensiveness from four databases and quality from the ABS ranking list. Therefore, this paper excludes conference papers and book chapters. A caveat regarding the 26 keywords of this study is worth mentioning, as there might surely be some articles that the query strings failed to retrieve; let alone in-press- publications, not yet available when the database search took place. Notwithstanding, a backward search of references allowed the verification of this review’s comprehensiveness, gauged near completion when no new concepts were identified in the literature set (Webster and Watson, 2002 ). However, the material upon which this scrutiny is based epitomizes an open invitation for other researchers, to compare and test whether or not the results herein stand up to close examination. After all, this is the ultimate way to expand and enrich the body of knowledge probing BI process research.

Linkage-exploring review matrix

BI process: an integrative framework

Synthesis of the covered and remaining areas of the literature

Systematic selection process of the articles

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Corresponding author

About the authors.

Yassine Talaoui is a researcher at the School of Management at the University of Vasa, where he teaches business models and strategic management theories. His research interests focus on delineating relationships between materiality, digitization and management and organization studies. He is the recipient of the 2018 SAP Interest Group Division Pushing The Boundary Award at the Academy of Management.

Marko Kohtamäki (PhD) is a Professor of Strategy at the University of Vaasa, and a visiting professor at the University of South-Eastern Norway, USN Business School and Luleå University of Technology. Kohtamäki takes special interest in strategic practices, strategic agility and business intelligence. Kohtamäki has published in distinguished international journals such as Strategic Management Journal, International Journal of Operations and Production Management, Industrial Marketing Management, Long Range Planning, Strategic Entrepreneurship Journal, International Journal of Production Economics, Technovation, Journal of Business Research , amongst others.

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Finished Theses

Dept. VII: Information Systems I

This is a list of finished theses written at the Chair of Information Systems I

Analysis-oriented data management for software test management in cross-company automotive development projects - a case study at the Dr. Ing. h. c. F. Porsche AG Master Thesis 2022

Operational Design Domain for Autonomous Vehicles – an information needs analysis based on an analytics-based evaluation of weather data Master Thesis 2022

Data provision for artificial intelligence in small and medium-sized enterprises – results of a quali-tative study Bachelor Thesis 2022

Applications of an automatic detection of overlapping and covered objects – conceptual design and prototypical implementation based on a case study in the care sector Master Thesis 2022

Possible applications and capabilities of semantic data models in the early stages of the product development process using the example of the Robert Bosch GmbH Bachelor Thesis 2022

Development of a role model for designing IoT-ecosystems in a small and medium-sized enterprise environment Master Thesis 2022

Development of a role concept for the application of Robotic Process Automation in the insurance industry Master Thesis 2021

Maturity Levels for Machine Learning Competencies – An Exploration in small and medium Enterprises Master Thesis 2021

Information Requirements of Hydrogen Value Networks – Exploring Industrial Use Cases for Model Design Master Thesis 2021

Capability Mapping in the context of Enterprise Architecture Management – an exploration of the status quo in practice Bachelor Thesis 2021

Deep Learning-based classification of test images in manufacturing environments – development and evaluation of a solution concept Bachelor Thesis 2021

Digitalization of payment processes in trade fair industry - Effects of digitalization of payment methods on trade fair specific business processes and IT systems Master Thesis 2021

Quality Management in Additive Manufacturing - Requirements Elicitation for Information Systems to Ensure the Quality of Additively Manufactured Parts Bachelor Thesis 2021

Classifiers in direct marketing of banks - A case study at the Volksbank in der Ortenau eG Bachelor Thesis 2021

Information requirements analysis for self-service business intelligence & analytics systems – results of a single case study at global support STIHL Master Thesis 2021

Applying no-code artificial intelligence for the implementation of process improvements for medium-sized online retail enterprises Master Thesis 2021

Geometrical Deep Learning Applications in Sheet Metal Component Analysis - Development of a Criteria Catalogue on the case of the Optimate GmbH Master Thesis 2021

Business Potential of Machine Learning in the Assessment of Warranty and Goodwill Costs - Conceptual design and further development of departmental planning and forecasting processes based at the example of an automotive manufacturer Master Thesis 2021

Using artificial intelligence to analyse the photovoltaic potential of cities – a prototype-based exploration Master Thesis 2021

Analytical szenarios in a smart charging context – a qualtiative study with a prototypical implementation Master Thesis 2021

Information requirements analysis for analyzes in the context of the development of mechatronic systems – an indiviual case study at TRUMPF GmbH + Co.KG Master Thesis 2021

Conception of a model governance framework for advanced and predictive analytics – results of a qualitative study Master Thesis 2021

Robotic Process Automation for the automation of data quality management – a case study at the Lidl Stiftung & Co. KG Bachelor Thesis 2021

Development of a concept to identify maverick buying using a machine learning based approach – Results of a case study at the Robert Bosch GmbH Master Thesis 2020

Analytical applications in the context of interorganisational data-sharing in the domain of industrial manufacturing Master Thesis 2020

Classifiers in direct marketing of banks – a case study at the Volksbank in the Ortenau eG Bachelor Thesis 2020

Application of Machine Learning to detect Data Quality issues in the domain of Management Accounting Master Thesis 2020

Development of a governance concept for handling and analyzing poly-structured data in the banking industry Master Thesis 2020

Conception of a meta data management for integrated data lake/data warehouse systems Master Thesis 2020

Support of analysis-oriented business units by augmented analytics – a qualitative study Master Thesis 2020

Exploration of Requirements for a Reorganization of the Global Master Data Managementin Manufacturing and Sales at the Festo AG & Co. KG Master Thesis 2019

Data Virtualization in Business Intelligence Architectures Bachelor Thesis 2019

Business Potential and limitations of the application of natural language processing for the analysis of customer feedback – results from a case study at the Daimler AG Bachelor Thesis 2019

Deep Learning-oriented data architecture for visual inspection – a holistic solution for manufacturing environments Master Thesis 2019

 The business potential of supply chain spanning real-time track & trace solutions -  a case study at the TRUMPF GmbH & Co. KG Master Thesis 2019

Application of Natural Language processing and Text Mining methods for the analysis of customer feedback data - a case study at Daimler AG Bachelor Project 2019

Prototypical implementation of a Deep Learning System to classify test images in a manufacturing environment Bachelor Project 2019

Development of Enterprise Architectures using Archimate and the Open Group Architecture Framework Bachelor Thesis, 2019

Business potential of classification methods for an early warning system along the product development process Master Thesis, 2018

Application of methods from the area of analytics in mid-sized manufacturing companies -prerequisites, requirements, business potential Bachelor Thesis, 2018

Business lntelligence in the Supply Chain – Requirement analysis in automotove industry i n the context of supply chain management Bachelor Thesis, 2018

Quality Management in Additive Manufacturing – Identification of Requirements for the Additive Manufacturing Process to Ensure Component Quality Bachelor Thesis, 2018

Development of a concept for Data Mining in the field of Tracking and Tracing - Insights from an exploration of the Daimler AG supply chain Master Thesis , 2018

Data Governance in the Context of Big Data – Approaches, Chances and Models Bachelor Thesis, 2018

Use Cases for Data Virtualization in Business Intelligence and Analytics Bachelor Thesis, 2018

Data Virtualization for Business Intelligence And Analytics  – Decision Criteria for the Use of Data Virtualization Bachelor Thesis, 2018

Agile Cloud Business and Analytics platform in the supplier management of the automotive industry Master Thesis , 2018

Business potential and realization options for the enrichment of customer data with external, polystructured data for marketing analyses  – results of a case study at the Daimler TSS GmbH Master Thesis , 2018

Consequences of applying Serverless Cloud Computing on Microservice Architectures and Software Quality - a prototpyical implementation and evaluation Master Thesis , 2018

Specification of end-user-focused Advanced Analytics solutions  – Development of a process model based on a case study at Data & Analytics Mercedes-Benz Cars Master Thesis , 2018

Advanced Analytics for defect prediction in advanced manufacturing processes Master Thesis , 2018

Expert systems – status, trends, challenges Master Thesis , 2018

Control of cyber-physical Production Systems  – Semantic Modelling of a concept to support coordiantion of Multi Agent Systems based on Distributed Ledger Technology Master Thesis , 2018

IT based feedback of field data to product development – a qualitative study Master Thesis , 2017

Conception of a Cognitive Dialogue System  – An Online Customer Support Service in the Automotive Industry based on IBM Watson Technology Master Thesis , 2017

Business potential of classification methods for an early warning system along the product development process Master Thesis , 2017

Introducing Enterprise Architecture Management in small and medium sized businesses – Design and Evaltution of guidelines Bachelor Thesis, 2017

Simulation in the context of Industrie 4.0  – Realisation of non-deterministric characteristics of cyberphysical systems in simulation models and tools Bachelor Thesis, 2017

Data quality in the fault elimination process  – An analysis at Mercedes-Benz Cars Master Thesis , 2017

Data Exchange along the product development process  – Potentials of AutomationML to control cyber-physical systems Master Thesis , 2017

Smart Products and Business Models – An Exploratory Maturity Level Analysis of Smart Products as a Foundation for Business Models within the Internet of Things Master Thesis , 2017

Optical Algorithms for Additive Manufacturing – Development of a Software Architecture to Support Quality Assurance of Additive Manufactured Components Bachelor Thesis, 2017

Data Mining in the context of Predictive Maintenance – Developing a concept based on an industrial use case Master Thesis, 2017

ldentification of Digital Value Drivers for Designing New Business Models – An Exploration of lnternet-of-Things-based Application Scenarios Master Thesis, 2017

Augmented Reality in the context of Smart Factory – Identification of Augmented Reality-based approaches for manufacturing enterprises Bachelor Thesis, 2017

Business Capabilities for digitalization and integration – An empiric inquiry concerning small and medium-sized companies of the industrial sector Master Thesis, 2017

Industrie 4.0 – Developing a competence-based approach to overcome challenges Master Thesis, 2017

Augmented Reality within Smart Factory Approaches Bachelor final project, 2017

Optical Algorithms for Quality Assurance in Additive Manufacturing Bachelor final project, 2017

Application of Advanced Analytics Methods for the All-time Prediction of Slow-moving Spare Parts - Development and Evaluation of a Model for the Automotive Industry Master Thesis, 2017

Agile analysis-oriented data management in big-data environments – A qualitative study on applications, business potential, and solution concepts Master Thesis, 2017

Department-spanning analyses in industry 4.0 scenarios for business model innovations in automotive manufacturing  – A case study at Volkswagen Master Thesis, 2017

The role of model-based analysis systems for for analysis-systems for applicant management. Development and test of an evaluation concept Bachelor Thesis, 2017

The role of expert systems in the context of Business Intelligence and Analytics Bachelor final project, 2017

Potential applications of blockchain technology for business data management Bachelor final project, 2017

Root cause analyses in additive manufacturing processes  – development of a prototype Bachelor final project, 2017

Application of methods from the area of analytics in mid-sized manufacturing companies  – prerequisites, requirements, business potential Bachelor final project, 2017

Data Virtualization in the context of the Internet of Things – A comparison of software tools Bachelor final project, 2017

Cloud-based Business Intelligence and Analytics solutions in the context of Industry 4.0: An exploration of potential applications in the manufacturing industry Master Thesis, 2017

Alternatives for Data Mining in der Cloud  – Development and test of a concept for the selection and evaluation Bachelor Thesis,2017

Design of a Small Factory Unit for a customized production in the context of Industrie 4.0 – an exploration Master Thesis, 2017

The Internet of things in various application domains  –   A comparing literature survey Diploma Thesis, 2016

Impact of sensors on data and information quality – An exploration Bachelor Thesis,2016

Conception of a planning and control system for the calculation of Kanban loops at the Robert Bosch GmbH Master Thesis, 2016

Implications of IoT Products on industrial enterprises – An empirical study Master Thesis, 2016

Design of an IT-based concept to support the flow of goods in the receiving department of medium-sized companies using Gaugler & Lutz OHG as an example Master Thesis, 2016

Portfolio-based selection of Industrie 4.0 projects in single piece production – A case study with Käfer Werkzeugbau GmbH Bachelor Thesis, 2016

Design of a concept for ETL-processes to analyze machine data – A case study with Gaugler & Lutz OHG Bachelor Thesis, 2016

Development of an IT-based concept for supporting the technical customer service of a machine tool manufacturer – Examplary shown at TRUMPF GmbH + Co. KG Master Thesis, 2016

Connected products: Exploration of potentials and challenges towards digital twins Bachelor Thesis, 2016

Investigation on requirements and effects of Customer Co-Creation to customize industrial products in the context of Industry 4.0 Bachelor Thesis, 2016

Data analysis systems for quality management in the automotive sector – Development of a concept using the example of Daimler AG Bachelor Thesis, 2016

An Exploration of the IT-Architecture in Additive Manufacturing Bachelor Thesis, 2016

Applications of neural networks for business analytics  – Results of a protypical validation Bachelor Thesis, 2016

Integration of smart sensors into a Cloud-based analysis environments  – Conclusions from a prototypical exploration Bachelor Thesis, 2016

Requirements for the application of a lambda architecture for the analysis of streaming data Bachelor Thesis, 2016

Hybrid Cloud-based analytics and reporting platforms for a benchmarking solution: A case study at the Horváth & Partners GmbH Bachelor Thesis, 2016

Cloud based analysis of smart sensor data for Internet of Things applications  – A prototypical evaluation Bachelor final project, 2016

Business Intelligence strategies in the context of Industry 4.0. Conclusions from a case study at the Robert Bosch GmbH Bachelor Thesis, 2016

Neural Networks for the identification of opportunities and challenges in business indicator data Bachelor final project, 2016

New business models for industry 4.0-approaches across enterprise borders – devel-opment of a concept for differentiation, analysis, and evaluation Diploma Thesis, 2016

The Internet of Things in several application areask – A comparing literature survey Diploma Thesis, 2016

The Internet of Things in different application domains – A comparative literature review Bachelor final project, 2016

Data Warehouse and Big Data platforms for an analysis-oriented data management – Development of a scenario- and criteria-based evaluation concept Master Thesis, 2016

Tools for Data Scientists / Data Analysts Diploma Thesis, 2016

Conception of an Enterprise Architecture Management Framework for IT  – And Management Consulting within the automotive sector Master Thesis, 2015

IT-supported value creation in the context of Industry 4.0 – Conclusions of a scenario analysis based on expert interviews Master Thesis, 2015

Sectoral and Structural Analysis of Additive Manufacturing Service Providers Bachelor Thesis, 2015

Exploration of possibilities to optimize business processes using web analytics on product-related websites in the B2C market Bachelor Thesis, 2015

Tool support for the analysis of project portfolios – concept development and prototypical implementation Bachelor Thesis, 2015

Visual Analytics – Approaches, Tools, business-oriented application scenarios Bachelor Thesis, 2015

Development of a business intelligence based concept for the supply of information to improve the decision support in the fault elimination process  – with respect to quality relevant needs for action Master Thesis, 2015

Concept development for documenting IT-Requirements and – Specifications in cooperation of automobile manufacturers - exemplary shown on a cooperation of Daimler AG Master Thesis, 2015

Development and Prototyping of an EV-Fleet Management Support Concept, illustrated at the example of the Frauenhofer IAO Master Thesis, 2015

Sales forecast with structured and unstructured data Bachelor final project, 2015

Development and evaluation of an IT-based concept for supporting order processing – An experimental analysis based on a simulation game Bachelor Thesis, 2015

Development of a concept to support EAM benchmarking Master Thesis, 2015

Development of a conception for the automated distribution of production orders in discrete parts manufacturing Master Thesis, 2015

Enterprise Architecture Management in SME – Relevant components and their realisation - an empirical study Bachelor Thesis, 2015

Status of ICT in the context of Industry 4.0 – An empirical study Bachelor Thesis (finished) Mayer, M. 2014 Establishing a requirements specification for an IT based system to support the auditor qualification and approval process - illustrated at the case of DEKRA Certification Group Bachelor final project, 2014

Collection, analysis, and distribution of smart meter data in Germany – A qualitative study Diploma Thesis, 2014

Conception and evaluation of an architecture for identity and rights government in fragmented supply chains  – represented using the exaple of motor industry Master Thesis, 2013

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MBA Dissertation Topics On Business Intelligence: 20 Best Ideas

Writing a dissertation on business intelligence is a major challenge for many MBA students. You need to choose a decent topic idea to make your research interesting and valuable. So, get started as soon as you get the assignment. The following guidelines and examples are designed to make this difficult task a bit easier for you.

Hints for Finding a Good Dissertation Topic for MBA Level

• Discuss your ideas with your professor and follow the guidance on generating, outlining, and finalizing your topic.
• Base your research in a real world of your study area, e.g. study business and management practices, investigate current issues, etc.
• Choose something that you feel comfortable and confident writing about, consider prompts that arouse your curiosity.
• Ensure that you’re knowledgeable about your subject, and it can be helpful for your future career.
• Avoid dissertation topics which are too complicated for you to research and write about; don’t aim to surprise your instructor.
• Consider an issue that you discussed in class, learn more about it, and come up with a perspective solution.
• Revise your textbook, class reading, and notes to think of something worth further exploration.

Top MBA Dissertation Prompts on Business Intelligence

• Creating a healthy business environment: data sharing issues.
• Time for changes: business intelligence innovations in the 21st century.
• Online data storage for enterprises: the effectiveness and ways for improvement.
• Meeting the company’s information requirements: useful strategies and possible complications.
• Using data discovery tools: the key advantages and disadvantages.
• Mobile business intelligence: the current state and development perspectives in different countries.
• Business data management: a comparison of different solutions.
• Using customer profiles: the core strategies for businesses regarding market share increase.
• The importance of market research for start-up companies.
• Applying high-cost analytic software: the main benefits.
• Ensuring the safety of data and information: security measures practiced by international companies.
• The ethical aspects of data sharing in the company’s environment: a case study of a particular organization.
• The issues related to large amounts of data accumulated throughout the decades.
• Supporting innovations in business intelligence: effective strategies.
• Improving the relationship between staff and clients: best practices of using various data.
• The most important functions of business intelligence.
• Optimization of key performance indicators: best practices in the developed countries.
• How much data in enough: the role of records amount and quality in business intelligence implementation.
• The vital features for a BI portal and a web portal in general: a comparison study.
• Problems that companies face with unstructured and semi-structured data.

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