Critical success factors for business intelligence systems

The implementation of a business intelligence (BI) system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of critical success factors (CSFs) for management reference because the BI market has been driven mainly by the IT industry and vendors. This research seeks to bridge the gap that exists between academia and practitioners by investigating the CSFs influencing BI systems success. The study followed a two-stage qualitative approach. Firstly, the authors utilised the Delphi method to conduct three rounds of studies. The study develops a CSFs framework crucial for BI systems implementation. Next, the framework and the associated CSFs are delineated through a series of case studies. The empirical findings substantiate the construct and applicability of the framework. More significantly, the research further reveals that those organisations which address the CSFs from a business orientation approach will be more likely to achieve better results.

Empirical investigation of critical success factors for implementing business intelligence systems in multiple engineering asset management organisations

Engineering asset management organisations (EAMOs) are increasingly motivated to implement business intelligence (BI) systems in response to dispersed information environments and compliance requirements. However, the implementation of a business intelligence (BI) system is a complex undertaking requiring considerable resources. Yet, so far, there are few defined critical success factors (CSFs) to which management can refer. Drawing on the CSFs framework derived from a previous Delphi study, a multiple-case design was used to examine how these CSFs could be implemented by five EAMOs. The case studies substantiate the construct and applicability of the CSFs framework. These CSFs are: committed management support and sponsorship, a clear vision and well-established business case, business-centric championship and balanced team composition, a business-driven and iterative develop ment approach, user-oriented change management, a business-driven, scalable and flexible technical framework, and sustainable data quality and integrity. More significantly, the study further reveals that those organisations which address the CSFs from a business orientation approach will be more likely to achieve better results.

Business intelligence systems implementation : testing a critical success factors framework in multiple cases

This paper presents and discusses the critical success factors (CSFs) influencing the implementation of business intelligence (BI) systems. Based on a preliminary critical success factors (CSFs) framework, multiple case studies approach was applied to investigate the CSFs influencing BI systems implementation in seven large engineering enterprises. The empirical findings demonstrate a clear trend towards multidimensional challenges involved in such resourceful and complex undertaking. The CSFs exist in various dimensions composed of organisation, process, and technology perspectives. More significantly, the study reveals that a more fundamental issue concerning the business needs of BI systems may, in the end, impede BI systems success. That is, BI stakeholders are urged to apply a business-orientation approach in tackling implementation challenges and ensuring buy-in from business stakeholders.

Managing the implementation of business intelligence systems : a critical success factors framework

The implementation of a BI system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of CSFs for management reference. This article represents a first step of filling in the research gap. The authors utilized the Delphi method to conduct three rounds of studies with 15 BI system experts in the domain of engineering asset management organizations. The study develops a CSFs framework that consists of seven factors and associated contextual elements crucial for BI systems implementation. The CSFs are committed management support and sponsorship, business user-oriented change management, clear business vision and well-established case, business-driven methodology and project management, business-centric championship and balanced project team composition, strategic and extensible technical framework, and sustainable data quality and governance framework. This CSFs framework allows BI stakeholders to holistically understand the critical factors that influence implementation success of BI systems.

Extending the understanding of critical success factors for implementing business intelligence systems

Extant studies suggest implementing a business intelligence (BI) system is a costly, resource-intensive and complex undertaking. Literature draws attention to the critical success factors (CSFs) for implementation of BI systems. Leveraging case studies of seven large organizations and blending them with Yeoh and Koronios's (2010) BI CSFs framework, our empirical study gives evidence to support this notion of CSFs and provides better contextual understanding of the CSFs in BI implementation domain. Cross-case analysis suggests that organizational factors play the most crucial role in determining the success of a BI system implementation. Hence, BI stakeholders should prioritize on the organizational dimension ahead of other factors. Our findings allow BI stakeholders to holistically understand the CSFs and the associated contextual issues that impact on implementation of BI systems.

A decision-making model to choose business intelligence platforms for organizations

In recent time, technology applications in different fields, especially Business Intelligence (BI) have been developed rapidly and considered to be one of the most significant uses of information technology with special position reserved. The application of BI systems provides organizations with a sense of superiority in the competitive environment. Despite many advantages, the companies applying such systems may also encounter problems in decision-making process because of the highly diversified interactions within the systems. Hence, the choice of a suitable BI platform is important to take the great advantage of using information technology in all organizational fields. The current research aims at addressing the problems existed in the organizational decision-making process, proposing and implementing a suitable BI platform using Iranian companies as case study. The paper attempts to present a solitary model based on studying different methods in BI platform choice and applying the chosen BI platform for different decisionmaking processes. The results from evaluating the effectiveness of subsequently implementing the model for Iranian Industrial companies are discussed.

Making sense of business intelligence : proposing a socio-technical framework for improved decision making in not-for-profit organisations

The authors of this paper argue that human intuition alone cannot be relied upon for strategic decision making in today’s business environment and that quality data intelligence is an imperative. The proposed project described in this paper is research-in-progress, action design research (ADR), to implement an appropriate information systems (IS) enabling enhanced organisational decision making. ADR is a new research method that draws on action research and design research in an organisational setting. In phase 1 of the project, a sociotechnical ‘sense-making’ approach is used to gather and analyse information and decision needs in a not-for-profit (NFP) association, Connections ACT. In phase 2, requirements are designed and modelled to build a conceptual framework that guides NFPs in improving business performance and reporting capability. Phase 3 is the evaluative stage when the framework is reflected upon and refined, with intervention in the organisation’s processes as a promising outcome.

A comprehensive diagnostic framework for evaluating business intelligence and analytics effectiveness

Business intelligence and analytics (BIA) initiatives are costly, complex and experience high failure rates. Organizations require effective approaches to evaluate their BIA capabilities in order to develop strategies for their evolution. In this paper, we employ a design scienceparadigm to develop a comprehensive BIA effectiveness diagnostic (BIAED) framework that can be easily operationalized. We propose that a useful BIAED framework must assess the correct factors, should be deployed in the proper process context and acquire the appropriateinput from different constituencies within an organization. Drawing on the BIAED framework, we further develop an online diagnostic toolkit that includes a comprehensive survey instrument. We subsequently deploy the diagnostic mechanism within three large organizations in North America (involving over 1500 participants) and use the results toinform BIA strategy formulation. Feedback from participating organizations indicates that the BIA diagnostic toolkit provides insights that are essential inputs to strategy development. This work addresses a significant research gap in the area of BIA effectiveness assessment.

Using business intelligence to support the process of organizational sensemaking

Making sense of an organization overwhelmed with data becomes a problem for decision makers at all levels of business planning and operation. Although scholars have suggested several technological solutions such as business intelligence as being useful in helping busy executives to make decisions, we still know little about assisting business stakeholders in the process of understanding their organizational complexity before such decisions could even be formulated. In this paper, we investigate the opportunities in using BI technologies to make sense of a business environment. We analyze the views and opinions of developers, analysts, consultants, and users of business intelligence, who are experienced in using the technology beyond decision making to support organizational sensemaking. Our results highlight the need for creating and maintaining individual; and organizational identity and enacting this identity on the business and its environment.

Using business intelligence to support the process of organizational sensemaking.

Morteza’s thesis investigated the opportunities in using BI technologies to make sense of a business environment. The results of his research highlighted the need for creating and maintaining an identity for Business Intelligence at both individual and organizational level and enacting this identity on the business and its environment.

Toward a synergy between simulation and knowledge management for business intelligence

The adoption of simulation as a powerful enabling method for knowledge management is hampered by the relatively high cost of model construction and maintenance. A two-step procedure, based on a divide and conquer strategy, is proposed in this paper. First, a simulation program is partitioned based on a reinterpretation of the model-view-controller architecture. Individual parts are then connected, in terms of abstraction, to guard against possible changes that resulted from shifting user requirements. We explore the applicability of these design principles through a detailed discussion of an industry case study. The knowledge-based perspective guides the design of architecture to accommodate the need of emulation without compromising the integrity of the simulation program. The synergy between simulation and a knowledge management perspective, as shown in the case study, has the potential to achieve the objectives of rapid development of models, with low maintenance cost. This could, in turn, facilitate an extension of the use of simulation in the knowledge management domain.

A brief review of recent research trends on applications of computational and statistical techniques in financial and business intelligence

Artificial neural networks and statistical techniques like decision trees, discriminant analysis, logistic regression and survival analysis play a crucial role in Business Intelligence. These predictive analytical tools exploit patterns found in historical data to make predictions about future events. In this paper we have shown some recent developments of a few of these techniques in financial and business intelligence applications like fraud detection, bankruptcy prediction and credit rating scoring.

A SOA-driven business intelligence architecture

A business intelligence architecture comprises of different unique components to collect, transform, analyze and present the structured and unstructured raw data in simple formats to assist decision makers in making timely decisions. The introduction of service-oriented architecture (SOA) enables reusable services which are accessible over a network on demand. However, there is still a lack of academic literatures on the business intelligence architecture with service-oriented concept. Based upon various references on BI architectures from major vendors, a novel BI architecture that is built on service-oriented concept is presented and described in this paper. The proposed service-oriented architecture enables enterprises to deploy a more agile, flexible, cheaper, reusable, reliable and responsive BI applications in supporting decision making process.

A maturity model of enterprise business intelligence

The implementation of an enterprise-level business intelligence initiative is a large-scale and complex undertaking, involving significant expenditure and multiple stakeholders over a lengthy period. It is therefore imperative to have systematic guidelines for business intelligence stakeholders in referring business intelligence maturity levels. Draw upon the prudent concepts of the Capability Maturity Model, this research proposes a multi-dimensional maturity model with distinct maturity levels for managing enterprise business intelligence initiatives. The maturity model, named Enterprise Business Intelligence Maturiy (EBIM), consists of five core maturity levels and four key dimensions, namely information quality, master data management, warehousing architecture, and analytics. It can be used to assist enterprises in benchmarking their business intelligence maturity level and identifying the critical areas to attain higher level of maturity.