Enterprise Business Intelligence Research Articles

Web Support System for Business Intelligence in Small and Medium Enterprises

Last decade belongs to business intelligence (BI) because it is one of the few concepts that have actually lived to the expectations. Not only the businesses have adopted it but also have reaped the fruits out of it. But the cost of BI solutions is very high and has restricted small and medium enterprises to use BI solutions. The human resource is also limited and resultantly expensive in this field. This research proposes a web support system for business intelligence which provides automated data mapping and loading from user application to BI framework and also validates it. The system also assists users in getting the outputs in terms of reports and dashboards. The implementation of the proposed framework demonstrates convenience of use and effective cost saving as it does not require any technical expertise. The beauty of this web support system is that all the steps are menu driven and any non-technical user can get hold of it easily.

Business Intelligence: Relationship between Capabilities, Success and Decision Environment

The concept of Business Intelligence (BI) was first proposed in the late 1980-s. Since then the field of BI has experienced the rapid extension and development. More than 50 billion dollars have already been invested in different BI tools and application software, such as new desktop data analysis tools, data warehousing technologies, data extraction middleware and many other tools and techniques into the market. But not all the BI initiatives are successful. This paper posits that a key antecedent of BI success is having the right BI Capabilities, and right BI Capabilities depend on the Decision Environment in which the BI is used. Research suggests that a lack of fit between an organization and its BI is one of the reasons for lack of success. The match between the Decision Environment and what a BI provides has been studied as an indicator of success. The theoretical framework is proposed in this paper to study relationship between BI Capabilities, BI Success and Decision Environment

Systematic Tracking of Key Performance Indicators for Enhanced Use of Enterprise Reports in the Soft Drinks Industry in Kenya

Computer based information systems that exist in organizations gather and store voluminous data that is a useful resource in supporting management decision making. Enterprise Reporting, which is the focus of this study, provides a means by which these organizations can unify and sift the vast amounts of electronic data available so as to glean insights that are handy in management decision making. This paper aims at finding a solution to the current underutilization of the existing volumes of electronic data through enterprise reporting. The study specifically assessed the status and the challenges of enterprise reporting, and determined appropriate Key Performance Indicators that can be tracked, and how they can be tracked to enhance the use of enterprise reports. Data was collected through the use of interviews, and thematic analysis method was used to analyze the responses obtained. Purposive sampling method was used to get respondents, drawn from business and Information Technology functions of the organization. The study was informed by systems theory, whereby enterprise reporting was regarded a component of a computer based information system of a typical organization. The study anticipated that utilization of electronic data through enterprise reporting can be improved through systematic tracking of Key Performance Indicators related to the factors that negatively influence the use of enterprise reports.Whereas this study was confined to specific company in the soft drinks industry, the findings can be of relevance to a wider group of related organizations. The study has generated an in-depth understanding of the issues related to the use of enterprise reports and anticipated to make a contribution to the existing knowledge on Enterprise Reporting and Business Intelligence in general in various organizations. The findings of the research will therefore help shape policies relating to the use of enterprise reports to facilitate better decision making and consequently better realization of organizational goals.

Studi Literatur tentang Faktor-Faktor yang Berpengaruh dalam Implementasi Enterprise System pada UKM

Small and Medium Enterprises (SMEs) are one of the pillars of Indonesian economy since they contribute to the national Gross Domestic Product in a large number. This study discusses some of the articles to determine the factors that influence the implementation of enterprise applications systems, business intelligence (BI) and cloud computing. SCM, ERP, CRM are applications that provide the benefits of efficiency and effectiveness to the organization that uses and implements them. SCM, ERP, CRM and Business Intelligence needs to be implemented to SMEs in order to improve their performance to compete efficiently and competitively. In addition, SMEs can try cloud computing services by implementing and integrating those applications, although obstacles may be considerable.

Effect of Virtualization on Enterprise Network, Server/Desktop Systems on Small and Mid-Size Businesses (SMB)

INTRODUCTION Enterprise network infrastructure has profoundly impacted information systems business world. As small, mid-size businesses, various devices and data move beyond the traditional security of the corporate landscape, cyber-attacks will continue to grow at an exponential proportion. In 2012, network security gurus experienced cyber dangers ranging from sophisticated advanced persistent threats, to firewire attacks, to lost or stolen laptop. Enterprise network's mobile endpoints are literally moving targets and until they are adequately secured against attacks, enterprise business intelligence, reputation or competitiveness are at risk The goal of this article included the following: 1. To identify the extent mid- size organizations have adopted or planned to adopt virtualization technology 2012. 2. Identify potential barriers that cause enterprise systems to postpone or decide not to adopt virtualization. 3. Identify among adopting firms, what virtualization products are most popular and which applications are most commonly virtualized? 4. Identify the core drivers that cause enterprise systems to be virtualized. LITERATURE REVIEW Berde et al. (2009) among others noted that virtualization is not a perfect solution to how organizations manage their resources but concluded that this technology provides tremendous capabilities on how enterprise systems manage and move operating systems into different hardware resources. Grid computing evolved as an innovative technology, and is distinguished from traditional distributed networks because of its large-scale resource sharing capabilities. The author further explained that grid computing allows large numbers of hardware components to act as a single device, thereby, pooling their capacity and re-allocating these components to different jobs. Ercan (2010) argued that in the next generation of Grids, applications will not necessarily be designed to run on certain piece of hardware or network, but will be written to consume certain types of resources, which could be provided anywhere on the network. He further summarized that to accomplish this, enterprise systems and technical gurus need more dynamic networks than are at the present time existence. However, noted that virtualization efforts the networking community are already moving the industry that direction. Fiedler and Gallenkamp (2008), their study reviled that virtualized infrastructure provides a layer of abstraction between computing storage, networking hardware, and the applications running on it. Their study further explained that the deployment of virtual infrastructure is nondisruptive to the system, because the user experiences are typically un-noticed or unchanged. The authors concludes by emphasizing that virtual infrastructure provide enterprise system management, the opportunity to manage pooled resources across the enterprise, thereby, allowing Information Technology (IT) managers to be more responsive to dynamic system needs to better leverage infrastructure investments. Early studies by Burry et al. (2004), Brandel (2004), Cannor (2005), found evidence that virtualization has been a part of the IT landscape for decades but today vendors are now conveying remuneration to industry-standard X86-based platforms which now encompass the preponderance of desktops, laptops and server shipments. They concluded by stating that a major benefit of virtualization is the ability of systems to run multiple operating systems on a single physical system while sharing the underlying hardware resources or partitioning. More recent studies have concluded that virtualization can apply to a range of system layers, including hardware-level virtualization, operating system- level virtualization, and high-level language virtual machines (Morana et al., 2011; Seyler et al., 2011; Tusa & Mikkilinemi, 2011) . …

The contingent effect of personal IT innovativeness and IT self-efficacy on innovative use of complex IT

While organisational investment in complex information technologies (IT) keeps growing, these technologies are often applied at a superficial level and fail to attain the promised benefits. To further extract the value potential of complex IT, this study investigates employee users' innovate with IT (IwIT), which is a post-acceptance behaviour that refers to individual users applying IT in novel ways to support their task performance. Drawing on the information system continuance (ISC) model, we propose a research framework with perceived usefulness (PU) and satisfaction (SAT) as the antecedents of IwIT. We further emphasise the contingent role of personal characteristics and include personal innovativeness with IT (PIIT) and IT self-efficacy (ITSE) as the moderators of the framework. We validate the model with data from users of two complex ITs: enterprise resource planning and business intelligence technologies. The results suggest that positioning personal factors as moderators significantly increases the explanatory power of the ISC model and offers a more comprehensive understanding about IwIT. Specifically, ITSE positively moderates the effect of PU and negatively moderates the effect of SAT on IwIT. The moderating role of PIIT, however, is subject to the specific type of IT of investigation.

State-of-the-Art Review and Critical Success Factors for Mobile Business Intelligence

Due to ubiquitous information requirements, market interest in mobile business intelligence (BI) has grown markedly. However, mobile BI market is a relatively new area that has been driven primarily by the IT industry. Yet, there is a lack of systematic study on the critical success factors for mobile BI. This research reviews the state-of-the-art of mobile BI, and explores the critical success factors based on a rigorous examination of the academic and practitioner literature. The study reveals that critical success factors of mobile BI generally fall into four key dimensions, namely security, mobile technology, system content and quality, and organisational support perspectives. The various research findings will be useful to organisations which are considering or undertaking mobile business intelligence initiatives.

Evaluation of the maturity level of BI initiatives in European Higher Education Institutions: initial report from the BI Task Force @EUNIS

Business intelligence (BI) encompasses a broad category of applications and technologies for gathering, storing, analyzing, sharing and providing access to data to help enterprise users make better business decisions. Nowadays, the value of BI is perceived as more than information dissemination or tactical responses to information requests from individual managers; it is highly linked to achieving organizational goals. Therefore, well-designed and deployed BI initiatives can play an important role in organizations enabling better management, leadership and decision-making, to achieve efficiency and financial benefit.

Integration of Business Intelligence and Enterprise Resource Planning within Organizations

Organizations have invested a considerable amount of resources in the implementation of Enterprise Resource Planning (ERP) and Business Intelligence (BI) systems. In today's competitive environment within the context of complex BI and ERP, these systems have become key strategic tools, which directly impact on the success of any project implementation. But little attention has been given for the integration of Business Intelligence and Enterprise Resource Planning (BIERP). A hand full of studies were carried out in the developed countries while developing countries received much less attention. Despite the effort has been devoted to explain the integration of these systems, the literature is still classified as fragmented and diversified. This paper attempts to review and evaluate articles published between 2000 and 2012 regarding the integration of BI and ERP.

Closing the Gap between Data Mining and Business Users of Business Intelligence Systems

Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations happened in the field of information systems and technologies, especially in the area of decision support systems. BI systems are widely used in organizations and their importance is recognized. These systems present themselves as essential parts of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasing in the BI field, as well as the acknowledgment of the relevance of its usage in enterprise BI systems. BI tools are friendly, iterative, and interactive, allowing business users an easy access. The user can manipulate directly data, having the ability to extract all the value contained into that business data. Problems noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools. The nonexistence of BI tools allowing business users the direct manipulation of DM models was identified as the problem. More of these issues, possible solutions and conclusions are presented in this article.

A framework for accessing an enterprise business intelligence maturity model (EBI2M): Delphi study approach

The implementation of enterprise-scale business intelligence (EBI) system is a complicated process involving considerable resources and numerous stakeholders over a lengthy time. To date, there has been only limited formal research work that explores the various key dimensions affecting EBI implementation efforts. This shortcoming arises from the fact that the business intelligence (BI) market is a relatively new phenomenon, with most of the pioneering work being driven in an informal, ad-hoc basis by various vendors in the IT industry. Therefore, the goal of this research is to seek to bridge the gap that exists between academia and practitioners. In particular, the research work will draw upon the maturity constructs underlying the prominent capability maturity model integration (CMMI) developed at Carnegie Mellon University. The driving motivation is to develop an EBI maturity model (EBI2M) that can serve as a useful guideline for enterprises which are planning, or undertaking large-scale EBI initiatives. In the first stage, the author utilises a Delphi study to conduct three rounds of enquiries with a panel of BI experts, and then refines the research into a preliminary EBI2M model. However, reliance on the Delphi study alone is not sufficient for the collection of data needed to rigorously address the research objective. Therefore, based on the research constructs and findings of Delphi study, multiple case studies will be carried out in the future. Key words: Business intelligence, maturity model.

A review of enterprise performance: Concepts and frameworks

In today's unpredictable business environment, enterprises face the challenge of expanding markets and meeting customer's requirements and expectations. Therefore, they need to consider their performance. Performance has always been a considerable influence on companies' activities, and there is need to evaluate performance. Thus, the means and ways of accurately measuring performance is an increasingly important field for research. The purposes of this paper are to: i) identify performance concept, performance indicators and performance measurement systems; ii) identify organization performance, organization performance frameworks, enterprise performance enablers such as enterprise resource planning (ERP), business intelligence (BI), and knowledge management (KM); iii) investigate enterprise performance indicators such as key performance indicators (KPIs) and ICIs and iv) present enterprise performance model. Key words: Performance, performance measurement, performance framework, performance indicators, enterprise performance model.

Business Intelligence Excellence: A Company’s Journey to Business Intelligence Maturity

This paper is a case study which describes a company’s Business Intelligence (BI) journey. Although Business Intelligence is seen as priority by many companies the level of benefits achieved varies significantly. Often this is dependent on the level of Business Intelligence maturity. This paper documents a company’s trials and tribulations with their BI implementations. The paper maps and discusses the company’s Business Intelligence best practices. KEYWORDS; Business Intelligence, Business Performance, Case Study INTRODUCTION Companies today have all come to realise the importance of providing accurate, relevant and timely information— information that allows their organisational personnel to engage in effective decision-making practices. Accenture interviewed 163 executives from large enterprises around the world to identify how companies were using Enterprise Resource Planning (ERP) systems to improve business performance and the specific practices that resulted in sustained value creation (Davenport et al, 2003). They found that the implementation of an enterprise-wide information system resulted in sustained value creation however; some companies realised far more comparable benefits than others. A more extensive follow up study in 2006 involving 450 executives from 370 companies identified the factors that drove value from their ERP system, as well how companies used these systems to enhance competitiveness and differentiation (Harris and Davenport, 2006). One of the key findings from this study was that improved decision making was the most desired benefit. Related to this finding was that the top performing companies aggressively used information and analytics to improve their decision making. These findings are supported by Gartner, a leading business analyst firm, who conducted a worldwide survey of 1,500 Chief Information Officers and identified Business Intelligence (BI) as the number one technology priority for companies, followed by ERP systems (Gartner, 2008). In a Cutter Consortium Report (2003), a survey of 142 companies it was found that 70% of the respondents had implemented data warehousing and Business Intelligence initiatives. The increased focus on BI is reflected in the forecasted BI vendor revenue expected of $7.7 billion by 2012 (Sommer, 2008). Although Business Intelligence is seen as a priority for many companies to survive in a competitive market there is uncertainty as to the path to follow. Researchers have identified that companies utilise Business Intelligence in different ways, with varying levels of success. A review of literature indicates that a number of companies often fail to realise expected benefits of Business Intelligence and sometimes consider the project to be a failure in itself (Chenoweth et. al., 2006; Hwang et al., 2004; Johnson, 2004; Arte 2003; Adelman and Moss 2002). Gartner predicted that more than half of the Global 2000 enterprises would fail to realise the capabilities of Business Intelligence and would lose market share to the companies that did (Dresner et al, 2002). A survey of 142 companies found that 41 percent of the respondents had experienced at least one Business Intelligence project failure and only 15 percent of respondents believed that their Business Intelligence initiative was a major success (Cutter Consortium Report, 2003). Moss and Atre (2003) indicated that 60% of Business Intelligence projects failed due to poor planning, poor project management, undelivered business requirements, or of those that were delivered, many were of poor quality. A number of authors believe that in many Business Intelligence projects the information that is generated is inaccurate or irrelevant to the user’s needs or indeed, delivered too late to be useful (Ballou and Tayi, 1999; Strong et al., 1997). Researchers have attempted to map Business Intelligence usage and best practices to provide a roadmap for companies to move forward in an attempt to maximise the benefits of their Business Intelligence initiatives. One approach for this roadmap has been the development of Business Intelligence Maturity Models (Watson et al, 2001; McDonald, 2004; Hamer, 2005; Eckerson, 2007, ASUG, 2007; Hewlett Packard, 2007). The purpose of these models is to provide companies with a roadmap to improve the management of their corporate data, as well as maximise the benefits obtained from Business Intelligence. Each year Gartner identifies companies for Business Intelligence Awards of Excellence. It would be reasonable to expect that a company which achieved such an award would be very mature as per the model. The remainder of the paper contains a case study documenting a company’s BI journey. This same company was a recent of the Gartner Business Intelligence Award of Excellence, (alias Acme Manufacturing).

A quantitative correlation coefficient mining method for business intelligence in small and medium enterprises of trading business

Business intelligence based on data mining has been one of the popular and indispensable tools for identifying business opportunity in sales and marketing of new products. The traditional data mining methods based on association rules may be inadequate in completely uncovering the hidden patterns of sales based on transaction records. This paper presents a qualitative correlation coefficient mining method which is capable of uncovering hidden patterns of sales and market. Hence, a prototype business intelligence system (BIS) named correlation coefficient sales data mining system (CCSDMS) has been developed and successfully trial implemented in a selected reference site. A series of experiments have been conducted to evaluate the performance of the proposed system. The results generated by the BIS are compared with a well known market available data mining system. The proposed quantitative correlation coefficient mining method is found to possess higher accuracy, better computational effectiveness and higher predictive power. With the new approach, associations for product relations and customer periodic demands are revealed and this can help to leverage organizational marketing capital to enhance quality and speed of promotions as well as awareness of product relations.

Experiences in building and scaling an enterprise application on multicore systems

SUMMARYEven though Java is the de facto programming language for enterprise applications, there exist only a limited number of Java‐based benchmarks to understand the performance on emerging multicore systems. To bridge this gap, this paper presents a report generation benchmark that is developed on top of Open Source Apache Geronimo's DayTrader benchmark. Report generation and rendering is at the heart of many enterprise business analytics and business intelligence software products, and it is used by many enterprise applications. We evaluate the performance scalability of this benchmark on a state‐of‐the‐art Power7 multicore system with 8 Power7 cores and 32 hardware threads. The benchmark throughput scales linearly up to eight hardware threads, but beyond that point, the throughput falls sharply. Significant locking in the Java class libraries for non‐shared objects results in this performance drop. Splitting the locks on these shared classes results in near linear scaling from eight to 32 threads and improved the throughput by 80%. We also show that the Linux operating system load balancing could result in a degraded application performance in hardware multithreaded systems and simultaneous‐multithreads‐aware task scheduling results in uniform core‐resource utilization as well as improved application performance. Copyright © 2011 John Wiley & Sons, Ltd.

BUSINESS INTELLIGENCE (BI) SUCCESS AND THE ROLE OF BI CAPABILITIES

Business intelligence (BI) has become the top priority for many organizations who have implemented BI solutions to improve their decision-making process. Yet, not all BI initiatives have fulfilled the expectations. We suggest that one of the reasons for failure is the lack of an understanding of the critical factors that define the success of BI applications, and that BI capabilities are among those critical factors. We present findings from a survey of 116 BI professionals that provides a snapshot of user satisfaction with various BI capabilities and the relationship between these capabilities and user satisfaction with BI. Our findings suggest that users are generally satisfied with BI overall and with BI capabilities. However, the BI capabilities with which they are most satisfied are not necessarily the ones that are the most strongly related to BI success. Of the five capabilities that were the most highly correlated with overall satisfaction with BI, only one was specifically related to data. Another interesting finding implies that, although users are not highly satisfied with the level of interaction of BI with other systems, this capability is highly correlated with BI success. Implications of these findings for the successful use and management of BI are discussed. Copyright © 2012 John Wiley & Sons, Ltd.

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.

Business Intelligence Competency Centers

Enterprises today continue to invest in business intelligence (BI) initiatives with the hope of providing a strategic advantage to their organizations. Many of these initiatives are supporting the tactical goals of individual business units and not the strategic goals of the enterprise. Although this decentralized approach provides short term gains, it creates an environment where information silos develop and the enterprise as a whole struggles to develop a single version of the truth when it comes to providing strategic information. Enterprises are turning toward a centralized approach to BI which aligns with their overall strategic goals. At the core of the centralized approach is the business intelligence competency center (BICC). This paper details why the centralized BICC approach should be considered an essential component of all enterprise BI initiatives. Examining case studies of BICC implementations details the benefits realized by real world companies who have taken this approach. It is also important to provide analysis of the two BI approaches in the areas of BI process and BI technology/data and people relations. The findings indicate the benefits of the centralized BICC outweigh the deficiencies of the decentralized approach.

Champion for Business Intelligence

Although Gartner’s EXP 2006 CIO Survey ranked Business Intelligence (BI) as the top technology priority, BI projects face tough competition from other projects in IT portfolios promising more tangible financial returns (Wu & Weitzman, 2006) Two major hurdles that prevent BI projects from shining in portfolios are vague requirements and weak benefits calculations. Both can be addressed by examining and learning from a number of case studies that prove tangible ROI on BI solutions when scoped and designed with a focus on specific, measurable, achievable, results-oriented, and time bound SMART business goals. In order for BI projects to compete in IT portfolios based on financial measures, like ROI, BI champions need to approach BI requirements gathering with the goal of addressing a specific business problem as well as employ standard ways of calculating BI benefits post project go live. By examining common failures with BI requirements and case studies which demonstrate how successful BI implementations translate into tangible benefits for the organization, BI champions develop a toolkit of tips, tricks, and lessons learned for successful requirements gathering, design, implementation, and measure of business results on BI initiatives.

What is Business Intelligence?

There has been growing corporate interest in business intelligence (BI) as a path to reduced costs, improved service quality, and better decision-making processes. However, while BI has existed for years, it has difficulties reaching what specialists in the field consider its full potential. In this paper, the authors examine disparities in how the constructs of business intelligence are defined and understood, which may impede an understanding of what BI represents to business leaders and researchers. The main objective of this study is to clearly understand this emerging concept of BI. In this regard, the authors analyze articles from the scientific and professional literature to have a comprehensive understanding of business intelligence as both a product and a process. This research proposes a global overview of the conceptual foundations of BI, which can help companies understand their BI initiative and leverage them to the strategic level.