Showing papers in "International Journal of Business Intelligence Research in 2011"
TL;DR: The agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence requirements, and the impact of agility on the business are discussed.
Abstract: Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well as technology has often changed considerably The result is disappointed stakeholders and frustrated development teams Agile development implements projects in an iterative fashion Also known as the sixty percent solution, the agile approach seeks to deliver more than half of the user requirements in the initial release, with refinements coming in a series of subsequent releases which are scheduled at regular intervals An agile data warehousing approach greatly increases the likelihood of successful implementation on time and within budget This article discusses agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence (BI) requirements, and demonstrates the impact of agility on the business
24 citations
TL;DR: It is proposed that test-driven development could be a useful methodology for data warehouse projects, in that it could help team members avoid some of the major pitfalls of data warehousing, and result in a higher-quality end product.
Abstract: Test-driven development is a software development methodology that has recently gained a great deal of traction in the software development community. It focuses on creating software-based test cases that define the business requirements of an application before beginning the coding of the application itself. This paper proposes that test-driven development could be a useful methodology for data warehouse projects, in that it could help team members avoid some of the major pitfalls of data warehousing, and result in a higher-quality end product.
22 citations
TL;DR: A business intelligence conceptual model (BISCOM) is proposed as a process-focused design theory for developing, understanding, and evaluating business intelligence (BI) systems that integrates and synthesizes existing research.
Abstract: A business intelligence conceptual model (BISCOM) is proposed as a process-focused design theory for developing, understanding, and evaluating business intelligence (BI) systems. Previous work has concentrated on subsets of the BI systems, use of BI tools, and specific business functional area requirements. BISCOM provides a unified and comprehensive design theory that integrates and synthesizes existing research. It extends existing research by proposing functionality that does not currently exist in BI systems. The BISCOM is validated through descriptive methods that demonstrate the model utility and through prototype creation to demonstrate the need for BISCOM.
21 citations
TL;DR: Data from first-year enrolling students for the 2006-2008 fall terms at a private, master’s-level institution in the northeastern United States was analyzed for the purpose of developing predictive models.
Abstract: Higher education often lags behind industry in the adoption of new or emerging technologies. As competition increases among colleges and universities for a diminishing supply of prospective students, the need to adopt the principles of business intelligence becomes increasingly more important. Data from first-year enrolling students for the 2006-2008 fall terms at a private, master’s-level institution in the northeastern United States was analyzed for the purpose of developing predictive models. A decision tree analysis, a neural network analysis, and a multiple regression analysis were conducted to predict each student’s grade point average (GPA) at the end of the first year of academic study. Numerous geodemographic variables were analyzed to develop the models to predict the target variable. The overall performance of the models developed in the analysis was evaluated by using the average square error (ASE). The three models had similar ASE values, which indicated that any of the models could be used for the intended purpose. Suggestions for future analysis include expansion of the scope of the study to include more student-centric variables and to evaluate GPA at other student levels.
20 citations
TL;DR: This paper will focus on why companies should use analytics (a subset of Business Intelligence (BI)) to transform and maximize the potential of their human capital.
Abstract: During the recent recession the number of jobs lost has been widely publicized. However, lurking among this obvious and simple metric of how human capital is involved in the workforce, there is the need to analyze and predict future talent. As economic conditions are slow to improve, decisions to simply cut the traditional costs, benefits, compensation and headcount are no longer enough. Companies have already started using business intelligence (BI) to transform and maximize the potential of their human capital. The use of human capital based business intelligence (BI) has increasingly become one of the vital strategic components for world-class companies. This paper will focus on why companies should use analytics (a subset of Business Intelligence (BI)) to transform and maximize the potential of their human capital.
19 citations
TL;DR: This paper addresses where BI developers have failed to create applications suited for the common end-user and provides a conceptual roadmap to address these shortfalls and suggests that until BI analysis tools become more human-centric, design-oriented and less from a technology-focused, engineering-oriented perspective, BI will continue to fail in its objective to routinely improve business decision-making.
Abstract: This paper addresses where BI developers have failed to create applications suited for the common end-user and provide a conceptual roadmap to address these shortfalls. It is argued that BI’s impact on analyses and decision-making depends on the development of less complex applications. Research conducted for this paper finds that BI lacks a common definition and standard, that BI tools are too complex for the common user, and that a shortage of analytical literacy relevant to BI among business professionals is a barrier to BI adoption. The paper suggests that until BI analysis tools become more “human-centric, design-oriented†and less from a “technology-centric, engineering-oriented perspective†, BI will continue to fail in its objective to routinely improve business decision-making.
16 citations
TL;DR: The authors aim to show how, in the economic war, engaging in committee-based standards development may be used for winning the competition battle.
Abstract: More and more companies operate today in a worldwide market under conditions of globalization, increased complexity, and competition. In such an environment, business decisions need to be made quickly yet intelligent, substantiated by the most salient and relevant information available. Under the global competition, with a diligent and measured manner, many companies are increasingly treating business like an economic war. Enterprises are methodically monitoring and investigating their competitors, while deploying all the resources they have at their disposal in order to beat their current or future rivals. Competitive Intelligence (CI) has become the ‘latest weapon in the world war of economics’. This paper contributes to the growing body of literature on competitive intelligence by synthesizing knowledge stemming from many years of experience in the standardization arena. The authors aim to show how, in the economic war, engaging in committee-based standards development may be used for winning the competition battle.
16 citations
TL;DR: The experiments described in this paper illustrate how qualified association rules supplement standard association rules data mining methods and provide additional information which can be used to better target corporate actions.
Abstract: Association rules mining is one of the most successfully applied data mining methods in today’s business settings (e.g. Amazon or Netflix recommendations to customers). Qualified association rules mining is an extension of the association rules data mining method, that uncovers previously unknown correlations that only manifest themselves under certain circumstances (e.g. on a particular day of the week), with the goal of improving action results, e.g. turning an underperforming campaign (spread too thin over the entire audience) into a highly targeted campaign that delivers results. Such correlations have not been easily reachable using standard data mining tools so far. This paper describes the method for straightforward discovery of qualified association rules and demonstrates the use of qualified association rules mining on an actual corporate data set. The data set is a subset of a corporate data warehouse for Sam’s Club, a division of Wal-Mart Stores, INC. The experiments described in this paper illustrate how qualified association rules supplement standard association rules data mining methods and provide additional information which can be used to better target corporate actions.
16 citations
TL;DR: The authors find that organizations are rapidly moving to an enterprise perspective on BI, but in an unsystematic way, and present a prescription for the future of BI called enterprise intelligence (EI).
Abstract: This paper examines the key issues associated with current and future implementations of business intelligence (BI). The authors review the literature and discover both the growing importance and emerging issues associated with BI. The issues are further examined with an exploratory, but detailed, case study of organizations from a variety of industries, yielding a series of lessons learned. The authors find that organizations are rapidly moving to an enterprise perspective on BI, but in an unsystematic way. The authors present a prescription for the future of BI called “enterprise intelligence†(EI). EI is described in a framework that combines elements of hierarchy theory, organization modeling, and intellectual capital.
15 citations
TL;DR: This paper details why the centralized BICC approach should be considered an essential component of all enterprise BI initiatives and analysis of the two BI approaches in the areas of BI process and BI technology/data and people relations.
Abstract: 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.
15 citations
TL;DR: This paper presents a discussion of industry factors such as airline routes, past passenger demands in different regions of the world and the sizes and types of aircraft that were required to support those demands, and how analysis of that information is integral to the projection of future demands within the commercial aerospace market.
Abstract: The world’s largest aircraft manufacturers like Boeing and Airbus have traditionally been dominant in the commercial aerospace industry, but due to the rise of several smaller commercial aircraft companies and in spite of air travel increasing each year, it will be paramount for Boeing and Airbus to thoroughly understand past and current market conditions and be able to combine their understanding with the proper analytical tools to anticipate the market demands of the future if they are to remain the world leaders in their industry. This paper presents a discussion of industry factors such as airline routes, past passenger demands in different regions of the world and the sizes and types of aircraft that were required to support those demands, and more importantly, how analysis of that information is integral to the projection of future demands within the commercial aerospace market which will facilitate Boeing and Airbus positioning themselves to provide their airline customers with the right product at the right time.
TL;DR: This study investigates this emerging field of opinion mining in terms of what it is, what it can do, and how it could be used effectively for business intelligence (BI).
Abstract: The online word-of-mouth behavior that exists today in the Web represents new and measurable sources of information. The automated discovery or mining of consumer opinions from these sources is of great importance for marketing intelligence and product benchmarking. Techniques are now being developed to effectively and easily mine the consumer opinions from the Web data and to timely deliver them to companies and individual consumers. This study investigates this emerging field named ‘opinion mining’ in terms of what it is, what it can do, and how it could be used effectively for business intelligence (BI). A rigorous review of the research literature on opinion mining is conducted to explore its current state, issues and challenges for its use in developing business applications for competitive advantage. The study aims to assist business managers to better understand the current opportunities and challenges in using opinion mining for deriving BI. Future research directions for further development of the field are also identified.
TL;DR: Security Information and Event Management (SIEM) has emerged within the last 10 years providing a centralized source to enable both real-time and deep level analysis of historical event data to drive security standards and align IT resources in a more efficient manner.
Abstract: Business Intelligence (BI) has often been described as the tools and systems that play an essential role in the strategic planning process of a corporation. The application of BI is most commonly associated with the analysis of sales and stock trends, pricing and customer behavior to inform business decision-making. There is a growing trend in utilizing the tools and processes used in the analysis of data and applying them to security event management. Security Information and Event Management (SIEM) has emerged within the last 10 years providing a centralized source to enable both real-time and deep level analysis of historical event data to drive security standards and align IT resources in a more efficient manner.
TL;DR: Ten major principles that organizations follow to ensure the failure of their BI solution are highlighted and how to avoid BI failure is described in terms of strategy and design, implementation management and communication, and technology and resource investment for BI solutions.
Abstract: Demand for business intelligence solutions continues to grow in the industry at record rates to combat competitive pressures and to attain business agility. Still organizations continue to struggle on how to implement successful business intelligence solutions. Despite its growing popularity and maturity as a field, it appears that organizations follow key guidelines that ensure the failure of their business intelligence implementation. This paper highlights ten major principles that organizations follow to ensure the failure of their BI solution and in so doing describes how to avoid BI failure in terms of strategy and design, implementation management and communication, and technology and resource investment for BI solutions.
TL;DR: 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.
Abstract: 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.
TL;DR: The authors give an overview of web analytics tools, key players, new technology trends and capabilities to integrate web analytics with BI so organizations can leverage intelligent analytics for new marketing initiatives.
Abstract: Organizations use web analytic tools and technologies to measure, collect, analyze, and report web usage data to help optimize websites. Traditionally, most of this data tends to be non-transactional and non-identifiable. In this regard, there has not been much integration with transactional data that is collected, stored, analyzed, and reported through Business Intelligence (BI). Emerging trends in web analytics provide organizations the ability to aggregate and analyze web analytics data with transactional data to provide valuable insights for building better customer relationship strategies. In this paper, the authors give an overview of web analytics tools, key players, new technology trends and capabilities to integrate web analytics with BI so organizations can leverage intelligent analytics for new marketing initiatives. While the benefits are significant, there are some challenges associated with the integration and a few possible solutions to address.
TL;DR: The history of selected systems development approaches is examined, the advantages and disadvantages of prevailing practices are weighed, and a path forward to succeeding in BI through the application of Agile methodologies is recommended.
Abstract: While the potential benefits from BI are vast, organizations have struggled to successfully deploy it. BI applies myriad advanced techniques, performed by the firm’s Information Technology (IT) group, to fulfill the reporting, analysis, and decision-support needs of the Lines of Business. Two of the greatest challenges in BI are accurately and continuously communicating requirements from the business to IT and quickly yet affordably delivering the requested functionality from IT to the business. Companies can overcome these challenges by embracing a prescribed set of Agile development methodologies for BI. This paper examines the history of selected systems development approaches, weighs the advantages and disadvantages of prevailing practices, and ultimately recommends a path forward to succeeding in BI through the application of Agile methodologies.
TL;DR: The paper demonstrates that there is synergy between corporate, government, and personal government performance measures and how business intelligence tools are making these relationships more transparent.
Abstract: Performance management is tied to external forces and stakeholders whose assessment of performance is more focused on societal outcomes than purely financial outcomes. Government, corporate, and even personal performance measurement should take into account societal indicators that link these disparate yet intertwined spheres of influence. New initiatives in both government and commercial sectors are bringing greater understanding of how societal indicators can measure performance. This paper highlights how societal indicators are used to measure performance in corporate and government sectors. Corporate societal indicators are explored primarily though literary research. Government societal indicators are explored through an examination of the EPA and Superfund program. The paper demonstrates that there is synergy between corporate, government, and personal government performance measures and how business intelligence tools are making these relationships more transparent.
TL;DR: A case study was designed to determine the efficacy of QA systems for generating answers to original, fusion, list questions (questions that have not previously been asked and answered, questions for which the answer cannot be found on a single web site), and results indicate that QA algorithms are not very good at producing complete answer lists.
Abstract: Web search engines typically retrieve a large number of web pages and overload business analysts with irrelevant information. One approach that has been proposed for overcoming some of these problems is automated Question Answering (QA). This paper describes a case study that was designed to determine the efficacy of QA systems for generating answers to original, fusion, list questions (questions that have not previously been asked and answered, questions for which the answer cannot be found on a single web site, and questions for which the answer is a list of items). Results indicate that QA algorithms are not very good at producing complete answer lists and that searchers are not very good at constructing answer lists from snippets. These findings indicate a need for QA research to focus on crowd sourcing answer lists and improving output format.
TL;DR: This paper reviews how the concept of centralization is defined, how it relates to the implementation of BI, and how it can effectively in overcome the common implementation hurdles.
Abstract: The implementation of BI into the business strategy and culture is laden with many potential points that could result in failure of the initiative, leaving BI to be underdeveloped and a source of wasted resources for the company. Due to the unique nature of BI in the business space, properly setting up BI within the organizational structure from the onset of integration minimizes the impact of the most common hurdles to BI implementation. Many companies choose to mitigate these problems by using a centralized approach by building a Center of Excellence, but their place in the company’s organizational structure needs to be well-defined and properly empowered to be effective. This paper also reviews how the concept of centralization is defined, how it relates to the implementation of BI, and how it can effectively in overcome the common implementation hurdles.