Key success factors to business intelligence solution implementation

Business Intelligence can bring critical capabilities to an organization, but the implementation of such capabilities is often plagued with problems. Why is it that certain projects fail, while others succeed? The aim of this article is to identify the factors that are present in successful Business Intelligence projects and to organize them into a framework of critical success factors. A survey was conducted during the spring of 2011 to collect primary data on Business Intelligence projects. Findings confirm that Business Intelligence projects are wrestling with both technological and non-technological problems, but the non-technological problems are found to be harder to solve as well as more time consuming than their counterparts. The study also shows that critical success factors for Business Intelligence projects are different from success factors for Information Systems projects in general. Business Intelligences projects have critical success factors that are unique to the subject matter. Major differences can be found primarily among non-technological factors, such as the presence of a specific business need and a clear vision to guide the project. Success depends on types of project funding, the business value provided by each iteration in the project and the alignment of the project to a strategic vision for Business Intelligence at large. Furthermore, the study provides a framework for critical success factors that, explains sixty-one percent of variability of success for projects. Areas which should be given special attention include making sure that the Business Intelligence solution is built with the end users in mind, that the Business Intelligence solution is closely tied to the company’s strategic vision and that the project is properly scoped and prioritized to concentrate on the best opportunities first.

Purpose Textile and contemporary apparel manufacturers are adopting and integrating cutting-edge technologies to reduce their impact on the environment and gain an advantage in the marketplace. Most previous studies have ignored business intelligence systems (BIS), notably in the textile and apparel industry (T&A), in favor of looking at the larger picture of how big data would affect retail and distribution in a company. This is especially true for the T&As.Design/methodology/approach The authors report that they conducted 14 semi-structured interviews with 12 international luxury tourism service providers. In this case, researchers use snowball features and systematic techniques to select participants. A qualitative content analysis strategy is used to capture the focus of the interviews.Findings Problems with T&A company sustainability, opportunities to increase value creation via use of industry-leading business intelligence (BI) solutions and perceived roadblocks to BIS adoption were all found by the poll. Garment retail and distribution sector has benefited greatly from the increased use of Industry 4.0 technologies, especially those that provide better BI solutions. Determine the extent to which industry participation slows down or speeds up the process. The Company Information System (BIS) will help convince non-tech-savvy business owners of the financial, economic and environmental benefits of adopting certain technologies developed as part of the industry 4.0 movement.Research limitations/implications The authors of this research claim theirs is one of the first to investigate what variables affect the uptake of BIS, ultimately hoping to find out how BIS may be used by T&A businesses to tackle environmental issues through the use of Industry 4.0 technologies. The purpose of this study was to see whether BIS might aid T&A firms with their sustainability issues.Practical implications In the last several years, there has been a meteoric rise in interest in big data and business analytics among firms and educational institutions alike. This paper tries to introduce readers to the concept of business analytics in a way that is both academic and accessible, considering both the present and future of the field. This paper begins with a quick introduction, followed by a summary of the three distinct forms of predictive modeling discussed.Originality/value In an effort to help aspiring analytics professionals, they have identified, categorized and evaluated the nine distinct players that are now active in the analytics market. Following this, they will provide a high-level summary of the many different research projects currently being worked on by their group.

Business Intelligence (BI) is an approach to optimize business decisions by utilizing existing information. BI has been supporting the organization to form effective strategic and tactical business strategies. For the past 2 decades, BI hasn't only had a great impact on large enterprises, but also it has aided in forming strong core strategies of small to mid-sized enterprises. This paper details the full BI solution's cycle; planning, design, and development with a focus on small enterprises. Fig. 1 (“System Development Cycle”) summarizes the process followed to develop a BI solution in this paper. The study involves a case study of a fictitious company, created for the sole purpose of applying the suggested BI solution. This case study elaborates on all the phases required to develop a BI solution for each phase. The challenges and benefits of the BI solution are thoroughly considered in this study. To evaluate the functionality of the proposed BI solution, several testing iterations were performed and the results of these iterations are presented at the end of this case study. The paper concludes by highlighting the areas for future research in BI.

Cloud computing is one of the major IT trends and Business Intelligence (BI) solutions are among of the top business applications wanted in the Cloud. Based on this demand, it is shown that BI solutions currently offered in the Cloud are missing important integration aspects. The paper illustrates how a fully integrated Cloud based BI solution can work incorporated into other Cloud based applications. The technical concept shows how the needed interface can be provided and gives an example.

In the era of fast-growing digital transformation, integrating business intelligence (BI) solutions in accounting decision-making has become essential for companies. This research reviews the critical role of BI solutions in optimizing the accounting decision-making process amid digital transformation. The method used in this research is a descriptive method with a qualitative approach, while the data used is secondary data obtained from previous studies. The data was collected through literature study techniques and analyzed qualitatively. The results of this study show that Business Intelligence (BI) plays a vital role in optimizing financial data presentation, adaptability to business changes, and providing competitive advantage through informed decision-making, especially in today's digital era. Implementing BI in an economic environment can improve efficiency, provide deep insights, and strengthen a company's competitiveness. BI is a technology tool and a strategic foundation supporting a company's success in the ever-changing digital era through its adaptability and ability to process data for decision-making.

Purpose – Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation. Design/methodology/approach – To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome. Findings – The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based and model-based solutions. Finally, it discusses BI implementation issues and outlines the security and privacy policies adopted in BI environment. Research limitations/implications – In this survey BI has been discussed in theoretical perspective whereas practical contribution has been given less attention. Originality/value – A comprehensive survey on BI which identifies areas lacking in recent research and providing potential opportunities for investigation.

Business Intelligence (BI) systems are applied by increasing numbers of organizations in a wide range of sectors. Despite the growing numbers of BI solutions and the experience augmented in many implementation projects worldwide, as well as the value that BI systems provide to organizations, IT literature lacks a coherent set of definitions through which BI systems can be classified, categorized, and assessed. Furthermore, BI implementation projects often do not succeed or do not fully accomplish the degrees of value and performance as expected, when firms fail to complete the system implementation, to satisfy the needs of users or when the benefits BI systems produce are lower than anticipated. This paper presents an analytical framework through which BI systems are defined and classified. On the basis of this framework, possible technical, organizational, and personal factors that affect the failure, partial or full success of BI system implementations are discussed. These factors are followed by a case study and empirical data analyses that exemplify and assess the extent to which various organizational attributes and properties of users influence the success or failure of BI implementation projects. Finally, implications regarding the management of BI system implementation projects and the organizations that apply them are derived.

Organizations are increasingly using Business Intelligence (BI) solutions to achieve success via data driven insights in today's fast paced business climate. Businesses have been able to make informed and strategic choices thanks to traditional BI systems like SAP BusinessObjects (BO), Cognos, and other legacy technologies. Nonetheless, the growing demand for real-time data analysis and foresight has driven businesses to include complex analytics into their business intelligence platforms. This paper examines the role of BI in driving business growth, focusing on the integration of legacy tools with advanced analytics. It demonstrates how this integration speeds up decision-making, increases operational efficiency, and reveals new income prospects. This paper reviews many case studies and business intelligence deployments to demonstrate how firms may use data insights to influence strategic goals, improve operational efficiency, and maintain a competitive advantage in market trends.

Business Intelligence (BI) solutions are among the popular applications that businesses demand in the Cloud. Although Business Intelligence functionality is already provided in the Cloud, vendors concentrate on providing existing products like Software as a Service, ignoring changing business and provisioning models of software. Business Intelligence functionality provided in the Cloud is lacking important integration features needed by other Cloud-based applications. While a simple upload of the data to be analyzed into a Cloud-based BI solution is already possible and well established, the visualization of the results and the interactive parts of the analysis are always provided inside the Cloud-based BI solution itself. However, as this chapter shows, common usage scenarios of integrated Cloud applications demand an integration of the analysis reports from BI applications into the user interface of other Cloud-based applications. This chapter describes how a fully integrated Cloud-based BI solution can collaborate with other Cloud-based applications. The technical concept illustrated with an example delineates how the required interface can be provided.

Foundations of SQL Server 2008 R2 Business Intelligence introduces the exciting gamut of business intelligence tools included with SQL Server 2008. Microsoft has designed SQL Server 2008 to be more than just a database. Its a complete business intelligence (BI) platform. The database is at the core, and surrounding the database are tools for data mining, modeling, reporting, analyzing, charting, and integration with other, enterprise-level software packages. SQL Server 2008 puts an incredible amount of BI functionality at your disposal. But how do you take advantage of it? Thats what this book is all about. Authors Guy Fouch and Lynn Langit show how to implement end-to-end BI solutions using SQL Server Analysis Services (SSAS), SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS), and other tools in the Microsoft BI toolkit. Youll learn about all-new features such as PowerPivot and Report Builder 3.0. Also provided are clear examples of predictive analysis made possible through powerful data mining features in SQL Server. If youre an analyst or developer working with SQL Server 2008 who is charged with delivering results that drive business success, you cant afford to be without this book; you cant afford to ignore the powerful BI suite that Microsoft has placed at your disposal. Provides the big picture of Microsofts BI tool suite Covers PowerPivot and other game-changing technologies introduced alongside SQL Server 2008 Release 2 Gives a practical analysis of features based on real-world practices What youll learn Apply dimensional- and fact-based modeling to create OLAP schemas Optimize data extraction and transformation with SQL Server Integration Services Develop OLAP and data-mining solutions using SQL Server Analysis Services Create compelling reports using the new Report Builder 3.0 in SQL Server Reporting Services Deliver self-service business-intelligence via Microsoft PowerPivot Manage and maintain control over your BI environment Who this book is for Foundations of SQL Server 2008 R2 Business Intelligence is for business intelligence analysts and developers wanting to employ the full stack of business intelligence tools that are part of SQL Server. The book also appeals to database administrators managing databases in support of business intelligence efforts. Table of Contents What Is Business Intelligence? OLAP Modeling Concepts Introducing OLAP Modeling with SSAS Intermediate OLAP Modeling with SSAS Advanced OLAP Modeling with SSAS Cube Storage and Aggregation Introducing SSIS Intermediate SSIS Advanced SSIS Reporting Tools Data Mining with Excel Introducing PowerPivot Introduction to MDX Introduction to Data Mining

Clinical laboratory information systems (LIS) rely on new solutions such as Business Intelligence (BI) to help manage their environments. Without proper BI solution, decision-making is based on instinct rather than on data. Using current tools and processes in the LIS, it is often difficult to deliver valuable information to the right people at the right time. This systematic research conducted between the years 2007 and 2012 suggests that as BI solutions come in a wide variety of flavors, an actionable insight into 8 areas in addition to 11 key factors should be considered selecting and evaluating a BI platform.

Business Intelligence is still in its infancy stages in developing countries, particularly Kenya. Research has shown that different models are available to assess the usage and adoption of Business Intelligence (BI). In this case, the technological, organizational, and environmental (TOE) model was proposed as a suitable model for developing economies like Kenya. The study investigates how the TOE constructs affect BI adoption, the BI systems in Kenya, and whether managers influence BI adoption. The equivocal nature of the TOE framework allowed the creation of a structured interview questionnaire that was divided into two parts; the demographic profile and questions based on the TOE framework. The results demonstrated that the TOE factors led to more intensive BI adoption, but there might be a lack of awareness or technical skills to adopt advanced BI technologies. On this basis, it is recommended that managers within small- and medium-sized enterprises (SME) learn about better BI solutions and how they can leverage the advantage to enable them to stay profitable, competitive, and data driven. Further research is needed to better understand BI usage within SMEs preferably with larger and representative sample sizes and across different counties within Kenya.

In 2012, the European Medicines Agency published guidelines for good pharmacovigilance practices. Within these guidelines, the Agency advised that marketing authorization holders (MAHs) establish safety signal tracking systems that capture outcomes related to signals that have been validated. Historically, MAHs have collected data and information to document signal detection and evaluation activities, and now many are developing robust approaches, to meet the guidelines, that systematically track and manage signals. Given the requirements, these systems could become highly reliant on business intelligence (BI) solutions. Expectations for these systems include the ability to document and track safety signals electronically through each step of the signal detection process, save source documents, and collect key performance indicators. They also include the ability to extract this information programmatically for regulatory or senior management reporting as well as for monitoring of compliance metrics. In addition, this type of systems-based approach facilitates resource management and assists MAHs to be regulatory inspection and audit ready. This article shares an experience of one mid-size pharmaceutical company.

SMEs play a very important part in the economy for countries and drive innovation and competition in many economic sectors. To be a strong competitor in the fast growing environment of the economy, smart strategic management decisions are essential for the SMEs. Chinese SMEs have a different character and responsibility compared to others in the developed countries as they make a bigger contribution in Chinese economy. The smart strategic decision and Business Intelligence(BI) solution seem especially import for them. This paper is focusing on analyzing the situation of SMEs, analyzing why they need BI, especial SaaS BI tool and how to implement SaaS BI in SMEs.

Business intelligence (BI) is a set of technologies and strategies allowing analyzing data and providing information to help in the decision-making process. Today, BI is used in many sectors and shows marvelous results. In this paper, we are interested in supporting decision-making in higher education. BI concepts when integrated to the process of academic affairs can make significant improvement. The present paper aims at describing a BI solution to support academic affairs at Taibah University. Using BI, we can take advantages of a set of analytical tools that support decision-making for different types of users (students, faculty members, administrators and decision makers). The proposed BI solution consists essentially of three mains tasks: (1) collecting data from different sources using the three operations (Extract, Transform and Load), (2) proposing a multidimensional solution that describes the academic processes, and (3) visualizing results through a set of dashboards and reports. Experiments are made using the SQL Server Data Tools. Three steps are detailed which are: SQL Server Integration Services, SQL Server Analysis Services and SQL Server Reporting Services. The proposed solution provides many statistical and predictive indicators needed in academic tasks.