To see the other types of publications on this topic, follow the link: BI (Business Intelligence).
Journal articles on the topic 'BI (Business Intelligence)'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'BI (Business Intelligence).'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Kemper, Hans-Georg. "Business Intelligence – BI." Controlling 14, no. 11 (2002): 665–66. http://dx.doi.org/10.15358/0935-0381-2002-11-665.
Full text
2
Al-khateeb, Bilal Ahmad Ali. "Business Intelligence (BI)." International Journal of Asian Business and Information Management 15, no. 1 (2024): 1–15. http://dx.doi.org/10.4018/ijabim.340387.
Full textAbstract:
Generally, BI plays a very significant role in promoting the success of any organization, particularly the universities; however, not all BI projects have been implemented successfully by the universities across the word, suggesting that only a few universities appear to have faith in BI as strategy for success and sustainability particularly in this era where data speak. As a result, this study attempts to establish the relationship between BI and university sustainability and success. Therefore, the major objective of the study is to find an empirical link between BI and university sustainability and success with particular interest in Saudi Arabia. Overall, the study found that business intelligence affects the university sustainability but not its success. It shows that BI assists the university in making good decisions that may sustain the university while ensuring that the university does not fall below break-even in terms of success. By implication, the university can use business intelligence as a strategy for sustainability but not work well for achieving the desired success.
3
Brodzinski, James, Elaine Crable, Thilini Ariyachandra, and Mark Frolick. "Mobile Business Intelligence." International Journal of Business Intelligence Research 4, no. 2 (2013): 54–66. http://dx.doi.org/10.4018/jbir.2013040104.
Full textAbstract:
Demand for business intelligence (BI) applications continues to grow at a rapid pace. Business intelligence via mobile devices is the latest frontier to drive demand among organizations interested in BI applications. However, mobile BI is still in its infancy. There are many opportunities to advance the way users use and interact with BI applications using mobile BI. Nevertheless, there are many challenges and issues that still require attention to attain mobile BI success. This paper highlights the state of mobile BI solutions and strategies to consider during a mobile BI implementation. It also discusses the challenges and opportunities mobile BI presents to organizations.
4
Chugh, Ritesh, and Srimannarayana Grandhi. "Why Business Intelligence?" International Journal of E-Entrepreneurship and Innovation 4, no. 2 (2013): 1–14. http://dx.doi.org/10.4018/ijeei.2013040101.
Full textAbstract:
Business Intelligence (BI) is one of the fastest growing software sector and software vendors are rapidly developing multiple BI tools to support the growing data analysis needs of organisations. In order to be sustainable in a briskly changing turbulent environment, organisations need to have access to information about their operational performance. BI tools play a vital role in supporting the decision makers at different organisational levels. As these tools are becoming critical in decision making, it has become not only an information technology concern but also a management concern. Without proper governance it would be impractical to achieve the value that BI tools offer. Adopting a BI governance framework in organisations will lead to common principles and clear ownership over information. Additionally, appropriate alignment between corporate governance and BI governance can yield more benefits. This paper provides an insight into the importance and value of BI tools. Key functionalities of BI tools have also been highlighted. Different challenges in gaining true value from BI tools have been examined. Four phases of developing a BI governance framework have been illustrated. The alignment between BI governance and corporate governance has also been explored with a recommended model. Exploratory analysis of two organisations (Premier Healthcare Alliance & BellSouth Telecommunications) to identify how they have utilised BI tools and adopted BI governance has been briefly carried out. The paper posits that if the steps for developing a BI framework are adopted by organisations and the BI framework is aligned with the corporate framework, BI deployment and usage will be successful with reduced risk levels.
5
Ahmed, Emad. "Utilization of Business Intelligence Tools among Business Intelligence Users." International Journal for Innovation Education and Research 9, no. 6 (2021): 237–53. http://dx.doi.org/10.31686/ijier.vol9.iss6.3172.
Full textAbstract:
The study was an investigation of the impact of perceived usefulness and perceived ease of use of business intelligence (BI) tools among users. The relationship between and among the dependent variable (utilization of BI tools) and the independent variables (perceived usefulness and perceived ease of use) was investigated through the lenses of technology acceptance model (TAM). Other objectives for the current research were to build a model to predict users’ utilization of the independent variables, and to generalize the results of the research to the IT population. Data for the current research was collected utilizing a survey questionnaire, designed by the researcher, with a 5-point Likert scale to interpret responses to the survey questions. The analysis consisted of descriptive statistics and multiple regressions models. A prediction model was structured using generalized linear models. The result of the study was the development of a prediction model for BI tools utilization through the lenses of a technology acceptance model (TAM). The model highlighted the importance of up-to-date information provided by current BI tools, ability of BI tools to provide users with more analytical tools to accomplish their jobs, the degree to which BI tools allow users to present convincing arguments, the ability of BI tools to provide users with more possible solutions, the ability of BI tools to reduce the time required to accomplish jobs, and the ability of BI tools to help users make relevant business predictions.
6
Pinto Ferreira, Antonio Augusto, Natalia Talan Freitas Alves, Silvio Francisco Morgon, and Solange Pereira dos Santos. "BUSINESS INTELLIGENCE." SITEFA - Simpósio de Tecnologia da Fatec Sertãozinho 3, no. 1 (2021): 268–74. http://dx.doi.org/10.33635/sitefa.v3i1.113.
Full textAbstract:
O conceito de Business Intelligence (BI) tem sido amplamente utilizado nas grandes empresas. São muitos os relatos de aumento de renda e redução de custos justificando o valor investido na implantação e manutenção das ferramentas e analistas necessários. Porém, a maior parte das empresas do Brasil é constituída por micro ou pequenas empresas e o conceito de BI não tem a mesma amplitude de utilização. Isso ocorre porque os recursos dessas empresas é limitado e o custo de um BI é alto. Utilizando a pesquisa bibliográfica e documental, este artigo mostra como as micro e pequenas empresas são caracterizadas e quais os benefícios e possíveis custos de implantação de um BI chegando à conclusão de que o custo é impeditivo para a implantação do conceito em micro empresas e na maior parte das empresas de pequeno porte.
7
McBride, Neil. "Virtuous Business Intelligence." International Journal of Business Intelligence Research 6, no. 2 (2015): 1–17. http://dx.doi.org/10.4018/ijbir.2015070101.
Full textAbstract:
This paper examines three approaches to ethics and focuses on the development of character and the practice of virtue in business intelligence (BI). The paper describes BI as a tool for mediating the relationships between pairs of stakeholders such as management and customer. Three aspects of the relationship which benefit ethically from the practice of virtues are discussed: the purpose of the BI, the prejudices behind the BI and the power of the stakeholders. The connection between the ethics of BI and the corporate ethics is discussed. Without the practice of virtues, BI may be recruited to support corporate vices of exploitation, exposure, exclusion, coercion, control and concealment. The paper seeks to highlight the importance of ethical issues in BI practice and suggests the development of an ethical balanced scorecard as a vehicle for developing ethical senstitivity.
8
Ask, Urban. "Business Intelligence Practices." International Journal of Business Intelligence Research 4, no. 2 (2013): 1–18. http://dx.doi.org/10.4018/jbir.2013040101.
Full textAbstract:
There is considerate interest in Business Intelligence (BI) from many perspectives, but little research describing design and use of BI in real companies is available (Granlund, 2011; Jourdan, Rainer & Marshall, 2008). The aim of this article is to add empirical evidence to the knowledge of BI practices, addressing calls for research. BI practices are reported from 193 large Nordic organizations with the aim to give a broad perspective. Nordic organizations are seen as early movers in the adoption of technology (Beise, 2004) and receptive to adopt innovations (Waarts & Van Everdingen, 2005). However, the picture this paper arrives at is that Nordic organizations design and use of BI solutions is fairly traditional, with a major focus on reporting and analysis that contain financial information. There are signs of “beyond traditional use” of BI, but more field based research is needed to better understand BI in practice.
9
Logunova, Inna. "NAVIGATING BUSINESS INTELLIGENCE TOOLS: STRATEGIES TO DRIVE BUSINESS GROWTH." American Journal of Engineering and Technology 6, no. 10 (2024): 126–33. http://dx.doi.org/10.37547/tajet/volume06issue10-14.
Full textAbstract:
This study explores the evolution and current state of business intelligence (BI) tools and their strategic role in driving business growth. The research utilizes a combination of market analysis, industry case studies, and theoretical frameworks, including the DIKW hierarchy and Resource-Based View, to examine BI adoption trends. The results highlight the importance of data quality, cross-functional collaboration, and user adoption in maximizing BI effectiveness. Key findings indicate that cloud-based and self-service BI tools significantly improve data-driven decision-making, while challenges remain in data governance and integration. To address these challenges, organizations must implement robust data policies and empower users through training and self-service capabilities. The study concludes that integrating BI tools into digital transformation initiatives provides a competitive edge, enabling strategic planning, operational efficiency, and innovation. This research offers new insights into how organizations can leverage BI tools for sustained growth and enhanced decision-makingl.
10
Logunova, Inna. "NAVIGATING BUSINESS INTELLIGENCE TOOLS: STRATEGIES TO DRIVE BUSINESS GROWTH." American Journal of Engineering and Technology 06, no. 10 (2024): 126–33. http://dx.doi.org/10.37547/tajet/volume06issue10-14a.
Full textAbstract:
This study explores the evolution and current state of business intelligence (BI) tools and their strategic role in driving business growth. The research utilizes a combination of market analysis, industry case studies, and theoretical frameworks, including the DIKW hierarchy and Resource-Based View, to examine BI adoption trends. The results highlight the importance of data quality, cross-functional collaboration, and user adoption in maximizing BI effectiveness. Key findings indicate that cloud-based and self-service BI tools significantly improve data-driven decision-making, while challenges remain in data governance and integration. To address these challenges, organizations must implement robust data policies and empower users through training and self-service capabilities. The study concludes that integrating BI tools into digital transformation initiatives provides a competitive edge, enabling strategic planning, operational efficiency, and innovation. This research offers new insights into how organizations can leverage BI tools for sustained growth and enhanced decision-making.
11
Dymarsky, Irina. "Champion for Business Intelligence." International Journal of Business Intelligence Research 2, no. 2 (2011): 22–36. http://dx.doi.org/10.4018/jbir.2011040102.
Full textAbstract:
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.
12
Mendoza, Rubén A. "Business Intelligence 2.0." International Journal of Business Intelligence Research 1, no. 4 (2010): 63–76. http://dx.doi.org/10.4018/jbir.2010100104.
Full textAbstract:
Business Intelligence 2.0 is an umbrella term used to refer to a collection of tools that help organizations extend their BI capabilities using Internet platforms. BI 2.0 tools can enable the automatic discovery of distributed software services and data stores, greatly increasing the range of market options for an organization. The development cycle for these tools is still in its early stage, and much work remains. However, some technologies and standards are already well understood in order to make a significant impact. This paper provides an overview of the eXtensible Markup Language (XML) and related technologies supporting the deployment of web services and service-oriented architectures (SOA). The author summarizes the critical importance of these technologies to the emergence of BI 2.0 tools. This paper also explores the current state of Internet-enabled BI activities and strategic considerations for firms considering BI 2.0 options.
13
Ebule, Amejuma Emmanuel. "Business Intelligence and Artificial Intelligence for Sustainable Business Operations." International Journal of Scientific Research and Management (IJSRM) 13, no. 01 (2025): 1917–35. https://doi.org/10.18535/ijsrm/v13i01.ec05.
Full textAbstract:
In the modern business landscape, sustainability has become a fundamental goal for organizations, driven by growing environmental concerns, social responsibility, and the need for long-term profitability (Bocken et al., 2014). Companies are under increasing pressure to reduce their environmental footprint, optimize resources, and improve operational efficiency, all while maintaining competitiveness. Business Intelligence (BI) and Artificial Intelligence (AI) have emerged as key technologies in this transition, offering organizations the ability to make data-driven decisions that promote sustainability (Chen et al., 2020). BI encompasses tools and techniques that convert raw data into actionable insights, helping businesses optimize operations and minimize waste (Shollo & Galliers, 2016). On the other hand, AI, particularly machine learning and predictive analytics, enhances decision-making by forecasting trends, automating processes, and providing deeper insights into complex datasets (Jeble et al., 2020). This article explores the integration of BI and AI in driving sustainable business operations. It examines their individual contributions and the synergistic benefits they bring when combined. Key applications discussed include energy management, where BI helps track energy consumption patterns, and AI optimizes resource allocation to minimize waste (Kemp et al., 2021). In supply chain optimization, BI analyzes supplier performance and inventory levels, while AI forecasts demand and automates processes to reduce carbon footprints (Saghafian et al., 2020). Waste reduction efforts are enhanced through predictive analytics, which help anticipate production needs and reduce excess output (Karim et al., 2021). Environmental monitoring, powered by AI and IoT sensors, allows for real-time analysis of environmental conditions, ensuring compliance with sustainability standards (Khan et al., 2021). However, the implementation of these technologies also presents challenges. Data integration remains a significant barrier, as companies often face difficulties in harmonizing large datasets from disparate sources (Laudon & Laudon, 2019). The initial investment in BI and AI technologies can be high, making it difficult for small and medium-sized enterprises (SMEs) to adopt these solutions (Zhang et al., 2020). Additionally, a shortage of skilled professionals in data science and AI poses another challenge, limiting the effective use of these technologies (Brynjolfsson & McAfee, 2014). Despite these challenges, the potential of BI and AI to foster sustainable business operations is substantial, and overcoming these barriers will be key to unlocking their full potential. The article concludes by discussing strategies for successful implementation and the future outlook for BI and AI in sustainable business practices.
14
Foley, Éric, and Manon G. Guillemette. "What is Business Intelligence?" International Journal of Business Intelligence Research 1, no. 4 (2010): 1–28. http://dx.doi.org/10.4018/jbir.2010100101.
Full textAbstract:
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.
15
Kasemsap, Kijpokin. "The Fundamentals of Business Intelligence." International Journal of Organizational and Collective Intelligence 6, no. 2 (2016): 12–25. http://dx.doi.org/10.4018/ijoci.2016040102.
Full textAbstract:
This article analyzes the recent literature in the search for the fundamentals of business intelligence (BI). The literature review covers the overview of BI; BI and technology acceptance model (TAM); BI, Big Data, and social media; the elements of BI; the characteristics of BI; enterprise information system (EIS) and cloud computing; the importance of BI; and the implementation of BI. BI involves creating any type of data visualization that provides insight into a business for the purpose of making a decision or taking an action.BI can assist organizations by facilitating better decisions in all facets of operations. The ideal BI system gives the organizations easy access to the information and the ability to analyze and share this information with other business enterprises. The findings present valuable insights and further understanding of the way in which BI perspectives should be emphasized.
16
Skyrius, Rimvydas, Igor Katin, Michail Kazimianec, Svetlana Nemitko, Gediminas Rumšas, and Raimundas Žilinskas. "Factors Driving Business Intelligence Culture." Issues in Informing Science and Information Technology 13 (2016): 171–86. http://dx.doi.org/10.28945/3483.
Full textAbstract:
The field of business intelligence (BI), despite rapid technology advances, continues to feature inadequate levels of adoption. The attention of researchers is shifting towards hu-man factors of BI adoption. The wide set of human factors influencing BI adoption con-tains elements of what we call BI culture – an overarching concept covering key managerial issues that come up in BI implementation. Research sources provide different sets of features pertaining to BI culture or related concepts – decision-making culture, analytical culture and others. The goal of this paper is to perform the review of research and practical sources to examine driving forces of BI – data-driven approaches, BI agility, maturity and acceptance – to point out culture-related issues that support BI adoption and to suggest an emerging set of factors influencing BI culture.
17
Glancy, Fletcher H., and Surya B. Yadav. "Business Intelligence Conceptual Model." International Journal of Business Intelligence Research 2, no. 2 (2011): 48–66. http://dx.doi.org/10.4018/jbir.2011040104.
Full textAbstract:
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.
18
Suša Vugec, Dalia, Vesna Bosilj Vukšić, Mirjana Pejić Bach, Jurij Jaklič, and Mojca Indihar Štemberger. "Business intelligence and organizational performance." Business Process Management Journal 26, no. 6 (2020): 1709–30. http://dx.doi.org/10.1108/bpmj-08-2019-0342.
Full textAbstract:
PurposeOrganizations introduce business intelligence (BI) to increase their performance, but often, this initiative is not aligned with the business process management (BPM) initiative, which also aims to improve organizational performance. Although some findings from the literature indicate that BI implementation has a positive impact on organizational performance, the impact seems to be indirect. Therefore, the purpose of this study is to enhance the understanding of how BI maturity is translated into organizational performance. Alignment of BI and BPM initiatives seems one possible way for creating business value with BI, particularly because BI enables process performance measurement and management, which allows the BI initiative to become more business focused.Design/methodology/approachA questionnaire was prepared and used to collect data in Croatian and Slovenian organizations with more than 50 employees. A BI–BPM alignment measurement instrument was developed for the purpose of this study using the recommended process of scale development and validation. A total of 185 responses were analyzed by the structural equation modeling technique.FindingsOur results provide evidence that the effect of BI on organizational performance is fully mediated by alignment of BI and BPM initiatives, and therefore, BI business value can be generated through the use of common terminology and methodologies, as well as a strong communication between BI and BPM experts, managers and teams in order to coordinate the two initiatives.Originality/valueThis study has responded to the call for better understanding of how the impact of BI on organization performance is realized. It confirmed that BI and BPM initiatives should be aligned in order to give BI a business value.
19
O’Neill, Daniel. "Business Intelligence Competency Centers." International Journal of Business Intelligence Research 2, no. 3 (2011): 21–35. http://dx.doi.org/10.4018/jbir.2011070102.
Full textAbstract:
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.
20
Yogev, Nir, Adir Even, and Lior Fink. "How Business Intelligence Creates Value." International Journal of Business Intelligence Research 4, no. 3 (2013): 16–31. http://dx.doi.org/10.4018/ijbir.2013070102.
Full textAbstract:
This study examines the business value associated with business intelligence (BI) systems, based on the premise that business value is largely contingent on system type and its unique contribution. The study adopts a process-oriented approach to evaluating the value contribution of BI, arguing that it stems from improvements in business processes. The study develops and tests a research model that explains the unique mechanisms through which BI creates business value. The model draws on the resource-based view to identify key assets and capabilities that determine the impact of BI on business processes and, consequently, on organizational performance. Analysis of data collected from 159 managers and IT/BI experts, using structural equation modeling (SEM) techniques, shows that BI largely contributes to business value by improving both operational and strategic business processes.
21
Johnson, Brian. "Business Intelligence Should be Centralized." International Journal of Business Intelligence Research 2, no. 4 (2011): 42–54. http://dx.doi.org/10.4018/jbir.2011100104.
Full textAbstract:
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.
22
Solanki, Vatsalya Vijay. "Evolution of Business Intelligence Tools." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1149–51. http://dx.doi.org/10.22214/ijraset.2023.54820.
Full textAbstract:
Abstract: Business Intelligence (BI) encompasses tools, technologies, and practices that help organizations collect, analyze, and transform data into valuable insights necessary for decision-making process. It involves data collection, warehousing, analysing, and visualization. BI enables companies to leverage their data for a competitive advantage, driving growth and innovation. Key trends include self-service BI, advanced analytics, and AI-driven insights. BI empowers organizations to unlock data value and make informed strategic decisions
23
Sudiantini, Dian, Ernita Shafa Nur Fadhilah, Maylinda Wijayanti, Rahmaliya Pratiwi, and Rizka Nurul Hanifah. "UTILIZATION OF BUSINESS INTELLIGENCE IN BUSINESS DECISION MAKING." SENTRI: Jurnal Riset Ilmiah 3, no. 6 (2024): 2873–83. http://dx.doi.org/10.55681/sentri.v3i6.2966.
Full textAbstract:
Making wise and well-informed company decisions at a time where data is the most valuable resource requires the application of business intelligence (BI). Big Data is handled by modern BI, which also benefits from more processing power, fast data access, opportunity detection, and risk mitigation. In order to correctly represent the facts, this research employs a qualitative technique and gathers data from several sources. The study's findings demonstrate the importance of business intelligence in corporate decision-making. In order to assist educated decision-making, plan development plans, and offer insight into internal and external performance, business intelligence (BI) gathers data from a variety of sources. Implementing BI necessitates a business and technological expertise, but it also makes difficult data analysis easier, boosts productivity, and helps identify possibilities and resolve issues. Business Intelligence (BI) is critical to success in the current day because it gives reliable information, increases operational efficiency, and assists management in making decisions that are responsive to changes in the market. The study's findings also demonstrate how BI gathers information from a variety of sources to offer perception into the market, consumer behavior, and internal business performance.
24
February, Sam Aubrey Fabian. "A Business Intelligence Effectiveness Model." International Journal of Strategic Decision Sciences 14, no. 1 (2023): 1–23. http://dx.doi.org/10.4018/ijsds.320513.
Full textAbstract:
Business intelligence (BI) is a technology-driven process that contributes toward revealing the position of an organisation in comparison to its competitors, market conditions, and future trends, and presents demographic and economic information. The objective of the research was to identify the elements that determine the effectiveness of BI for organisations. The research proposes a BI effectiveness model to enhance decision-making support by ensuring that decision-makers receive the right information at the right time in the most appropriate format. A quantitative research approach was followed, and purposive sampling was used for selecting research participants within an organisation in the telecommunications sector. The effectiveness of a BI department has a direct impact on the strength of an organization's decision-making capability. The components of the BI effectiveness model suggest focus areas for more effective information flow throughout the organisation, improved information accessibility, improved decision-making, and ultimately, improved productivity.
25
Wikamulia, Nathaniel, and Sani Muhamad Isa. "Predictive business intelligence dashboard for food and beverage business." Bulletin of Electrical Engineering and Informatics 12, no. 5 (2023): 3016–26. http://dx.doi.org/10.11591/eei.v12i5.5162.
Full textAbstract:
This research was conducted to provide an example of predictive business intelligence (BI) dashboard implementation for the food and beverage business (businesses that sell fast-expired goods). This research was conducted using data from a bakery's transactional database. The data are used to perform demand forecasting using extreme gradient boosting (XGBoost), and recency, frequency, and monetary value (RFM) analysis using mini batch k-means (MBKM). The data are processed and displayed in a BI dashboard created using Microsoft Power BI. The XGBoost model created resulted in a root mean square error (RMSE) value of 0.188 and an R2 score of 0.931. The MBKM model created resulted in a Dunn index value of 0.4264, a silhouette score value of 0.4421, and a Davies-Bouldin index value of 0.8327. After the BI dashboard is evaluated by the end user using a questionnaire, the BI dashboard gets a final score of 4.77 out of 5. From the BI dashboard evaluation, it was concluded that the predictive BI dashboard succeeded in helping the analysis process in the bakery business by: accelerating the decision-making process, implementing a data-driven decision-making system, and helping businesses discover new insights.
26
Banica, Logica, Liviu Cristian Stefan, and Mariana Jurian. "Business Intelligence For Educational Purpose." Balkan Region Conference on Engineering and Business Education 1, no. 1 (2014): 333–38. http://dx.doi.org/10.2478/cplbu-2014-0049.
Full textAbstract:
AbstractThe paper follows three main directions: business intelligence – as a software tool, companies – as an application field and top management – as target of intelligent efforts. From this symbiosis does result an advantage, scientific and data based educational tool, having the goal to give the students a tool to explore data collections and analysis methods in order to improve the management of a company and forecast its evolution. The purpose of Business Intelligence (BI) software is to help the firms on acquiring knowledge about highlights and dangerous trends, to observe the connections and to forecast the future market evolutions. From this perspective, we consider that students need to learn theory and practical application about BI. After an overview of the BI main concepts, we choose to use the facilities of Jaspersoft BI software; to model the most frequently used analysis requirements, displaying the most relevant data and key indicators, following the steps of a BI system.
27
Ebule, Amejuma Emmanuel. "Leveraging Artificial Intelligence in Business Intelligence Systems for Predictive Analytics." International Journal of Scientific Research and Management (IJSRM) 13, no. 01 (2025): 1862–79. https://doi.org/10.18535/ijsrm/v13i01.ec02.
Full textAbstract:
Artificial Intelligence (AI) and Business Intelligence (BI) are rapidly emerging as the next big things for organizations to analyze data and gain insights. As this article will go on to examine, the concept of using AI for BI is one that has significant implications about the possible integration of AI into various Business Intelligence systems examined in this article will focus on the application of AI for BI in the use of predicting analytics. When integrating Machine learning, natural language processing, and intelligent automation, these AI-Advanced BI systems assist organizations to go beyond data reporting or simple descriptive analytics and gain an insight to use BI systems to discover and pre-empt issues, besides noticing them using proactive decision making. In discussing the elements of AI-embedded BI systems, this article analyzes how organizations across industries use real-time intelligence and predictive models as indispensable resources for the generation of competitive edge. Some of the advantages highlighted includes improved accuracy for predictions, efficiency of cost on data handling, scalability on large data and the shorter delays on decision making. However, alongside these benefits, the article also addresses key challenges, such as data privacy concerns, biases in AI algorithms, and the complexities of integrating AI into legacy BI platforms. These limitations are critical considerations for organizations seeking to implement AI-driven BI systems effectively. Furthermore, this work discusses the issues relating to the implementation of AI for BI, for example, the integration of AI into existing BI platforms, data quality issues, ethical issues, and the skill gaps in specialized AI talents. The article also discusses new developments in AI integration to BI systems including the growing incorporation of deep learning techniques, automation of decision making and BI democratization for small businesses. They suggest that BI must evolve new business strategies to be effective and meet the information demands needed for corporate competitiveness in today’s data-centric economy. The convergence of advanced analytics and operational decision making makes AI driven BI system the tool with tremendous potential to become the lingua franca of business strategy and growth.
28
Al-Zadjali, Mohamed, and Kamla Ali Al-Busaidi. "Empowering CRM Through Business Intelligence Applications." International Journal of Knowledge Management 14, no. 4 (2018): 68–87. http://dx.doi.org/10.4018/ijkm.2018100105.
Full textAbstract:
The application of business intelligence (BI) techniques for knowledge discovery and decision support empowers organizations in different functions. This article examines the impacts of BI on customer relationship management (CRM) functions (marketing, sales and customer services) in the telecommunications sector. The literature found that BI application in CRM in a telecommunications context is limited but necessary due to the high rate of competition between service providers and the massive data generated by subscribers. This study surveyed employees' perspectives from telecommunications companies in Oman, and results demonstrated mixed impacts. First, the results showed that implementing BI in marketing has a positive impact on business processes values, customer values, but a negative impact on employees' values. Second, implementing BI in sales has a positive impact on financial values and employees' values, but a negative impact on business processes values, and customers' values. Finally, implementing BI in customer service has a positive impact on employees' values. The study provides valuable guidelines for practitioners in the area of CRM, BI, and telecommunications to help understand why to invest in BI in specific CRM functions.
29
Kokin, Sergey, and Tien An Wang. "Empirical Research on Business Intelligence Success." Advanced Materials Research 842 (November 2013): 754–58. http://dx.doi.org/10.4028/www.scientific.net/amr.842.754.
Full textAbstract:
The present paper is a research on problems of measuring Business Intelligence (BI) System Success. The well-known system of Information System Success by DeLone and McLean and Business Intelligence Capabilities Framework were reviewed to develop a framework for evaluating Business Intelligence (BI) Success. We hypothesize that BI Capabilities (Data Type Quality, Data Source Quality, Flexibility, Interaction with other Systems, User Access Quality) are positively connected with BI Success. Our hypotheses are tested with survey data. The respondents are CEOs, CIOs and BI specialists of Russian-based companies. Structural equation modelling exhibits a good fit with the observed data. Our results show, that BI Capabilities have a strong influence on BI System Success. However, there is more work to be done, because some of the hypotheses are not supported. The present paper contributes theoretically to Information System Success domain by expanding research about Business Intelligence System Success.
30
Epple, Johannes, Robert Winter, Stefan Bischoff, and Stephan Aier. "Business Intelligence is No ‘Free Lunch'." International Journal of Business Intelligence Research 9, no. 1 (2018): 1–15. http://dx.doi.org/10.4018/ijbir.2018010101.
Full textAbstract:
Cost allocations for business intelligence (BI) costs create cost awareness, enhance cost transparency, and support the management of BI systems. Although BI cost allocation is highly relevant in practice, the field is widely uncharted in current scholarly research. In this article, the state of the art in scientific literature is analyzed. The review is comprised of three iterations. It shows that certain general approaches for information systems cost allocation are suitable candidates if being combined and tailored to BI systems. Based on synthesis, an agenda is derived for future research into cost allocation for BI systems.
31
Chee, Chin-Hoong, William Yeoh, Shijia Gao, and Gregory Richards. "Improving Business Intelligence Traceability and Accountability." Journal of Database Management 25, no. 3 (2014): 28–47. http://dx.doi.org/10.4018/jdm.2014070102.
Full textAbstract:
A Business Intelligence (BI) system provides users with multi-dimensional information (a so-called ‘BI product') to support decision-making. However, existing BI systems overlook the lineage metadata which supports individual data quality dimensions such as data believability and ease of understanding. Using a design science research paradigm, this paper proposes and develops an integrated framework (known as BI Product and Metacontent Map - ‘BIP-Map') to facilitate the traceability and accountability of BI products. Specifically, the business workflow layer of the integrated framework is modelled using business process modelling notation, and an information product map is used to model the second layer's information manufacturing process, whilst the third layer represents the metacontent detail of the data validation stage, from source system through to ETL, to the data warehousing stage. Also, the authors develop a BIP-Map informed prototype in collaboration with an online job advertising firm, the framework then being validated by key BI stakeholders of the firm. The integrated framework addresses individual-related data quality issues and builds user confidence by enhancing the traceability and accountability of a BI product.
32
Matthew, N. O. Sadiku, Omotoso Adedamola, and M. Musa Sarhan. "Healthcare Business Intelligence A Primer." International Journal of Trend in Scientific Research and Development 4, no. 2 (2020): 497–501. https://doi.org/10.5281/zenodo.3854888.
Full textAbstract:
To succeed in a modern digital world, healthcare industry must be data driven. Hospitals and healthcare institutions desire to make their workflows more efficient in order to meet demand. One way they can achieve this is with the help of business intelligence BI software. BI refers to the acquisition, correlation, and transformation of data into insightful and actionable information through analytics. Utilizing a BI software is an indispensable part of the growth process toward becoming data driven. In the modern healthcare environment, almost all BI initiatives will be driven by data analytics. This paper provides a brief examination of the deployment and constraints of business intelligence in healthcare. Matthew N. O. Sadiku | Adedamola Omotoso | Sarhan M. Musa "Healthcare Business Intelligence: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30041.pdf
33
Tofan, Dragoş Ovidiu. "Business Intelligence Security." Review of Economic and Business Studies 9, no. 1 (2016): 157–69. http://dx.doi.org/10.1515/rebs-2016-0030.
Full textAbstract:
AbstractExcess information characteristic to the current environment leads to the need for a change of the organizations’ perspective and strategy not only through the raw data processing, but also in terms of existing applications generating new information. The overwhelming evolution of digital technologies and web changes led to the adoption of new and adapted internal policies and the emergence of regulations at level of governments or different social organisms. Information security risks arising from the current dynamics demand fast solutions linked to hardware, software and also to education of human resources. Business Intelligence (BI) solutions have their specific evolution in order to bring their contribution to ensure the protection of data through specific components (Big Data, cloud, analytics). The current trend of development of BI applications on mobile devices brings with it a number of shortcomings related to information security and require additional protective measure regarding flows, specific processing and data storage.
34
Paradza, Dignity, and Olawande Daramola. "Business Intelligence and Business Value in Organisations: A Systematic Literature Review." Sustainability 13, no. 20 (2021): 11382. http://dx.doi.org/10.3390/su132011382.
Full textAbstract:
Organisations must derive adequate business value (BV) from Business Intelligence (BI) adoption to retain their profitability and long-term sustainability. Yet, the nuances that define the realisation of BV from BI are still not understood by many organisations that have adopted BI. This paper aims to foster a deeper understanding of the relationship between Business Intelligence (BI) and business value (BV) by focusing on the theories that have been used, the critical factors of BV derivation, the inhibitors of BV, and the different forms of BV. To do this, a systematic literature review (SLR) methodology was adopted. Articles were retrieved from three scholarly databases, namely Google Scholar, Scopus, and Science Direct, based on relevant search strings. Inclusion and exclusion criteria were applied to select ninety-three (93) papers as the primary studies. We found that the most used theoretical frameworks in studies on BI and BV are the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), Technology-Organisation-Environment (TOE), and Contingency Theory (CON). The most acknowledged critical factors of BV are skilled human capital, BI Infrastructure, data quality, BI application and usage/data culture, BI alignment with organisational goals, and top management support. The most acclaimed inhibitors of BV are data quality and handling, data security and protection, lack of BI Infrastructure, and lack of skilled human resource capital, while customer intelligence is the most acknowledged form of BV. So far, many theories that are relevant to BI and BV, critical factors, inhibitors, and forms of BV were marginally mentioned in the literature, requiring more investigations. The study reveals opportunities for future research that can be explored to gain a deeper understanding of the issues of BV derivation from BI. It also offers useful insights for adopters of BI, BI researchers, and BI practitioners.
35
Grytz, Raphael, and Artus Krohn-Grimberghe. "Business Intelligence and Analytics Cost Accounting." International Journal of Systems and Service-Oriented Engineering 8, no. 3 (2018): 37–59. http://dx.doi.org/10.4018/ijssoe.2018070103.
Full textAbstract:
As data driven decision-making using business intelligence and analytics (BI&A) becomes standard in companies, the importance of mitigating the accompanying growth in costs increases. Research shows that the increasing transparency of individual BI&A artefacts such as reports or analytic applications is necessary, but in practice and implementation lags behind. This article addresses the status quo for three types of stakeholders: users, developers, and managers. The results show where a strong need for action exists and this article identifies challenges for further research. These findings indicate that managers see BI&A cost accounting as having a high potential benefit - and believe the degree of implementation to be higher than other stakeholder types do. The authors identified comprehensibility as an important factor for user acceptance of BI&A cost accounting systems; this could be supported by a service-oriented approach. The authors conclude that BI&A professionals have to consider these different perceptions and their implications in order to gain traction for BI&A cost accounting.
36
Thakur, Mr Sanjeev, and Mr Krishna Raaz. "Mechanization’s Impact on Business Intelligence Tools." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43363.
Full textAbstract:
Business intelligence (BI) solutions have transformed organizational decision-making processes by giving useful data insights. In recent years, there has been a substantial trend toward automation in BI systems, allowing firms to streamline operations and improve productivity. This article investigates how automation enabled by BI technologies affects several elements of corporate operations. We investigate how automation has transformed data processing, analysis, and reporting procedures by reviewing the literature and conducting case studies. In addition, we explore the effects of automation on workforce dynamics, organizational structures, and strategic decision-making. The study's findings give significant insight for firms looking to use BI tools for increased automation and a competitive advantage in today's data-driven environment
37
Orina, Dennis Olondo, and Andrea Tick. "Application of Business Intelligence in Kenyan SMEs." Journal of Central and Eastern European African Studies 4, no. 3-4 (2025): 239–70. https://doi.org/10.12700/jceeas.2024.4.3-4.299.
Full textAbstract:
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.
38
Shi, Hao, Cong Peng, and Mao Zeng Xu. "Business Intelligence in Construction: A Review." Advanced Materials Research 594-597 (November 2012): 3049–57. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.3049.
Full textAbstract:
Business Intelligence (BI) has been viewed as sets of powerful tools and approaches to improving business executive decision-making, business operations, and increasing the value of the enterprise. This literature review presents the development of business Intelligence in recent years. Architecture, technologies, performance evaluation and applications four aspects are discussed to illustrate the BI used technology and the future trend. It presents how to improve the BI efficiency and what method is adopted for enterprise to solve the problems which they encounter.
39
Miranda, Eka, Firmansyah Firmansyah, and Davies Ezra Emerald. "Desain Business Intelligence untuk Manajemen Rumah Sakit." JURNAL SISTEM INFORMASI BISNIS 11, no. 1 (2021): 62–69. http://dx.doi.org/10.21456/vol11iss1pp62-69.
Full textAbstract:
Organizational management, as well as hospital management, could not work precisely without defining the performance indicators to control all business process. This situation encourages the need for information and data analysis availability. BI includes applications, infrastructure, tools and practices that enable organizations to access and analyze data and information to improve and optimize the decisions and organization performance. BI has the potential to improve the quality, efficiency and effectiveness of hospital health services as well. The objective of this study was to design business intelligence prototype for the hospital. BI design was carried out with a Business Intelligence Roadmap approach which has 6 main stages, namely: (1) Justification, (2) Planning, (3) Business Analysis, (4) Design, (5) Construction and (6) Deployment. Data were collected from hospital activities includes registration, Electronic Medical Record (EMR) in the Imaging, Laboratory, Pharmacy, Operating Theater and Medical Check-Up departments activities. Designing BI was preceded by identifying technical and non-technical needs, then continued by designing BI itself. BI roadmap approach was used for this propose. Technical requirements for designing BI include hardware and software infrastructure readiness, while non-technical requirements include Business Analysis which consists of Project Requirements Definition, Data Analysis, Application Prototyping and Metadata repository Analysis. Designing BI itself includes: Designing a multidimensional database and designing ETL. The user interfaces for BI was shown in the Performance Dashboard, which allows organizations to track all aspects of their daily business activities and performance.
40
Richards, Gregory S. "Business Intelligence and Analytics Research." International Journal of Business Intelligence Research 7, no. 1 (2016): 1–10. http://dx.doi.org/10.4018/ijbir.2016010101.
Full textAbstract:
The tremendous growth of data of all forms has led to an increase in research on the uses and outcomes of Business Intelligence and Analytics (BI&A). Much of the current research however, focuses on the technological aspects. The process of decision making with data is treated more or less like the proverbial black box. If one is to better understand how BI&A can help managers make informed decisions, then more effort is needed to explore the decision making process. This paper argues that decision-making in organizations is enacted by a sociotechnical system in which human information processing forms the key constraint. By considering the stages of cognition and the use of rules-based versus heuristic-based decision making, the paper identifies a number of core questions related to the contribution of a BI&A system to the decision making process in organizations.
41
Handzic, Meliha, Kursad Ozlen, and Nermina Durmic. "Improving Customer Relationship Management Through Business Intelligence." Journal of Information & Knowledge Management 13, no. 02 (2014): 1450015. http://dx.doi.org/10.1142/s0219649214500154.
Full textAbstract:
This paper examines empirically the role of business intelligence (BI) in customer relationship management (CRM). Drawing on relevant literature on BI and CRM, the research model for the current investigation proposes that BI approaches of an organisation and its competition influence organisational business strategy which in turn impacts its customer strategy. The model is tested empirically using survey data of 165 respondents from 73 different private and state owned businesses operating in a transitional economy of East Europe. Empirical evidence confirms a key role of BI in CRM through its impact on organisational business and customer strategies development. Such findings make two important contributions. For research, they provide an improved understanding of the factors and processes involved in realising benefits from BI. For practice, they show managers how BI can be leveraged to achieve performance gains through competitive actions. Further research is recommended to confirm and extend the current investigation.
42
Talaoui, Yassine, Rodrigo Rabetino, and Marko Kohtamäki. "Structuring Business Intelligence (BI)-related research." Academy of Management Proceedings 2018, no. 1 (2018): 15938. http://dx.doi.org/10.5465/ambpp.2018.15938abstract.
Full text
43
Firmansyah, Denny. "Business Intelligence (BI) Implementation - COBIT 4.1." Journal of Applied Information, Communication and Technology 5, no. 1 (2018): 1–9. http://dx.doi.org/10.33555/ejaict.v5i1.5.
Full textAbstract:
The present paper aims to propose a general procedure to sizing and improve the Information Technology in an organization.
 The use of information systems has led to the recognition of the importance of quality management in the competitive environment. However, only few companies have taken actions to measure and enhance the quality of information. Hidden costs and poor quality of information may adversely measuring the performance of Business Intelligence systems based on COBIT 4.1 [1].
 The value of the study is the ability to use business intelligence for the purpose of implementation and management capability.
44
Aruldoss, Martin, Miranda Lakshmi Travis, and V. Prasanna Venkatesan. "A survey on recent research in business intelligence." Journal of Enterprise Information Management 27, no. 6 (2014): 831–66. http://dx.doi.org/10.1108/jeim-06-2013-0029.
Full textAbstract:
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.
45
Ali, Md Shaheb, and Shahadat Khan. "Organizational Capability Readiness Towards Business Intelligence Implementation." International Journal of Business Intelligence Research 10, no. 1 (2019): 42–58. http://dx.doi.org/10.4018/ijbir.2019010103.
Full textAbstract:
Business intelligence (BI) has widely been recognized due to its growing applications in improving the decision-making support. The growing trend of BI applications necessitates a study on organizational capability readiness which offers significant effects in BI implementations. However, a limited number of studies focused on issues related to organizational capability readiness for BI implementations. Therefore, this study aims to analyze and explore an integrated view of literature in relation to organizational capability readiness for BI implementations. A qualitative content analysis of relevant sample articles was conducted for exploring the thematic understanding on organizational capability readiness for BI implementations. The main findings of this review study are classified in two categories, i.e., readiness of managerial proficiency and readiness of IS-led environment. The first category “readiness of managerial proficiency” integrates several factors such as the readiness of awareness, commitment, and absorptive capacity. The second category “readiness of IS-led environment” integrates the readiness of technological support, personnel capability, and data quality management. The findings of this review study would significantly contribute to the researchers and practitioners for the sustainable development of BI implementation in business.
46
Ratia, Milla, Jussi Myllärniemi, and Nina Helander. "The new era of business intelligence." Meditari Accountancy Research 26, no. 3 (2018): 531–46. http://dx.doi.org/10.1108/medar-08-2017-0200.
Full textAbstract:
Purpose As the health care sector is changing rapidly, there is a growing need to develop new ways to make data-driven decisions, especially at the organizational level. Data utilization, like business intelligence (BI) activities, benefits health care organizations. The purpose of this paper is to study the potential of Big Data and the utilization of BI tools in creating value in the private health care industry in Finland. Design/methodology/approach Intellectual capital (IC) components and Möller et al.’s (2005) work on value capabilities are used as a framework to point out the roles of data utilization and BI tools in value creation. Thematic interviews enable understanding of the value creation based on Big Data potential and utilization of BI tools in the Finnish private health care industry. Findings The findings will provide an understanding of the existing data sources and BI tools used in private health care. In addition, it provides an insight into the future-oriented Big Data potential, which can create new business concepts. The approach provides valuable insights for value identifying the future needs of data utilization and creates an understanding on the current state within the private health care sector. Originality/value Data-driven value creation is one of the most discussed topics in private health care sector. By analyzing the current data-source utilization, challenges with data and BI tool utilization and the future vision and development roadmaps, the authors gain a better understanding of the IC components and value creation capabilities.
47
Samia, NACIRI. "A Systematic mapping Review of Business Intelligence." International Journal of Management Sciences and Business Research 7, no. 10 (2018): 58–71. https://doi.org/10.5281/zenodo.3490030.
Full textAbstract:
Experience in software engineering in general and Business Intelligence(BI) and analytics, in particular, involved various approaches, models, tools and criteria to select and implement the right BI system that meets the decisionmaker expectations. Those multiple disciplines were appointed in the BI industry, addressed in research, and depicted thought surveys conducted by well-known advisor organizations similar to Gartner, Forester, and others. However, to our knowledge, few systematic reviews have been conducted to identify and analyze Business Intelligence (BI) disciplines explored and cited in the literature. This paper aims to identify and analyze BI and analytics publications according to five perspectives: publication channel, type of contribution, trends over time, domain and covered area. It performs a systematic mapping review of papers published in the period 2000–2017 and reviews them based on an automated search. This mapping study revealed that most researchers focus on proposing or analyzing models and approaches related to BI systems
48
Jagals, Marvin, Erik Karger, and und Marco Boehle. "Self-Service Business Intelligence." Controlling 33, no. 6 (2021): 50–56. http://dx.doi.org/10.15358/0935-0381-2021-6-50.
Full textAbstract:
Self-Service BI gestattet Fachbereichen innerhalb einer Organisation in Eigenverantwortung Analysen zu erstellen. In der Praxis existieren dazu jedoch besonders auf Managementebene diverse Herausforderungen. Im Rahmen dieses Beitrags werden Gestaltungsempfehlungen erörtert, die der Managementpraxis zu einer erfolgreichen Implementierung verhelfen.
49
Spruit, Marco, and Tim de Boer. "Business Intelligence as a Service." International Journal of Business Intelligence Research 5, no. 4 (2014): 26–43. http://dx.doi.org/10.4018/ijbir.2014100103.
Full textAbstract:
Demand for business intelligence (BI) applications continues to grow even at a time when demand for most information technology (IT) products is low, showing the importance of BI products for a modern organization. However, globalization changes the way organizations use BI, where geographic location and time independency is becoming more and more important. Gartner's hype-cycle on BI depicts the technology of BI as a Service as being almost on top of the hype cycle, indicating there are high expectations of this new technology. This research advances on existing literature on business intelligence and cloud computing from a development perspective by introducing the concept of business intelligence as a service (BIaaS). The most important deliverable is the BIaaS capability maturity model (CMM) that is introduced here. The BIaaS CMM explains the conceptual model of BIaaS by the creation of the first BIaaS capability model containing key capabilities of BIaaS. The capability model is further enhanced with maturity levels depicting the importance of each BIaaS capability, a maturity matrix suggesting a roadmap for BIaaS solution development, and a BIaaS assessment model introducing a tool for finding problem areas in existing BIaaS solutions. The developed BIaaS CMM ought to support (starting) BIaaS vendors to develop BIaaS solutions by providing an assessment tool for BIaaS solutions. The assessment outcome provides the current maturity of the BIaaS solution and also includes problem areas for solution improvement. The introduction of the CApability Maturity Positioning (CAMP) method for the development of a maturity matrix, which results in the BIaaS maturity model, is significantly different from conventional maturity modeling. To calculate the weight of each capability from the BIaaS capability model, a thorough product review of existing business intelligence and cloud computing products is performed. Analysis of the results and normalizing the outcome of that analysis together with the introduction of a calculation mapping, is input for the creation of the maturity matrix. The maturity matrix is the essential foundation for the developed business intelligence as a Service capability maturity model, which is the main deliverable of this research.
50
Marjanovic, Olivera. "The Importance of Process Thinking in Business Intelligence." International Journal of Business Intelligence Research 1, no. 4 (2010): 29–46. http://dx.doi.org/10.4018/jbir.2010100102.
Full textAbstract:
The growing field of Operational Business Intelligence (BI) has resulted in increasing interest in BI-supported Business Processes (BPs), including their management and ongoing improvement. This has led BI practitioners to consider another field–Business Process Management (BPM)–that is closely related to business performance management. However, current approaches to the BPM and operational BI integration have been limited and reduced to the problem of technical integration of BPM and BI systems. This paper argues that by adopting process- thinking in BI, further opportunities for business value creation could be discovered through systematic analysis of the non-technical aspects of BI and BPM integration, including strategy alignment, human-centered knowledge management, and ongoing improvement of BI supported processes. The authors propose a theoretical framework founded in the related research in BPM, BI, and Knowledge Management (KM) fields, describing the ways it has been used to guide ongoing empirical research in diverse case organizations across different industry sectors.