Business Intelligence Architecture Research Articles

Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Systematic Literature Review

Higher education institutions (HEIs) make decisions in several domains, namely strategic and internal management, without using systematized data that support these decisions, which may jeopardize the success of their actions or even their efficiency. Thus, HEIs must define and monitor strategies and policies essential for decision making in their various areas and levels, in which business intelligence (BI) plays a leading role. This study presents a systematic literature review (SLR) aimed at identifying and analyzing primary studies that propose a roadmap for the implementation of a BI system in HEIs. The objectives of the SLR are to identify and characterize (i) the strategic objectives that underlie decision making, activities, processes, and information in HEIs; (ii) the BI systems used in HEIs; (iii) the methods and techniques applied in the design of a BI architecture in HEIs. The results showed that there is space for developing research in this area since it was possible to identify several studies on the use of BI in HEIs, although a roadmap for its implementation was not identified, making it necessary to define a roadmap for the implementation of BI systems that can serve as a reference for HEIs.

Towards a Conceptual Framework for Data Management in Business Intelligence

Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate data from several operational databases. BI further incorporates a combination of analytics, data management, and reporting tools, together with associated methodologies for managing and analyzing data. An important goal of BI initiatives is to improve business decision-making for organizations to increase revenue, improve operational efficiency, and gain a competitive advantage. In this article, we analyze qualitatively various prominent business intelligence (BI) frameworks in the literature and develop a comprehensive BI framework from these. Through the technique of qualitative propositions, we identify the properties, respective advantages, and possible disadvantages of the said BI frameworks to develop a comprehensive framework aimed mainly at data management, incorporating the advantages and eliminating the disadvantages of the individual frameworks. The BI landscape is vast, so as a limitation, we note that the new framework is conceptual; hence, no implementation or any quantitative measurement is performed at this stage. That said, our work exhibits originality since it combines numerous BI frameworks into a comprehensive framework, thereby contributing to conceptual BI framework development. As part of future work, the new framework will be formally specified, followed by a practical phase, namely, conducting case studies in the industry to assist companies in their BI applications.

Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework

Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework

Achieving Competitive Sustainable Advantages (CSAs) by Applying a Heuristic-Collaborative Risk Model

Increasing disruption and turmoil continuously challenges organizations regarding the achievement of short- and long-term objectives. Such a hostile environment results from both the natural evolution of the business landscape complexity and the emergence of unpredictable disruptive evets such as the COVID-19 pandemic. More than ever, organizations should continuously develop business strategies that help them to become more agile, adaptative, sustainable, and effectively respond to the countless business risks (threats and opportunities). Innovation, such as the development and implementation of new technology, new ways of thinking and executing work, are just some of the major factors that can help organizations to increase their likelihood of success. In this work, is proposed the incorporation of a heuristic risk model into a typical organizational business intelligence architecture, to identify collaborative critical success factors across the different phases of a project life cycle which can be used to guide, monitor, and increase the success outcome likelihood of ongoing and upcoming projects. Some benefits of the incorporation include: a higher speed regarding the collection and treatment process of project collaborative data, the output of more accurate results with residual bias associated, a timely and efficient 360° view regarding the identification of project collaborative risks, and the impact (positive or/and negative) of these on a project’s outputs and outcomes. Finally, the model capabilities of performing descriptive, predictive, and prescriptive analysis, enables the generation of unique and actionable project’s lessons learned which can be used to make more data-informed decisions, and thus enhances the achievement of sustainable competitive advantages. The development and implementation of the proposed incorporation is illustrated with a with a real case study.

Conformance Analysis of Student Activities to Evaluate Implementation of Outcome-Based Education in Early of Pandemic using Process Mining

The learning management system has a core component of a system event log that contains data on activities carried out by students and lecturers in the system. Educational process mining is a field in educational data mining that is concerned with finding, analyzing, and improving the overall educational process based on information hidden in educational data sets and event logs. The learning process in student lectures through the learning management system will produce a process flow according to the event data. In one semester in the information technology-based study program, the subjects taken are data from programming and non-programming courses in the 5th semester of the information systems department, namely Data Warehouse and Business Intelligence (DWBI) and Enterprise Architecture (EA). The Data Warehouse and Business Intelligence course is chosen because the main role in the graduate profile is as a data engineer. While the Enterprise Architecture course is chosen because being an IT Consultant requires knowledge of EA. Each course has different measured learning outcomes and each course has a different pattern in obtaining learning outcomes. To get a pattern for each learning achievement, an analysis of learning patterns, Bloom’s taxonomy level, and CLO pass scores was carried out using process mining. Course Learning Outcomes (CLO) is a competency standard or minimum qualification criteria for graduates’ abilities which include attitudes, knowledge, and skills assigned to courses. The existence of a bloom level indicates the level of expected learning achievement, where the higher the bloom level, the higher the expected ability. The mining process is carried out using Disco and PROM 5.2. The modeling process uses a heuristic miner algorithm because of its ability to express the main behavior recorded in the event log well. Heuristic miner algorithm can also take into account the frequency of the relationship between activities in the log to determine causal dependencies. The results of this study indicate that there is a difference between those that pass the course learning outcomes and those that do not pass. The passed CLO is indicated by the realization value of passing the course exceeding the threshold of 85.50%, while the failed CLO is indicated by the realization value of course graduation that is less than the threshold. In addition, control-flow, the frequency of activities that are often carried out indicate the appropriate learning path and are carried out by students to achieve a minimal assessment of course learning outcomes. In the Enterprise Architecture course, the activity that has the highest frequency in CLO1 is Attempt Quiz, while in CLO6 is View Course. In the Data Warehouse and Business Intelligence course, the activity that has the highest frequency in CLO3 is View Course, while in CLO4 is Attempt Quiz. The initial activity of the learning pattern produced in the two courses begins always with the view course activity. The highest bloom level in the Data Warehouse and Business Intelligence course is C6 Creation, while in the Enterprise Architecture course is C5 Evaluation. Thus, it can be said that Data Warehouse and Business Intelligence courses have a higher level of difficulty than Enterprise Architecture. Previously, in the DWBI course there was one CLO that failed in its implementation. With this research, it is hoped that this research can have a positive impact on adding new insights regarding the use of event logs in the field of education, so implementation of outcome-based education can be used as a benchmark for student learning to succeed in the course which include attitudes, knowledge, and skills.

A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business

Efficient cooperation between organizations across all the phases of a project lifecycle is a critical factor to increase the chances of project success and drive sustainable business. However, and according to research, despite the large benefits that efficient organizational cooperation provides to organizations, they are still often reluctant to engage in cooperative partnerships. The reviewed literature argues that the major reason for such a trend is due to the lack of efficient and actionable supportive models to manage organizational cooperative risks. In this work we propose a model to efficiently support the management of organizational cooperative risks in project environments. The model, MCPx (management of cooperative projects), was developed based on four critical scientific pillars, (1) project risk management, (2) cooperative networks, (3) social network analysis, and (4) business intelligence architecture, and will analyze in a quantitative way how project cooperative behaviors evolve across a bounded time period, and to which extent they can turn into a cooperative project risk (essentially potential threats). For this matter, the MCPx model will quantitatively analyze five key project cooperative behavioral dimensions, (1) communication, (2) information sharing, (3) trust, (4) problem solving and (5) decision making, which show how dynamic interactions between project stakeholders evolve across time. The implementation and functioning principles of the MCPx model are illustrated with a case study.

MEASUREMENT OF THE EFFECTS OF BUSINESS INTELLIGENCE APPLICATIONS ON PERFORMANCE IN HOSPITALS ACCORDING TO THE MANAGERIAL LEVELS: A CHAIN HOSPITAL APPLICATION

MEASUREMENT OF THE EFFECTS OF BUSINESS INTELLIGENCE APPLICATIONS ON PERFORMANCE IN HOSPITALS ACCORDING TO THE MANAGERIAL LEVELS: A CHAIN HOSPITAL APPLICATION Mustafa IŞIK [1] Onur YARAR [2] Didem Söylemez SUR [3] ABSTRACT Problem of the Research: Hospitals are matrix and complex organizations with many areas of expertise. In hospitals, multi-modular and integrated systems that can have multiple software and databases provide support in the execution of business processes within this complex organizational structure. Getting the right data at the right time for decision support purposes is often an important problem. In overcoming these problems, the problem of the research is what the contribution of integrated business intelligence applications solutions can be in decision-making stages from all levels of management. Purpose of the research: This research was conducted to investigate the effect of business intelligence reporting practices on performance according to managerial levels in hospital management systems. The research is a study conducted to measure the effect of business intelligence practices on performance in a chain hospital group that uses business intelligence in connection with the ERP (Enterprise Resource Planning) system with the highest number of hospitals in Turkey. The questionnaire study was conducted on 383 people. In this context, BI (Business Intelligence) architecture has been examined with all its components and information has been given about the benefits it provides to the enterprises. In the application developed as an example, a data warehouse modeling in accordance with the information required by the top management in hospitals, preparation of analyses, creation of presentation layer and presentation of the prepared analyses and reports on the control table were carried out. Material &Method: The universe of the research is composed of people who have the authority and access to use BI at MLP care group for this study. The number of authorized people on the ERP system was 591 and 65% of them have filled out the requested survey. 231 was our minimum number and for increasing the reliability we have modified our number of samples to 383. So 383 samples were reached to strengthen the validity and the reliability of the survey. In this sample range, it was also aimed to compare the performance impact between senior, intermediate and operational level managers. Conclusion: As a result of the research, it was determined that corporate business intelligence application screens at all types of managerial levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor and control their operational activities through corporate business intelligence, it has been concluded that performance indicators provide less time loss, high reliability, integrated data, quality and accurate valuation advantages in the evaluation process. In the research, it was observed that the effect of performance results of operational managers' business intelligence applications from management levels was higher than that of senior and middle level managers. Keywords: Business Intelligence, Big Data, Corporate Performance Management, Hospital Information Management Systems, Data Analysis [1] Mustafa IŞIK, Asst. Prof. İstinye University Faculty of Economics, Administrative and Social Sciences, Department of Health Management/ mustafa.isik@mlpcare.com [2] Onur YARAR, Asst. Prof. Okan University, Healthcare Management, Turkey / onur.yarar@okan.edu.tr [3] Didem Söylemez SUR, Asst. Prof. Kent University, Healthcare Management, Turkey /didem.sur@kent.edu.tr

BITOUR: A Business Intelligence Platform for Tourism Analysis

Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.

Adaptive Business Intelligence: A New Architectural Approach

Healthcare systems face enormous challenges, fundamentally due to the amount of data generated daily in a hospital environment, which forces entities to reflect on how to organize and use the same data. Currently, the number of studies at this level is growing, focusing on the innovation to be implemented, so that this same sector can adopt new methodologies, architectures and technologies that allow a more efficient support of existing hospital processes, as well as the results to be provided to all professionals involved in this area. In this research, an Adaptive Business Intelligence architecture is proposed, whose contribution was supported by the realization of an adequate conceptual and technological framework describing its development at different levels. Thus, a possible modernization of several working methods is initiated, with the introduction of an architecture capable of contributing to several factors, both at clinical and administrative levels, meeting the needs of a hospital system, regarding the design, development, implementation and demonstration of results.

Social BI to understand the debate on vaccines on the Web and social media: unraveling the anti-, free, and pro-vax communities in Italy

The debate on vaccines in Italy has greatly intensified in recent years. The promulgation of a law that makes a set of ten vaccines obligatory has pushed this formerly niche topic to a mainstream level. The law itself is an answer to the progressive erosion of the vaccine coverage. The debate has become a political topic with three main positions: supporters of the importance of vaccines, opponents who claim that vaccines are harmful to health, and the new position of those contesting only the mandatoriness of vaccinations. In this paper, we build on a Social Business Intelligence architecture to propose an in-depth analysis of the emerging social debate. Our analysis spans over more than three years, covering all the Web and social media. We adopt several techniques, including community detection and text analytics, to understand the evolution of the debate, the discussed topics, and the structure and peculiarities of the main social communities. The study reveals that the communities are well characterized, especially from a political perspective, and provides useful insights to official health organizations to improve their communication strategies.

Construction of business intelligence in dadih product affective design

Business intelligence is a primary system for almost all conditions that involve business decision making and strategy formulation. Manufacturers usually want to collect more information about the product before being traded. They consider the opinions of consumers in making decisions on consumer purchases. Among the entire spectrum of consumer needs, functional and affective needs have been recognised as of primary importance. In this study, we focused on the affective design of the dadih product. Dadih is one of the typical fermented milk products from West Sumatera and potentially develops as the functional food. However, the obstacle of the dadih product today is not yet in the modern mass commercialisation way due to less attractive packaging that makes it difficult for marketing and market expansion. The main challenges are to understand consumers’ affective needs accurately and then design a dadih product by consumers’ needs. The objectives of this research are to identify the construction of business intelligence architecture, to identify consumers’ affective needs for dadih products and to map the need for the affective design of dadih products based on consumer opinion. The architecture of business intelligence was compiled from data sources, data warehousing, analytical techniques, subject analysis, and decision making. The analysis techniques used to map consumer affective needs were Naive Bayes Classifier and Association Rules Mining. The results of the analysis shown the orientation of consumer opinion and the rules of affective design of dadih products.

Challenges and Opportunities for Publishing IIoT Data in Manufacturing as a Service Business

Cloud manufacturing (CMfg) can manage mass manufacturing (MM) resources and capabilities, and provide them as services via the Internet. Undoubtedly, multiple manufacturing clouds will have abundant services in terms of function, price, reliability, location, etc. The implementation of such concept in a wider structure than a single MC requires to provide explicit data about production sequence, machine allocation, production parameters, quality of service (QoS), and several other key performance indicators (KPIs). However, the state of the art approach is based on private access to such data, under specific contract requirements, and it becomes a limitation to the evolution of the concept itself. In particular it becomes limiting when the CMfgs involve multiple process owners distributed throughout a complex value creating network, such as production, logistics, quality control, but also, third parties like auditor interested in energy consumption, or machine maintenance provider, also interested in monitoring the effective usage of the machines, etc. This paper will analyze deeply the structural and functional characteristics of such CMfgs and then, it will propose a business intelligence architecture that aims to enable publishing relevant KPIs related to interested process data, with the convenient layer of trustworthiness. Such public access will enable the assessment to different agents at different time frequency and, finally, with different motivation, thus bringing practical information about time delays for the data availability and scalability factors.

The power big data-based energy analysis for intelligent community in smart grid

Smart grid deploys large numbers of intelligent terminals, to monitor or control the operating status and improve the energy efficiency and functional applications. In this paper, we study the power efficiency problem of intelligent cell based on the power big data, and present the system architecture and key algorithm for the intelligent community and smart industrial park: 1) we propose a business intelligence architecture based on cloud computing to meet the requirements of efficient storage and analysis of massive data; 2) we also establish a multivariable, multi-dimensional intelligent electricity energy analysis model which improves the orderly power consumption efficiency; 3) also we present a novel parallel algorithm to achieve the data mining algorithms and data analysis algorithms, and solve the issue of processing speed of large-scale data analysis. The analysis results guided the efficient electricity, and increased the functions of the intelligent community and the intelligent life preliminarily.

The power big data-based energy analysis for intelligent community in smart grid

Smart grid deploys large numbers of intelligent terminals, to monitor or control the operating status and improve the energy efficiency and functional applications. In this paper, we study the power efficiency problem of intelligent cell based on the power big data, and present the system architecture and key algorithm for the intelligent community and smart industrial park: 1) we propose a business intelligence architecture based on cloud computing to meet the requirements of efficient storage and analysis of massive data; 2) we also establish a multivariable, multi-dimensional intelligent electricity energy analysis model which improves the orderly power consumption efficiency; 3) also we present a novel parallel algorithm to achieve the data mining algorithms and data analysis algorithms, and solve the issue of processing speed of large-scale data analysis. The analysis results guided the efficient electricity, and increased the functions of the intelligent community and the intelligent life preliminarily.

Investigating the Impact of System Quality on Service-Oriented Business Intelligence Architecture

Despite the recognized value that Business Intelligence (BI) systems bring to organizations, our understanding of the system quality and its impact on service-oriented architecture is limited. This study seeks to examine the impact of system quality on service-oriented BI architecture. Following the theory in the Information System success model, this study assesses the factors that can create impact to the individual and the organization. A total of 60 sample data were collected and analyzed using the partial least squares method. Majority of the respondents are the IT practitioners who hold managerial positions in their respective companies. The results suggest there are two sets of system quality factors. This study contributes to the literature of BI architecture. The managerial findings will be useful for BI stakeholders in the planning, reviewing, and implementing of SOA-based BI architecture.

Architecture for Business Intelligence in the Healthcare Sector

Healthcare environment is growing to include not only the traditional information systems, but also a business intelligence platform. For executive leaders, consultants, and analysts, there is no longer a need to spend hours in design and develop of typical reports or charts, the entire solution can be completed through using Business Intelligence software. The current paper highlights the advantages of big data analytics and business intelligence in the healthcare industry. In this paper, In this paper we focus our discussion around intelligent techniques and methodologies which are recently used for business intelligence in healthcare.

Revisiting Ralph Sprague’s Framework for Developing Decision Support Systems

Ralph H. Sprague Jr. was a leader in the MIS field and helped develop the conceptual foundation for decision support systems (DSS). In this paper, I pay homage to Sprague and his DSS contributions. I take a personal perspective based on my years of working with Sprague. I explore the history of DSS and its evolution. I also present and discuss Sprague’s DSS development framework with its dialog, data, and models (DDM) paradigm and characteristics. At its core, the development framework remains valid in today’s world of business intelligence and big data analytics. I present and discuss a contemporary reference architecture for business intelligence and analytics (BI/A) in the context of Sprague’s DSS development framework. The practice of decision support continues to evolve and can be described by a maturity model with DSS, enterprise data warehousing, real-time data warehousing, big data analytics, and the emerging cognitive as successive generations. I use a DSS perspective to describe and provide examples of what the forthcoming cognitive generation will bring.

A Generic Functional Architecture for Operational BI System

In recent years, Operational Business Intelligence has emerged as an important trend in the Business Intelligence (BI) market. Majority of BI application architectures are bespoke in nature which have several architectural limitations like tightly coupled, static, historic, subjective, no performance measurement of business processes, limited user access, limited analytical processing, querying and reporting features. In this article, a generic functional architecture for Operational BI systems based on software architecture principles is presented. All functional modules of the system are derived from the key features of the system and by using top down approach of software design principles. The similar functional modules are grouped into sub-systems and a set of these sub-systems constitutes overall functional architecture. The proposed architecture overcomes the limitations of traditional BI architectures.

A Business Intelligence Approach to Monitoring and Trend Analysis of National R&D Indicators

The economic growth and competitiveness of countries is strongly influenced by their national research and development (R&D) systems. Hence, national R&D managers must make appropriate decisions to develop their R&D systems. For this purpose, an integrated view on the dimensions of national R&D systems is much needed. Due to the distribution of relevant data sources for national, R&D indicators and the complexity of national R&D systems, establishing an integrated view is challenging. In this article, we propose a business intelligence architecture to monitor, analyze, and forecast national, R&D indicators. Data for these indicators are integrated from heterogeneous data sources. In a case study focusing on Iranian R&D indicators, a prototype system is designed and implemented to evaluate its effectiveness in practice. After monitoring the indicators using online analytical processing, trends for the indicators across 11 years are forecasted using time series analysis models. The prototype system provides an integrated view of the Iranian R&D system, so that national R&D managers can use the developed system for monitoring and trend analysis of Iranian R&D indicators. As a result of the analysis of these trends, implications and recommendations for the future of the Iranian R&D system are presented.

Metodologías Sugeridas de Evaluación y Selección de Software de Arquitectura Empresarial para la Digitalización del Conocimiento

La gestión del conocimiento (GC) es una práctica de administración que permite la creación, uso, distribución y transferencia del conocimiento en organizaciones. Existen marcos de referencia de GC que incluyen componentes de inteligencia de negocios o de arquitectura empresarial (AE) para la implementación de proyectos de GC en organizaciones. Las herramientas de AE son usadas para digitalizar, relacionar y visualizar varias dimensiones de conocimiento como son: estructura organizacional, procesos de negocio, aplicaciones e infraestructura tecnológica. Este artículo tiene como objetivo evaluar el rol de la AE como componente clave de la GC y sugerir metodologías que pueden ser usadas para evaluar herramientas de software de AE. Para esto se realizó una revisión bibliográfica de las metodologías de evaluación de software existente en el mercado y se eligieron aquellas que pueden ser adaptadas para el campo de la AE. Está investigación fue cualitativa y exploratoria a través de un caso de estudio desarrollado en un proveedor de servicios logísticos con presencia internacional. El caso de estudio muestra el proceso realizado para elegir la metodología de evaluación. Además, se describen los pasos para la digitalización del conocimiento organizacional.