Business Analytics in the Context of Big Data: A Roadmap for Research

This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice

Business Analytics in the Context of Big Data: A Roadmap for Research

This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice

Business Intelligence and Big Data in Higher Education: Status of a Multi-Year Model Curriculum Development Effort for Business School Undergraduates, MS Graduates, and MBAs

Business intelligence (BI), “big data”, and analytics solutions are being deployed in an increasing number of organizations, yet recent predictions point to severe shortages in the number of graduates prepared to work in the area. New model curriculum is needed that can properly introduce BI and analytics topics into existing curriculum. That curriculum needs to incorporate current big data developments even as new dedicated analytics programs are becoming more prominent throughout the world. This paper contributes to the BI field by providing the first BI model curriculum guidelines. It focuses on adding appropriate elective courses to existing curriculum in order to foster the development of BI skills, knowledge, and experience for undergraduate majors, master of science in business information systems degree students, and MBAs. New curricula must achieve a delicate balance between a topic’s level of coverage that is appropriate to students’ level of expertise and background, and it must reflect industry workforce needs. Our approach to model curriculum development for business intelligence courses follows the structure of Krathwohl’s (2002) revised taxonomy, and we incorporated multi-level feedback from faculty and industry experts. Overall, this was a long-term effort that resulted in model curriculum guidelines

Towards the Implementation of an Intelligent ERP System: Guidelines for Building Intelligent ERP Systems

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe digital age has forced companies to change the way they operate their businesses and adapt quickly to the digital transformation driven by increased global competitiveness in recent years. To remain competitive, organizations must implement management solutions that allow them to efficiently control all business areas through an Enterprise Resource Planning (ERP) system. Management systems have had to evolve to keep up with technological advancements by incorporating intelligent tools. As a result, ERP companies have created new systems known as intelligent ERP (i-ERP). Given the variety of improvement opportunities, it has become necessary to develop a series of guidelines for i-ERP manufacturing as well as for companies that want to implement intelligent solutions in their different business areas, in order to assist technical and non-technical people selecting the best existing option. A design science research (DSR) methodology was used to accomplish the study's goal. It was mandatory to start by defining what an i-ERP system is. Furthermore, their seven capabilities have been clarified, such as intelligent behaviour, learning management, advanced analytics, process automation, intelligent interfaces, dark analytics, and simplification of customization. These capabilities are based on technologies such as artificial intelligence, machine learning, big data, and cloud computing. The guidelines were based on these seven capabilities and were applied to the four major modules of an ERP, which are financial, purchasing, sales, and human resources. As a result, it was possible to create a table with recommendations in general by i-ERP capabilities, followed by guidelines focusing on the financial, purchasing, sales, and human resources areas, and an assessment tool that allowed creating measures to evaluate an ERP system, considering its level of intelligence based on the recommendations created. Finally, the evaluation system was used to rate the latest system developed by SAP SE, SAP S4/HANA, demonstrating its usefulness, followed by expert interviews to validate the recommendations for the four areas identified in terms of their use and acceptance. The relevant literature review and my personal work experience were used as the basis for this master's thesis. It is expected that this study will contribute to the scientific community's understanding of intelligent information systems as well as arouse curiosity in future studies.A era digital forçou as empresas a mudarem a forma como operam os seus negócios e a adaptarem-se rapidamente à transformação digital impulsionada pelo aumento da competitividade global nos últimos anos. Para se manterem competitivas, as organizações devem implementar soluções de gestão que lhes permitam controlar eficazmente todas as áreas de negócio através de um sistema de planeamento de recursos corporativos (ERP). Os sistemas de gestão tiveram de evoluir para acompanhar os avanços tecnológicos, incorporando ferramentas inteligentes. Como resultado, as empresas de sistemas ERP criaram produtos conhecidos como ERP inteligentes (i-ERP). Dada a variedade de oportunidades de melhoria, tornou-se necessário desenvolver uma série de orientações para fabricantes de i-ERP bem como para empresas que pretendam implementar soluções inteligentes nas diversas áreas de negócio, a fim de ajudar as pessoas técnicas e não técnicas na seleção da melhor opção existente. Uma metodologia de desenho de investigação científica (DSR) foi utilizada para atingir o objetivo do estudo. Foi obrigatório começar por definir o que é um sistema i-ERP bem como as suas sete capacidades identificadas, como ter um comportamento inteligente, gestão da aprendizagem, análise avançada, automatização de processos, interfaces inteligentes, análise escura, e simplificação da personalização, que têm como base tecnologias como inteligência artificial, aprendizagem de máquinas, grandes dados e armazenamento em nuvem. As orientações utilizaram como base estas sete capacidades e foram aplicadas aos quatro principais módulos de um ERP, que são o financeiro, compras, logística e recursos humanos. Como resultado foi possível criar uma tabela de recomendações gerais por capacidades de um i-ERP, seguida de recomendações com foco na área financeira, compras, logística e recursos humanos e por último uma ferramenta de avaliação que permitiu criar medidas para avaliar um sistema ERP, considerando o seu nível de inteligência com base nas recomendações criadas. Por último, o sistema de avaliação foi utilizado para classificar o mais recente sistema desenvolvido pela SAP SE, o SAP S4/HANA, demonstrando a sua utilidade, seguido de entrevistas a especialistas para validar as recomendações para as quatro áreas identificadas em termos de respetiva utilização e aceitação. Uma relevante revisão bibliográfica e a minha experiência profissional foram utilizadas como base para esta tese de mestrado. Espera-se que este estudo contribua para a compreensão dos sistemas de informação inteligentes pela comunidade científica, assim como despertar curiosidade em estudos futuros

Big data reduction framework for value creation in sustainable enterprises

Value creation is a major sustainability factor for enterprises, in addition to profit maximization and revenue generation. Modern enterprises collect big data from various inbound and outbound data sources. The inbound data sources handle data generated from the results of business operations, such as manufacturing, supply chain management, marketing, and human resource management, among others. Outbound data sources handle customer-generated data which are acquired directly or indirectly from customers, market analysis, surveys, product reviews, and transactional histories. However, cloud service utilization costs increase because of big data analytics and value creation activities for enterprises and customers. This article presents a novel concept of big data reduction at the customer end in which early data reduction operations are performed to achieve multiple objectives, such as a) lowering the service utilization cost, b) enhancing the trust between customers and enterprises, c) preserving privacy of customers, d) enabling secure data sharing, and e) delegating data sharing control to customers. We also propose a framework for early data reduction at customer end and present a business model for end-to-end data reduction in enterprise applications. The article further presents a business model canvas and maps the future application areas with its nine components. Finally, the article discusses the technology adoption challenges for value creation through big data reduction in enterprise applications

Revolutionising the quality of life: the role of real-time sensing in smart cities

To further evolve urban quality of life, this paper explores the potential of crowdsensing and crowdsourcing in the context of smart cities. To aid urban planners and residents in understanding the nuances of day-to-day urban dynamics, we actively pursue the improvement of data visualisation tools that can adapt to changing conditions. An architecture was created and implemented that ensures secure and easy connectivity between various sources, such as a network of Internet of Things (IoT) devices, to merge with crowdsensing data and use them efficiently. In addition, we expanded the scope of our study to include the development of mobile and online applications, emphasizing the integration of autonomous and geo-surveillance. The main findings highlight the importance of sensor data in urban knowledge. Their incorporation via Tepresentational State Transfer (REST) Application Programming Interface (APIs) improves data access and informed decision-making, and dynamic data visualisation provides better insights. The geofencing of the application encourages community participation in urban planning and resource allocation, supporting sustainable urban innovation.This work was supported by FCT-Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER-000086. Rui Miranda was supported by grant no. UMINHO/BID/2021/137; Carlos Alves was supported by grant nos. 2022.12629.BD and UMINHO/BID/2021/134; Regina Sousa was supported by grant no. UMINHO/BID/2021/136; António Chaves was supported by grant no. UMINHO/BID/2021/135; Larissa Montenegro was supported by grant no. UMINHO/BID/2022/53

Deepint.net: A rapid deployment platform for smart territories

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.This work has been partially supported by the European Regional Development Fund (ERDF) through the Interreg Spain-Portugal V-A Program (POCTEP) under grant 0677_DISRUPTIVE_2_E, the project My-TRAC: My TRAvel Companion (H2020-S2RJU-2017), the project LAPASSION, CITIES (CYTED 518RT0558) and the company DCSC. Pablo Chamoso’s research work has been funded through the Santander Iberoamerican Research Grants, call 2020/2021, under the direction of Paulo Novais