Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Managing the Innovation Process: Infusing Data Analytics into the Undergraduate Business Curriculum (Lessons Learned and Next Steps)

The designing of a new, potentially disruptive, curricular program, is not without challenges; however, it can be rewarding for students, faculty, and employers and serve as a template for other academics to follow. To be effective, the new data analytics program should be driven by business input and academic leadership that incorporates innovation theory and practice concepts. Similar to many innovative projects, our journey began with a business problem, i.e., the explosion of data from a plethora of sources, the realization that data transformed into information and intelligence can generate business value, and the recognition that there are currently too few graduates with the necessary skillset to make this happen in the foreseeable future. The approach developed here may provide other universities with a path toward an information systems curriculum that is more in tune with the emerging big data world

A conceptual framework for the direct marketing process using business intelligence

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Direct marketing is becoming a key strategy for organisations to develop and maintain strong customer relationships. This method targets specific customers with personalised advertising and promotional campaigns in order to help organisations increase campaign responses and to get a higher return on their investments. There are, however, many issues related to direct marketing, ranging from the highly technical to the more organisational and managerial aspects. This research focuses on the organisational and managerial issues of the direct marketing process and investigates the stages, activities and technologies required to effectively execute direct marketing. The direct marketing process integrates a complex collection of marketing concepts and business analytics principles, which form an entirely ‘self-contained’ choice for organisations. This makes direct marketing a significantly difficult process to perform. As a result, many scholars have attempted to tackle the complexity of executing the direct marketing process. However, most of their research efforts did not consider an integrated information system platform capable of effectively supporting the direct marketing process. This research attempts to address the above issues by developing a conceptual framework for the Direct Marketing Process with Business Intelligence (DMP-BI). The conceptual framework is developed using the identified marketing concepts and business analytics principles for the direct marketing process. It also proposes Business Intelligence (BI) as an integrated information system platform to effectively execute the direct marketing process. In order to evaluate and illustrate the practicality and impact of the DMP-BI framework, this thesis adopts a case study approach. Three case studies have been carried out in different industries including retailing, telecommunication and higher education. The aim of the case studies is also to demonstrate the usage of the DMP-BI framework within an organisational context. Based on the case studies’ findings, this thesis compares the DMP-BI framework with existing rival methodologies. The comparisons provide clear indications of the DMP-BI framework’s benefits over existing rival methodologies

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Big Data and Analytics: Issues, Solutions, and ROI

Recently, the topic of big data and analytics has received renewed attention from academia and practitioners. There has been an increase in demand for skills in big data and analytics due to the increasing speed, variety, and volume of information. Several research reports have shown that big data and analytics remain top priority for CIOs. A recent study shows how a company accurately predicted a teen girl’s pregnancy via the company’s big data algorithm. However, there are dark sides to big data and analytics. A panel discussion addressed topics concerning how companies ensure that big data projects clearly define measurable goals up front, methods that companies use to ensure maximum return and most effectively, and ways that companies evolve culture, processes, and technology to simultaneously maximize return. Most companies are looking at how they can effectively manage their business more through using their data assets. Companies today target an average return of $3.50 dollars for every dollar spent on big data projects. However, most are only returning a fraction of that today, which leaves room for improvement and the possibility that organizations will push back against new analytic technologies. In this paper, we cover these topics that a panel of researchers at AMCIS 2014 in Savannah, GA, discussed

Comparative Study Of Implementing The On-Premises and Cloud Business Intelligence On Business Problems In a Multi-National Software Development Company

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNowadays every enterprise wants to be competitive. In the last decade, the data volumes are increased dramatically. As each year data in the market increases, the ability to extract, analyze and manage the data become the backbone condition for the organization to be competitive. In this condition, organizations need to adapt their technologies to the new business reality in order to be competitive and provide new solutions that meet new requests. Business Intelligence by the main definition is the ability to extract analyze and manage the data through which an organization gain a competitive advantage. Before using this approach, it’s important to decide on which computing system it will base on, considering the volume of data, business context of the organization and technologies requirements of the market. In the last 10 years, the popularity of cloud computing increased and divided the computing Systems into On-Premises and cloud. The cloud benefits are based on providing scalability, availability and fewer costs. On another hand, traditional On-Premises provides independence of software configuration, control over data and high security. The final decision as to which computing paradigm to follow in the organization it’s not an easy task as well as depends on the business context of the organization, and the characteristics of the performance of the current On-Premises systems in business processes. In this case, Business Intelligence functions and requires in-depth analysis in order to understand if cloud computing technologies could better perform in those processes than traditional systems. The objective of this internship is to conduct a comparative study between 2 computing systems in Business Intelligence routine functions. The study will compare the On-Premises Business Intelligence Based on Oracle Architecture with Cloud Business Intelligence based on Google Cloud Services. A comparative study will be conducted through participation in activities and projects in the Business Intelligence department, of a company that develops software digital solutions to serve the telecommunications market for 12 months, as an internship student in the 2nd year of a master’s degree in Information Management, with a specialization in Knowledge Management and Business Intelligence at Nova Information Management School (NOVA IMS)

Where are we headed in business analytics? A framework based on a paradigmatic analysis of the history of analytics

The explosion of interest in business analytics (BA) comes with multiple problems. With as many as eleven distinct disciplines teaching analytics, it is not clear which areas of study constitute the BA field. If the information systems (IS) field is to exert a significant influence in analytics, what the IS researcher and practitioner need to focus on has to be made clear. Using a paradigmatic historiographical analysis of the field of analytics this study provides evidence for the bifurcation of analytics into data science and BA as founding disciplines of computer science, mathematics and statistics, machine learning and IS contribute to the analytics movement. The results from this analysis also identify a set of conceptual foundations for BA that takes advantage of both the intellectual strengths of the IS field without sacrificing the necessary depth of data science