Enhancing Decision Support for Secondary Education with OLAP

Decision-making is one of the most critical processes taking place in a modern school. It is a necessary competence for school administrators and managerial staff especially in Education Directorates who often have to make decisions regarding the implementation of education strategies and policies. It is also important for teaching staff and school curriculum designers in order to plan teaching methods and monitor student performance. Nowadays many school functions are supported by dedicated information systems. Business Intelligence (BI) is a widely used set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. They include Online Analytical Processing (OLAP) in order to provide historical, current and predictive views of business operations. Schools in secondary education can be viewed as small organizations where effective decision making is required at many areas and levels. The aim of this project is the research of feasibility of applying OLAP Decision Support Systems in Education and Education Management, any possible benefits as well as possible enhancements. The outcome is the design and implementation of an enhanced OLAP system applied in a specific educational setting based on our case study

A model for Business Intelligence Systems’ Development

Often, Business Intelligence Systems (BIS) require historical data or data collected from var-ious sources. The solution is found in data warehouses, which are the main technology used to extract, transform, load and store data in the organizational Business Intelligence projects. The development cycle of a data warehouse involves lots of resources, time, high costs and above all, it is built only for some specific tasks. In this paper, we’ll present some of the aspects of the BI systems’ development such as: architecture, lifecycle, modeling techniques and finally, some evaluation criteria for the system’s performance.BIS (Business Intelligence Systems), Data Warehouses, OLAP (On-Line Analytical Processing), Object-Oriented Modeling

Implementing data-driven decision support system based on independent educational data mart

Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions

Implementation of Business Intelligence on Banking, Retail, and Educational Industry

Information technology is useful to automate business process involving considerable data transaction in the daily basis. Currently, companies have to tackle large data transaction which is difficult to be handled manually. It is very difficult for a person to manually extract useful information from a large data set despite of the fact that the information may be useful in decision-making process. This article studied and explored the implementation of business intelligence in banking, retail, and educational industries. The article begins with the exposition of business intelligence role in the industries; is followed by an illustration of business intelligence in the industries and finalized with the implication of business intelligence implementation

Developing Strategic Reports for National Co-Operative of Malaysia (Angkasa) Using Data Warehouse and Decision Tree Model

Managing an organization requires access to information in order to monitor activities and assess performance. Business Intelligence (BI) solutions provide organizations with timley, itegrated information that is crucial to the understanding of their business. Data Warehouse (DW) technology is one of the important strategic management approaches for decision making in an organizations. The BI combines architectures, tools, databases, analytical tools, and methodologies to enable the implementation of interactive information in generating analytical reports. Strategic reports, which influence the enduring way of the whole company, are typically used by top managers. These kinds of decisions are repeatedly complex and the outcomes unsure, because existing information is habitually incomplete. Managers at this point must normally depend on history experiences and their instincts when making strategic decisions. DW is a technology allows integrating and transforming enterprise data for strategic decision making. Furthermore, Decision Tree (DT) is a decision support tool that uses a tree-like graphof decisions and their possible consequences, including chance event outcomes, resource costs, and utility. The organization, which is, responsible to manage people activities need strategic decisions making. This paper will be focused how to design and develop Strategic Reports using DW and DT Model for National Co-operative Organization of Malaysia (ANGKASA) called DSRNCO, as a case study. This system has been evaluated through the system user feedback by using Computer System Usability Questionnaire (CSUQ), which measures system usability and user satisfaction

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

Business intelligence to support NOVA IMS academic services BI system

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceKimball argues that Business Intelligence is one of the most important assets of any organization, allowing it to store, explore and add value to the organization’s data which will ultimately help in the decision making process. Nowadays, some organizations and, in this specific case, some schools are not yet transforming data into their full potential and business intelligence is one of the most known tools to help schools in this issue, seen as some of them are still using out-dated information systems, and do not yet apply business intelligence techniques to their increasing amounts of data so as to turn it into useful information and knowledge. In the present report, I intend to analyse the current NOVA IMS academic services data and the rationales behind the need to work with this data, so as to propose a solution that will ultimately help the school board or the academic services to make better-supported decisions. In order to do so, it was developed a Data Warehouse that will clean and transform the source database. Another important step to help the academic services is to present a series of reports to discover information in the decision making process

Teaching ERP Systems by a Multiperspective Approach

In this paper we describe how a market-leading ERP system can be used to demonstrate theoretical knowledge of Enterprise Systems. Students of business administration with a focus on information systems are taught theoretical backgrounds about modeling, technical architectures, development, and conceptions of Enterprise Systems. They know about business processes like production planning or material management. However, links between business knowledge, theoretical IT-knowledge, and practical IT-experience are not revealed to the students. To close this gap we propose an integrative ERP curriculum, which maps different theoretical IT-knowledge to Enterprise Systems. Therefore an ERP system is determined from different perspectives like end-users or IT-specialists. Using exemplary processes like purchasing or MRP, the integrative aspect of Enterprise Systems is demonstrated. The introduced concept is not about teaching a specific ERP system but illustrates concepts of ERP like data integration, data structure integration, process integration, and maps information models onto ERP systems

Developing and Delivering a Data Warehousing and Mining Course

This paper describes the content and delivery of a Data Warehousing and Mining course that was developed for students in the Eberly College of Business at Indiana University of Pennsylvania. This elective course introduces students to the strategies, technologies, and techniques associated with this growing MIS specialty area. Students learn what is involved in planning, designing, building, using, and managing a data warehouse. Students also learn about how a data warehouse must fit into an over-all corporate data architecture that may include legacy systems, operational data stores, enterprise data warehouses, and data marts. In addition, students are exposed to the different data mining techniques used by organizations to derive information from the data warehouse for strategic and long-term business decision making