Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.

Knowledge and Process Based Decision Support in Business Intelligence System

Abstract: As a kind of data-driven decision support systems, business intelligence tools focus too much on data and have low efficiency of decision making. Companies in today are more process-oriented than in the past and process-driven decision support system is emerging to help enterprises improve the speed and effectiveness of business operations. In order to provide the business intelligence system with the ability of process-driven decision making, we introduce the concept of business process management to the current business intelligence system. We add the process model component in our business intelligence model base. With the implementation of case-based reasoning and rule-based reasoning technology, the process models can be built and managed efficiently. In this paper we also provide a strategy for knowledge management in business intelligence system.

Academic analytics landscape at the University of Phoenix

Abstract: The University of Phoenix understands that in order to serve its large population of non-traditional students, it needs to rely on data. We have created a strong foundation with an integrated data repository that connects data from all parts of the organization. With this repository in place, we can now undertake a variety of analytics projects. One such project is an attempt to predict a student's persistence in their program using available data indicators such as schedule, grades, content usage, and demographics.

Formal structures for data mining, knowledge discovery and communication in a knowledge management environment

Abstract: Current Business Intelligence (BI) initiatives customize DM-KDD techniques into business analytics, which cannot be used in applications other than business. A review of current methodology at the strategic level of the KM/KDD domains indicates that there exists no general formal framework which can be adopted in new applications, or new application areas. There are no established procedures for the domain expert to express their prior knowledge, understanding and aims in a way which can be linked to KDD/DMM processes and subsequent deployment of discovered knowledge. It is suggested that the sequential life-cycle project-management approach of CRISP-DM needs to be complemented by a dynamic interactive view of a conceptual data/information/knowledge hierarchy in the KM context. It is also suggested that a graphical/visual knowledge representation framework needs to be developed as the basis of a knowledge and discovery and communication framework (KDCF). A review of the limitations in DM methodology at the technical/technological level leads to the conclusion that there is no coherent DM methodology to guide the choice of models and their evaluation, that the DM discipline is fractionated, and that the fundamental search and sampling paradigms have been insufficiently utilized in DM development. It is proposed that development of linked data and model ontologies, together with a DM-epistemology, and associated with full exploitation of search and sampling could lead to improved cohesion and efficacy of the DM discipline.

Role of big data and predictive analytics

Abstract: Big data has emerged as an important area of interest pertaining to the study and research of practitioners and academicians. The exponential growth of data is fuelled by the exponential growth of various internet and digital devices. Advancement in technology has made it economically feasible to store and analyse huge amounts of data. Big data is a juxtaposition of structured, semi-structured and unstructured real time data originating from a variety of sources. Predictive analytics provides the methodology in tapping intelligence from large data sets. Many visionary companies such as Google, Amazon, etc. have realised the potential of big data and analytics in gaining competitive advantage. These techniques provide several opportunities like discovering patterns or better optimisation algorithms. Managing and analysing big data also constitutes a few challenges - namely size, quality, reliability and completeness of data. This paper provides an extensive review of literature on big data and predictive analytics. It gives the reader details of the fundamental concepts in this emerging field. Finally, we conclude with the findings of our study and have outlined future research directions in this field.