Opportunities for Business Intelligence and Big Data Analytics in Evidence Based Medicine

Evidence based medicine (EBM) is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. Each year, a significant number of research studies (potentially serving as evidence) are reported in the literature at an ever-increasing rate outpacing the translation of research findings into practice. Coupled with the proliferation of electronic health records, and consumer health information, researchers and practitioners are challenged to leverage the full potential of EBM. In this paper we present a research agenda for leveraging business intelligence and big data analytics in evidence based medicine, and illustrate how analytics can be used to support EBM

A Frame Work for Customer Relationship Management in Nigerian Banks Using Data Analytics

One of the most crucial challenges that Nigeria banks have to face is in the jurisdiction of customers’ satisfaction. Customers’ satisfaction has become one of the most important factors of success in today’s banking industry in Nigeria. Today Nigeria banks customer’s increases every day, as it is essential for many Nigerian to have proper savings with any bank of their choice; if the performance of bank falls short of their expectations, the very survival of such bank would be difficult. In this paper, a   framework for customer relationship management for Nigeria banks using big data analytics approach was developed. Qualitative research was used to identify customer satisfaction through customer management system information publish annually.  The data were collected from complaint data for financial report 2017 from the Customer Relationship Management System for WEMA Bank Plc. The data were analyzed using excel spread sheet and later converted into CSV and ARFF file format respectively. Data were  exported into WEKA for data analytics which then generated results. The formulated hypotheses are subjected to empirical test using Logistic regression and Machine learning. This new strategy provided solution of these problems identified. Keywords: Data Analytics, Linear regression, Banking,  Customer Satisfactio

Exploring the Impact of Business Intelligence (BI) Use on Organisational Power Dynamics: A National Health Service (NHS) Case Study

The public sector, particularly healthcare organisations are under ever increasing pressure to do more with less. This coupled with the need to keep up to the constant technological changes and ever increasing abundance of information has led to many public sector organisations adopting Business Intelligence (BI) in order to leverage business value and improve decision-making. However, many organisations such as the National Health Service (NHS) continue to fail in their Information Technology (IT) related initiatives. While the rise of BI and its growing influence in organisations has attracted much academic attention, this has largely been from architectural, design and technological perspectives, whilst little is known about how BI is used by various organisational actors to reach decisions, nor much is understood regarding its resulting impact on organisational power dynamics. Thus, there remains an under researched area of discussion in the literature from the perspective of BI users. While studies report how BI can impact organisational effectiveness, facilitate data driven decision making and supposedly overcome intuitive decision making, the extent to which BI impacts and alters power dynamics between organisational actors across the organisation has received little attention. Accordingly, this research adopts a qualitative case study approach to explore power resulting from BI use within a large NHS trust by conducting 30 semi-structured interviews consisting of operational managers and BI analysts. Through taking a human-centric approach, this research uncovers how BI is altering power dynamics between organisational actors, whereby BI analysts are becoming increasingly influential as a result of their analytical skills. It was found that operational managers are becoming more reliant upon data analysts, resulting in the analysts having more and more influence. However, this research finds it is only when the analysts supplement their technical skill-set with their institutional knowledge, that they have the ability to influence and enact power within the organisational settings. The research also offers insights into the contestations and conflicts which arise from the use of BI, between operational managers and analysts as well as between in-house analysts, based in the operation setting and the centralised analysts, operating across the entire trust. Accordingly, this research empirically validates a BI Power Enactment Framework and proposes the BI Power Matrix, which may assist policy makers in identifying determining key factors which are contributory to the success or failure of technological initiatives