Benchmarking performance management systems

The Balanced Scorecard and associated performance management approaches, has become a widely practiced and popular management reporting method in recent times. Moreover, enabling technology, which assists in the delivery and personalisation of corporate performance information, is having a deeper and more rapid impact than ever before. This paper presents a brief comparative benchmarking study of leading enterprise performance management systems. Also, the author discusses the merits of bespoke internet technology development and out-of-the-box portal functionalities. An analysis of key business drivers and implementation risks of such approaches is highlighted via a case study example, and concludes the paper

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

The Use of Olap Reporting Technology to Improve Patient Care Services Decision Making Within the University Health Center

The purpose of this paper is to demonstrate that it is feasible for the student health center to leverage existing clinical data in a data warehouse by using OLAP reporting in order to improve patient care and health care services decision making. Historically, University health care centers have relied mainly on operational data sources for critical health care decision making. These sources of data do not contain enough information to allow health officials to recognize trends or predict how future changes in health care services might vastly improve overall heath care. Four proof of concept artifacts are constructed through design science research methodology, and a feasibility study is presented to build the case for the adoption of OLAP reporting technology. The study concludes that it is feasible to implement an OLAP reporting infrastructure at the student health center if physicians, clinical staff, and administration clearly define the need for the new technology, develop proper data extraction, loading, and transformation strategy, and adequately provide project management and data warehouse design towards the implementation of the solution

Designing OLAP Cubes: A Teaching Case

This teaching case models the process used to create a “proof of concept” data warehouse and On-line Analytical Processing (OLAP) Cube for admissions at small, private, Midwestern university. Students will use test data to design Star Schemas and create OLAP cubes to show how they could be used for data mining purposes. This case attempts to build an understanding of the design and use of data warehouses and OLAP cubes within a larger data mining course

Business Intelligence for Financial Risk Management

This project evaluated the implementation of Business Intelligence (BI) platforms and alternative visualization techniques for risk management in fixed income trading at the sponsor, BNP Paribas. The project examined literature related to fixed income products and risk, the implementation of common BI solutions, analytical tools, and modeling techniques, as well as interviews with key stakeholders at the firm. Using this research, a proposed solution for capture, storage, analysis, and presentation was designed, and a prototype of the solution was implemented as a proof-of-concept. Testing with key users involved in portfolio risk management indicated the prototype was a marked improvement in usability, data access, and intuitive display of the information needed from the system

DACTyL:towards providing the missing link between clinical and telehealth data

This document conveys the findings of the Data Analytics, Clinical, Telehealth, Link (DACTyL) project. This nine-month project started at January 2013 and was conducted at Philips Research in the Care Management Solution group and as part of the Data Analysis for Home Healthcare (DA4HH) project. The DA4HH charter is to perform and support retrospective analyses of data from Home Healthcare products, such as Motiva telehealth. These studies will provide valid insights in actual clinical aspects, usage and behavior of installed products and services. The insights will help to improve service offerings, create clinical algorithms for better outcome, and validate and substantiate claims on efficacy and cost-effectiveness. The current DACTyL project aims at developing and implementing an architecture and infrastructure to meet the most demanding need from Motiva telehealth customers on return on investment (ROI). These customers are hospitals that offer Motiva telehealth to their patients. In order to provide the Motiva service cost-effectively, they need to have insight into the actual cost, benefit and resource utilization when it comes to Motiva deployment compared to their usual routine care. Additional stakeholders for these ROI-related data are Motiva customer consultants and research scientists from Philips for strengthening their messaging and service deliveries to arrive at better patient care

Evaluation of the Contemporary Issues in Data Mining and Data Warehousing

Over the past years data warehousing and data mining tools have evolved from research into a unique and popular business application class for decision support and business intelligence. This paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Theoretical and empirical literature was reviewed and various gaps in literature were identified. Besides the analysis of data mining and the business areas that can successfully apply it, the paper suggested and concluded that firms and scholars need to carry out more empirical research in the area of integrity of data mining and data warehousing since this will help eliminate marketing errors in operations and practice

Hybrid Solution for Integrated Trading

Integrated applications are complex solutions, whose complexity are determined by the economic processes they implement, the amount of data employed (millions of records grouped in hundreds of tables, databases, hundreds of GB) and the number of users. Service oriented architecture (SOA), is now the most talked-about integration solution in mainstream journals, addressing both simple applications, for a department but also at enterprise level. SOA can refer to software architecture or to a way of standardizing the technical architecture of an enterprise and it shows its value when operating in several distinct and heterogeneous environments.System Integration, Data Integration, Web Services, Java, XML, Stock Market