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A Business Intelligence Tool Designed for Governing Sustainability

Computer technology and administrative procedures are used to provide business intelligence (BI), which aids in decision-making. In order to balance financial and non-financial factors as well as short- and long-term measurements, the balanced scorecard is a type of business intelligence tool for performance measurement and management control. The original balanced scorecard was modified to specifically take governance, social, environmental, and ethical issues into account. As a result, sustainability concepts may be incorporated into the organization's strategy and management. This is known as the sustainable balanced scorecard. Despite being one of the best instruments for incorporating sustainability into management, the sustainable balanced scorecard has few developed and implemented instances that can serve as reference models. In order to address this issue, this paper discusses the case study's findings and suggests a technique for creating a sustainable balanced scorecard that takes into account several stages including planification, analysis, design, or the use of computer tools. As a result, the research methodology used to conceptualize and carry out the case study was split into seven stages: defining the objectives and questions of the study; formulating a theoretical model; identifying units of analysis; choosing a case; defining research methods and resources; conducting fieldwork; gathering data; classifying and triangulating information; developing an improved theory, model, or methodology; and confirming the thoroughness and caliber of the investigation. The paper shows a methodology organized in phases and activities that allow a sustainable balanced scorecard to be planned, designed, built, computerized, and controlled to integrate sustainability within the management systems of organizations.

Social Collaboration Analytics Framework: A framework for providing business intelligence on collaboration in the digital workplace

Enterprise collaboration systems (ECS) have become the core of the digital workplace in many organizations. More and more companies have introduced this new business software for supporting computer-mediated collaboration among employees. The emergence of ECS in the technical and strategical landscape of companies leads to new challenges in business intelligence and reporting. In contrast to traditional business software such as ERP systems, there are no established methods for decision-making in ECS projects. The collaboration professionals responsible for managing the ECS require insights into the use of an ECS to understand how employees use these systems. By developing the Social Collaboration Analytics Framework, this work provides a framework for establishing the analysis of collaboration activities in the digital workplace (social collaboration analytics) as part of organizations' business intelligence. The Social Collaboration Analytics Framework consists of distinct phases and guides the analysis of ECS data. It includes working steps, recommendations, and guiding questions developed based on the findings from longitudinal research in a university-industry collaboration context and a comparison of established data mining process models.

Levelling bed occupancy: reconfiguring surgery schedules via simulation

PurposeThe purpose of this paper is to present simulation modelling to reconfigure a 700-bed Hong Kong hospital’s master surgery schedule (MSS), aiming to improve patient flow, capacity management and resource allocation through levelling bed occupancy within the hospital.Design/methodology/approachA discrete-event simulation model was developed to understand how changes to the MSS would affect bed occupancy, thereby providing business intelligence for short- and long-term hospital planning. A decision tool was subsequently developed for hospital managers to test different scenarios.FindingsSimulation modelling showed that significant bed occupancy levelling could be achieved through small and practicable changes to the MSS. Optimisation routines conducted using the simulation model then gave additional insights into how the schedule should be revamped for the long term.Practical implicationsThe authors show how operations research methods are useful for guiding hospital operational planning. The authors show that a data-driven and evidence-based model enables hospital managers to critically explore various scheduling changes, while also providing a scientific common ground for discussion among important stakeholders. It is a crucial step forward when adopting advanced analytics for Hong Kong hospital operational planning.Originality/valueThe authors provide a robust method for evaluating the relationship between Hong Kong hospital’s MSS and its bed occupancy. Through simulating various changes to the surgical schedule, valuable and practicable insights were made available for hospital managers to make short- and longer-term changes that enhance the system’s overall efficiency and service quality.

Tackling business intelligence with bioinspired deep learning

To tackle the complex problem of providing business intelligence solutions based on business data, bioinspired deep learning has to be considered. This paper focuses on the application of artificial metaplasticity learning in business intelligence systems as an alternative paradigm of achieving a deeper information extraction and learning from arbitrary size data sets. As a case study, artificial metaplasticity multilayer perceptron applied to the automation of credit approval decision based on collected client data is analyzed, showing its potential and improvements over the state-of-the-art techniques. This paper successfully introduces the relevant novelty that the artificial neural network itself estimates the pdf of the input data to be used in the metaplasticity learning, so it is much closer to the biologic reality than previous implementations of artificial metaplasticity.

Creating a Decision-Making Model Using Association Rules

ABSTRACTBeing a highly significant and complex function of management, decision making requires methods and techniques that simplify the process of selecting one choice among all available options. Decision making is therefore selection of that particular choice over any of several alternatives. Because of the process complexity, a continuous research and improvement of the methods and techniques modern decision making involves is required. One of many modern business challenges is to discover any possible improvement in the decision-making process managers shall use in making the right decision. Any decision made by managers directly impacts the realized profit, business, and company’s position on the market. The fact is that mankind faces the decision-making problem in each phase of its social development, which has resulted in increased need for learning more about it. In this work, both the significance and application of association rules will be analyzed on an example of car sales business. The research was conducted on a sample of 1728 transactions in order to recognize and establish the association rules and then determine their impact on the sales and profit. For the purpose of this research, a large car sales database was used as a source of information, which is also described in this work. Once these association rules were established, they were then used to create a better and more complete market supply. The main contribution of the paper is providing business intelligence model for performing association rules in real-term business settings.

A Hybrid Classification Algorithm to Classify Engineering Students Problems and Perks

The social networking sites have brought a new horizon for expressing views and opinions of individuals. Moreover, they provide medium to students to share their sentiments including struggles and joy during the learning process. Such informal information has a great venue for decision making. The large and growing scale of information needs automatic classification techniques. Sentiment analysis is one of the automated techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful to draw decisions in education system since they classify the sentiments into merely three pre-set categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or negative category does not provide deeper insight into their problems and perks. In this paper, we propose a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several categories to help future students and education system in decision making.

Integrated Business Intelligent System for E-Health: A Case for Dermatology Diseases

In the current era, information technology has boosted every field of life either business industry or healthcare to integrate the internal processes of it. Due to the demand of managing huge data related to these fields numerous information systems play different roles in making the organizational processes robust and up to date. This paper discusses the integrated business intelligence implication specifically for healthcare to provide the fast and precise information on time. Therefore, this paper discusses the idea of building intelligent system based on Enterprise Resource Planning (ERP) databases using exclusively for dermatology diseases by applying data mining techniques. Firstly, classification mining has been applied for categorization data based on patient’s record. Then rules and patterns generated from the categorized data related to dermatology diseases, symptoms and treatments. The proposed system will retrieve the corresponding information related to the given symptoms along with medication and complete treatment. This research aims to integrate different ERP processes with centralized ERP database to provide business intelligence effectively for the dermatologists. The paper has provided with the comprehensive conceptual framework and each step has been discussed in detail.

Mining anomalous events against frequent sequences in surveillance videos from commercial environments

In the UK alone there are currently over 4.2 million operational CCTV cameras, that is virtually one camera for every 14th person, and this figure is increasing at a fast rate throughout the world (especially after the tragic events of 9/11 and 7/7) ( Norris, McCahill, & Wood, 2004). Security concerns are not the only factor driving the rapid growth of CCTV cameras. Another important reason is the access of hidden knowledge extracted from CCTV footage to be used for effective business decision making, such as store designing, customer services, product marketing, reducing store shrinkage, etc. Events occurring in observed scenes are one of the most important semantic entities that can be extracted from videos ( Anwar & Naftel, 2008). Most of the work presented in the past is based upon finding frequent event patterns or deals with discovering already known abnormal events. In contrast, in this paper we present a framework to discover unknown anomalous events associated with a frequent sequence of events ( A EASP); that is to discover events, which are unlikely to follow a frequent sequence of events. This information can be very useful for discovering unknown abnormal events and can provide early actionable intelligence to redeploy resources to specific areas of view (such as PTZ camera or attention of a CCTV user). Discovery of anomalous events against a sequential pattern can also provide business intelligence for store management in the retail sector. The proposed event mining framework is an extension to our previous research work presented in Anwar et al. (2010) and also takes the temporal aspect of anomalous events against frequent sequence of events into consideration, that is to discover anomalous events which are true for a specific time interval only and might not be an anomalous events against frequent sequence of events over a whole time spectrum and vice versa. To confront the memory expensive process of searching all the instances of multiple sequential patterns in each data sequence an efficient dynamic sequential pattern search mechanism is introduced. Different experiments are conducted to evaluate the proposed anomalous events against frequent sequence of events mining algorithm’s accuracy and performance.

Part I: Research: Much more than search and retrieval

The development of enabling technologies for data collection has led to an exponential growth in the availability of information, presenting challenges in finding relevant information, organizing it and using it to advantage. But it also offers opportunities to independent information professionals who can leverage their knowledge of the information landscape, of ever changing information search techniques, of information use patterns and of information management technologies to provide services of value to their clients. In this group of articles Cindy Shamel and Liga Greenfield, who work with clients in the biomedical industry, discuss the value and process of helping clients articulate their information needs to locating relevant resources efficiently and the value of analyzing organizational information flows in defining information management solutions. Tom Wolff and Stephen Adams, specialists in patent information, discuss the many ways IIP patent consultants provide business intelligence, support legal opinions and business decisions and provide strategic guidance using patent analytics and services like intellectual property management and database development and training. Jane John, Jocelyn Sheppard and Jan Knight use a question-and-answer format to present their views on getting started in business, reaching and meeting target clients, the challenges of marketing and the unique opportunities and benefits of working with early stage sci-tech companies and entrepreneurs. Peggy Garvin's article discusses the value IIPs provide to professionals working in policy, politics and journalism in navigating the information-rich and constantly evolving space of government information on the Internet. Phyllis Smith describes the value of her business to a Canadian federal government department. Arthur Weiss and Ellen Naylor offer insights into the use of secondary and primary research for competitive intelligence. Eiko Shaul discusses how an IIP fluent in the language and culture of a country (Japan, as an example) would be an asset to cross-country research. Finally, Missy Corley uses the business approaches and products of three IIPs, as well as projects her own business has undertaken, to show how IIPs in genealogical research, a specialized niche, provide value-added services.

Understanding Data-Driven Decision Support Systems

It is important for managers and Information Technology professionals to understand data-driven decision support systems and how such systems can provide business intelligence and performance monitoring. Data-driven DSS is one of five major types of computerized decision support systems and the features of such systems vary across specific implementations. Different development packages also impact the capabilities of data-driven DSS and hence criteria for evaluating data-driven DSS development software are important to understand. Overall, this article builds on an historic foundation of prior decision support systems theory.

Building the Executive Dashboard

Executive dashboards are systems that provide business intelligence to company executives and managers by presenting data from a wide variety of sources in ways that support effective monitoring and decision making. They are linked to a company's data warehouses or other sources of data from operations, finance, marketing, and other domains and thus must provide visualizations that allow users to comprehend what can be an enormous quantity of data. In order to do so, the designer must consider many of the formats that have been identified in human factors practice to support various kinds of data and tasks. Many executive dashboards include capabilities to aggregate data from multiple sources, drill down to examine specific data, analyze historical or conceptual trends, and other functionality. These added capabilities add a significant amount of complexity to the design that must be addressed carefully. Users are often experts in their domains (ie. marketing) but not necessarily proficient with information technology or data analysis. The design must satisfy the needs of the users while providing a simple and context-dependent interface in order to be effective. This paper describes many of the human factors issues involved in creating executive dashboard systems and suggests some areas for future research.