Business Intelligence Dashboards Research Articles

Business intelligence and cognitive loads: Proposition of a dashboard adoption model

Decision makers in organizations strive to improve the quality of their decisions. One way to improve that process is to objectify the decisions with facts. Data-driven Decision Support Systems (data-driven DSS), and more specifically business intelligence (BI) intend to achieve this. Organizations invest massively in the development of BI data-driven DSS and expect them to be adopted and to effectively support decision makers. This raises many technical and methodological challenges, especially regarding the design of BI dashboards, which can be seen as the visible tip of the BI data-driven DSS iceberg and which play a major role in the adoption of the entire system. In this paper, the dashboard content is investigated as one possible root cause for BI data-driven DSS dashboard adoption or rejection through an early empirical research. More precisely, this work is composed of three parts. In the first part, the concept of cognitive loads is studied in the context of BI dashboards and the informational, the representational and the non-informational loads are introduced. In the second part, the effects of these loads on the adoption of BI dashboards are then studied through an experiment with 167 respondents and a Structural Equation Modeling (SEM) analysis. The result is a Dashboard Adoption Model, enriching the seminal Technology Acceptance Model with new content-oriented variables to support the design of more supportive BI data-driven DSS dashboards. Finally, in the third part, a set of indicators is proposed to help dashboards designers in the monitoring of the loads of their dashboards practically.

Visualisation of building life cycle assessment results using 3D business intelligence dashboards

Visualisation of building life cycle assessment results using 3D business intelligence dashboards

Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

As big data becomes an apparent challenge to handle when building a business intelligence (BI) system, there is a motivation to handle this challenging issue in higher education institutions (HEIs). Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources. This paper reviews big data and analyses the cases from the literature regarding quality assurance (QA) in HEIs. It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper. The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data. The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’ QA systems. This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard

A Conceptual UX Model for Designing and Developing the Business Intelligence Dashboards

A Conceptual UX Model for Designing and Developing the Business Intelligence Dashboards

Development and Implementation of Cloud Open Source Cryptocurrency BI Dashboards System

Cryptocurrencies have received a lot of attention from investors, researchers, regulators, and the media. It is a known fact that the cryptocurrency price is very volatile and has anomaly effects. However, not enough scientific research has been done in the Business Intelligence cryptocurrency calendar anomaly detection area. This study presented the development of Business Intelligence dashboards with real data. As a result of this application, anomaly detection was carried out in the values from the Finance Yahoo API collected data set. With the corresponding BI application, a time-dependent representation of cryptocurrency price can be captured and analyzed, and anomalies can be selected and evaluated. The factors that play a role in forming the model to be applied in detecting anomalies with the experimental results were formed and investigated.

The effect of long-term climatic variability on wild mammal populations in a tropical forest hotspot: A business intelligence framework

Anthropogenic climate change has emerged as a new threat to biodiversity over the last decades. It affects climate systems such as El Niño Southern Oscillation (ENSO) with possible impacts on mammal populations due to changes in local climate. Since this issue had not been addressed in any previous work, the main contribution of this research was the development of a Business Intelligence (BI) system which significantly supports the analysis of patterns in the variation of the population of small mammals in the tropical forest and its response to ENSO phenomenon. In this sense, a Data Warehouse (DW) and Online Analytical Processing (OLAP) tool were built to support the analyses of the data amassed by a 22-year monitoring study of small mammal populations in a highly threatened biodiversity hotspot, the Atlantic Forest. The BI system was applied for three purposes: evaluating the population size trends throughout time, analyzing the relationship between ENSO events and local weather, and evaluating the effects of the most intense ENSO events on the population size of a small, endemic marsupial, Marmosops incanus. The data exploration started by the interactive and visual analyses of the BI dashboards, followed by a statistical analysis which confirmed some trends observed in the dashboards. Very strong and strong La Niña episodes reduced the average temperature and increased precipitation in the study area. These changes affected the population of M. incanus with a reduction in population size in short-term (4 and 6 months) as well as long-term (24 months), indicating the negative effects of reduced temperatures on the population dynamics. Since this BI system is available to the scientific community, we believe that it can stimulate further studies involving the relationship between the population dynamics of other Atlantic Forest species and changes in local and global climate.

Dashboard Business Intelligence Vusialisasi Data Akreditasi Sekolah Pada SMP Negeri 1 Sembawa

This study aims to create a business intelligence dashboard to support school accreditation at SMP Negeri 1 Sembawa in the form of data visualization. In this study, several business intelligence dashboards were produced with the aim of providing convenience for schools in supporting the school accreditation process. Business intelligence is a tool to manage data and perform data analysis that can support school accreditation which will be collected into a data mart and the analysis process is carried out in the form of a cube. For the ETL process using the help of SQL Server Management Studio and SQL Server Development software and as a design for the dashboard creation using the Power BI software. This research has several dashboards including student achievement dashboard based on achievement level, student achievement dashboard based on year of achievement, and student achievement based on graduation which can be seen by year and gender of students.

LINGUISTIC SUMMARIES IN EVALUATING ELEMENTARY CONDITIONS AND THEIR APPLICATION IN SOFTWARE ENVIRONMENT

Data users are generally interested in two types of aggregated information: summarization of the selected attribute(s) for all considered entities and retrieval and evaluation of entities by the requirements posed on the relevant attributes. Less statistically literate users (e.g., domain experts) and the business intelligence strategic dashboards can benefit from linguistic summarization, i.e. a summary like most customers are middle-aged can be understood immediately. Evaluation of the mandatory and optional requirements of the structure P1 and most of the other posed predicates should be satisfied beneficial for analytical business intelligence dashboards and search engines in general. This work formalizes the integration of the aforementioned quantified summaries and quantified evaluation into the concept of database queries to empower their flexibility by, e.g., the nested quantified query conditions on hierarchical data structures. Later in our work, we adapted our research into practical application. We created a software environment for evaluating data based on a dataset retrieved from The Statistical Office of the Slovak republic. These datasets are aimed mainly on landscape characteristics like altitude, area sizes of towns and villages, and similar parameters. Based on user's preferences, our system recommends the most suitable place for holidays to spend on.

Investigating occupational and operational industrial safety data through Business Intelligence and Machine Learning

Learning from previous events represents a crucial element to improve the design and operations of industrial processes, especially considering the many variables characterizing the functioning of a plant. This learning process aims to reduce the frequency of incidents and/or mitigate their severity, which are both continuous and open challenges.This paper is grounded on a large incident repository, i.e., the Major Hazard Incident Data Service (MHIDAS) database, which was developed in 1986 by the Health and Safety Executive (HSE) to provide a reliable source of data on major hazard incidents involving hazardous materials. The database includes more than 9000 reports collected over five decades (1950s–1990s). This paper aims to provide a novel understanding of the industrial incidents reported in MHIDAS and unveil possible ways of exploring occupational/operational incidents through descriptive and quantitative analyses. Consequently, this paper proposes the implementation of Business Intelligence (BI) tools to facilitate dynamic data visualization and Machine Learning (ML) algorithms for the extraction of knowledge from different data entries. Therefore, after engineering the MHIDAS data model, a set of BI dashboards was designed and complemented with a ML-driven categorization of incidents through representative key variables for occupational/operational incidents.The manuscript describes the process necessary to create a BI model for safety data management in an industrial context, and its integration with ML solutions that may support an in-depth multi-variate investigation of reported data. The investigation provides evidence on the importance of a precise reporting of safety events, thus unveiling the potential for lessons learned in the process industry.

Adoption of Business Intelligence to Support Cost Accounting Based Financial Systems — Case Study of XYZ Company

Abstract XYZ is a company engaged in the port sector. To support the company’s business processes, XYZ uses two applications to carry out operational activities, namely the CARTOS application to manage invoices and the Finance application to record company costs and revenues. To produce a cost accounting report, XYZ is still processing and visualizing it manually with data sources from the two applications mentioned earlier. This resulted in quite a long time processing data into information. So that reporting to management cannot be done in real time. Therefore XYZ needs a system that can help management to analyze and manage data into information in real time. The Business Intelligence (BI) method is one of the solutions for company needs, especially in analyzing and providing access to data to help make better decisions. This study discusses the design and implementation of business intelligence solutions ranging from architecture, data warehouse, ETL processes and visualization in the form of a dashboard in accordance with the needs of XYZ. The method used in developing business intelligence dashboards refers to the executive information system life cycle method which consists of justification, planning, business analysis, design, construction, and dissemination. The results of this research are dashboard visualization using the Power BI tool that displays information and knowledge needed in the monitoring process and becomes material to produce management decisions related to cost accounting reports.

Case-mix adjusted postanaesthesia care unit length of stay and business intelligence dashboards for feedback to anaesthetists

BackgroundDespite advances in business intelligence software and evidence that feedback to doctors can improve outcomes, objective feedback regarding patient outcomes for individual anaesthetists is hampered by lack of useful benchmarks. We aimed to address this issue by producing case-mix and risk-adjusted postanaesthesia care unit (PACU) length of stay (LOS) benchmarks for integration into modern reporting tools. MethodsWe extended existing hospital information systems to calculate predicted PACU LOS using a neural network trained on patient age, surgery duration, sex, operating specialty, urgency, weekday, and insurance status (n=100 511). We then calculated the difference between observed mean and predicted PACU LOS for individual doctors, and compared the results with and without case-mix adjustment. We report practical implications of using visual analytics dashboards displaying the difference between observed and predicted PACU LOS to provide feedback to anaesthetic doctors. ResultsThe neural network accounted for over half of observed variation in individual doctors' mean PACU LOS (mean predicted and mean actual LOS Spearman's r2=0.57). Account for case-mix reduced apparent spread, with 80% of individual doctors falling in a band of 4.3 min after case-mix adjusting, compared with a range of 24 min without adjustment. Case-mix adjusting also identified different individual doctors as outliers (Weighted Cohen's kappa [κ]=0.27). Finally, we demonstrated that we were able to integrate the adjusted metrics into routine reporting tools. ConclusionWith caution, case-mix adjustment of anaesthetic outcome measures such as PACU LOS potentially provides a useful continuous quality improvement tool. Unadjusted outcome measures are imprecise at best and misleading at worst.

Learning from Incidents: A Supply Chain Management Perspective in Military Environments

Supply chain management (SCM) represents a crucial role in the military sector to ensure operation sustainability. Starting from the NATO handbook for military organizational learning, this paper aims at investigating the link between technical inconveniences and sustainable supply chain operations. Taking advantage of the learning from incidents (LFI) models traditionally used in the risk and safety management area, this paper proposes an information management system to support organizational learning from technical inconveniences in a military supply chain. The approach is discussed with reference to the Italian context, in line with international and national standards for technical inconvenience reporting. The results of the paper show the benefits of adopting a systematic LFI system for technical inconveniences, providing related exemplar business intelligence dashboards. Further implications for the generalization of the proposed information management system are presented to foster a healthy and effective reporting environment in military scenarios.

DISTRIBUTING SERVICES THROUGH THE CLOUD INFRASTRUCTURE: THE CASE OF A NORWEGIAN SOFTWARE FIRM

This paper examines how professional practices of software developers forge global assemblages in the oil and gas industry by shedding light on the implementation of cloud technologies within a Norwegian-based digital service company. Delivering digital solutions to oil and gas extracting corporations, this company primarily develops proprietary software providing engineers with business intelligence dashboards that assist in managing the assets involved in the extraction of resources. This extended case study seeks to gain a better understanding of the materialities emerging in cloud environments by illuminating transnational divisions of labor within global assemblages. Committed to a holistic contextualization, this mixed-method investigation is primarily based on ethnographic fieldwork, including participation in industry events and a three-month secondment in a small-scale digital service company. Drawing from a materialistic approach to internet technologies, the study provides a comprehensive account of the digital service company since its founding in 2001. Based on evidence from industry events and a long-term immersion in the working lives of software developers, I suggest that the implementation of cloud technologies in the oil and gas industry prompted new digital divisions of labor and replaced the physical travel of professionals with a remote control system facilitating an enhanced circulation of data. The findings of this investigation imply that cloud computing continues to restructure the global economy and accelerates the migration of data through internet technologies.

Performance Evaluation of Clinical Decision Support Systems (CDSS): Developing a Business Intelligence (BI) Dashboard.

This document describes the development of a Business Intelligence (BI) dashboard for tracking the drug-drug interaction (DDI) alerts implemented as Clinical Decision Support Systems (CDSS) in Electronic Health Records (EHR). CDSS are known for their potential to reduce medical error. The use of requirements in the development of BI dashboards is crucial to obtain successful software. In this work, the requirements were analysed using a score methodology, considering the relevance of the indicators and visualization methods. CDSS effectiveness and acceptance have been questioned, so it is fundamental to monitor their behaviour and performance. The dashboard was designed in order to satisfy the needed indicators. Using BI as a tool for monitoring the CDSS performance made it possible to operationalize the EHR content repository, maximizing the understanding in relation to the override and, by inference, to optimize the CDSS system by opening new lines of work.

Displaying Background Maps in Business Intelligence Dashboards

Business data in geographic maps, called data maps, can be displayed via business intelligence dashboards. An important emerging feature is the use of background maps that overlap with existing data maps. Here, the authors examine the usefulness of background maps in dashboards and investigate how much cognitive effort users put in when they use dashboards with background maps as compared to dashboards without them. To test the extent of cognitive effort, the authors conducted an eye-tracking study in which users performed a decision-making task with maps in dashboards. In a separate study, users were asked directly about the mental effort required to perform tasks with the dashboards. Both studies identified that when users use background maps, they required less cognitive effort than users who use dashboards in which the information on the background map is represented in another form, such as a bar chart.

Data Mining and Business Intelligence Dashboards

Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A detailed understanding and knowledge is required to analyze the business environment at different levels. This can be achieved by implementing value added information throughout the organization. There has been an increased demand in the development of IT that performs data manipulation operations to report and analyze the data. While sifting through all that data, organizing it, and then using it to make the business run more profitably has become a bigger challenge. Decision markers can use such information to implement business strategies that best meet the needs of the ultimate customer. Making use of that information is all the more urgent.