Business Intelligence Tools Research Articles

Integration: the quest for true equity, beyond accessibility

Integration: the quest for true equity, beyond accessibilityPlanning the location of healthcare services and funding them appropriately is complex, as it requires several measures that pull in different directions. Normally, more complex services tend to be placed in larger population nucleus, without full consideration of how the population uses each resource. This favors accessibility from a wider area and efficiency, but goes against equity, as it implies longer travel time for part of the population. Also, it complicates integrated care, as the same person may have to be using geographically separate resources, some more local, some more distant, which are harder to coordinate.We posit that we need tools to help discover and evaluate these tradeoffs, and plan for resource allocation + treatment plans. These tools should, at the very least,1) Be able to ingest data from several tiers of the healthcare systems (acute hospitals, emergency services, primary care, intermediate care…), and analyze them jointly.2) Analyze how citizens with different profiles are actually using the different resources, including travel distance, frequency of use, and continuity of attention.3) Analyze in detail the variability within a given coarse classification (such as DRG or ICD codes): Why some patients in the same DRG and for the same procedure leave their zones and why others stay? Are these differences justified by variability within DRG and patient history? Or are they arbitrary patient choices? Or differences in protocol among different centers?We describe our tool ALOE which was co-designed with the Catalan Health Service for this purpose. The tool monitorizes attraction to different services and flows from different geographical areas, and shows and describes the anomalies in flows: Significant numbers of people that go where they shouldn’t, and how these people differ from similar people that stay in their zones.We will describe a few actual cases studied using the tool. Two involve eating disorder problems and autistic spectrum disorders, respectively in a subregion of Catalonia where there seemed to be unjustified disparity in the centers where people were being treated. In another study, we discovered that a number of heart failure patients in a medium-size town had a tendency to go to a larger tertiary hospital over 80 km away, instead of being treated locally. It turns out that these were the people who had neoplasic antecedents, and they routinely went to the tertiary hospital where they are regularly checked for more complex attention.In still another study, we discovered a (partial) explanation why two otherwise similar hospitals had widely differing lengths of stay after knee replacement surgery (4 days vs. 8 days). It turned out that the 4-day hospital is close to a large tertiary hospital, and that most “complex” patients went there, while the 8-day hospital took care of all knee replacement cases in its territory, both the easy and the difficult ones.A conclusion of the use of the tool is that such in-depth analysis is impossible, or highly time-consuming, to perform with the traditional indicator-based business intelligence tools.

Monitoring and Optimization of E-Services in IT Service Desk Systems

Information management systems have become the most important system for successfully growing firms. This paper gives techniques and tools for measurement and analysis of e-Services in IT Service Desk Systems. The subject of the research is to propose Business Intelligence tools for their monitoring and optimization. Object of the study are these e-Services. Therefore, the aim of this paper is to look into and analyze the performance of these e-Services in IT Service Desk Systems.

Leveraging technology and supply chain to improve family planning logistics in Pakistan.

Pakistan and USAID have invested in improving the contraceptive supply chain data and commodity security. In 2011, the first digital contraceptive logistics management information system (cLMIS) was launched, enabling supply chain data visibility from the federal level to health facilities. The system has built-in modules on forecasting and supply planning, inventory management, consumption reporting, business intelligence tools, automatic email and SMS alerts. Using these features, policy-makers and health managers annually forecast needs, and procure contraceptives accordingly. The objective of this research was to understand the existing technological platforms for family planning (FP) supply chain data visibility and the potential impact on contraceptive commodity security. The authors reviewed available published and grey literature papers on contraceptives and supplies in Pakistan. We extracted data from the cLMIS, evaluated indicators including reporting compliance, reported stock-out rates, and contraceptive performance. The analysis was validated by reviewing supply chain and FP indicators, such as average monthly consumption, months of stock, and couple years of protection. The cLMIS has resulted in improved distribution, early warning and accountability at the lowest tiers in the FP supply chain in the public sector. At the facility level, FP commodity availability increased from 40% in 2009 to 84% in 2018. Contraceptive supply chain has seen significant growth over the past decade to meet expanding reproductive health evidence to inform strategic decisions; cLMIS is a prime contributor to improvements registered in FP stock availability at public sector facilities.

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.

The Role of Information Dashboards as a Business Intelligence Tool for Managing the Corona Virus Pandemic

Information dashboards were one of the best ways to manage Covid disease. The concept of information dashboards and their important benefits are explained in the present study.

SELECTION OF THE MOST ACCEPTABLE TOOL FOR BUSINESS INTELLIGENCE FOR WORK IN PUBLIC ADMINISTRATION

The development intelligence tools is gaining a new dimension every day, and this is an area of dynamic development. The importance of this paper is to select the tool that represents the best solution for the given needs in public administration. A multi-stage, broad- based survey with clear selection criteria leads to the choice of three tools. Criteria for tool evaluation were set, virtual machines were created, tests and analyzes were performed. The evaluations carried out give the choice of the most accepTable business intelligence tool for use and interoperability in public administration.

Alignment and Identification of the Relationships between Key Performance Indicators in a Multilevel Tree Structure to Support Transportation Agency Decisions

Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

The evolution of Amazon redshift

The evolution of Amazon redshift

A two-stage workflow to extract and harmonize drug mentions from clinical notes into observational databases

BackgroundThe content of the clinical notes that have been continuously collected along patients’ health history has the potential to provide relevant information about treatments and diseases, and to increase the value of structured data available in Electronic Health Records (EHR) databases. EHR databases are currently being used in observational studies which lead to important findings in medical and biomedical sciences. However, the information present in clinical notes is not being used in those studies, since the computational analysis of this unstructured data is much complex in comparison to structured data. MethodsWe propose a two-stage workflow for solving an existing gap in Extraction, Transformation and Loading (ETL) procedures regarding observational databases. The first stage of the workflow extracts prescriptions present in patient’s clinical notes, while the second stage harmonises the extracted information into their standard definition and stores the resulting information in a common database schema used in observational studies. ResultsWe validated this methodology using two distinct data sets, in which the goal was to extract and store drug related information in a new Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) database. We analysed the performance of the used annotator as well as its limitations. Finally, we described some practical examples of how users can explore these datasets once migrated to OMOP CDM databases. ConclusionWith this methodology, we were able to show a strategy for using the information extracted from the clinical notes in business intelligence tools, or for other applications such as data exploration through the use of SQL queries. Besides, the extracted information complements the data present in OMOP CDM databases which was not directly available in the EHR database.

Increasing the Effectivity of Business Intelligence Tools via Amplified Data Knowledge

Increasing the Effectivity of Business Intelligence Tools via Amplified Data Knowledge

Success Factors Affecting the Intention to Use Business Analytics: An Empirical Study

ABSTRACT In the era of knowledge-based economy, the firm’s ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilising BA which is designed to help business executives and policy makers make well-thought and informed decisions. To have a clear picture of what will lead to the serious consideration of BA as a business intelligence tool, this paper identifies contextual variables that include privacy, security, risk concerns, information technology (IT) capability and perceived value that may significantly influence the firm’s intention to use BA. This paper conducted confirmatory factor analyses and used the structural equation model to determine what either motivate or inhibit the BA adoption Through a series of hypothesis testing, we discovered that higher security and risk concerns along with a lack of IT capability became important deterrents to BA adoption. Also, we found that firms which recognised the value of BA were more inclined to adopt BA than the others. This paper is one of the first attempts to develop practical guidelines for the potential adopters of BA based on the empirical study of BA practices among the Korean firms.

Business Intelligence for the Analysis of Industrial Accidents Based on Mhidas Database

Reducing the frequency and severity of accidents in industrial processes is a continuous open challenge. Learning from previous events represents a crucial instrument to ensure an improved design of industrial plants, especially considering the complexity arising in everyday operations. This article is grounded on a database of industrial accidents involving hazardous substances and materials. The Major Hazard Incident Data Service (MHIDAS) was developed in 1986 by the Health and Safety Executive (HSE) to provide a reliable source of data on major hazard incidents and to learn for the past accidents. The database has more than 9000 accident reports covering the periods from 1950 to the end of the 1990s caused by hazardous substances/materials. This paper aims are to provide an understanding of MHIDAS data through quantitative analyses that can be obtained by exploiting the information collected through appropriate data management tools. Therefore, Information Technology (IT) services such as Business Intelligence (BI) tools have been used in this research. The paper describes the process of creating a BI model for data management on MHIDAS database to generate useful information on previous industrial safety events, allowing a detailed search engine as well through any event stored in MHIDAS.

An Effective Model for Fraud Risk Management in Mitigating Telecommunication Fraud Incidences

In the context of Malaysia, the most common types of cyber attacks are denial of service, intrusion attempts, spam, vulnerability report, fraud, malicious code, and content-related attacks as reported by Cyber Security Malaysia. However, since 2011, cyber attacks such as intrusion attempts, denial of service and spam are decreasing steadily as a result of security measures that have been taken by companies or internet users. Nevertheless, cyber attacks which are steadily increasing are cyber harassment, intrusion and fraud. Approximately 5,328 fraud incidences were reported in 2011 while in 2020, the number of cases rose to 7,593 cases. Out of these cyber attacks in Malaysia, the most proliferating ones have been identified as fraud incidences. Thus, it is highly needed to come up with and propose the best fraud risk management strategy to handle fraud incidences among internet users and internet providers through the utilization of business intelligence tools, and quality enhancement via quality systems in place, quality information, and quality users. With these tools in hand, it is hoped that the proposed model will serve as a framework to mitigate and/or prevent the occurrence of fraud incidences.

Utilization of Business Intelligence Tools among Business Intelligence Users

The study was an investigation of the impact of perceived usefulness and perceived ease of use of business intelligence (BI) tools among users. The relationship between and among the dependent variable (utilization of BI tools) and the independent variables (perceived usefulness and perceived ease of use) was investigated through the lenses of technology acceptance model (TAM). Other objectives for the current research were to build a model to predict users’ utilization of the independent variables, and to generalize the results of the research to the IT population. Data for the current research was collected utilizing a survey questionnaire, designed by the researcher, with a 5-point Likert scale to interpret responses to the survey questions. The analysis consisted of descriptive statistics and multiple regressions models. A prediction model was structured using generalized linear models. The result of the study was the development of a prediction model for BI tools utilization through the lenses of a technology acceptance model (TAM). The model highlighted the importance of up-to-date information provided by current BI tools, ability of BI tools to provide users with more analytical tools to accomplish their jobs, the degree to which BI tools allow users to present convincing arguments, the ability of BI tools to provide users with more possible solutions, the ability of BI tools to reduce the time required to accomplish jobs, and the ability of BI tools to help users make relevant business predictions.

Managing the Process of Evaluation of the Academic Teachers with the Use of Data Mart and Business Intelligence

Purpose: The purpose of this research is to present the original model of management of academic teachers' remote work evaluation that implements an analytical approach. Design/Methodology/Approach: Desk research and interview method have been applied in this paper. The literature review concerned two areas, i.e., the analytical approach to management and the management of university educational processes. Unstructured interviews conducted with university authorities located in Poland were the second research method applied. Findings: Based on literature studies, conducted interviews and authors' personal experience, the original model of academic teachers' remote work evaluation that implements an analytical approach and utilises data mart concept and business intelligence tools has been created. This model contains the following elements, the conceptual model of data mart allowing the evaluation of courses realisation and teachers job, KPIs utilised in this evaluation, and the dashboard presenting KPI's values. Two main areas representing courses evaluation and teachers' evaluation have been considered. Practical Implications: The authors propose the solution that utilises an analytical approach to improve the management of the evaluation process of educational processes at the university. Created model may be a valuable source of inspiration for university authorities responsible for ensuring the high efficiency of these processes, and for people responsible for developing teachers' competencies. According to the authors, the implementation of this model can contribute to the improvement of the academic education. Originality/Value: An analytical approach in evaluation of educational processes proposed by authors is innovative in nature and is particularly important in the context of increasingly widespread remote education. Its adoption to management states a big challenge for university authorities.

The implication of business intelligence in risk management: a case study in agricultural insurance

The increasing data scales in today’s business sectors coupled with the necessity of risk management raise the importance of business intelligence tools as an integrated solution for the insurance industry. These tools have mostly been used to achieve effective risk management. Although methods of risk management in the insurance industry have been proposed many years ago, the research effort has primarily been focused on predictive analyses. This study aimed to investigate the role of business intelligence as a solution to illustrate its potential in risk management particularly for decision-makers in agricultural insurance. We hypothesized that this would make a preferable decision in uncertain conditions. Sample data from the online transaction process system of Iran agricultural insurance fund were preprocessed in SQL server. Multidimensional online analytical processing architecture was analyzed using Targit business intelligence tool. Our results identified financial risks that lead to a framework of controlling risk based on business intelligence in the agricultural insurance fund.

A multi-criteria assessment of decision support systems in educational environments

<span>Decision support systems (DSS) are useful business intelligence (BI) tools as they help managers in large organizations make the best out of many decisions. Decisions are based on various types of raw data, models, documents, knowledge, and past experiences. This paper examines numerous criteria of decision support systems in the educational environment. Two effective methods were discovered and applied in this research, the analytic hierarchy process (AHP) and simple multi-attribute rating technique (SMART). These methods were selected due to their abilities to deal with complex decisional environments in general and widely used in practice for the educational environment in specific. The performance of methods is compared using two datasets called xApi-Education and IPEDS datasets. The obtained results based on the measurement of space complexity showed the level of convergence and similarity between these two methods. However, the experiments show that the Simple Multi-Attribute Rating Technique outperformed the analytic hierarchy process in terms of accuracy, deviation, and time complexity measurement.</span>

Affordability of IBM Cognos business intelligence tool features suitable for small-and medium-sized enterprises’ decision-making

Background: Business intelligence (BI) tools are generally associated with organisations that have resources to purchase and implement these tools. Evidences abound regarding the correlation between BI tools and improved business decision-making. This study’s unit of analysis is affordability as a feature of IBM Cognos making it suitable for small-and medium-sized enterprises (SMEs).Objective: The research aim was to identify the fundamental features of IBM Cognos which would address decision-making needs of SMEs. The objective was to determine the significance of BI tool features by means of identifying affordable features suitable for SMEs’ decision-making.Method: Quantitative research design and a deductive approach were best suited for assessing the fundamental features of IBM Cognos for SMEs’ decision-making needs. The signification framework variables, such as presumed, prized and perceived value of BI tool features, werequantified and measured using statistical analysis tools. A non-probability convenience sampling technique was used with a sample size of 200, that is, 80 BI consultants, 60 SME BI developers and 60 SME managers.Results: Affordable key features of BI tools in the context of SMEs’ business decision-making include consistency and comfort, intuitive interface, avoiding impulsivity, cost effectiveness, availability of information, best programmed visualisations, reporting quickly and easily and financial decision-making.Conclusion: The signification framework’s presumed, prized and perceived value indicators link affordable BI tool features to the consistency of the decision-making process and present an alternative view of affordability. An intuitive interface relates to convenience and ease of authoring content, designing, building and securing reports to the SME, which helps inimproving consistent decision-making.

Business intelligence tools to optimize the appropriateness of the diagnostic process for clinical and epidemiologic purposes in a multicenter veterinary pathology service.

Laboratory tests provide essential support to the veterinary practitioner, and their use has grown exponentially. This growth is the result of several factors, such as the eradication of historical diseases, the occurrence of multifactorial diseases, and the obligation to control endemic and epidemic diseases. However, the introduction of novel techniques is counterbalanced by economic constraints, and the establishment of evidence- and consensus-based guidelines is essential to support the pathologist. Therefore, we developed standardized protocols, categorized by species, type of production, age, and syndrome at the Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), a multicenter institution for animal health and food safety. We have 72 protocols in use for livestock, poultry, and pets, categorized as, for example, "bovine enteric calf", "rabbit respiratory", "broiler articular". Each protocol consists of a panel of tests, divided into 'mandatory' and 'ancillary', to be selected by the pathologist in order to reach the final diagnosis. After autopsy, the case is categorized into a specific syndrome, subsequently referred to as a syndrome-specific panel of analyses. The activity of the laboratories is monitored through a web-based dynamic reporting system developed using a business intelligence product (QlikView) connected to the laboratory information management system (IZILAB). On a daily basis, reports become available at general, laboratory, and case levels, and are updated as needed. The reporting system highlights epidemiologic variations in the field and allows verification of compliance with the protocols within the organization. The diagnostic protocols are revised annually to increase system efficiency and to address stakeholder requests.

Business Intelligence + Lean Manufacturing: uma revisão sistemática da literatura (2008-2018)

Lean Manufacturing (LM) é uma filosofia de gestão apoiada por um grupo de técnicas que, quando combinadas e amadurecidas, reduzem o tempo e o custo de produção, maximizam o valor ao cliente e minimiza desperdícios. Para isto a tomada de decisão desempenha um papel fundamental e se torna um ponto crítico para esta filosofia de gestão. A ferramenta Business Intelligence (BI) fornece uma abordagem baseada em dados para vincular as metas estratégicas das empresas às políticas gerenciais e ações operacionais, podendo ser de ajuda para as empresas enxutas. Assim, o presente estudo pretende analisar as principais aplicações das ferramentas de BI para dar suporte à tomada de decisão às empresas que aplicam LM. Com apoio do software de revisão sistemática StArt, foi feita uma análise da literatura atual relacionada ao tema de pesquisa. A análise permitiu definir oportunidades de aplicação de BI nas empresas que, de alguma forma, utilizam o LM.