Applying Data Analytics Research Articles

Balanced Scorecard Internal Process Perspective: Applying Data Analytics to Monitor Police Department Performance

ABSTRACT Employers expect graduates to perform data analytics using both organizational and publicly available data. Interestingly, few teaching cases provide students with needed exposure to data analytics in managerial or governmental accounting. In this case, students assume the role of a managerial accountant employed at Municipal Consulting, LLC. Students assist the Police Commissioner in using a Balanced Scorecard approach to analyze how her department responds to crime patterns and to develop next year’s budget. This case requires students to critique, analyze, and evaluate Internal Business Process Perspective Balanced Scorecard measures supported by publicly available data using data visualization software. The case includes instructions for both Tableau and Power BI. The case covers three skills: (1) extract, transform, and load data; (2) develop data visualization software experience; and (3) analyze data. The case is designed for senior level undergraduate and graduate courses in managerial, data analytics, and governmental accounting.

Applying Data Analytics to Analyze Activity Sequences for an Assessment of Fragmentation in Daily Travel Patterns: A Case Study of the Metropolitan Region of Barcelona

Sequence analysis is a robust methodological approach that has gained popularity in various fields, including transportation research. It provides a comprehensive way to understand the dynamics and patterns of individual behaviors over time. In the context of the Metropolitan Region of Barcelona, applying sequence analysis to mobility surveys offers valuable insights into the sequencing of travel activities and modes, shedding light on the complex interrelationship between individuals and their travel choices and the built environment. Sequence analysis allows us to examine travel behaviors as dynamic processes and reveal the underlying structure and evolution of travel patterns over a day. Here, we describe a data analytics approach that enables the identification of common travel patterns and the exploration of variations across demographic groups or geographical regions. We propose a method for discovering similarities in travel behavior segments from diaries included in travel surveys in order to refine transport policies for selected segments. Unfortunately, the data collected by the authorities in the analyzed travel surveys do not include family structure, which seems critical in explaining the segmentation of travel sequences.

Detection of Cardiovascular Disease Applying Machine Learning and Data Analytics

Detection of Cardiovascular Disease Applying Machine Learning and Data Analytics

How Big data Analytics Supports Project Manager in Project Risk Management – Cases from UAE Health Sector

Abstract
 
 Big data analysis allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or used. Companies can use advanced analytics techniques such as text analysis, machine learning, predictive analytics, data mining, statistics, and natural language processing to gain new insights and insights from previously untapped data sources independently or with existing enterprise data. Significant challenges are still in how to deal with this data and maximize their use, so large data have become the clock that has become the most national, regional, and international institutions, and a broad range of database management, methodologies and measures that can be adopted for the exploitation of large data in all areas of life.
 This paper investigates the effects of big data analytics on project risk management with examples from Healthcare sector in UAE. Inclusive research has been done by searching approximately more than 20 references resulted in a literature review studied the effect of implementing data analytics in business, technology, industry, and society businesses aspects. A research methodology has been done by interviewing professionals from healthcare field investigating further the role of Data analytics in analyzing and managing data in healthcare, its benefits in predicting risks and improving healthcare outcomes and future insights.
 This research’s result reveals that collecting data and applying data analytics even in businesses or healthcare represent an important kind of digital transformation. An obvious finding from this research is that business intelligence and data analytics has been implemented widely in UAE healthcare sector by both government and privet sectors resulting in opportunities that develop and emphasize positive changes to this sector.

WHAT MAKES A STREET WALKABLE? A DATA ANALYTIC APPROACH TO INVESTIGATING WALKABILITY FACTORS

Walkability is a hot topic for variety of disciplines, as well as everyday walker. It affects the health, the environment and the liveliness of our neighbourhoods. Walkable streets are necessary for a better lifestyle and sustainable planet. The problem with walkability is that we still don’t have a general understanding of the concept. Every study differs in the way they define walkability, thus making walkability a subjective topic. However, the subjectivity causes contradiction in science. In this study, the aim to answer the question of what makes a street walkable by using a data analytic approach. The features used in other studies, as well as new attributes specific to this study, were investigated. Street images were used to extract data. The data was divided into nine categories: Street, Sidewalk, Obstacles, Urban Blocks, Amenities, Transportation, Attractiveness, People, and Vehicles. Data collection was carried out by measuring physical attributes through Remote Sensing images in QGIS, visually analyzing qualitative attributes with Google Street Maps/View and double checking data in Open Street Map Overpass Turbo API. Attributes were translated into scores and normalized where possible. Mutual Information Matrix and Correlation processes were conducted in Rapidminer. The attributes were processed in relation to overall assessment of walkability which was defined with personal rating. As a result, Mutual Information and Correlation matrices are useful in figuring out the relationship and dependencies between different attributes. Applying data analytics to a more comprehensive dataset will help identify the global factors of walkability.

An Approach to BEPS Risk Assessment: Applying Data Analytics Techniques to Country-by-Country Reporting Data

This article discusses the application of data analytics techniques on data provided by multinationals in the country-by-country reporting to analyse, interpret and understand financial and other data with ease. Thus, it would provide a better understanding of multinationals’ global structures, economic activities and footprints to identify the material risk areas, thereby providing multinationals and tax authorities with a technique to undertake preliminary base erosion and profit shifting risk assessment. The authors have primarily relied on the tax risk indicators laid down in the OECD Handbook on Effective Tax Risk Assessment to build different stages of risk assessment. The possibility and the advantages of setting up a unified risk assessment tool based on the various risk assessment are also briefly discussed. The authors believe that such a tool, when publicly available, would enable different stakeholders to interpret the available data in country-by-country reporting more uniformly and requisite, timely action can be taken to address the identified risk areas.

The role of data analytics within operational risk management: A systematic review from the financial services and energy sectors

Operational risks are increasingly prevalent and complex to manage in organisations, culminating in substantial financial and non-financial costs. Given the inefficiencies and biases of traditional manual, static and qualitative risk management practices, research has progressed to using data analytics to objectively and dynamically manage risks. However, the variety of operational risks, techniques and objectives researched is not well mapped across industries. This paper thoroughly reviews the emerging research area applying data analytics to operational risk management (ORM) within financial services (FS) and energy and natural resources (ENR). A systematic literature search resulted in 2,538 publications, from which detailed bibliometric and content analyses are performed on 191 studies of relevance. The literature is classified using a novel multi-layered framework, informing critical analyses of the analytics techniques and data employed. Five core themes emerge, relevant to practitioners, researchers, educators and students across any sector: risk identification, causal factors, risk quantification, risk prediction and risk decision-making. Generally, ENR studies focus on identifying causal factors and predicting specific incidents, whereas FS applications are more mature surrounding risk quantification. To conclude, the comprehensive review reveals areas where further research is needed to advance ORM within and beyond FS and ENR, in pursuit of improved decision-making.

Scheduling on-site service deliveries to minimise the risk of missing appointment times

This paper studies the stochastic service task scheduling and vehicle routing problem for a telecommunication provider where each vehicle is driven by an engineer who performs service tasks at customer premises. There is an agreed time window for starting each service task. The service times and travel times are subject to uncertainties, e.g., task taking longer or shorter than expected, traffic situation causing delays. The problem is to schedule the tasks and route the vehicles to minimise the risks of missing appointment times. Models are presented to express the risks and describe the problem. Simulated annealing and tabu search are applied for generating an initial schedule of the day and for re-optimisation during the day based on real-time information updates. The study reported is based on the work in an industrial case. The stochastic nature of the travel times and durations of different task types as well as their distribution parameters have been obtained by applying data analytics on large sets of operations data. These are used in calculating the risks and in making scheduling and routing decisions. Real-time data updates sent back from the engineers are used for re-optimisation to adjust the schedule and routes so that the risks are kept at a lower level. Simulation results show that using risk minimisation as objective and re-optimisation during the day help enhance the on-time start of tasks. With this approach organisations can achieve robust task scheduling and improved customer satisfaction, and so become more competitive in the market.

Data Analytics Determines Co-occurrence of Odorants in Raw Water and Evaluates Drinking Water Treatment Removal Strategies.

A complex dataset with 140 sampling events was generated using triple quadrupole gas chromatography-mass spectrometer to track the occurrence of 95 odorants in raw and finished water from 98 drinking water treatment plants in 31 cities across China. Data analysis identified more than 70 odorants with concentrations ranging from not detected to thousands of ng/L. In raw water, Pearson correlation analysis determined that thioethers, non-oxygen benzene-containing compounds, and pyrazines were classes of chemicals that co-occurred, and geosmin and p(m)-cresol, as well as cyclohexanone and benzaldehyde, also co-occurred, indicating similar natural or industrial sources. Based on classification and regression tree analysis, total dissolved organic carbon and geographical location were identified as major factors affecting the occurrence of thioethers. Indoles, phenols, and thioethers were well-removed through conventional and advanced treatment processes, while some aldehydes could be generated. For other odorants, higher removal was achieved by ozonation-biological activated carbon (39.3%) compared to the conventional treatment process (14.5%). To our knowledge, this is the first study to systematically identify the major odorants in raw water and determine suitable treatment strategies to control their occurrence by applying data analytics and statistical methods to the complex dataset. These provide informative reference for odor control and water quality management in drinking water industry.

A clustering solution for analyzing residential water consumption patterns

Water utility companies in urban areas face two major challenges: ensuring there is enough water for everyone during prolonged drought and maintaining adequate water pressure during the hours of peak demand. These issues can be overcome by applying data analytics and machine learning to the data gathered from digital water meters. For water conservation and demand management strategies to be effective, utility companies need to gain a better understanding of consumer behaviours, habits and routines. To accomplish this goal, we adapted a clustering approach to reveal residential water consumption patterns within metered data. In the experiment, we used two data sets (engineered features data set as well as the times of use and weighted probabilities of use data set) based on the data collected over 10 months from 306 households in Melbourne, Australia. For the engineered features data set, first, we identified the number of optimal clusters. We then performed extensive experiments to find the best clustering approach in terms of performance evaluation and clustering quality. We chose the hierarchical agglomerative clustering technique based on the nature of the data and the objective of the study. We observed that for the engineered features data set, k-means is the best performing clustering technique after considering performance metrics. For the other data set, we found that the number of clusters varies based on the type of water-consumption event, type of day (i.e., weekday or weekend), profiling interval and probability of use. In addition, we observed that insight into tap-water usage could be used to determine the population’s adaptation of hygiene practices in an unprecedented time, such as the COVID-19 pandemic. Finally, we recommend that future clustering studies also employ aligned socio-demographic data and other key features.

Benefits and Challenges of Applying Data Analytics in Government Auditing

Benefits and Challenges of Applying Data Analytics in Government Auditing

Application of Data Analytics in the Events Industry

This research examined the data analytics practices used within the events industry and the value of such applications. This study consisted of an interpretive case review of current companies within the events industry. The interview process explained the current practices being used to collect and analyze data. The common themes revealed data analytics are being used to evaluate, redesign, and enhance company performance, marketing strategy, decision guidelines, and economics. The study shows data collection and analysis is mostly focused on determining what consumers want and are looking for within the industry. The findings of this study support the importance of applying data analytics within industry-related companies to be financially successful and maintain market-share. Both the results from this study and the literature used indicate the significance of data analytics and the tremendous amount of opportunity buried beneath the application of data, although there is still room for growth.

Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction

Data analytics methods have been increasingly applied to understanding materials chemistry, processing due to the manufacturing approach, and uni-axial and cyclic property relationships in the highly complex space of alloy design. There are several benefits to applying data analytics to this space, including the ability to manage non-linearities in the responses of the alloy attributes and the resulting mechanical properties. However, key difficulties in applying and understanding the results of data analytics include the often lack of reported assumptions and data processing steps necessary to improve interpretation and reproducibility in derived results. In this work, the methods used to generate clustering and correlation analyses for experimental 9% Cr ferritic-martensitic steel data were investigated and the resulting implications for mechanical property predictions were assessed. This work uses principal component analysis, partitioning around medoids, t-SNE, and k-means clustering to investigate trends in composition, processing and microstructure information with creep and tensile properties, building on work done previously using a smaller version of the same dataset. The initial assumptions, preprocessing steps and methods are investigated and outlined in order to depict the fine level of detail required to convey the steps taken to process data and produce analytical results. The variations in the resulting analyses are explored due to the influence of new and more varied data.

Application of data analytics for research of digital administrative services in Bulgaria

The article examines issues on the topic of digital administrative services in Bulgaria. In this sense, the paper presents the opportunities for application of data analytics, which are suitable for research of this type of services. In this case, the article uses data from online media content on the researched topic. The main purpose of the article is to apply opportunities for data analytics and to present the relevant analysis on the topic of digital administrative services in Bulgaria. To achieve its goal, the article uses various scientific methods, including study, analysis, research, modeling and experimentation. The results support the main hypothesis of the study, namely defining the benefits of applying data analytics to research the media positioning of digital administrative services in Bulgaria. In conclusion, the paper points out the opportunities for improvement of the research in the subject area with the application of the respective data analysis.

Server-Edge dualized closed-loop data analytics system for cyber-physical system application

• We define six essential components for the data analytics system for CPS application. • We propose an architecture of dualized closed-loop data analytics with server and edge-computing devices. • A PMML-based data analysis model information parsing structure is proposed to deal with the automation issue of the data analytics application. • A case study that applies the proposed server-edge dualized closed-loop data analytics system for CPS application to the die-casting factory in Korea is introduced. In this research, we propose a system architecture of the server-edge dualized closed-loop data analytics system for cyber-physical system (CPS) application. We define six essential components for the data analytics system for CPS application: (1) the cyber model, (2) the data analytics module, (3) the data analysis model execution module, (4) the decision making module, (5) the system control module, and (6) the visualization module. We then propose an architecture of dualized closed-loop data analytics with server and edge-computing devices. The proposed dualized architecture of the data analytics system has advantages in handling the three issues of applying data analytics systems to the manufacturing context: (1) the system overload issue of the data analytics module due to large volumes of data, (2) the automation issue in the sequences of data analysis model generation, data analysis module execution, and system control, and (3) the real-time issue of data analysis model execution. In particular, a PMML-based data analysis model information parsing structure is proposed to deal with the automation issue. A case study that applies the proposed server-edge dualized closed-loop data analytics system for CPS application to the die-casting factory in Korea is introduced.

Data analytics in control and operation of municipal wastewater treatment plants: qualitative analysis of needs and barriers.

This study aims to identify barriers and needs for the application of data analytics in municipal wastewater treatment. The study was conducted through a series of interviews with stakeholders involved in instrumentation, control, and automation of wastewater treatment plants. Opportunities and limitations observed by different stakeholders were assessed with a thematic analysis. Thematic analysis enabled a broader consideration of social and organizational aspects related to process control, operation, and maintenance. Identified key barriers for applying data analytics included laborious instrumentation maintenance, unstable control loops, and deficient customization of digital tools for users at wastewater treatment plants. Development needs include easier data processing tools, quality assurance of instrumentation, and controller tuning. Results indicate that the perceived potential of data analytics is highly dependent on the performance of underlying physical and digital systems, as well as the control strategies and operating environment of the plant. Despite the barriers, users and developers see many potential applications for data analytics and expect them to have a central role in the control and operation of wastewater treatment plants in the future.

DEVELOPING A SMART, INTEGRATED FRAMEWORK BY EMPLOYING DATA ANALYTICS TOOLS AND TECHNIQUES TO MITIGATE AND EFFECTIVELY MANAGE THE RISKS ASSOCIATED WITH FINTECH COMPANIES

The rapid integration of technology in fintech companies has significantly increased the risks in this sector. Data analytics, particularly in fraud detection, prevention, and risk management, has emerged as a crucial tool for addressing these risks, offering more accurate predictions of potential outcomes. The growing volume of data has led to a heightened awareness of the importance of risk management through data analytics. Research in this field is essential for helping companies understand the emergence of risk factors more effectively. However, applying data analytics to risk management in fintech companies presents several challenges, including data quality and availability, lack of expertise, cybersecurity and data privacy issues, bias, and ethical concerns. Fintech companies must navigate these challenges while balancing the benefits of effective data analytics models. The necessity of collaboration with other companies, industry associations, and regulators becomes evident in the face of these challenges, as it can help fintech firms stay informed about the latest risks and best practices and identify potential threats early. This research paper provides a comprehensive overview of the role of data analytics in managing risks in fintech companies. It highlights the need for robust policies to ensure data management, transparency, and reliability.

Application of Data Analytics Principles in Healthcare

Information technology has transformed the healthcare field worldwide. In many areas of the healthcare industry, implementations of data analytics tools are commonly used recently. Applying data analytics principles in medical sciences appropriately transforms the mere storage of medical records in to discovery of drugs. Data science and analytics are essential tools because they can help make better decisions when it comes to spending and reducing inefficiencies in healthcare. The proposed model of healthcare data analytics provides a framework to accelerate the adoption and implementation of predictive analytics in healthcare. Healthcare data analytics can be applied to prove formulated hypotheses, test those using standard analytics models and predict patient health conditions. It can be used to classify patients at risk of developing diseases such as diabetes, asthma, and other life-long illnesses. In spite of the challenges faced while applying data science predictive analytics in the healthcare environment, there is an enormous opportunity for its usage in providing quality healthcare for patients

Towards an Approach Integrating Various Levels of Data Analytics to Exploit Product-Usage Information in Product Development

AbstractBy applying data analytics to product usage information (PUI) from combinations of different channels, companies can get a more complete picture of their products’ and services’ Mid-Of-Life. All data, which is gathered within the usage phase of a product and which relates to a more comprehensive understanding of the usability of the product itself, can become valuable input. Nevertheless, an efficient use of such knowledge requires to setup related analysis capabilities enabling users not only to visualize relevant data, but providing development related knowledge e.g. to predict product behaviours not yet reflected by initial requirements.The paper elaborates on explorations to support product development with analytics to improve anticipation of future usage of products and related services. The discussed descriptive, predictive and prescriptive analytics in given research context share the idea and overarching process of getting knowledge out of PUI data. By implementation of corresponding features into an open software platform, the application of advanced analytics for white goods product development has been explored as a reference scenario for PUI exploitation.

Applying Data Analytics to Address Social Determinants of Health in Practice.

As public health and health care increase focus toward addressing social determinants of health (SDH), the growth of data and analytics affords new, impactful tools for data-informed community health improvement. Best practices should be established for responsible use, meaningful interpretation, and actionable implementation of SDH data for community health improvement.