Power BI Research Articles

Applying Microsoft Power Platform to Improve Primary Medical Equipment Maintenance Processes.

Post-COVID, our hospital shifted to online systems using Microsoft Power Platform to mitigate errors and delays. SharePoint and Power Apps manage equipment maintenance records and submissions, enhancing immediacy and transparency. Power Automate automates report generation and reminders, improving task completion rates. Power BI visualizes data for streamlined management. Adjustments to real-time reporting enhanced efficiency, reducing manual efforts and improving completion rates.

Constructing a User-Centric Visual Real-Time Surgical Dashboard.

The existing surgery timetable is very simple, not user-friendly, and has very limited information. This article outlines the creation of a surgical dynamic dashboard for users, using Microsoft Power BI. The dashboard was developed based on in-depth interviews with the surgical team members to determine their requirements. The outcome of dashboard was evaluated by 5-likert scale questionnaire to measure satisfaction of all surgical team members. All team members were very satisfied with the dashboard compared to the existing surgery timetable. In addition, with the function of auto-generated analytic reports through data criteria selection on the dashboard saved time on manually generating reports. This study supports to replace the existing surgery timetable by the user-centric and real time surgical dashboard.

Using evidence‐based decision‐making and cognitive apprenticeship approach to develop students' entrepreneurial mindset

AbstractDeveloping one's entrepreneurial mindset is important for all students, regardless of discipline. Evidence‐based decision‐making (which has the potential to lower costs and improve quality of life) is one approach for applying entrepreneurially minded learning in the undergraduate classroom. This approach allows students to understand trends related to data, in general, and big data, specifically. Furthermore, it better prepares graduates to evaluate and identify effective data science‐based solutions. The purpose of this study is to report on one pedagogical approach to developing the entrepreneurial mindset through integrating evidence‐based decision‐making into the engineering and technology classroom using Microsoft Power BI Desktop (a freely available tool released by Microsoft in September 2013, where “BI” implies Business Intelligence). A mixed methods assessment was conducted including a rubric to measure students' effectiveness in applying the entrepreneurial mindset and a metacognitive reflection to better understand student motivation, awareness of learning, and engagement. First, the rubric was applied, and students were categorized by performance group (e.g., high, mid, low). Second, each performance group was analyzed to identify themes within the reflections. Our findings suggest that students in the high‐performing group communicated overall high levels of motivation, while students in the low‐performing group shared overall moderate levels of motivation. The relationship between performance and motivation among students in the mid‐performing group was inconclusive. Findings from our study suggest that there may be a relationship between students' performance and motivation. The key study implications relate to the use of new literacies, such as technological literacy, data literacy, and human literacy, as practices for promoting the development of an entrepreneurial mindset. Our findings suggest that our approach was effective in accomplishing this goal, but there is also room for improvement. Lessons learned and recommendations are provided.

Data-Driven Quality Improvement for Sustainability in Automotive Packaging

This study addresses sustainability challenges in automotive packaging by introducing a novel data-driven approach to audit and improve packaging quality. Traditional manual auditing processes are both time-consuming and prone to errors. To counter these inefficiencies, we developed an automated system utilizing Microsoft Power Apps and SQL databases to streamline data collection and processing. The automated tools facilitate real-time data capture and visualization through Microsoft Power BI, enabling precise tracking and management of returnable and expendable packaging materials. Our results show that our approach offers a significant enhancement in the accuracy and speed of data analysis, leading to more informed decision-making. Our methodology supports sustainability by reducing waste and reliance on expendable packaging and improving the economic and operational efficiency of automotive packaging systems.

Implementation and Visualization of Business Intelligence Based Budget in Ngawi District Financial Agency 2021-2023

This research aims to determine the s of budget implementation and visualization from 2021 to 2023 in the Financial Agency of Ngawi Regency which uses Business Intelligence Power BI as a basis for decision-making. This is quantitative research which used a descriptive-quantitative approach method. To collect the data, the researchers used a literature study and documentation of the summary of APBD (Anggaran Pendapatan Belanja Daerah/APBD) data in the Financial Agency of Ngawi Regency from 2021 to 2023. The results indicate that there are fluctuations in the Regional Income, Regional Expenditures, and Regional Financing when implementing and visualizing budget data (APBD) using Business Intelligence Power BI. Cumulatively, the total APBD is Rp 13,732,224,315,602.00, with a total growth of 210.3%. The next result is the Financial Agency of Ngawi Regency can use Business Intelligence Power BI to analyse and visualize the APBD budget data for decision-making purposes. It shows that this system can be used for decision-making by this agency because it is user-friendly, fast, accurate, and informative.

Regional resource provision map: methodology and key approaches

The achievement of sustainable development goals with the help of the implementation of a systematic approach to managing the resource potential of regions through sustainable development goals is one of the actual objectives in regional management. Mapping is known as an approach, which allows combining several data sources with different scaling. This study aims to develop regional resource provision map for creating sustainable development conditions. Multidisciplinary research is a valuable source of this research that allows to unit ESG criteria and their regional commitment through cartographic science tools. The methodology is presented in the form of a sequence of actions to draw up a resource supply map. Using the presented map of Western Kazakhstani region confirms the validity of the scientific and applied methodology. The research outcomes contain proven arguments for the further research based on the issues of constructing integrated resource provision maps for the Kazakhstani regions. Key cartography approaches make it possible to form recommendations for similar maps use in terms of decision-making based on interregional interaction, taking into account resource potential, consisting of natural, labor, financial, and infrastructural capabilities of the regions and environmental risk assessments. Developed recommendations were tested with the help of Microsoft Power BI and SuperMap (laboratory of “Geoinformation Cartography” of Kazakh National University named after al-Farabi Kazakh).

STRATEGIC DECISION SUPPORT: DEVELOPING A DASHBOARD FOR MANAGERIAL INSIGHTS IN HIGH-LEVEL TVET STUDENTS' ENROLMENT AT UNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH

The purpose of this study was to create a dashboard with information visualisation to assist Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) in managerial decision-making and to critically and hierarchically present data regarding the enrolment of Malaysian Diploma Vocational (MDV) students into high-level Technical Vocational Education and Training (TVET) programs at UMPSA. The Cross Industry Standard Process for Data Mining (CRISP-DM) model was applied in this research followed by dashboard development using Microsoft Power BI. The CRISP-DM model consists of six continuous and connected phases, namely business understanding, data understanding, data preparation, data modelling, data visualisation, and data synchronise and update. The data was obtained from the Undergraduate Records Management Unit, Centre for Academic Management, UMPSA. The database was used in the development of the dashboard to store information in the form of reports and for data visualisation purposes. The findings hope to assist the management of UMPSA in making better decisions and planning the enrolment process of prospective students. The dashboard outlines several initiatives and best practices that should be implemented by the Malaysian Government, the Ministry of Higher Education, and UMPSA through the UMPSA 2021–2025 Strategic Plan to empower the high-level TVET agenda.

ENHANCING LIFELONG LEARNING: A DATA-DRIVEN DASHBOARD APPROACH FOR STRATEGIC DECISION-MAKING AT UNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH

The purpose of this study was to develop a dashboard with information visualisation for the lifelong learning (LLL) programs at Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) and provide recommendations for UMPSA to improve the strategies and decision-making for future LLL program offerings. This study used the Cross Industry Standard Process for Data Mining (CRISP-DM) model created by the International Business Machines Corporation (IBM) and dashboard development using Microsoft Power BI. The updated database was created by obtaining data from the Institute of Graduate Studies (IPS), UMPSA. Such data was then prepared through the process of selecting, cleaning, constructing, integrating, and formatting data. The dashboard was developed using a database for data visualisation and information in the form of reports. Such information can ensure a better decision-making process and the UMPSA management’s readiness in planning the admission process for future students. However, the data management and data quality at UMPSA must be improved. Future research can expand the present investigation by including data from all categories of LLL students at UMPSA to facilitate a more comprehensive analysis. It will also enable the university management to make accurate decisions about program offerings in the future.

The Use of Business Intelligence Software to Monitor Key Performance Indicators (KPIs) for the Evaluation of a Computerized Maintenance Management System (CMMS)

The increasing use of electromedical equipment in hospital care services necessitates effective management of complex devices often unsupported by existing control systems. This paper focuses on developing a pool of evaluation indices for the Clinical Engineering Department (CED) of the ASST Grande Ospedale Metropolitano Niguarda in Milano (Italy), aiming to enhance awareness of the economic value, assess operational units, and optimize maintenance processes. Leveraging business intelligence, this study identifies 18 key performance indicators (KPIs) across logistics, technical, and equipment management categories. An interactive dashboard, implemented using Power BI, facilitates dynamic analysis and visualization of these KPIs, providing insights into the maintenance efficiency and obsolescence of medical devices. It offers a comprehensive framework for ongoing monitoring and decision-making. The results showcase the potential of the developed KPIs and dashboard to enhance operational insights and guide improvements in the healthcare facility’s maintenance processes.

Enhancing decision‐making in higher education: Exploring the integration of ChatGPT and data visualization tools in data analysis

AbstractThis chapter explores the potential of integrating conversational AI tools such as ChatGPT with data visualization (DV) tools such as Power BI in higher education settings. A brief history of chatbots is summarized and challenges and opportunities in higher education are outlined. The highlights include AI's prospects for enhancing data‐informed decision‐making while needing safeguards to mitigate risks. Through a pioneering exercise, we integrated ChatGPT's conversational capabilities with Power BI's interface via API and tested functionality. Suggestions for good practice and implications for higher education are discussed.Practical Takeaways The importance of data security, recognizing current limitations in accuracy, and good practices for integrating DV tools with ChatGPT. The exercise shows that AI tools have the potential to aid higher education leaders, but still face many challenges, and human judgment remains essential. Integration of ChatGPT with Power BI in higher education aims to enable natural language interaction with data visualizations, facilitating dynamic discussions and insights for leaders. Considerations include data security protocols, technical challenges, financial constraints, and the potential and limitations of AI. Technical challenges include limitations in ChatGPT's interpretation of complex queries, API performance issues, and data processing inefficiencies. Affordability of enterprise AI versions poses a barrier to adoption for institutions with budget constraints despite recognizing potential benefits. ChatGPT represents a significant advancement in AI capabilities but has limitations, including generating inaccurate information and lacking awareness of its limitations. Human oversight and critical evaluation are crucial in navigating the evolving AI landscape and leveraging its benefits effectively while mitigating risks.

A market intelligence model for the health care business system

Health in Brazil is an old problem that has worsened with the Covid-19 pandemic and resources have become scarce. Studying market behavior and growth trends has become fundamental. This research aims to build a Market Intelligence Model, based on information obtained from official secondary data sources, using the Fuzzy logic methodology. The concepts of Data Analytics and Decision Making applied in the Health Area are addressed. The model was developed in RSdudio software, stored in SQL Database, and presented in Power BI and QGis. The model was validated in private health units in the southeast and northeast regions of Brazil for the treatment of chronic diseases, mainly in the areas of nephrology and oncology. The results were obtained, and it can be concluded that the proposed model has become very effective in estimating the growth of chronic diseases.

Implementing a natural language processing pipeline to expedite clinical chart review to assess germline genetic testing in veterans with breast cancer.

e13640 Background: The Making Advances in Mammography and Medical Options for Veterans Act of 2022 mandated that the Department of Veterans Affairs (VA) perform a study of germline genetic testing in Veterans with breast cancer (BC). Assessing whether a Veteran with BC was offered germline genetic testing based on electronic health records requires time-consuming review of unstructured clinical notes by clinical experts who are also extremely busy providing care. To expedite this review process, we implemented a productionized natural language processing (NLP) pipeline to identify relevant concepts and highlight them within a custom-made chart review interface. Methods: Clinical subject-matter experts (SMEs) were convened to identify keywords suggestive of genetic testing. These were categorized into 3 sets: gene-related concepts, VA-specific services, and exams and tests. We then used entity recognition and entity linking to link these concepts to a knowledge base. These were enriched with entities similarly extracted from an open-source reference clinical text on germline genetic testing in BC. The resulting concepts were visualized as a word cloud and reviewed by the SMEs. We implemented a Power BI and Power Apps interface to display clinical notes from 200 randomly selected Veterans, with relevant concepts highlighted. Each Veteran’s notes were reviewed independently by 2 of the 5 SMEs, who assessed whether the Veteran was offered testing, referred to a genetic counseling service, or otherwise had evidence of testing. Disagreements were adjudicated by the whole team. Results: 11622 notes (mean 58/Veteran, 3028 characters/note) were reviewed. Table shows the most prevalent entities among genes, mutations, and exam names. Of the 5 reviewers, 3 annotated 80 patients each in a single session, lasting less than 50 minutes on average. Reviewers had an inter-annotator agreement of 85.5%. Mention of target concepts was associated with germline genetic testing with an accuracy of 80.4%, precision of 76.4%, recall of 99.1%, and F1 of 86.3%. Germline genetic testing rates are detailed in a separate submission. Conclusions: Modern informatics tools such as productionized NLP, the Microsoft Power Platform, and high-performance computing environments allow for rapid implementation of health information technology tools that can enable clinicians to gain critical insights quickly and efficiently from electronic health record data. [Table: see text]

Enhancing Business Intelligence with Real-Time Sales Dashboards: Integrating SQL Databases and Power BI

Abstract: In today's fast-paced business environment, the ability to make informed decisions swiftly is crucial for maintaining a competitive edge. This project proposes the integration of SQL databases and Power BI to develop a real-time sales dashboard, aimed at transforming how sales data is visualized and analysed within the organization. By harnessing the robust capabilities of SQL databases for structured data management and Power BI for data visualization and analytics, the project seeks to provide comprehensive insights into sales trends, performance metrics, and customer behaviours. The integration will enable the automatic synchronization of sales data from SQL databases to Power BI, facilitating the creation of interactive and dynamic dashboards. These dashboards will not only display key sales indicators and metrics but also allow users to drill down into specific data points for deeper analysis. The real-time nature of the data presented will empower sales managers and executives to make data-driven decisions rapidly, adjust strategies on the fly, and identify opportunities for growth and improvement with unprecedented speed and accuracy.

Retail Insights: Unveiling E-Commerce Dynamics with Power Bi

Retail Insights: Unveiling E-Commerce Dynamics with Power Bi

Business Intelligence HRIS Monitoring Dashboard to Increase User Interaction Using Power BI

Business Intelligence HRIS Monitoring Dashboard to Increase User Interaction Using Power BI

IoT-Enabled Flood Monitoring System for Enhanced Dam Surveillance and Risk Mitigation

According to the Indian scenario, the majority of reservoirs for holding water are operated independently, which is problematic when there are crises (abnormal inflow, cloudy conditions), which causes the surrounding communities and agricultural areas to be submerged those aquifers. Due to the vast geographic region and depth, it is challenging to manually measure the essential reservoir life metrics. Therefore, this research work suggests a cutting-edge system of reservoir management that includes sensors that are appropriate for measuring variables such as pressure, water level, outflow velocity, inflow velocity, tilt, vibration, etc. The Arduino Uno integrates all of the sensors, and Microsoft Power BI receives the data in real time, where each parameter is shown in an appropriate format for visualization. In case of an emergency water level rise, the alarm is set off. The procedure begins with the collection of data from sensors and concludes with the presentation of that data on a dashboard in a control room situated in a distant place that links to a website where the relevant information can be seen by visitors.

Prevalence and trends in physical therapy interventions for erectile dysfunction: A Scopus-based bibliometric analysis (1989-2022).

Erectile dysfunction (ED) is a condition that affects many males. Physical therapy (PT) is the one potential treatment for ED that may improve blood flow, muscle strength, and other factors that may contribute to the issue. Data on the prevalence and trends of research on PT for ED are lacking. This study aimed to evaluate the literature trends in PT for ED via bibliometric and visualized analysis. Data on publications were collected from Scopus covering the period between 1989 and 2022. To refine the data, bibliometric analyses were conducted using Microsoft Access, Microsoft Excel, an online visualization platform, and BiblioAnalytics. Power BI and Bibliomaster were used to generate figures and tables, while Biblioshiny and VOSviewer were used for visualization. A total of 494 documents were identified. The year 2019 generated the largest number of publications, with a total of 54. These studies have received 12,917 citations related to PT for ED. The most common document type was the original article with 283 publications. The University of California, USA, was the most productive institution on this topic, with 21 publications and 2,035 citations. The USA led all countries with 114 publications on the topic. The Journal of Sexual Medicine secured the top ranking with an h-index of 18. The main topics studied were erectile dysfunction, shockwave therapy, and physiotherapy. The number of publications on PT for ED has demonstrated an upward trend over the last three decades.

Exploring associations between accident types and activities in construction using natural language processing

Existing accident analysis methods often fail to integrate the relationship of construction accidents with construction activities, restrict the potential for visualizing the accidents frequency in Building Information Modelling (BIM). This study proposes a decision support system that provides visual insights, and the frequency of the core construction accidents in relation to typical construction activities. K-mean clustering and keyword-matching techniques are used to group the leading causes of construction accidents and then compare them with the construction activities. The average hit rate computed for construction accidents was 91%, whereas the hit rate calculated for construction activities connected to these frequent accidents was 75%. The classification models are integrated into the Power BI platform to offer decision-makers deeper insights regarding the relationship of prevalent construction accident types and associated activities. The practical application of the system is demonstrated with a case study, exemplifying the fall accident data integrated in a BIM environment.

Sustainable Energy Consumption Analysis through Data Driven Insights

Energy is the backbone of our society, supporting our daily activities and driving progress. It plays a crucial role in shaping our modern way of life. The future of global energy consumption is influenced by many factors, including demographics, economic dynamics, technological developments, political actions, environmental demands and geopolitical considerations. As the world's population continues to grow and urbanize, the demand for energy is increasing. At the same time, rapid technological innovations are shaping the energy landscape and changing production, distribution and consumption patterns. In the midst of this development, it is very important to optimize energy consumption, accurately anticipate needs, curb climate change, limit emissions of greenhouse gasses, fight against pollution and promote sustainability. This study includes an in-depth analysis of historical consumption trends, assessing the multiple benefits of renewable energy integration, estimating carbon emissions, formulating practical policy recommendations and providing empirically informed insights. The work is based on various data obtained from platforms such as Kaggle and using advanced visualization techniques such as Power BI dashboards. The study provides invaluable perspectives on future energy needs, the penetration of renewable sources into the energy mix, and the strategic needs to achieve sustainable energy use.

Implementasi Intelejen Bisnis dengan Visualisasi Data Gaji dan Algoritma Linear Regresion

To understand these employment dynamics, it is important to explore how long Data Scientists typically work in this field and what sectors they are more likely to be involved in. This information is very important for companies looking to recruit Data Scientists or determine the average salary of their employees. With this activity, managers can more clearly know the employee's salary range based on their work experience and area of specialization. This gives managers a more precise tool for assessing compensation appropriate to the level of experience and expertise possessed by employees. Nowadays, the concept of Business Intelligence can be applied by all industrial sectors, as long as the industry has a database system to develop its business. Business Intelligence processes and analyzes large amounts of raw data and then displays it in a business report with interactive visuals, namely a dashboard. This research aims to contribute to the understanding of average salary predictions and provide valuable information for business owners to make informed decisions. The results show that linear regression is accurate and forms a linear regression model with an R-square result of 0.263429. The salary prediction calculation process is carried out by considering influencing factors, and the results are then presented visually using Power BI to provide business owners with more interactive information.