Business Intelligence Platform Research Articles

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.

Revealing the Supply Chain 4.0 Potential within the European Automotive Industry

With the rapid advancements in Information and Communication Technologies (ICT) and the widespread enthusiasm of both theoreticians and practitioners, the broader transition to Industry 4.0 (I4.0) in major industries appears imminent. This empirical study analyzes business data from 1140 automotive companies operating in Europe, utilizing various business intelligence platforms and employing decision tree analytics to establish connections between enablers, drivers, company size, and financial resources. The goal is to identify persistent barriers hindering the rational transition to Industry 4.0. The findings reveal an uneven transformation within the industry nexus. While larger companies possess the financial means to allocate collective intelligence, technical resources, and drive necessary for fulfilling I4.0 requirements, smaller members of the nexus lag behind despite their enthusiasm and intent. This imbalanced evolution poses a threat to the comprehensive transformation required for realizing all the benefits of Industry 4.0 within the sector. The primary discovery indicates that small to medium-sized enterprises do not exhibit the same rates of Industry 4.0 adoption, a lag highly correlated with their available financial and human resources for digital transition. The decision tree proposed in this study offers guidelines for achieving an Industry 4.0-compliant nexus. Given its diversity and substantial global impact, the case study from the automotive industry proves intriguing and may later be generalized to other sectors. The study’s outcome could empower engineering managers and researchers to implement, execute, and assess the impact of digital strategies based on the financial capabilities of industrial institutions.

Αποτύπωση της Οικονομικής Κατάστασης των Κρατικών Νοσοκομείων: Χ ρηματοοικονομική Ανάλυση των Δημοσιευμένων Ισολογισμών (2019-2021)

Financial statements of public hospitals are prepared by accountants and are audited by Certified Public Accountants and they are the only reliable information that reflects the real financial performance. The accounting information provided is useful to users for transparency, accountability purposes, and decision-making regarding the public management of hospitals. The purpose of this study is to investigate the preparation of audited financial statements, the benchmarking of key operational and financial figures, the analysis of financial figures with cost indicators, and the qualitative analysis of certified public accountants’ audit reports per hospital and per health region for the period from 2019 until 2021. The audited Financial Statements as published on the website of the Ministry of Health and the yearly hospital activity open data from the Business Intelligence (BI-Health) Platform were collected and processed. The analysis demonstrates key financial figures of the Balance Sheet and the Profit and Loss Statement and useful cost indicators for the period from 2014 until 2021. Overall, this study strengthens the idea that accrual accounting and the preparation of financial statements in public hospitals should be a management tool and not only a legal obligation. The financial reporting enhances performance management, effective management of public assets, and policymaking for public health.

Unleashing the Power of Big Data: Designing a Robust Business Intelligence Framework for E-commerce Data Analytics

E-commerce companies are struggling to make use of the enormous amounts of data collected from diverse sources in the big data era. Designing and implementing a strong business intelligence (BI) framework that makes use of big data analytics is essential to overcoming this difficulty. The study examines how cloud computing affects mobile e-commerce, stressing its benefits for real-time data processing, scalability, and analysis. which boosts the competitiveness of Chinese e-commerce businesses. This study's goal is to deploy a comprehensive BI platform designed especially for e-commerce research to unleash the power of big data. Furthermore, the deployment of precision marketing techniques based on the RFM model and historical data analysis increases client segmentation, leading to targeted marketing efforts, greater customer happiness, and higher conversion rates. The major goal of this research is to equip e-commerce companies with the tools they need to take advantage of big data's potential and make decisions that will give them a competitive edge. Data storage, retrieval, and data mining are made possible by the integration of big data technologies, such as relational and distributed databases, along with parallelization via MapReduce. Ordinary, the findings of this newsletter spotlight the importance of embracing massive information technologies and methodologies in the e-trade sector. Leveraging cloud computing, records mining, and enterprise intelligence strategies can free up the capability of tremendous records assets, permitting enterprises to make informed choices, drive innovation, and gain a competitive facet. The paper highlights the value of security safeguards and risk assessment models in e-commerce systems, offering suggestions for spotting and reducing potential dangers and preserving the integrity of the system.

Data Analysis and Visualization of Road Accident Using Power BI

Abstract: Power BI has taken the world of business intelligence, data visualization and analytics by storm. Power BI is an online service that enables searching data, transforming it, visualizing it, and sharing the developed reports and dashboards with other users in the same or different department/organizations or even with the general public. As of February 2017, more than 200,000 organizations across 205 countries are using Power BI. Power BI is having a free option that has adequate features and functionality , it has become a serious contender for use as a business intelligence platform in small and medium organizations. One of the innovative features of Power BI is its Quick Insights feature (Michael Hart, 2017) that is built on a growing set of advanced analytical algorithms. After uploading a dataset to Power BI, a click of a button can be used to invoke this feature that automatically builds many reports based on its analysis of the data, without any human intervention being required. This also helps reduce human errors, in calculations and statistical techniques, which lead to un-verifiable research. Accepting even Excel spreadsheets as input, Power BI is easy to use and ripe for adoption as a platform for Research Data Analysis. In this paper, an attempt has been made to show how easily a dataset of research data can be transformed by Power BI into a set of analytical reports and dashboards, and which can be shared with ease.

Implementasi Business Intelligence Untuk Menganalisis Data Proses Manufaktur Menggunakan Google Data Studio

This study aims to apply the concept of Business Intelligence (BI) by implementing it using Google Data Studio tools in analyzing production data. The BI approach utilizes technology and analytical methods to collect, integrate, analyze, and present the business information needed by company leaders. Google Data Studio is the latest BI platform and provides convenience in interactive data visualization. In this study, the implementation of BI was carried out using Google Data Studio to analyze cardboard box production data. This approach involves the process of collecting production data from various related sources, such as manufacturing systems, inventory systems, and sales systems. The data is then integrated and presented in the form of an interactive and easy-to-understand visualization. The research method used includes literature studies on BI concepts and technology, as well as experiments using Google Data Studio to analyze the production data that has been collected. During the experiment, various indicators of production performance were identified and analyzed, such as productivity, efficiency and error rate. The results of this analysis are interpreted to gain valuable insights in improving production processes and making better business decisions. It is hoped that this research will provide a better understanding of BI implementation using Google Data Studio in the context of production data analysis. It is hoped that the findings of this research can become the basis for companies to obtain better information and make more effective decisions in improving production performance.

STUDY OF THE RELATIONSHIP OF CITIZENS’ APPEALS TO THE OPEN PORTAL OF THE GOVERNMENT OF MOSCOW WITH THE SOCIO-ECONOMIC INDICATORS OF THE ADMINISTRATIVE DISTRICT

The article discusses the possibilities of studying the state of the social sphere according to the repository of the open data portal of the Government of Moscow for administrative districts and urban districts using business intelligence platforms and intelligent technologies of Data Science and machine learning platforms. The possibilities of using machine learning technologies for business intelligence platforms to identify hidden patterns in order to make informed management decisions are presented.
 Purpose – is to familiarize with business intelligence platforms; the use of intelligent BI-platform technologies for monitoring socio-economic indicators.
 Method or methodology of the work: the clustering method and statistical research methods were used in the article.
 Results: to confirm the hypothesis of a positive relationship between the number of families receiving subsidies in the city of Moscow, in various sections, and the number of appeals of citizens to the open portal of the Government of Moscow
 Practical implications: the objective information obtained as a result of monitoring can be used to develop a strategy and make management decisions on the development of districts and improving the quality of life in Moscow

Digital transformation for the water industry: how a data-driven business intelligence platform can improve operations

Abstract The management of a water network is a complex and challenging job. Nowadays, thanks to the technology available, there is the opportunity to move to a data-driven decision process. To meet the increasingly stringent technical quality requirements from the Authority, water utilities need to increase the operational efficiency. The solution is to integrate and organize operational and business-related data into a common data lake and use these data as a source for a business analytics software able to assist and support daily operations and future investment programs. This paper describes the activities and results of a digitalization case study carried out in Italy where a leading water utility serving over 2 million residents implemented an integrated water management system to enhance the business performance by leveraging a data-driven approach. The project resulted in the establishment of an advanced control room at the company's headquarters, featuring real-time dashboards that are fully integrated with the utility's operational tools and software. The integrated system allows faster responses to operational needs and supports business decisions through streamlined access to relevant data.

Use of Big Data and Business Intelligence Platforms to Boost the Tourism and Travel Sector in the Province of Fianarantsoa, Madagascar

The dynamization of Tourism in the Haute Matsiatra Region (Isalo, Andringitra, Ranomafana) is considered favorable to the promotion of Tourism and important for the sustainable development of the rural areas concerned. They are exploited by local visitors. However, currently, there is an inspiration for new openings and new and more original horizons throughout Madagascar. The idea of launching the "dynamization of tourism", using Big Data and Business Intelligence, is somewhat conceived as an exclusive speech to incite tourists to discover new destinations even more authentic than many others. However, we would like to propose that the aim of the dynamization of tourism is to make the assets dynamic, to valorize and capitalize on the natural wealth of the region for the purpose of tourism, and to develop the areas in a sustainable way.

The Brazilian System for Monitoring Workers and General Population Exposed to Asbestos: Development, Challenges, and Opportunities for Workers' Health Surveillance.

a descriptive study of the Datamianto development process, examining all the stages of system planning, development, improvement, validation, availability, and training of health services for its use, in addition to presenting the main challenges and opportunities for its implementation. The system was developed by a group of software developers, workers' health specialists, and practitioners, and it was recently incorporated by the Ministry of Health to be used for workers' health surveillance. It can facilitate the monitoring of exposed individuals, epidemiological data analysis, promote cooperation between health services, and ensure periodical medical screening guaranteed to workers by labor legislation. Moreover, the system has a Business Intelligence (BI) platform to analyze epidemiologic data and produce near real-time reports. Datamianto can support and qualify the healthcare and surveillance of asbestos-exposed workers and ARD, promoting a better quality of life for workers and improving companies' compliance with legislation. Even so, the system's significance, applicability, and longevity will depend on the efforts aimed at its implementation and improvement.

Enhancing Clinical Management of Bariatric Surgery Using Business Intelligence

There is a problem with collecting information in healthcare services as it is scattered among various sources. This leads to potential impact on patient care focus. To address this issue, a Business Intelligence platform was developed and implemented at the Centre for Surgical Treatment of Obesity at Centro Hospitalar do Tâmega e Sousa. The platform developed enables knowledge extraction and aids healthcare professionals to easily access helpful information and perform better decisions, specifically in regards to the growing global concern of obesity and the increasing prevalence of bariatric surgery.

Critical success factors for business intelligence and bank performance

This research attempts to investigate the technological, organizational, and environmental aspects that impact the banking industry's use of business intelligence (BI). In addition, the present research aims to quantify the effect of business intelligence adoption on bank performance to contribute to the current understanding of business intelligence adoption. Upon the completion of the sample verification procedure, 232 samples are collected. Throughout the research investigation, the SEM software is used to process all the acquired data. The study's findings indicate that TOE had a direct and beneficial effect on the adoption of BI systems by banks. Based on the results of the study, the researchers believe that decision-maker and managers would define all activities, responsibilities, and work processes using business intelligence platforms in order to increase their organization's versatility and performance.

BUSINESS INTELLIGENCE: STATIC AND DYNAMIC ASPECTS OF THE WORLD MARKET

Objective. Determination of the state and features of the development of Business Intelligence as a type of decision support system in the world. Methods. The following methods and techniques of cognition were used in the research process: theoretical generalization and comparison, analysis and synthesis, induction and deduction, generalization and systematization. Results. It was established that business intelligence (BI) is a type of decision support system (DSS), which is a large-scale, interactive information system designed to ensure the collection, processing (in particular, analysis and visualization), storage and use of information for decision-making, the demand for which, in view of the significant and exponentially growing amount of information that must be taken into account by the management of the enterprise when making management decisions, the imperatives of transparency and the validity of management decisions, is constantly growing. It was determined that BI began to be actively discussed in scientific and business circles since the 80s of the 20th century. It was established that at the current stage of development, the global BI market is dynamically developing and is characterized by the following features: 1) significant and growing market capacity; 2) complex market architecture; 3) asymmetry and disproportionality of development; 4) significant structural features of the offer and a high level of market concentration; 5) significant structural features of demand. It was determined that as of 2022, the world market of BI is estimated at 29.42 billion dollars, in 2030 - 54.27 billion dollars.It is substantiated that the BI market is quite segmented, it includes the following segments: market of BI software, market of BI platforms and services; the local BI market (On-Premise BI) and the cloud BI market (Cloud BI); BFSI for Banking, Financial Services and Insurance (BFSI), IT BFS, Retail BFS, Healthcare BFS, etc.; the North American BI market, the European BI market, the Asia-Pacific region BI market, etc., the BI market of individual countries. Based on information about the state and features of the development of the segments of the BI market, it was established that the largest and most developed segments of the BI market are: the BI software market, the cloud BI market, the BI market for IT, the North American market; the fastest growing segments of the BI market are: BI platforms, applications and services market, cloud BI market, mobile BI market, Asia Pacific BI market, BI market for BFSI. It has been proven that despite the fact that the IT market is characterized by a fairly developed subject structure, the leading companies of the IT market are: IBM (USA), Oracle (USA), Microsoft (USA), AWS (USA), Salesforce (USA), Sisense (USA), MicroStrategy (USA), Teradata (USA), etc., this market is characterized by a high level of concentration, because Microsoft's share is 36% of the IT software market, 71% of cloud IT. It has been established that BI is most actively implemented by large companies.

Augmented Analytics Driven by AI: A Digital Transformation beyond Business Intelligence.

Lately, Augmented Analytics (AA) has increasingly been introduced as a tool for transforming data into valuable insights for decision-making, and it has gained attention as one of the most advanced methods to facilitate modern analytics for different types of users. AA can be defined as a combination of Business Intelligence (BI) and the advanced features of Artificial Intelligence (AI). With the massive growth in data diversity, the traditional approach to BI has become less useful and requires additional work to obtain timely results. However, the power of AA that uses AI can be leveraged in BI platforms with the use of Machine Learning (ML) and natural language comprehension to automate the cycle of business analytics. Despite the various benefits for businesses and end users in converting from BI to AA, research on this trend has been limited. This study presents a comparison of the capabilities of the traditional BI and its augmented version in the business analytics cycle. Our findings show that AA enhances analysis, reduces time, and supports data preparation, visualization, modelling, and generation of insights. However, AI-driven analytics cannot fully replace human decision-making, as most business problems cannot be solved purely by machines. Human interaction and perspectives are essential, and decision-makers still play an important role in sharing and operationalizing findings.

Visual Analytics to Leverage Anesthesia Electronic Health Record.

Visual analytics is the science of analytical reasoning supported by interactive visual interfaces called dashboards. In this report, we describe our experience addressing the challenges in visual analytics of anesthesia electronic health record (EHR) data using a commercially available business intelligence (BI) platform. As a primary outcome, we discuss some performance metrics of the dashboards, and as a secondary outcome, we outline some operational enhancements and financial savings associated with deploying the dashboards. Data were transferred from the EHR to our departmental servers using several parallel processes. A custom structured query language (SQL) query was written to extract the relevant data fields and to clean the data. Tableau was used to design multiple dashboards for clinical operation, performance improvement, and business management. Before deployment of the dashboards, detailed case counts and attributions were available for the operating rooms (ORs) from perioperative services; however, the same level of detail was not available for non-OR locations. Deployment of the yearly case count dashboards provided near-real-time case count information from both central and non-OR locations among multiple campuses, which was not previously available. The visual presentation of monthly data for each year allowed us to recognize seasonality in case volumes and adjust our supply chain to prevent shortages. The dashboards highlighted the systemwide volume of cases in our endoscopy suites, which allowed us to target these supplies for pricing negotiations, with an estimated annual cost savings of $250,000. Our central venous pressure (CVP) dashboard enabled us to provide individual practitioner feedback, thus increasing our monthly CVP checklist compliance from approximately 92% to 99%. The customization and visualization of EHR data are both possible and worthwhile for the leveraging of information into easily comprehensible and actionable data for the improvement of health care provision and practice management. Limitations inherent to EHR data presentation make this customization necessary, and continued open access to the underlying data set is essential.

Business Intelligence for MSMEs : Role of Management Accountants

The aim of this article is to bring out the role of the Management Accountants in facilitating Micro, Small and Medium Enterprises (MSMEs) in embracing Business Intelligence (BI) as a tool for decision making. The article is exploratory in nature and draws the data from both primary and secondary sources. A sample survey of MSMEs was conducted to collate the relevant primary data. Post pandemic and owing to the digital transformation, the ecosystem of BI has undergone a radical shift. MSMEs are grappling with the technology disruption and the advent of big data. Management Accountants are handy in marshalling and decoding the big data for MSMEs. The findings of this study reveal two important things namely (1) Digital transformation is the key for deploying BI for MSMEs in the post pandemic era and (2) Management Accountants play a strategic role in hand holding the MSMEs to have a robust BI platform for achieving operational performance.

Business Intelligence’s Self-Service Tools Evaluation

The software selection process in the context of a big company is not an easy task. In the Business Intelligence area, this decision is critical, since the resources needed to implement the tool are huge and imply the participation of all organization actors. We propose to adopt the systemic quality model to perform a neutral comparison between four business intelligence self-service tools. To assess the quality, we consider eight characteristics and eighty-two metrics. We built a methodology to evaluate self-service BI tools, adapting the systemic quality model. As an example, we evaluated four tools that were selected from all business intelligence platforms, following a rigorous methodology. Through the assessment, we obtained two tools with the maximum quality level. To obtain the differences between them, we were more restrictive increasing the level of satisfaction. Finally, we got a unique tool with the maximum quality level, while the other one was rejected according to the rules established in the methodology. The methodology works well for this type of software, helping in the detailed analysis and neutral selection of the final software to be used for the implementation.

Data Analysis and Visualization of Sales Dataset using Power BI

Abstract: Power BI has completely revolutionized the worlds of business intelligence, data visualization, and analytics. Power BI is an online service that allows users to search for data, transform it, visualize it, and share the reports and dashboards they create with other users in the same or different departments/organizations, as well as the general public. Power BI is used by over 200,000 organizations in 205 countries as of February 2017. Power BI's Quick Insights feature is an innovative feature that is built on a growing set of advanced analytical algorithms. This function may be activated with a single click after uploading a data set to Power BI, and it generates a number of reports depending on the data's analysis without any need for human participation. This also aids in reducing human errors in calculations and statistical techniques, which can lead to research that isn't verifiable. Power BI is simple to use and ready for adoption as a platform for Research Data Analysis, accepting even Excel spreadsheets as input. The pandemic of the coronavirus has hastened the adoption of mobile-friendly analytics and business intelligence platforms. Companies all over the world now want their employees and customers to have access to data and analytics from any location. The mobile BI market will be worth more than $20 billion by 2024, according to Mordor Intelligence. Furthermore, mobile devices such as smartphones and tablets are expected to account for more than 72 percent of all internet traffic. As a result, implementing mobile-friendly BI platforms across your organization in 2022 makes even more sense from a business perspective. The purpose of this paper is to show how Power BI can quickly transform a piece of research data into a set of shareable analytical reports and dashboards.

Learning Methods of Business Intelligence and Group Related Diagnostics on Patient Management by Using Artificial Dynamic System

Every recognized hospital’s patient management unit (PMU) has focused its efforts on improving clinical patient care, with a process approach, analyzing it from adult emergency overcrowding to prolonged stays in clinical services. It is generating many patients waiting for beds in the emergency service. The PMU does not have a business intelligence (BI) platform that provides information in real-time, generating a blind browsing problem. The purpose is to demonstrate the need for a BI platform using Artificial Intelligence (AI) to analyze in real-time the relevant information for decision making. The methodology consists of analyzing qualitative and quantitatively the statistics of the last three years, both from the emergency service and from the clinical services. This study shows that the saturation of the emergency service responds to the number of patients waiting for beds, which interferes with outpatient care. The projections for 2020 underestimated the demand, and the efforts to open hospital beds and home hospitalization quotas allowed to shovel said excess demand. The average stay numbers continue to increase, as does the number of hospitalized patients for emergencies, generating a progressive growth in demand. It is necessary to have a BI system adapted with AI to perform real-time analysis of the GRD, to be able to act during hospitalization and not afterward.

Do Life-cycle and Generation Moderate Sentiment and Diffusion?

ABSTRACT This study investigates the relationship between diffusion of new digital products and sentiment of product reviews with respect to the moderating effects of product generation and product life cycle. Results for a sample of 154 products and 105 thousand adopters show that the effect of sentiment on diffusion is significantly moderated by both product life cycle and production generation. Specifically, while there is a reciprocal causality between sentiment and diffusion in the early stage, the relationship is significantly weakened in the late stage of product life cycle. Moreover, while sentiment can trigger an effect on diffusion in a similar manner for both single- and multiple-generation products, the effect is likely to last longer for single-generation products than for multiple-generation products. The study discusses theoretical contributions to the Information System theory as well as practical contributions to the use of business intelligence platforms to manage product life cycle and product generation.