Business Process Intelligence Research Articles

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.

Understanding the Nexus: Business Intelligence and its Impact on Customer Experience Across Diverse Work Sectors

In the 21st-century hyper-competitive business landscape, comprehending and improving customer experience (CX) is highly quintessential for sustainable success across diverse industry sectors. Customers today hold more options in pursuing a luxurious lifestyle compared to their predecessors, yet their expectations continue to be on a constant surge. To meet these rising expectations, today's organisations strongly emphasize incorporating the best industry practices to ensure a high-end customer experience [1]. One such practice that has been included in recent times is integrating business intelligence and analytics. By leveraging advanced analytics tools and techniques, businesses can extract valuable insights from vast datasets, enabling informed decision-making and proactive strategies [2]. Business intelligence and analytics play a vital role in this practice by providing real-time data analysis, predictive modelling, and actionable recommendations, empowering organizations to optimize operations, enhance customer experiences, and stay ahead in today's dynamic marketplace [3]. The following paper aims to explore the vital role Business Intelligence (BI) plays in enhancing customer experience across various sectors. By applying a comprehensive review and analysis methodology, the study will seek to investigate how BI tools, strategies, and analytics influence user experience metrics, organizational performance, and competitive advantage in disparate sectors. Moreover, it further aims to determine the significance of leveraging data-driven insights garnered through business intelligence processes to comprehend customer behaviours, preferences, and sentiments across diverse sectors, ranging from retail and hospitality to finance and healthcare. The paper will bolster the aforementioned analysis by synthesising empirical case analysis, diving into the multifaceted impacts of Business Intelligence and Analytics on Customer Experience. Furthermore, the paper aims to examine the role of BI in optimizing operational efficiencies, streamlining processes, and fostering innovation to meet evolving customer expectations and market demands. Alongside this, it will dive deeper into issues such as data privacy concerns, and underscore the importance of organizational culture, leadership support, and cross-functional collaboration in fostering a BI-driven CX-centric mindset within diverse work environments. Through empirical evidence and industry case studies, the paper will seek to demonstrate how Business Intelligence adoption correlates with improved customer satisfaction scores and profitability metrics across diverse sectors. Towards the end, the paper will present a concluding argument by highlighting key insights, implications, and the imperative of investing in robust data infrastructure, analytics capabilities, and talent development initiatives to unlock the full transformative power of Business Intelligence in driving superior customer experiences. It will eventually underscore the need for continuous evaluation, adaptation, and innovation in Business Intelligence strategies to stay abreast of evolving customer preferences, technological advancements, and competitive dynamics. By illuminating the transformative impact of Business Intelligence on Customer Experience metrics, organizational performance, and competitive advantage, the study aims to provide valuable insights and guidelines for leveraging data-driven strategies to drive customer-centric innovation and sustainable growth in an increasingly dynamic business landscape.

Business Intelligence System and Firms’ Adaptive Ability: Evidence from Developing Country

Purpose: This study assessed the relationship between business intelligence system and firm ability for adaptation Method: A survey research design and sample of one hundred and twenty-four (124) employees of Fintech firms were used. Essentially, three (3) business intelligence systems were used such as technology skill, business forecasting and strategic planning. Data obtained in the survey were analyzed using independent sample t-test statistical too Results and Conclusion: Findings revealed strong and significant variations in business forecasting, strategic planning and technology skill and ability of Fintech companies to adapt. The study concludes that there is relationship between business intelligence system and firm ability for adaptation. Implication of the Research: The implication of the finding is that systems of business intelligence are vital particularly in areas of technology skill, forecasting and strategic planning which aid the ability of Fintech companies to adapt Originality/Value: This study assessed how business forecasting, strategic planning and technology skills which are vital elements of business intelligence can be carried out constantly and collaboratively by including all units/departments concerned with process and implementation of business intelligence

КЛЮЧЕВЫЕ ИНСТРУМЕНТЫ BI-СИСТЕМ, ПРЕИМУЩЕСТВА ИСПОЛЬЗОВАНИЯ И ПРОБЛЕМЫ ВНЕДРЕНИЯ

The article examines the relevance of systems of business intelligence in the field of management in order to improve the quality and efficiency of business decisions. The authors cover the stages of the business intelligence process, providing a detailed analysis of the main tools used in soft business intelligence. This scientific article analyzes the main problems faced by organizations in the process of designing and developing business intelligence systems. Aspects such as data collection and integration, data quality, management support, defining user requirements, personnel training, security considerations, and technology selection are covered in detail.

Building a business intelligence dashboard for a Lebanese company

This project developed a business intelligence process for a Lebanese company to enhance their financial management. The process involved creating a data model based on the company's needs and data availability, integrating their data into a physical database using Python and PostgreSQL, and generating dashboards with Power BI to visualize and analyse their revenue numbers. The project outcomes will help the company gain insights into their business performance, optimize their revenue and profit, and identify data entry issues that may affect their results. The project also provides a foundation for further data integration and analysis.

Information systems for automating work flows: a comparative analysis

Workflow is a sequence of repeated and controlled steps aimed at completing a specific task or work. The urgency of the task of optimizing such works contributed to the development of methods and means of operations research to optimize such processes for the needs of various subject areas. Information technologies to support such workflow are workflow engines that enable faster automation, ensure compliance with norms and standards, formalize business processes, improve communication, etc. The workflow management systems are divided into the following categories: automation using robotics, intelligent integration platforms, intelligent business process management systems, open-source engines, the cloud-supported, as well as data flow processing systems. For a more detailed analysis, it is advisable to choose the following engines and services: built-in, cloud-oriented, and those to support both scenarios (jBPM, Camunda, Zeebe, Amazon Step Functions). It is appropriate to define evaluation criteria and compare such workflow automation systems to form further recommendations regarding their selection and application. Such criteria are development activity, stability and history of commercial use, versioning support, standards support, support for timers and asynchronous execution, support for human-oriented and manual tasks, integration with other solutions, monitoring and logging, scaling, cloud support, the possibility of scanning in private infrastructure, the presence of a visual interface, the convenience of local development and testing, open source code, the necessary programming for implementation and cost. A comparison of advantages and disadvantages can be used to decide on a workflow automation system.

Business Intelligence Enabling Competitiveness: A Multi-Theoretical Analysis of South African Metals Manufacturers

The competitiveness of the South African metals manufacturers is under the spotlight, since new investments in the metals and engineering industry in South Africa have been poor, and growth in the Chinese trade has had a negative competitive effect on domestic metals manufacturing output and employment. The purpose of the study was to determine how supportive the business intelligence process is in enabling successful competitiveness in practice. A theoretical model was designed and empirically tested by metals manufacturers, where four theories were selected to act as mediators between the business intelligence process and successful competitiveness. This study tested four hypotheses by applying Spearman's rank-order correlations and a structural equation model. A total of 63 responses were received and analysed. The study found that the building blocks of the business intelligence process combined in a single factor do not directly lead to successful competitiveness, but only when all the theories' principles, combined into a single factor, act as mediators.

Modeling and automatic generation of data warehouse using model-driven transformation in business intelligence process

The work presented in this paper focuses on the modeling and implementation of business intelligence processes, specifically how to apply model-driven architecture (MDA) throughout the entire development process of a data warehouse to automatically generate the multidimensional schema. As a result, this work specifies different rules for automating the process of obtaining an online analytical processing (OLAP) cube and implementing it using a collection of metamodels and automatic transformations. The data warehouse relational and OLAP cube models are then created using a model transformation from the conceptual model (class diagram). Finally, the XML file and SQL code are generated for creating the OLAP cubes and relational data warehouse, respectively. Using transformation languages like query-view-transformation (QVT) and Acceleo, all model transformations are performed.

A Systematic Review of Consensus Mechanisms in Blockchain

Since the birth of Bitcoin, blockchain has shifted from a critical cryptocurrency technology to an enabling technology. Due to its immutability and trustworthiness, blockchain has revolutionized many fields requiring credibility and high-quality data for decision making. Particularly in business intelligence and business process management, users can use blockchain to build their blockchain-enabled collaboration and data-sharing ecosystem with their partners. In this paper, we present the development process of blockchain and consensus mechanisms, where important blockchain consensus mechanisms are introduced. The consensus mechanism is the kernel among various blockchain components to ensure security and performance. Again, we present a comparison of these consensus mechanisms from different perspectives. We take the blockchain-enabling business as an example and analyze the relationship between blockchain and business process characteristics and the ideas and principles for selecting consensus mechanisms. Finally, we describe the differences and connections among many consensus mechanisms while laying a foundation for selecting appropriate consensus mechanisms for different scenarios and fields of application.

MCDM and FaaS to Automate the Eligibility of Business Rules in the Decision-Making Task during Business Processes

Serverless Computing, also named Function as a Service (FaaS) in the Azure cloud provider, is a new feature of cloud computing. This is another brick, after managed and fully managed services, allowing to provide on-demand services instead of provisioned resources and it is used to strengthen the company’s ability in order to master its IT system and consequently to make its business processes more profitable. Knowing that decision making is one of the important tasks in business processes, the improvement of this task was the concern of both the industry and the academy communities. Those efforts have led to several models, mainly the two Object Management Group (OMG) models: Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) in order to support this need. The DMN covers the decision-making task in business processes mainly the eligibility of business rules. This eligibility can be automated in order to help designers in the mastering of this important task by the running of an algorithm or a method such as the Multiple Criteria Decision Making (MCDM). This feature can be designed and implemented and deployed in various architectures to integrate it in existing Business Process Management Systems (BPMS). It could then improve supporting several business areas such as the Business Intelligence (BI) process. In this paper, our main contribution is the enrichment of the DMN model by the automation of the business rules eligibility through Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using FaaS to further streamline the decision-making task in business processes. Results show to strengthen business-IT alignment and reduce the gap between the real world and associated IT solutions.

Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes

Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance.This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects:•Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures.•Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises.

THE IMPACT OF BUSINESS INTELLIGENCE AND BUSINESS PROCESS AGILITY ON ACHIEVING COMPETITIVE ADVANTAGE

Modern businesses face new challenges as a result of the shifting environments in which they conduct organizational operations. These include the requirement for value creation in partnership with customers, preparedness for change, and the capacity to act quickly in response to consumer needs and to gain competitive advantage. Agile companies are seen as a new paradigm for businesses and are defined by these characteristics. This research aimed to seek the signified role of business intelligence and business process agility by attempting to create a conceptual model in order to dig more knowledge in this concept a literature based research carried to contribute in literature and academic source.

Creating business value with process mining

Information systems research has a long-standing interest in how organizations gain value through information technology. In this article, we investigate a business process intelligence (BPI) technology that is receiving increasing interest in research and practice: process mining. Process mining uses digital trace data to visualize and measure the performance of business processes in order to inform managerial actions. While process mining has received tremendous uptake in practice, it is unknown how organizations use it to generate business value. We present the results of a multiple case study with key stakeholders from eight internationally operating companies. We identify key features of process mining – data & connectivity, process visualization, and process analytics – and show how they translate into a set of affordances that enable value creation. Specifically, process mining affords (1) perceiving end-to-end process visualizations and performance indicators, (2) sense-making of process-related information, (3) data-driven decision making, and (4) implementing interventions. Value is realized, in turn, in the form of process efficiency, monetary gains, and non-monetary gains, such as customer satisfaction. Our findings have implications for the discourse on IT value creation as we show how process mining constitutes a new class of business intelligence & analytics (BI&A) technology, that enables behavioral visibility and allows organizations to make evidence-based decisions about their business processes.

CAO/CAE-инженерное сопровождение производства малых предприятий

The creation of high-tech and science based productions allows designing products with high consumer qualities and manufacturing globally competitive products. New trends associated with the digitalization of design and technological projects are accelerating the integration of material and virtual production. A dynamic environment requires the flexibility of management in the face of accelerated adaptation of manufacture. Such challenges involve long-term forecasting and making quick decisions while maintaining the competitiveness of goods and services over time. The technological transformation of small and medium-sized enterprises as part of the transition to digital production determines their integration into the space of distributed factories. Innovative changes in manufacturing technology are accompanied by the introduction of efficient business processes at all levels. The article considers the formation of a group of competencies ensuring the integration of CAD / CAE / CAO into a single information data flow, as well as their distribution in the field of small and medium-sized machine-building enterprises. A single system of intelligent business process model provides support for parallel work of developers.

Enhancing Decision-Making Consistency in Business Process using a Rule-Based Approach

Decision-making in Business Process is a real challenge, given its technical complexity and organizational impact. Mostly, decision-making is based on business rules fired by an inference engine using facts reflecting the context of the current process task. Focus on a task alone and in isolation from the rest of the process can easily lead to inconsistency in decision-making. In this paper, we aim to improve the importance of consistency of decision-making throughout the process. To fulfill this aim, our contribution is to propose Consistency Working Memory RETE (CWM-RETE): a Framework based on the Rete Algorithm as a pattern-matching algorithm to simulate inference; and MongoDB as a document-oriented database to serialize business rules. This framework enables the compatibility of decision-making throughout the business process. The experimentation is based on the Business Intelligence process as a case study and it is shown that the decision-making process can generate different results depending on whether consistency functionality is enabled or not.

Improving Data Quality in Clinical Research Informatics Tools.

Maintaining data quality is a fundamental requirement for any successful and long-term data management. Providing high-quality, reliable, and statistically sound data is a primary goal for clinical research informatics. In addition, effective data governance and management are essential to ensuring accurate data counts, reports, and validation. As a crucial step of the clinical research process, it is important to establish and maintain organization-wide standards for data quality management to ensure consistency across all systems designed primarily for cohort identification, allowing users to perform an enterprise-wide search on a clinical research data repository to determine the existence of a set of patients meeting certain inclusion or exclusion criteria. Some of the clinical research tools are referred to as de-identified data tools. Assessing and improving the quality of data used by clinical research informatics tools are both important and difficult tasks. For an increasing number of users who rely on information as one of their most important assets, enforcing high data quality levels represents a strategic investment to preserve the value of the data. In clinical research informatics, better data quality translates into better research results and better patient care. However, achieving high-quality data standards is a major task because of the variety of ways that errors might be introduced in a system and the difficulty of correcting them systematically. Problems with data quality tend to fall into two categories. The first category is related to inconsistency among data resources such as format, syntax, and semantic inconsistencies. The second category is related to poor ETL and data mapping processes. In this paper, we describe a real-life case study on assessing and improving the data quality at one of healthcare organizations. This paper compares between the results obtained from two de-identified data systems i2b2, and Epic Slicedicer, and discuss the data quality dimensions' specific to the clinical research informatics context, and the possible data quality issues between the de-identified systems. This work in paper aims to propose steps/rules for maintaining the data quality among different systems to help data managers, information systems teams, and informaticists at any health care organization to monitor and sustain data quality as part of their business intelligence, data governance, and data democratization processes.

A Novel Approach in Business Intelligence for Big Data Analytics Using an Unsupervised Technique

Business intelligence process analyzed data and found certain insights so that managers, executives, or higher authorities of the enterprise can take appropriate decision. Business intelligence provides past, current, and future views of the business actions. Big data indicates big amount of data that is growing exponentially with time. Data analytics check the huge amount of data and indicates some insights. The objective of big data analytics is to discover information like hidden patterns, correlations, market performance, and preferences of customer, so organization can make business decision. This paper presents a new methodology for analysis of huge amount of data using an unsupervised technique of machine learning.

Building interpretable models for business process prediction using shared and specialised attention mechanisms

Predictive process analytics, often underpinned by deep learning techniques, is a newly emerged discipline dedicated for providing business process intelligence in modern organisations. Whilst accuracy has been a dominant criterion in building predictive capabilities, the use of deep learning techniques comes at the cost of the resulting models being used as ‘black boxes’, i.e., they are unable to provide insights into why a certain business process prediction was made. So far, little attention has been paid to interpretability in the design of deep learning-based process predictive models.In this paper, we address the ‘black-box’ problem in the context of predictive process analytics by developing attention-based models that are capable to inform both what and why is a process prediction. We propose i) two types of attentions—event attention to capture the impact of specific events on a prediction, and attribute attention to reveal which attribute(s) of an event influenced the prediction; and ii) two attention mechanisms—shared attention mechanism and specialised attention mechanism to reflect different design decisions between whether to construct attribute attention on individual input features (specialised) or using the concatenated feature tensor of all input feature vectors (shared). These lead to two distinct attention-based models, and both are interpretable models that incorporate interpretability directly into the structure of a process predictive model. We conduct experimental evaluation of the proposed models using real-life dataset and comparative analysis between the models for accuracy and interpretability, and draw insights from the evaluation and analysis results.The results demonstrate that i) the proposed attention-based models can achieve reasonably high accuracy; ii) both are capable of providing relevant interpretations (when validated against domain knowledge); and iii) whilst the two models perform equally in terms of prediction accuracy, the specialised attention-based model tends to provide more relevant interpretations than the shared attention-based model, reflecting the fact that the specialised attention-based model is designed to facilitate better interpretability.

Business intelligence applied to the consumption of iodinated contrast agents in computed tomography scans

BackgroundThe management of the use of iodinated contrast agents (ICA) in the computed tomography (CT) has clinical and financial impacts; however, the approaches in the current research setting have limitations with regard to their exploration of the theme. This work describes the application of the stages of a process of business intelligence (BI), from the formulation of business questions, the building of a research database, and the adaptation of a multidimensional model, to the creation of dashboards to give support to the decision-making process in a hospital. This research aims to apply and document a BI process that provides support to the decision making of managers, so the use of ICA can be better managed, allowing for the identification of situations in which the material was wasted using a study applied to the hospital field.MethodsAn applied exploratory research with a quantitative approach in a database made up by 24 variables and 35,388 records extracted from the RIS (Radiology Information System) that is used by the General Hospital of Porto Alegre—HCPA. The software used, supplied by AGFA Healthcare, were the Qdoc system (version 6.2.0) and the Impax BI (Version 11.1.1) for, respectively, data entry and data exploration. At the end of the process, a total of 48 variables was considered.ResultsThe BI process applied allowed for the identification of situations in which ICA was being wasted during the operationalization of the volume/mass ratio of the agent injected in the patient. It also offered the necessary substantiation for the managers to formulate plans, actions, and controls associated to the use of the material. This work made it possible to diminish in 15.65% the total consumption of ICA injected in the patients who underwent the CTAB1 exam (full CT scan of the abdomen), with a projected economy of US$ 10,039.95, for the performance of this exam from 2020 on. The measuring of the impact and the relevance of the process was 99.6% positive, according to the evaluation of the managers.ConclusionsThis research generated clinical and financial benefits for the HCPA, a positive evaluation by the managers and the generation of new knowledge, which can be shared with other public or private health organizations.

Internet of Things Data Visualization for Business Intelligence.

This study contributes to the research on Internet of Things data visualization for business intelligence processes, an area of growing interest to scholars, by conducting a systematic review of the literature. A total of 237 articles published over the past 11 years were obtained and compared. This made it possible to identify the top contributing and most influential authors, countries, publishers, institutions, papers, and research findings, together with the challenges facing current research. Based on these results, this work provides a thorough insight into the field by proposing four research categories (Technology infrastructure, Case examples, Final-user experience, and Big Data tools), together with the development of these research streams over time and their future research directions.