Business Intelligence Tools Research Articles

REVOLUTIONIZING BANKING SECURITY: INTEGRATING ARTIFICIAL INTELLIGENCE, BLOCKCHAIN, AND BUSINESS INTELLIGENCE FOR ENHANCED CYBERSECURITY

This paper outlines the methodology and implementation strategies necessary to revolutionize banking security and ensure a resilient financial ecosystem. In the dynamic landscape of banking, security stands as a cornerstone for financial institutions. The rise of digital banking and the proliferation of online transactions have heightened the need for robust cybersecurity measures to protect sensitive financial data and ensure the integrity of transactions. Traditional security approaches are often reactive and struggle to keep pace with the evolving tactics of cybercriminals. Consequently, there is a pressing need for innovative solutions that can adapt to emerging threats in real-time. The integration of Artificial Intelligence (AI), Blockchain, and Business Intelligence (BI) offers a paradigm shift in banking security. AI, with its ability to analyze vast amounts of data and identify patterns indicative of suspicious behavior, serves as a proactive defense mechanism against cyber threats. By continuously monitoring transactions and network activities, AI-powered systems can swiftly detect anomalies and potential security breaches, enabling banks to respond effectively and mitigate risks before they escalate. Blockchain technology introduces a decentralized and immutable ledger that enhances the security and transparency of transactions. By utilizing cryptographic principles, Blockchain ensures that transactional data remains tamper-proof and verifiable, reducing the risk of fraud and unauthorized access. This technology not only secures financial transactions but also enables the secure sharing of data among stakeholders, facilitating seamless collaboration while maintaining data integrity. Business Intelligence (BI) complements AI and Blockchain by providing actionable insights derived from data analytics. BI tools enable banks to gain a deeper understanding of their security posture, identify vulnerabilities, and prioritize remediation efforts.
 Keywords: Banking, Security, AI, Integration, Blockchain.

323 Applying MeSH Tree Structures and Condition-to-MeSH Mapping to Catalog and Characterize Clinical Trials Research Focus Areas

OBJECTIVES/GOALS: Characterizing and analyzing research studies presents several challenges given the various ways studies may be labeled or organized. The Medical Subject Headings (MeSH) thesaurus is a hierarchical vocabulary that can index and organize research foci using common business intelligence tools to enable rapid exploration of research portfolios. METHODS/STUDY POPULATION: Metadata from ClinicalTrials.gov on 455,437 trials were downloaded and all MeSH terms associated with trials in the condition_browse section were loaded into a database. The corresponding MeSH trees for each term were then identified and mapped to their ancestor terms within the tree. Trials were then indexed based on top four hierarchical levels for each associated MeSH term. Trials performed at the University of Miami (UM) were identified based on locations associated with the trial as well as matching National Clinical Trial (NCT) numbers identified from internal research administration systems. Business intelligence software (Microsoft PowerBI) was applied to the corresponding dataset to enable end user exploration and analysis of the trials within ClinicalTrials.gov. RESULTS/ANTICIPATED RESULTS: A total of 3,271 studies associated with UM were identified, of which, 3,054 (93.3%) had at least one condition MeSH term linked. A total of 7,711 MeSH terms were associated with the trials overall, representing 1,112 unique MeSH terms; the most common terms were carcinoma (164), lymphoma (155), HIV Infections (139), neoplasms (136), and leukemia (122). Utilizing MeSH hierarchy, trials were characterized were categorized into 36 different trees. The most common top tree nodes were neoplasms (1,181), followed by pathological conditions/signs and symptoms (913), immune system diseases (574), nervous system diseases (513), and digestive system diseases (483). Within trees, a total of 184, 681, and 1057 different MeSH terms were specified at the second, third, and fourth nodes in the hierarchy respectively. DISCUSSION/SIGNIFICANCE: Utilizing existing metadata from trials posted on ClinicalTrials.gov and MeSH tree structures can enable organizations to readily explore the foci of clinical trials research. High rates of MeSH term association to research study conditions are necessary to ensure adequate representation of research foci.

Business intelligence tools in finance: A review of trends in the USA and Africa

This paper provides a comprehensive overview of the current trends in Business Intelligence (BI) tools within the finance sector, with a specific focus on the divergent landscapes of the USA and Africa. As the financial industry evolves, the integration of advanced BI tools has become imperative for informed decision-making and sustainable growth. This review examines key developments, challenges, and opportunities shaping the implementation of BI tools in both regions. In the USA, financial institutions are witnessing a surge in BI tool adoption, driven by the increasing need for real-time data analytics, regulatory compliance, and risk management. The integration of artificial intelligence and machine learning algorithms in BI tools is enabling predictive analytics, empowering financial professionals to make strategic decisions in a rapidly changing market environment. Additionally, the emergence of cloud-based BI solutions is enhancing scalability and accessibility, facilitating seamless collaboration and data-driven insights. Contrastingly, the African financial landscape is experiencing a gradual but promising shift towards BI tool utilization. The continent, characterized by diverse economies and regulatory frameworks, faces unique challenges such as infrastructure limitations and varying levels of technological readiness. However, the potential for BI tools to revolutionize financial operations, improve transparency, and facilitate financial inclusion is evident. Governments and financial institutions are investing in technology infrastructure and skill development to harness the transformative power of BI tools. This review underscores the importance of understanding regional nuances in the adoption of BI tools, recognizing that effective implementation requires tailored strategies. As both the USA and Africa navigate the evolving financial landscape, the convergence of technological advancements and strategic planning will undoubtedly play a pivotal role in shaping the future of BI tools in the finance sector

Enhancing strategic decision-making: The role of business intelligence tools and organizational ambidexterity

The study aims to investigate the influence of business intelligence on decision-making quality, considering organizational ambidexterity as a moderating factor. The sample included a broad group of professionals from the high-technology segment, surveying 450 respondents; 254 valid responses were obtained from senior executives, supervisors, and analysts. These respondents were selected as they had a clear ability for a deeper dive into strategic goals and international expansion strategies, effectiveness in capacities, and daily financial management improvement. The results showed that with the use of business intelligence tools, organizations were in a better position to provide a higher quality of strategic decision-making (p < 0.01) as it helped in the functioning of new opportunities to identify and capitalize, while keeping an eye on the current capabilities. The study underscores that organizational ambidexterity is one of the most dominant causes (p < 0.01) for the effective realization of the benefits of business intelligence. Thus, for effective decision-making, it is crucial for organizations to synergize business intelligence tools with their ambidextrous capabilities. This underscores the significant role of organizational ambidexterity as a moderator and further demonstrates its importance in optimizing the use of business intelligence to improve strategic decision-making processes.

Modeling e‐Truck adoption for sustainable transport in Jordan: A system dynamics approach

AbstractThis study has developed a model that predicts the quantity of CO2 emissions reduced and the adoption of e‐Trucks until the year 2100 in Jordan. Using system dynamics software (Powersim) and business intelligence tools (Microsoft Power BI), the most significant factors affecting three modules: environment, government, and customer were examined. Twelve scenarios were modeled during the study (three factors and four cases for each factor). The factors were marketing effectiveness, environmental awareness, and government acquisition subsidy, and their values were (0.2, 0.3, 0.4, 0.5), (0.3, 0.4, 0.5, 0.6), and (500, 1500, 3000, 6000 Jordanian Dinar—JOD), respectively. Under normal conditions, with a marketing effectiveness of 0.2, environmental awareness of 0.3, and a subsidy of 500 JOD, the annual sales of e‐Trucks and Conventional Fuel Trucks (CFT) will break‐even in 2052 in Jordan. The cumulative CO2 emissions reduction at the same year will be 6165.213 Tons. Among the three factors, marketing effectiveness was found to have the highest impact followed by the environmental awareness and last came the acquisition subsidy. Based on our findings, it is recommended that policymakers prioritize improving marketing efforts to accelerate the shift towards e‐Trucks, raise environmental awareness among consumers, and provide subsidies for acquisition of e‐Trucks in Jordan. Additionally, further research is needed to incorporate other factors, such as ICT infrastructure, technology maturity and other social factors that can affect the adoption of e‐Trucks. The study's originality lies in its focus on e‐Trucks, a relatively underexplored area in the sustainable transport literature, especially in the context of Jordan as many other emerging nations. The practical implications include leading the switch to e‐Trucks, identifying infrastructural needs, and informing legislative choices. By analyzing long‐term dynamics and encouraging the deployment of e‐Trucks, it seeks to lower CO2 emissions, enhance air quality, and promote a sustainable transportation system in Jordan.

Leveraging Business Intelligence Systems for Enhanced Corporate Competitiveness: Strategy and Evolution

This study contextualizes the transformative role of Business Intelligence (BI) over the past two decades, emphasizing its impact on business strategy and competitive advantage. Employing a dual-method approach, it integrates a bibliometric analysis using SciMAT with a qualitative examination of six key articles from the Web of Science (WoS), analyzed through the Gioia methodology, focusing on BI and competitiveness. The aim is to examine the metamorphosis of Business Intelligence (BI) and how it has evolved from a traditionally supporting role to a central strategic player in shaping corporate strategy and business competitive advantage over the past two decades. It discusses the overall transformation of BI and provides an in-depth examination of the specific ways in which Business Intelligence tools have redefined the landscape in contemporary business practices. Key findings reveal BI’s pivotal role in enhancing knowledge management, innovation, and marketing capabilities. Challenges in BI implementation, such as the necessity for skilled personnel and adaptability to swift technological shifts, are also highlighted. Results advocate for a dynamic BI approach, adaptable to market trends and technological evolutions. The research demonstrates that BI tools, especially when integrated with technologies like AI, IoT, and machine learning, significantly enhances decision making and efficiency in socio–technical and management systems, leading to a paradigm shift in handling complex systems and adapting to changing environments.

Sales Analysis on Garment Industry with Datawarehouse and ETL Implementation on Star Schema

In the current industrial era, clothing companies face increasingly complex challenges in maintaining the quality and sales of their products. To overcome these obstacles, companies must have a good vision, mission, and decision-making capabilities supported by efficient data management. Data is becoming a valuable asset in Industry 4.0, pivotal in process management, increased productivity, and competitive advantage. This research explores the implementation of Data Warehouse (data warehouse) and ETL (Extraction, Transformation, and Load) processes on star schema for PT Golden Flower Tbk. A data warehouse is a container for storing company data, supporting decision-making, and providing business insights. Meanwhile, the ETL process ensures that the data entered into the warehouse is clean, structured, and ready for analysis. PTA garment manufacturing and exporter company, USPT Golden Flower Tbk, uses the Pentaho application to carry out the ETL process. Pentaho, a Business Intelligence tool, facilitates easy data processing and analysis. This research also explains the use of dimension tables and fact tables in star schemas, which form the basis for more in-depth data analysis. Through visualization and analysis using star schema, Mondrian, and schema workbench, companies can identify sales patterns in each branch and understand which products are most in demand by consumers. The results of the star schema, moMondrianand schema workbench provide a better understanding of the company's sales performance, enabling more informed decision-making and more effective strategies.

Supporting Asset Management with GIS and Business Intelligence Technologies: The Case Study of the University of Turin

Despite the promising outcomes achieved over time in Asset Management, data accessibility, correlation, analysis, and visualization still represent challenges. The integration, readability, and interpretation of heterogeneous information by different stakeholders is a further concern, especially at the urban scale, where spatial data integration is required to correlate virtual information with the real world. The Geographic Information System (GIS) allows these connections, representing and digitizing extensive areas with significant benefits for asset analysis, management, and decision-making processes. Such benefits are central for managing large and widespread university campuses as they are comparable to small cities, covering a wide urban region and including resources highly integrated into the urban context. The paper presents how GIS integrated into Business Intelligence (BI) tools can support university Asset Management System (AMS) creation for the optimal use of resources, illustrating the University of Turin case study. The results discussion considers the relationship between the different elements of the assets and their synergy with the city. It focuses on four themes, dealing with the asset identification of buildings and resources, especially the educational ones, asset spatiotemporal evolution, and buildings’ distances for proximity analysis. The benefits achievable through the AMS, related challenges, and possible future developments are highlighted.

Data analysis as a digital competence for information technology professionals: implementation and use of business intelligence

Competitiveness in the business market is becoming increasingly strong due to the constant advancement of technology, which makes it necessary to search for positive results and the systematization of processes, in such a situation, new opportunities arise in which it is possible to extract the maximum potential of all data obtained in a systemic way, applying the concepts of data analysis based on business intelligence. This research aims to present the main methodologies and tools used to perform in-depth data analysis, exploring and defining the use of the concepts of data warehouse, data marts, OLAP, data mining, ETL, business analytics and also business intelligence tools. With the aim of demonstrating the importance of such concepts as a digital competence to IT professionals and also to support decision-making in organizations, so that an understanding on the aforementioned subject becomes possible. You will also be able to understand how data analysis along with all the other concepts mentioned impact on the life of a company, helping in the definition of strategies, the assembly of plans and especially in decision making.

Transforming Primary Healthcare Services with Centralized Health Intelligence: A Case Study from Qatar

Centralized Health Intelligence (CHI) represents an ecosystem where data, technology, and expertise converge to elevate the standards of healthcare services. In this case study, we explore the pivotal role of CHI in reshaping the healthcare paradigm at the Primary Health Care Corporation (PHCC) in Qatar. Adopting clinical information systems led to a wealth of data, necessitating the transformation of the conventional health information management team into the Business and Health Intelligence (BHI) Department. The establishment of a centralized enterprise data warehouse (EDW) and the use of business intelligence tools by the BHI Department further contributed to improved data governance. A meticulous Centralized Health Intelligence Framework was devised to ensure the effective use of PHCC's data assets. This framework encompasses policies and procedures related to data stewardship, information needs assessment, data classification, data privacy, and data literacy requirements. Through this study, we demonstrate how PHCC has harnessed CHI to redefine healthcare delivery by providing relevant stakeholders with easy access to advanced data analytics and data-driven decision-making tools. Implementing CHI infrastructure presents many challenges, including financial, technical, and organizational hurdles. Despite these challenges, CHI significantly benefits relevant stakeholders by providing enhanced access to health data, fostering active engagement. However, addressing data comprehension, security, and privacy concerns remains critical.

DATA MINING AND BUSINESS INTELLIGENCE IN MANAGING THE BUSINESS PROCESSES OF THE PROJECT OFFICE OF AN OUTSOURCING COMPANY

The article carries out a comprehensive study of the possibilities of applying modern business analytics and intelligent data analysis methods to optimize project management processes in the conditions of an outsourcing IT company. The work justifies the relevance of the problem associated with the avalanche-like growth of data volumes during the implementation of IT projects and the possibilities of innovative technologies for their intellectual analysis to identify hidden patterns. Scientific works of leading experts in project management and informatization have been critically analyzed, but a lack of comprehensive studies regarding data analysis specifically for project offices was noted. To achieve the research goal, a thorough analysis of the essence and possibilities of business analytics and intelligent data analysis was carried out, their role in managing modern organizations was examined in detail, and the scientific advantages of implementation in project offices of outsourcing companies were justified. The study of the practical use of business analytics and intelligent data analysis tools to solve the specific task of searching for new projects in the marketing activities of the IT outsourcing company was conducted. Effective methods of data collection, processing, and analysis concerning clients, market, competitors, communication channels, etc., were thoroughly analyzed. Thus, the article carries out a comprehensive study of the theoretical foundations and applied aspects of using advanced business analytics and intelligent data analysis tools with the aim of improving and optimizing the marketing activities of the modern project office of an outsourcing IT company by making timely well-founded managerial decisions based on deep analysis of available data sets.

Enablers and barriers of Adopting BI-tool for data analytics in Large Enterprises of Bangladesh

The objective of this research is to examine the factors that facilitate or hinder the incorporation of Business Intelligence (BI) tools for data analysis and decision-making in enterprises in Bangladesh, using an adapted TOE framework. A quantitative methodology was used, consisting of a purposive survey conducted on 290 individuals who had experience in BI-Applications for the purpose of analytics. 191 valid survey responses were collected and analyzed using PLS-SEM. A conceptual framework consisting of 8 first order reflective constructs and 3 second order constructs were developed to examine the hypotheses, which portrays the main reflective elements as a set of variables that serve as reflective indicators for second-order constructs, encompassing technical, organizational, and environmental factors. The reported data indicated that factors such as relative advantage, compatibility, simplicity, top management support, organizational fit, competitive pressure, and vendor support have a substantial effect on the adoption of BI tools

The potential of Logistics 4.0 technologies: a case study through business intelligence framing by applying the Delphi method.

The growing competitiveness and the importance of data availability for organizations have created a demand for intelligent information systems capable of analyzing data to support strategy and decision-making. Organizations are generating more and more data due to new technologies associated with Industry 4.0 and Logistics 4.0, making it essential to transform this data into relevant information to streamline decision-making processes. This paper examines the influence of these technologies on gaining a competitive advantage, specifically in a logistics company, which is scarce in the literature. A case study was conducted in a Portuguese company using the Delphi method with 61 participants-employees who use the company's integrated BI tool daily. The participants were presented with a questionnaire via the online platform Welphi, requiring qualitative responses to various statements based on the literature review and the results of semi-structured meetings with the company. The study aimed to identify areas where employees believe more investment/ development is needed to optimize processes and improve the use of the BI tool in the future. The results indicate that BI is a crucial technology when aligned with a company's objectives and needs, highlighting the necessity of top management's involvement in optimizing the BI tool. Encouraging employees to use the BI tool emerged as a significant factor, underscoring the importance of leadership in innovative projects to achieve greater competitive advantage for the company. This study aims to understand the importance of Business Intelligence (BI) and how its functionalities should be adapted according to a company's strategy and objectives to optimize decision-making processes. Thereby, the discussion focused on the essential role of BI technologies in leveraging the company's competitive advantage.

Non-Financial Reporting in SMEs: a new approach to measure corporate well-being based on employee perception

The paper focuses on social sustainability within manufacturing industries by measuring workplace well-being. Firstly, the study focused on what the scientific literature says about the main variables and aspects that impact on employee well-being and deepen the productivity and efficiency of industries. To better understand the problems and address the best solution to improve the company environment, the paper analyses more than 20 innovative startups operating in the field of well-being and Human Resource Management. After having defined the impactful and meaningful variables, it was structured a particular case study on a manufacturing field, regarding a Calabrian SME. Using Business Intelligence tools and applying machine learning techniques, comes to light an overall result of company well-being and the value of the main areas. In addition, different company cluster analyses are conducted. Moreover, it shows a correlation network between variables highlighting the most affecting ones on well-being. Thanks to the information obtained, a strategic investment can be made to enhance people's well-being and to make companies more productive.

Analysis of Hospital Length of Stay in Each Diagnostic -Related Groups (DRGs) Carried Out Using the Smart Hospital Research Application

Abstract Background The application of business intelligence (BI) tools in hospitals can enhance the quality and efficiency of care by providing insights into diagnostic, therapeutic, and business processes. BI tools aid in infection monitoring, clinical decision -making, and analysis of hospitalisation durations within Diagnostic-Related Groups (DRGs), identifying inefficiencies and optimizing resource use. Objectives This study aims to analyse hospital length of stay and identify the DRGs with the most inefficient hospitalization times using the BI -driven Smart Hospital application. Materials and methods The Smart Hospital application, developed on the Qlik Sense BI platform, analysed data from the National Health Fund (NFZ), Statistics Poland, e -health Centre (CEZ), and hospitalisations billed by DRG sections. The dataset included 20,376,405 hospitalisations from 2017–2019. Results The average length of stay (ALOS) was 6.2 days, with an effective length of stay (ELOS) of 4.33 days. Ineffective hospitalisation days totalled 30,307,086, accounting for 28.99% of all hospitalizations. The most inefficient DRGs were E53G (Cardiovascular failure), A48 (Complex stroke treatment), N01 (Childbirth), T07 (Trauma conservative treatment), and D28 (Respiratory and thoracic malignancies), contributing to about 14% of all ineffective hospital days. Conclusions Understanding the factors influencing hospitalisation durations in DRGs can improve patient flow management. Future research should compare treatment effectiveness concerning hospitalisation duration to develop optimal strategies for specific patient groups.

The dynamic role of business intelligence in developing effective planning strategies through analyzing data as an influential variable: Case of engineering the pharmaceutical sector in Jordan

In the current pharmaceutical context of Jordan, the significance of Business Intelligence (BI) has emerged as a crucial factor in designing efficient planning methods. This research investigates the transformative impact of business intelligence (BI) in four key domains: drug development enhancement, operational optimization, compliance assurance, and market dynamics comprehension. The research highlights the need to utilize a data-driven methodology to emphasize the value of business intelligence (BI) tools in extracting valuable insights, facilitating strategic decision-making, and promoting operational efficiency. The results indicate that pharmaceutical organizations that utilize business intelligence (BI) can uncover concealed patterns, recognize chances for growth, and make well-informed decisions. Additionally, the capacity of business intelligence (BI) to integrate novel data has accelerated the development of resilient technical frameworks, thereby reinforcing its essential position within the pharmaceutical sector in Jordan. This research serves as evidence of the potential of business intelligence (BI) in facilitating innovation, surmounting obstacles, and eventually improving patient outcomes.

Development of Financial Literacy Through a Self-advising Stock Investment Model: an Integration of Machine Learning and Business Intelligence Tools to Guide Novice Investors

Development of Financial Literacy Through a Self-advising Stock Investment Model: an Integration of Machine Learning and Business Intelligence Tools to Guide Novice Investors

Digital Twin and Asset Management for Italian highways decision support towards scheduled maintenance, repair and rehab

This paper describes Sina’s current inventory and inspection system (SIOS) development with BIM (Building Information Modelling) applications and BMS (Bridge Management System) strategies towards an Asset Management system implementation. Procedures and data flows using Sina databases SIOS (Sistema Ispettivo Opere Sina inventory and inspection data, seismic vulnerability analysis database, etc.) as inputs supporting BIM modelling are described. Starting from a first project, which is currently being applied to an entire highway agency and which also envisages a later integration of tunnels, a BIM modelling procedure was developed to reach information models for an “inspection” “BIM USE”. Such information models, populated with the information necessary for bridge management system processing, have been connected to Business Intelligence tools to create dashboards supporting decision-making criteria based on management and maintenance approaches (Bridge and Asset Management System). In addition, for Asset Management development, the approach of decision-making tools, integrated with a multi-level approach of Italian Guidelines For The Classification And Management Of Risk, The Assessment Of Safety And The Monitoring Of Existing Bridges, can convey information flows to support criteria planning to choose routine and extraordinary maintenance and restoring interventions. The project illustrated in this paper shows how integrating technologies, methodologies, experience and knowledge must be supported by consistent information management towards companies’ digital transformation and asset management development as a decision-making tool based on enabling technologies.

A Survey on Business Intelligence Approach Based on Deep Learning

The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and generate predictions from complicated datasets. This paper introduces the concepts and principles and highlights recent developments and applications in different domains of research: education, organizations, stock market, forecasting, decision-making in real-time, and security. However, the fundamental problem with the business intelligence approach is that there is no learning involved. Other limitations and challenges include the capacity that affects the data analysis process, the variety of data in results, and the need for a complete presentation of results in the form of dashboards, scorecards, reports, and portals. The approach choice hinges on the problem's context and requirements and the nature and characteristics of the data. Although BI and DL are widespread, alternative methods may suit well too, such as machine learning, data mining, and statistical analysis. Justifying the selection based on precise needs and goals is crucial. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent units (GRU), and Business intelligence tools are used in the research problem to address these limitations and explore the potential advantages and difficulties of integrating BI and DL to achieve an advantage in a given sector. Index Terms— Business Intelligence Tools; decision making; deep learning algorithms.

A Business Intelligence Tool Designed for Governing Sustainability

Computer technology and administrative procedures are used to provide business intelligence (BI), which aids in decision-making. In order to balance financial and non-financial factors as well as short- and long-term measurements, the balanced scorecard is a type of business intelligence tool for performance measurement and management control. The original balanced scorecard was modified to specifically take governance, social, environmental, and ethical issues into account. As a result, sustainability concepts may be incorporated into the organization's strategy and management. This is known as the sustainable balanced scorecard. Despite being one of the best instruments for incorporating sustainability into management, the sustainable balanced scorecard has few developed and implemented instances that can serve as reference models. In order to address this issue, this paper discusses the case study's findings and suggests a technique for creating a sustainable balanced scorecard that takes into account several stages including planification, analysis, design, or the use of computer tools. As a result, the research methodology used to conceptualize and carry out the case study was split into seven stages: defining the objectives and questions of the study; formulating a theoretical model; identifying units of analysis; choosing a case; defining research methods and resources; conducting fieldwork; gathering data; classifying and triangulating information; developing an improved theory, model, or methodology; and confirming the thoroughness and caliber of the investigation. The paper shows a methodology organized in phases and activities that allow a sustainable balanced scorecard to be planned, designed, built, computerized, and controlled to integrate sustainability within the management systems of organizations.