Design and Implementation of Enterprise-level Business Intelligence Analysis System

Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, clinical blood analysis offers superior accessibility to a comprehensive assessment of physiological parameters. These parameters can be integrated and automated at scale, allowing for in-depth clinical inference and cost-effectiveness compared to other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value of routine blood tests leveraged by artificial intelligence (AI), using the ICD-10 classification as a reference. A significant gap exists between standard disease-associated features and those selected by machine learning models. This suggests an amount of non-perceived information in traditional decision support systems that AI could leverage with improved performance metrics. Nonetheless, AI-derived support for clinical decisions must still be harmonized regarding external validation studies, regulatory approvals, and clinical deployment strategies. Still, as we discuss, the path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.

The continued growth in the volume of available domain and technical data has been facilitated by a corresponding advancement in and communication technology. This information overload can result in inefficient use of time and resources as well as the creation of recommended courses of action that are overruled by the decision makers' judgment and experience. In order to address these problems, multiple knowledge sources and an inference process capable of mirroring the human thought processes (especially judgment and experience) must be available at the right time to the persons or groups needing the knowledge for decision making. Such a concept can be referred to as MANAGED QUALITATIVE THINKING SUPPORT SYSTEMS (MQTSS). Traditional decision support systems (DSS) rely upon decision maker or staff expertise to render knowledge in support of decision making. If the decision maker or staff has insufficient domain or technical expertise to utilize the DSS's embedded models, interpret results, or implement the recommendations, knowledge delivery may be compromised or rendered ineffective. MQTSS can alleviate these support problems and improve knowledge delivery for decision making by reducing knowledge search times, streamlining decision-making tasks, reducing decision time, and promoting appropriate qualitative thinking,. The MQTSS approach theoretically can enhance the decision making process and decision outcomes. This paper attempts to replicate an earlier study by this author to test the theory. First, the MQTSS approach is presented. Next, an system is created to deliver the technology to management. Finally, a simulation experiment is reported that compares the effectiveness of support rendered by a traditional decision support system and the created MQTSS system. The paper closes with conclusions and implications for systems research.

It is shown that, although many aspects of a traditional decision support system (TDSS) and an organizational decision support system (ODSS) are similar, there are important differences that must be understood if the ODSS is to be successful. Differences in purposes lead to differences in design, differences in managing the development effort, differences in implementation, and differences in maintenance. In fact, some aspects of building an ODSS are more similar to building a traditional information system than to building a TDSS. The suggested approach to building an ODSS combines principles and good practice for building both types of systems. The points made are illustrated with examples from the ODSS that are currently being implemented by the US Air Force. The system, called the Enlisted Force Management System, is being used to help members of the Air Staff in the Pentagon make decisions related to their enlisted personnel. >

Business organisations are increasing their influence and presence throughout the world as domestic economies become oversaturated. Organisations increasingly turn to international commerce to discover new markets. Globalisation requires that firms adapt their management and decision-making practices to recognise localised cultural, political, legal, social, and security requirements. In response, organisations are revamping their decision processes, tailoring them as cultures evolve, political assumptions and realities change, laws are enacted, social issues come to the forefront, and security requirements mature. Traditional decision support systems (DSS) are incapable of readily incorporating localised variables into decision-making processes and must be enhanced. Geographic Information Systems (GIS), have the potential to enhance traditional management decision-making (DSS) tools by adapting them for use in global environments. This paper proposes the melding of GIS and DSS toolsets to create a new framework, hereinafter referred to as Geographically-Enhanced Decision Support Systems (GEDSS), for enhanced management and decision-making for businesses.

As businesses grow across continents in terms of reach, scale, and diversity in products, the complexity of management problems and efforts to maximize the profit in corporations have increased significantly. To assist managers and employees at all levels in decision making, corporate modeling is essential to provide decision support information. Corporate modeling within business organizations has moved from standalone database applications to integrated and flexible enterprise-wide information systems. Computer modeling and planning tools such as decision support systems (DSS) are widely used by corporate management to select alternatives based on key business performance measures. Enterprise resource planning (ERP) systems have also been introduced as a new form of corporate modeling that does the same thing as traditional DSS, but they are not confined to the boundaries of business applications. This article reviews the various types of corporate models along with development and applications of advanced DSS and ERP systems. Keywords: corporate modeling; customer-centric enterprise information systems; decision support systems; enterprise resource planning; evolutionary multiagent systems

Adopting Artificial Intelligence in a Decision Support System – Learnings from Comment Moderation Systems

In this study, we deploy review centric research of User Satisfaction, Cost Reducing, and Time Optimization impacting decision making on whether to implement Business Intelligence in organizations. The purpose of this paper is to present a literature review to examine the association between Business Intelligence and these three different factors. The primary goal of this study was to investigate the user’s satisfaction, cost reducing, and time optimization implications in order to choose Business Intelligence as a business tool. A comprehensive literature review of business intelligence created a theoretical foundation for this paper. Using the grounded theory, a model was developed and evaluated based on how User Satisfaction, Cost Reducing and Time Optimization impacts managerial decision-making using Business Intelligence. Using data from JSTOR and Academic Search Premier databases a new model is presented to encapsulate the highly dynamic interaction of user satisfaction, cost reducing, and time optimization with business intelligence to provide elements to consider the implementation of BI in a firm. This model highlights three key aspects that administrators consider in order to determine the possibility of taking Business Intelligence as an instrument in their daily duties. The theoretical model is limited to three factors only, which could be extended in future studies on this topic. Moreover, this model has been discussed using a theoretical perspective whereas practical contributions has been given less attention. This study provides an exciting opportunity to advance our knowledge of business intelligence to efficiently and flexibly help companies make the right decisions in real time, grasp business opportunities, and gain competitive advantage.

Traditional decision support systems (DSS) and executive information systems (EIS) gather and present information from several sources for business purposes. It is an information technology to help the knowledge worker (executive, manager, analyst) make faster and better decisions. So far, these data were stored statically and persistently in a database, typically in a data warehouse. Data warehouses collect masses of operational data, allowing analysts to extract information by issuing decision support queries on the otherwise discarded data. In a typical scenario, an organization stores a detailed record of its operations in a database, which is then analyzed to improve efficiency, detect sales opportunities, and so on. Performing complex analysis on these data is an essential component of these organizations’ businesses. Chaudhuri and Dayal (1997) present an excellent survey on decision-making and online analytical processing (OLAP) technologies for traditional database systems. ?n many applications however, it may not be possible to process queries within a database management system (DBMS). These applications involve data items that arrive online from multiple sources in a continuous, rapid and time-varying fashion (Babcock et. al., 2002). These data may or may not be stored in a database. As a result, a new class of data-intensive applications has recently attracted a lot of attention: applications in which the data is modeled not as persistent relations but rather as transient data streams. Examples include financial applications (streams of transactions or ticks), network monitoring (stream of packets), security, telecommunication data management (stream of calls or call packets), web applications (clickstreams), manufacturing, wireless sensor networks (measurements), RFID data, and others. In data streams we usually have “continuous” queries (Terry et. al., 1992; Babu & Widom, 2002) rather than “one-time.” The answer to a continuous query is produced over time, reflecting the stream data seen so far. Answers may be stored and updated as new data arrives or may be produced as data streams themselves. Continuous queries can be used for monitoring, alerting, security, personalization, etc. Data streams can be either transactional (i.e., log interactions between entities, such as credit card purchases, web clickstreams, phone calls), or measurement (i.e., monitor evolution of entity states, such as physical phenomena, road traffic, temperature, network). How to best model, express and evaluate complex queries over data streams is an open and difficult problem. This involves data modeling, rich querying capabilities to support real-time decision support and mining, and novel evaluation and optimization processing techniques. In addition, the kind of decision support over data streams is quite different from “traditional” decision-making: decisions are “tactical” rather than “strategic.” Research on data streams is currently among the most active areas in database research community. Flexible and efficient stream querying will be a crucial component of any future data management and decision support system (Abiteboul et al., 2005).

In present business competition, organizations are emphasizing on optimal decision support systems (DSS) to enhance its growth-oriented decision support system process. To meet these requirements, the enterprise solutions facilitate certain robust and productive business intelligence (BI) applications. On the other hand, to retain market, organizations are emphasizing on service innovation discovery and its redesign (SIDRD) paradigm using BI applications. To accomplish SIDRD, DSS-oriented BI applications require huge feedback datasets to process where the data could be retrieved from certain feedback channel or data warehouses (DWs). The data warehouses encompass datasets from varied resources, and to ensure optimal data extraction and retrieval, these DWs need robust data mining schemes compatible with multidimensional data model (MDDM). On the other hand, to provide most precise and accurate DSS applications, the data security and its privacy preservation are inevitable. Considering all these requirements, in this paper, a robust privacy-preserved mining model called Commutative RSA (CRSA) has been developed and implemented with C5.0 decision tree algorithm to achieve SIDRD objectives with BI utilities. The developed paradigm has exhibited optimal performance for optimized accuracy, overheads, and secure rule set generation for BI. The performance for the developed SIDRD has been analyzed in terms of coverage accuracy and F1 scores, and the overall system has performed optimal.

Business intelligence (BI) has traditionally functioned as a descriptive and diagnostic decision-support system, relying largely on structured and historical data to evaluate past organizational performance. However, increasing data volume, velocity, and variety, coupled with the demand for faster and forward-looking decisions, have revealed significant limitations in conventional BI architectures. This paper examines the evolution of business intelligence from hindsight-driven reporting systems to predictive and augmented intelligence platforms through the integration of data science techniques. Drawing exclusively on secondary literature published from 2015 onward, the study explores how machine learning, automation, real-time analytics, and augmented analytics are reshaping BI capabilities and organizational decision-making processes. A conceptual and comparative analysis is conducted between traditional BI systems and modern augmented analytics platforms, focusing on analytical depth, data processing architecture, user accessibility, and strategic value creation. The findings indicate that the convergence of BI and data science enables predictive foresight, reduces dependence on technical expertise, and supports real-time strategic decision-making. By narrowing the gap between data availability and actionable intelligence, modern BI systems evolve from retrospective reporting tools into proactive, enterprise-wide strategic assets. This study contributes to existing literature by clarifying the strategic role of data science in redefining business intelligence and enhancing organizational agility.

As actuarial analysis and evaluation is a time-consuming, complex and error-prone process, it can be improved or enhanced considerably by automated reasoning. Efforts to reduce the inaccuracy and incorrectness of analyses and to enhance the confidence levels of actuarial analysis have led to the development of an intelligent decision support system called ActuaExpert, which assists, not replaces, actuaries. ActuaExpert assumes the role of a hypothetical actuary capable of assessing risks, creating policies that minimize risk and its financial impacts on companies, and maximizing the profits for insurance companies. It has a knowledge base containing the expertise of statistics, finance, and business, and a case base of past episodes and consequences of decisions. By combining knowledge-based problem solving with case-based reasoning, ActuaExpert demonstrates forms of intelligent behavior not yet observed in traditional decision support systems and expert systems.

It is well known that security analysis is a time-consuming and error-prone process. However, it can be improved or enhanced considerably by automated reasoning. Efforts to reduce the inaccuracy and incorrectness of analyses and to enhance the confidence levels of stock selection have led to the development of an intelligent decision support system called TradeExpert, which assists, not replaces, portfolio managers. TradeExpert assumes the role of a hypothetical securities analyst capable of analyzing stocks, calling market turns, and making recommendations. It has a knowledge base of stock trading expertise, and a case base of past episodes and consequences of decisions. By combining knowledge-based problem solving with case-based reasoning and fuzzy inference, TradeExpert demonstrates forms of intelligent behavior not yet observed in traditional decision support systems and expert systems. The novelty of this research lies in its application to analogical reasoning, fuzzy reasoning, and knowledge-based decision making.

As causality becomes non-linear, data-driven decision making becomes challenging because traditional Decision Support Systems (DSSs) do not support these environments. Existing analytical methods struggle in complex environments where variables that interact with each other as well as themselves. DEAR is a new framework that supports complex decision making. This framework performs change detection, evaluates causal relationships and interactions, assesses the risk of known interventions, and recommends action using a guided exploration strategy that blends historically successful solutions with possible alternatives.

A decision support system is an interactive human–computer decision-making system that supports decision makers rather than replaces them, utilizing data and models. It solves unstructured and semi-structured problems with a focus on effectiveness rather than efficiency in decision processes. In the early 1970s, scholars in this field began to recognize the important roles that decision support systems (DSS) play in supporting managers in their semistructured or unstructured decision-making activities. Over the past five decades, DSS has made progress toward becoming a solid academic field. Nevertheless, since the mid-1990s, the inability of DSS to fully satisfy a wide range of information needs of practitioners provided an impetus for a new breed of DSS, business intelligence systems (BIS). The academic discipline of DSS has undergone numerous changes in technological environments including the adoption of data warehouses. Until the late 1990s, most textbooks referred to “decision support systems.” Nowadays, many of them have replaced “decision support systems” with “business intelligence.” While DSS/BIS began in academia and were quickly adopted in business, in recent years these tools have moved into government and the academic field of public administration. In addition, modern political campaigns, especially at the national level, are based on data analytics and the use of big data analytics. The first section of this article reviews the development of DSS as an academic discipline. The second section discusses BIS and their components (the data warehousing environment and the analytical environment). The final section introduces two emerging topics in DSS/BIS: big data analytics and cloud computing analytics. Before the era of big data, most data collected by business organizations could easily be managed by traditional relational database management systems with a serial processing system. Social networks, e-business networks, Internet of Things (IoT), and many other wireless sensor networks are generating huge volumes of data every day. The challenge of big data has demanded a new business intelligence infrastructure with new tools (Hadoop cluster, the data warehousing environment, and the business analytical environment).

- In today’s data-intensive business environment, organizations increasingly depend on Business Intelligence (BI) tools to support evidence-based decision-making and sustain competitive advantage. This study examines how managers at Mind Box LLC perceive and utilize BI tools as Decision Support Systems (DSS) in their routine and strategic decision processes. Using a descriptive research design, primary data were collected from 100 managers across departments through a structured questionnaire, complemented by secondary data from journals and company documents. The analysis employed percentage statistics, chi-square tests, correlation, and one-way ANOVA to evaluate BI usage patterns, perceived benefits, challenges, and satisfaction levels. Results show high adoption of BI tools, with 75% of managers using them at least weekly and a majority reporting moderate to advanced BI knowledge. Managers report that BI tools improve decision quality, reduce uncertainty and risk, enable faster decisions, and contribute to competitive advantage. Key barriers include insufficient training, data quality issues, and system complexity, while no significant differences in satisfaction were found across managerial levels. The findings underscore that BI tools are effective DSS enablers when supported by robust data governance, continuous training, user-friendly design, and a data-driven culture. The study offers practical recommendations for strengthening BI adoption and maximizing its decision support value at Mind Box and similar technology-driven organizations. This study contributes to the growing literature on BI-enabled decision-making in technology-driven organizations. Keywords: Business Intelligence, Decision Support Systems, managerial perception, data-driven decision-making, Mind Box.