Dinkum Journal of Economics and Managerial Innovations (DJEMI).

Publication History

Submitted: May 11, 2023
Accepted: June 01, 2023
Published: August 01, 2023




Al-Moontasir Shifat, Hyunsu Agnihotri, Ming Chen and Yasir Rafique (2023). Reforming data into decisions: investigating the integrated paradigm of business intelligence (B.I.). Dinkum Journal of Economics and Managerial Innovations, 2(08):490-496.


© 2023 DJEMI. All rights reserved

Reforming Data into Decisions: Investigating the Integrated Paradigm of Business Intelligence (B.I)Review Article

Al-Moontasir Shifat1 *, Hyunsu Agnihotri 2, Ming Chen3, Yasir Rafique  4

  1. Army Institute of Business Administration, Bangladesh University of Professionals, Dhaka, Bangaldesh; shifat251997@gmail.com
  2. European University of Lisbon, Portugal; hyunsuag@gmail.com
  3. European University of Lisbon, Portugal; chenming23@gmail.com
  4. Southwest University of Science and Technology, China; yasirrafiquebscs@gmail.com

*             Correspondence: shifat251997@gmail.com

Abstract: Intelligence systems for businesses blend operative and past statistics with statistical instruments to provide business administrators and decision-makers with valuable and competitive information. Intelligence for Business (BI) enhance the precision and accessibility of information and empower managers to have an enhanced understanding of company’s position relative to competitors. Tools and technologies for business intelligence can assist companies in analyzing altering market share trends, customer behavior and spending patterns, customer preferences, company capabilities, and market conditions. Business intelligence can assist analysts and managers in determining which modifications are most likely to be effective in response to shifting patterns. The warehouse of data advent as a storage facility, advancements in information purification, improved hardware and software capabilities, development of web architecture combining all build to a more robust intelligence for business ecosystem used to be available. In light of this paper, endeavor made to establish a foundation for developing an intelligence for business and its system.

Keywords: business intelligence, empower managers, data management, decision making, competitive advantage, machine learning


The onset of the data age has provided businesses with unparalleled possibilities. The creation and implementation of Business Intelligence (BI) are fundamental to this revolution. BI is a technology-driven process that employs intelligence to guide both operational and strategic business decisions (Tavera et al., 2021). Effective BI integration can convert raw data into useful data, thereby assisting decision-makers in comprehending the efficiency of the company, forecasting future results, and formulating innovative approaches (Duan, Cao and Edwards, 2020). The dangers of globalization decentralization, rivalry with innovations in technology have compelled businesses to rethink their approaches to operations, and large corporations and businesses have turned to Intelligence for Business (BI) strategies to assist them comprehend and supervise company workflow in order to achieve edge over their competitors (Niu et al., 2021). Primarily, BI utilized for enhancing the accuracy and reliability of information, and to help supervisor greater comprehend competitive places in theirs organization. Skyrius, (2021) inferred that the BI applications and technologies assist businesses in analyzing shifting market share trends, alterations in customer behavior and purchasing habits, customer preferences, corporate capacities, and industry circumstances. Tripathi, Bagga and Aggarwal, (2020) inferred that supervisors and executives in determining changes that are likely to implement to accommodate altering patterns employ it. It was developed as an approach for analyzing collected data in order to assist decision-making divisions in gaining a more comprehensive understanding of corporation’s activities and making suitable business goals as a result.


BI is a subset of Decisions Support System (DSS), which is a utilized computerized technology to facilitate challenging choices of decision and the resolution of multifaceted, informal, or ill-structured issues (Aws, Ping and Al-Okaily, 2021). Being anchored, in the discipline of DSS, BI has undergone a significant transformation in recent years and is now a DSS subfield that attracts an abundance of enthusiasm from both business leaders and academics. It is typically a structure, tool, engineering, or mechanism accumulates and organizes information, examines it with statistical instruments, facilitates monitoring and querying, and eventually supplies organizations with information or expertise permitting them to make more choices that are informed (Trieu et al., 2022). BI is the transformation of facts towards information and then wisdom. Despite the fact that Intelligence for Business is a one kind of DSS, it frequently affects larger significance. It is the method of gathering outstanding and pertinent facts regarding the research topic enabling the researchers to examine the data, formulate inferences, or provide judgments (Huang, Savita and Zhong-jie, 2022). Intelligence for Business refers to the practice of analyzing massive amounts of information data, examining that information, and delivering a series of analysis that distill key insights of that information on the the peak of corporations initiatives, thereby empowering executives to undertake basic, day-to-day company choices.  Purnomo et al., (2021) argued that BI is the means or process for enhancing company outcomes through aiding making choices at the top-level makers with the assistance necessary to have relevant data on hand. Intelligence for Business equipment regarded as a form of technology that improves efficacy of corporation operations by improving the enterprise’s wisdom and, consequently, its utilization.  BI is “the procedure of collecting, processing, and disseminating information with the objective of reducing uncertainty in all strategic decision-making.” While it is argued that intelligence in business is a “business management term used to describe applications and technologies that are used to compile, deliver access to, and analyze data and information about an organization in order to assist them in making more informed business decisions,” the term “business intelligence” (Intelligence for Business) is more commonly used (Lopes, Guimarães and Santos, 2020). BI differs from its antecedent, “decision support,” in the sense that it is a tactic developed to aid in forecasting and performance evaluation, as opposed to Purdy decisions pertaining to operations.  Executive Information Systems (EIS) and Decision Support Systems (DSS) have transformed into Business Intelligence (BI) tools that support querying, disclosure, custom analysis, and multivariate analysis, also known as Online Analytical Processing (OLAP). This broad range of capabilities encouraged companies to invest in the creation of this aptitude structure types. In spite of this, companies ought to incorporate a discrete BI strategy within their technological framework. Zohuri and Moghaddam, (2020) said that enterprise Intelligence for Business is a technique offers integration to business procedures and novel improvements throughout business sectors. Chen and Lin, (2021) said that business intelligence (BI) refers to an organization’s ability to comprehend and use information to its advantage. Intelligence for Business offers enterprises “one version of the truth” by supplying each department with consistent and standardized data. For attaining information consistency throughout multiple applications in a tough organization, three essential objectives that must met:

  • Effectiveness: The system’s data must be coordinated with all other programs;
  • Precision: The information related to data should include all data from any other application;
  • Recognition: Users ought to have able to engage in the use of the system to generate decisions aid if they are convinced of the timeliness and accuracy of the data (Basile et al., 2023).

The rise of globalization, deregulatory policies, consolidations and acquisitions, and technological advancement have compelled businesses to reconsider their organizational tactics (Choi et al., 2020). In this atmosphere of competition, intelligence for business (BI) provides an essential part enabling the decision-making mechanism to enhance productivity, establishing strong connections among organization plans and technology. Technology for Business has been growing and upgrading to address increasingly complex business needs. Data warehousing (DW), on-line analytical processing (OLAP), and data mining (DM) have emerged as the most extensively utilized BI enabling technologies (Khatibi, Keramati and Shirazi, 2020). The goal of Intelligence of Business technology is to assist individuals making “better” decisions for business by providing them with precise, timely, and pertinent data when they require it (Bordeleau, Mosconi and de Santa-Eulalia, 2020). In certain instances, competitive organizations may view BI as a valuable core competency because it enables them to assess the environment in order to obtain a sustainable competitive advantage.

2.1 Bases of Business Intelligence (B.I)

Regardless, BI customized by companies according to their requirements, past times, and setting in addition to generate client-focused up-to-date and profitable decisions are precious and lucrative, the primary strategies are:

The conventional approach to business intelligence focuses on data aggregation, analytics for business, along with information visualization. Richardson et al., (2020) viewed that according to this strategy, BI investigates a variety of technological instruments, generating reports and forecasts in order to enhance the effectiveness of decision-making. Data Warehouse (DW), Extract-Transform-Load (ETL), On-Line Analytical Processing (OLAP), Data Mining (DM), Text Mining, Web Mining, Data Visualization, Geographic Information Systems (GIS), and Web Portals are examples of such tools. Concerns exist at the subsequent level regarding the integration of business processes with BI. BI is a mechanism for bridging the divide among business procedure management and business strategy, according to this approach. Tools such as Business Performance Management (BPM), Business Activity Monitoring (BAM), Service-Oriented Architecture (SOA), Automatic Decision Systems (ADS), and dashboards also comprised (Tong-On, Siripipatthanakul and Phayaphrom, 2021). Flexible intelligence for business involves dealing with autonomous, responsive, action-recommendation-generating learning procedures and draw lessons from earlier choices in order to continuous improve (Shao et al., 2022). This is how Artificial Intelligence integrated into BI mechanism. Nevertheless, a basic structure for professionals, scholars, and researchers to comprehend and follow depicted here.

The foundation of intelligence of business divided by three components:

(i)Acquiring/Capturing Data, (ii) Preservation of Data, and (iii) Accessing information & examination. Internal and external sources used to collect data. The corporation’s working database and data repository are internal sources of data (Ji and Tia, 2022). External data sources include clients, suppliers, regulatory bodies, rivals and online databases etc. Following Extract, Transform, and Load (ETL) procedures, collected heterogeneous information kept in a warehouse of data. Finally, the data warehouse stored information analyzed for making judgement.

i- Capturing Data/ Collection

The acquisition element is the system’s back end and comprises mechanism interact with systems of operation to input information into the data warehouse (Rana et al., 2022). Records originally inputted or controlled by a continuous company procedure centered on an online transaction processing (OLTP) setting thereafter saved in a functioning database, such as Oracle, DB2, Informix, SQL Server, SAP R/3, etc. Before functional database and external origin of data inserted into the warehouse of data, it must undergo following processing steps:

  • Extraction and Cleansing: Information/Data extracted from various avenues, such as working procedures, during information extraction (Srivastava, Venkataraman and V, 2022). The selected data consolidated and cleansed of various types of contamination. Validating and cleansing extracted data in order to fix inconsistent, absent, or inaccurate values. This step involves the application of triggers, error reports, and remediation processes.

Conversion of Data:

The conversion of information involves the integration of information into functional formats and the application of company principles, which correspond data to the storage layout (Bhatiasevi and Naglis, 2020). The creation of composites (e.g., a list of facts) and derived attributes.


Process of importing updated information into a storage of information.

ii-Preservation of Information:

Data retained for future analysis in a storage of information or mart of data.

Data Repository:

A data warehouse is a structured copy of data related to transactions that aimed through data analysis, retrieval, and assistance with decisions instead of functional or processing transactions (Cheng, Zhong and Cao, 2020). A data warehouse is an amalgamation of information that originates from systems that operate and other sources of information (Niño, Niño and Ortega, 2020). A data warehouse is a subject-based, incorporated, and time-varying, nonvolatile collection of data whose primary purpose is to support managerial decision-making (Gupta and Jiwani, 2021). The operational databases kept distinct from the data warehouses designed for decision support. In routine, data warehouse infrastructure can take various shapes. Nevertheless, beforehand constructing a data warehouse, the requirements and resources of the business must be consider. However, organizations may choose from the following architecture alternatives depending on the circumstances: Data Mart; Central Data Warehouse; Distributed Data Warehouse; Virtual Data Warehouse.

Data Marts:

Data marts, commonly referred to as specialized data warehouses, are restricted data warehouses built by particular organizational units or sections for assisting individual decision-making duties (Chen and Siau, 2020). For example, a data mart developed for specific products or processes, including managing client relationships, financial management, marketing, etc. When it comes for constructing data marts, it is necessary obtaining a working model without anticipating the establishment of a broader corporate data warehouse, as it is tiny and simple to construct (Hamad et al., 2021). However, organizations with multiple data marts face operational challenges when integrating them into an independent data mart are discordant from one another, requiring a corporate data warehouse structure. Meta Information/Data: Clients require information regarding the data warehouse infrastructure and its components for this purpose (Biagi, Patriarca and Di Gravio, 2022. This data comprises the data’s algorithms for encoding and decoding, domain limitations and descriptions. It known as metadata. In addition, it contains business definitions, data quality alerts, organizational changes, business rules and assumptions, and other business-related information. Metadata aid business users in comprehending the information that is available, the way to gain it, what it signify which information useful and when, etc. Meta information explorers offer intuitive perspective of the data repository (Nyanga, Pansiri and Chatibura, 2020).

iii- Accessing and Analyzing Data:

Velu, (2021) opined that BI’s access element frequently described as its front end. It comprises accessible techniques with instruments offer simple, interactive, or batch access to data for business users while concealing the technical difficulties of information recovery. The interface delivers an instinctive; business like information displaying that is user-friendly for not-technically users. BI tools, a collection of software tools with a graphical user interface (GUI) and robust industry monitoring and evaluation characteristics, used to achieve it. Usually multiple instruments utilized in a cohesive way to aid the requirements of various user categories, such as:

  • Query and analytical tools bundled in software
  • Advanced statistical assessment equipment (OLAP/ROLAP)
  • Data exploration or acquisition of expertise tools
  • Machine Learning instruments and
  • Visualization equipment

OLAP: The most popular techniques Online Analytical Processing (OLAP) and data mining (DM) are methods of wisdom gathering. OLAP enables individual to investigate and evaluate vast quantities of information containing intricate calculations, related connections and the visual presentation of findings from multiple perspectives. OLAP tools combine analytical processing techniques and graphical user interface. Numerous measurement concepts, calculation-intensive abilities, as well as time experience differentiate OLAP programs (Phillips-Wren, Daly and Burstein, 2021). Typically utilized in OLAP applications, a multidimensional view of data enables fast and flexible access to data and information. Roll-up (information is broken down based on growing simplification), zooming in (higher depth levels are exposed), cutting and dicing (dimension extension functions), and rotating (crossing tally) are typical applications performed on multidimensional data views. Complex analyses, forecasting includes series of events (event sequence) and algorithm visualization, modeling, probability/mathematical, “what-if” examination, are conceivable. Methods to analyze analytically defined results are mechanism for identifying various types of data required for decision-making.

Extraction of Data: It alludes to the process of identifying morsels locating information or judgment-making expertise within large data sets and retrieving it for use in fields like assistance decisions, anticipating, predicting, and evaluation using a variety of techniques. Frequently, the data is vast, but it has little value because it cannot be use directly; the useful information is conceal within the data. Mining of Data is the non-trivial gathering in the form of unspoken, past unidentified and convincingly beneficial knowledge resulting from data. This includes a variety of technical approaches, including clustering, data distillation, learning segmentation rules, discovering dependency relationships, analyzing shifts, and spotting oddities. Data extraction is the quest for latent relationships and worldwide trends within massive databases, for example- the connections among patient information and clinical conclusions.

Data exploration involves mainly the investigation of information including application of mechanism related to software to discover routines and trends within sets of data. It is accountable for discovering trends through distinguishing data’s fundamental standards or characteristics. The concept is that it is possible to find gold in places that are not expected, as mining in data software consisting trends that were discernible previously or were so apparent which none of them had observed them earlier. In addition to data mining, the discovery of valuable trends in information historically been described to as wisdom of the mining procedure, information findings, archaeology of information, harvesting of information, and trends in data assessment. As the development of data extraction has progressed, it has come regarded as a singular stage in the Discovery of Knowledge in Databases (KDD) life cycle. KDD includes expansive mechanism of discovering information wisdom repositories. The activities preceding genuine analysis of information, which includes assessment and implementation of results. A number of companies anticipate that BI solutions will play an important part in their ability to swift respond to market demands and in the creation of their strategies; those that fail to evolve to take market difficulties carefully will be at risk of extinction (Shiau et al., 2023). Corporations are able to regulate costs and efficiency, as well as increase their profitability, when they have simple utilization of enormous quantities of intricate information derived from numerous locations.

Machine-Learning (ML):

ML is an emerging form of AI that used to automate sophisticated choices and problem-solving tasks in an increasing number of domains across the last few years. ML is an extended set of systems that attempts to educate machines problem solving by subjecting them to past information instances (Al-Okaily et al., 2022). Between the different approaches offered, Artificial Neural Network (ANN) is frequently used. It began as an endeavor to represent human learning capabilities and modeled after the brain’s physical neural structures that humans have. Further techniques include learning by inference, individualized argumentation, algorithms based on genetics, and natural language synthesis, etc.


Business Intelligence incorporated comprehensively into an organization’s structure, it has the potential to significantly enhance decision-making, boost economic viability, and establish a long-term competitive edge. As the information ecosystem continues to change, so does the holistic approach to BI, embracing emergent technologies and adapting to shifting business requirements and market circumstances. In day-to-day extremely fierce environment, the caliber and punctuality of an organization’s information related to business is not just a matter of earnings and loss; it could be a matter of existence/failure. There is not a single entity that can deny BI’s benefits. Latest market forecasts indicate that thousands of users will utilize BI visual tools and analytics daily in the future years.


  • Tavera Romero, C.A., Ortiz, J.H., Khalaf, O.I. and Ríos Prado, A., 2021. Business intelligence: business evolution after industry 4.0. Sustainability, 13(18), p.10026.
  • Gupta, K. and Jiwani, N., 2021. A systematic Overview of Fundamentals and Methods of Business Intelligence. International Journal of Sustainable Development in Computing Science, 3(3), pp.31-46.
  • Tripathi, A., Bagga, T. and Aggarwal, R.K., 2020. Strategic impact of business intelligence: A review of literature. Prabandhan: Indian Journal of Management, 13(3), pp.35-48.
  • Niu, Y., Ying, L., Yang, J., Bao, M. and Sivaparthipan, C.B., 2021. Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), p.102725.
  • Trieu, V.H., Burton-Jones, A., Green, P. and Cockcroft, S., 2022. Applying and extending the theory of effective use in a business intelligence context. MIS Quarterly: Management Information Systems, 46(1), pp.645-678.
  • Duan, Y., Cao, G. and Edwards, J.S., 2020. Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), pp.673-686.
  • Aws, A.L., Ping, T.A. and Al-Okaily, M., 2021. Towards business intelligence success measurement in an organization: A conceptual study. J. Syst. Manag. Sci, 11, pp.155-170.
  • Huang, Z.X., Savita, K.S. and Zhong-jie, J., 2022. The Business Intelligence impact on the financial performance of start-ups. Information Processing & Management, 59(1), p.102761.
  • Chen, Y. and Lin, Z., 2021. Business intelligence capabilities and firm performance: A study in China. International Journal of Information Management, 57, p.102232.
  • Bordeleau, F.E., Mosconi, E. and de Santa-Eulalia, L.A., 2020. Business intelligence and analytics value creation in Industry 4.0: a multiple case study in manufacturing medium enterprises. Production Planning & Control, 31(2-3), pp.173-185.
  • , Zohuri, B. and Moghaddam, 2020. From business intelligence to artificial intelligence. Journal of Material Sciences & Manufacturing Research. SRC/JMSMR/102 Page, 3.
  • Purnomo, A., Firdaus, M., Sutiksno, D.U., Putra, R.S. and Hasanah, U., 2021, July. Mapping of business intelligence research themes: four decade review. In 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) (pp. 32-37). IEEE.
  • Niño, H.A.C., Niño, J.P.C. and Ortega, R.M., 2020. Business intelligence governance framework in a university: Universidad de la costa case study. International Journal of Information Management, 50, pp.405-412.
  • Choi, J., Yoon, J., Chung, J., Coh, B.Y. and Lee, J.M., 2020. Social media analytics and business intelligence research: A systematic review. Information Processing & Management, 57(6), p.102279.
  • Richardson, J., Sallam, R., Schlegel, K., Kronz, A. and Sun, J., 2020. Magic quadrant for analytics and business intelligence platforms. Gartner ID G00386610.
  • Tong-On, P., Siripipatthanakul, S. and Phayaphrom, B., 2021. The implementation of business intelligence using data analytics and its effects towards on performance in the hotel industry in Thailand. International Journal of Behavioral Analytics, 1(2).
  • Shao, C., Yang, Y., Juneja, S. and GSeetharam, T., 2022. IoT data visualization for business intelligence in corporate finance. Information Processing & Management, 59(1), p.102736.
  • Rana, N.P., Chatterjee, S., Dwivedi, Y.K. and Akter, S., 2022. Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness. European Journal of Information Systems, 31(3), pp.364-387.
  • Lopes, J., Guimarães, T. and Santos, M.F., 2020. Adaptive business intelligence: A new architectural approach. Procedia Computer Science, 177, pp.540-545.
  • Srivastava, G., Venkataraman, R. and V, K., 2022. A review of the state of the art in business intelligence software. Enterprise Information Systems, 16(1), pp.1-28.
  • Phillips-Wren, G., Daly, M. and Burstein, F., 2021. Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Decision Support Systems, 146, p.113560.
  • Chen, X. and Siau, K., 2020. Business analytics/business intelligence and IT infrastructure: impact on organizational agility. Journal of Organizational and End User Computing (JOEUC), 32(4), pp.138-161.
  • Nyanga, C., Pansiri, J. and Chatibura, D., 2020. Enhancing competitiveness in the tourism industry through the use of business intelligence: A literature review. Journal of Tourism Futures, 6(2), pp.139-151.
  • Biagi, V., Patriarca, R. and Di Gravio, G., 2022. Business Intelligence for IT Governance of a Technology Company. Data, 7(1), p.2.
  • Velu, A., 2021. Influence of business intelligence and analytics on business value. International Engineering Journal For Research & Development, 6(1), pp.9-19.
  • Al-Okaily, A., Al-Okaily, M., Teoh, A.P. and Al-Debei, M.M., 2022. An empirical study on data warehouse systems effectiveness: the case of Jordanian banks in the business intelligence era. EuroMed Journal of Business, (ahead-of-print).
  • Bhatiasevi, V. and Naglis, M., 2020. Elucidating the determinants of business intelligence adoption and organizational performance. Information development, 36(1), pp.78-96.
  • Basile, L.J., Carbonara, N., Pellegrino, R. and Panniello, U., 2023. Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making. Technovation, 120, p.102482.
  • Cheng, C., Zhong, H. and Cao, L., 2020. Facilitating speed of internationalization: The roles of business intelligence and organizational agility. Journal of Business Research, 110, pp.95-103.
  • Shiau, W.L., Chen, H., Wang, Z. and Dwivedi, Y.K., 2023. Exploring core knowledge in business intelligence research. Internet Research, 33(3), pp.1179-1201.
  • Skyrius, R., 2021. Business Intelligence. Springer International Publishing.
  • Hamad, F., Al-Aamr, R., Jabbar, S.A. and Fakhuri, H., 2021. Business intelligence in academic libraries in Jordan: Opportunities and challenges. IFLA journal, 47(1), pp.37-50.
  • Ji, F. and Tia, A., 2022. The effect of blockchain on business intelligence efficiency of banks. Kybernetes, 51(8), pp.2652-2668.
  • Khatibi, V., Keramati, A. and Shirazi, F., 2020. Deployment of a business intelligence model to evaluate Iranian national higher education. Social Sciences & Humanities Open, 2(1), p.100056.

Publication History

Submitted: May 11, 2023
Accepted: June 01, 2023
Published: August 01, 2023




Al-Moontasir Shifat, Hyunsu Agnihotri, Ming Chen and Yasir Rafique (2023). Reforming data into decisions: investigating the integrated paradigm of business intelligence (B.I.). Dinkum Journal of Economics and Managerial Innovations, 2(08):490-496.


© 2023 DJEMI. All rights reserved