The Impact of Business Intelligence on Decision-Making in Public Organisations

Berhane, A.; Nabeel, Mohamad; Grose, Carolyn

doi:10.1109/ieem45057.2020.9309763

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…Both papers [22], and [28] examine the effect of business intelligence on decision-making. The first paper investigates whether decision-makers respond to warnings and adjust their decisions accordingly through two online experiments, using probability theory and studying the impact of BI and advanced analytics warnings.…”

Section: B Discussionmentioning

confidence: 99%

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A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

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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.

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Section: B Discussionmentioning

confidence: 99%

“…Both (Dursane, 2018), (Berhane, 2020) studies highlight the potential benefits and drawbacks of using business intelligence and analytics in decision-making. While BI can enhance data quality and provide valuable insights, it can also be costly and may neglect the importance of intuition and emotions in decision-making.…”

Section: B Discussionmentioning

confidence: 99%

A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

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“…Ultimately, being able to access data, actively use it, and perform complex analytical tasks on the data are all necessary steps that rely on the analytic and decision-making culture to achieve the ultimate goals of BIA-insight and action. The Analytical and decisionmaking culture dimension measures the extent to which the university incorporates a shift from intuitive to data-driven decision-making across the organization [56]. This relates to what some authors call the "embeddedness of BI systems" [55], in which the BIA solution becomes part of the organizational activities supporting the decision-making of all user groups in HEIs.…”

Section: Peoplementioning

confidence: 99%

Designing a Business Intelligence and Analytics Maturity Model for Higher Education: A Design Science Approach

Cardoso

2022

Applied Sciences

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Business Intelligence and Analytics (BIA) systems play an essential role in organizations, providing actionable insights that enable business users to make more informed, data-driven decisions. However, many Higher Education (HE) institutions do not have accessible and usable models to guide them through the incremental development of BIA solutions to realize the full potential value of BIA. The situation is becoming ever more acute as HE operates today in a complex and dynamic environment brought forward by globalization and the rapid development of information technologies. This paper proposes a domain-specific BIA maturity model (MM) for HE–the HE-BIA Maturity Model. Following a design science approach, this paper details the design, development, and evaluation of two artifacts: the MM and the maturity assessment method. The evaluation phase comprised three case studies with universities from different countries and two workshops with practitioners from more than ten countries. HE institutions reported that the assessment with the HE-BIA model was (i) useful and adequate for their needs; (ii) and contributed to a better understanding of the current status of their BIA landscape, making it explicit that a BIA program is a technology endeavor as well as an organizational development.

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“…We assume this is caused by the trend in new data generation within the last years. [8] claims although analytics does not reduce the volume of decisions, it enables a decision-maker or organisation to control and monitor the decision basis, which suggests that decision quality increases if the decision concerns issues that rest on statistics and facts.…”

Section: Literature Reviewmentioning

confidence: 99%

Project Performance Analysis Using Hierarchical Clustering Method

Halaj¹

2021

IJETAE

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This paper deals with the project management performance analysis in automotive industry and usage of the advanced analytics to optimize the performance of the engineering projects within the vehicle manufacturing facility. The aim is to identify and mitigate the common causes of project delay (slippage) in various engineering projects that are independent of the technical discipline where a particular project belongs to. We have used the hierarchical clustering technique here to identify the sub-groups of similar projects and further deeply analysed those ones with the highest average slippage. Keywords—Cluster analysis, Data, Delay, Distance, Hierarchical clustering, Metric, Project, Slippage, Strategy

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The Impact of Business Intelligence on Decision-Making in Public Organisations

Cited by 8 publications

References 14 publications

A Survey on Business Intelligence Approach Based on Deep Learning

A Survey on Business Intelligence Approach Based on Deep Learning

Designing a Business Intelligence and Analytics Maturity Model for Higher Education: A Design Science Approach

Project Performance Analysis Using Hierarchical Clustering Method

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