Application of Business Intelligence Solution Development and Implementation in a Small-Sized Enterprise

Alaskar, Hind; Saba, Tanzila

doi:10.1109/smart-tech49988.2020.00051

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…In the organization's performance sector, these papers [1], [24], [25], [29], [32] focus on various aspects of business intelligence (BI) and how it can be improved and automated using machine learning algorithms (ML), data mining techniques, and knowledge management practices [36], [37]. They identify problems such as integrating ML with BI, automating manual processes, extracting hidden information from business data, ensuring data security and privacy, and leveraging critical knowledge for organizational performance.…”

Section: B Discussionmentioning

confidence: 99%

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%

A Survey on Business Intelligence Approach Based on Deep Learning

2023

IJCCCE

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“…Getting data in, traditionally called data warehousing, involves moving data from a set of source systems into an integrated data warehouse. The enterprise resource planning (ERP), customer relationship management (CRM), and supply chain management (SCM) tools databases are examples of different sources [15]. The second activity, BI, consists of business users and applications accessing data analytics.…”

Section: Introductionmentioning

confidence: 99%

Developing Integrated Performance Dashboards Visualisations Using Power BI as a Platform

Gonçalves,

Campante

2023

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The rapid advance of business technologies in recent years has made knowledge an essential and strategic asset that determines the success or failure of an organisation. Access to the right information in real time and with high selectivity can be a competitive advantage in the business environment. Business intelligence systems help corporate executives, business managers, and other operational workers make better and more informed business decisions. This study aimed to assess the impact of using business intelligence tools on the decision-making process in organisations, specifically in sales marketing. The methodology applied to realise the study’s objective was the Vercellis methodology. A set of data available on the sales marketing website SuperDataScience was used to implement a set of pressing KPIs for the business decision-making process in the area. Using these data, a complete business intelligence system solution was implemented. A data warehouse was created using the ETL (extract–transform–load) process, and the data were then explored using a set of dynamics dashboards with a view of the business metrics. The results showed the use of business intelligence systems that allow the integration and transformation of data from various sources stored in data warehouses, where it is possible to implement KPIs and carry out quick, concise, easy-to-interpret graphical analyses. This paper contributes to a better understanding of the importance of data-integrated dashboard visualisation for the decision-making process.

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