Data Analytics Tools: A User Perspective

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002 ◽  
Author(s):  
Paul Town ◽  
Fadi Thabtah
Keyword(s):  
Data Analysis ◽  
Business Intelligence ◽  
Data Analytics ◽  
End Users ◽  
Point Of View ◽  
Future Research ◽  
User Perspective ◽  
Evaluative Criteria ◽  
Decision Making Processes ◽  
User Groups

Business Intelligence Tools (BI Tools) can be an intelligent way for individuals to undertake data analysis and reporting for guiding decision-making processes. There are many different BI Tools available in the market today, as well as information to assist organisations in evaluating their effectiveness. This paper focusses on two commercially available BI Tools: Tableau and Microsoft Power BI. It aims to determine which BI Tool is better for data analysis and reporting from an end user’s point of view. This paper undertakes an evaluation of both tools and compares which is more suitable for students using interface (navigation), cost, presence in the market, and available training and help as the evaluative criteria. Results produced in this paper found that overall, Tableau was more highly ranked than Power BI based on the evaluative criteria for end users for data analysis and reporting at least among the samples of the study. Tableau ranked higher than Power BI with its presence in the market, and available training and help. Power BI was rated more highly on its interface and both BI Tools were ranked the same in terms of cost to end users. This research is exploratory and may assist in formulating future research on BI Tools for specific user groups.

Integrated Data Architecture for Business

2019 ◽  
pp. 478-490
Author(s):  
Richard Kumaradjaja
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Point Of View ◽  
Future Research ◽  
Integration Framework ◽  
Architecture Framework ◽  
Data Architecture ◽  
Future Research Directions

This chapter describes data integration issues in big data analytics and proposes an integrated data integration framework for big data analytics. The main focus of this chapter is to address the issues of data integration from the architectural point of view. Addressing the issues of data integration from the architectural point of view will lead to a better understanding of the current situation and better construction of proposed solutions to those issues since architectural approach can give us a holistic and comprehensive view of the problems. The chapter also discusses future research directions of the proposed integrated data architecture framework.

Business Intelligence Implementation Critical Success Factors

2019 ◽  
pp. 112-129
Author(s):  
Ahad Zare Ravasan ◽  
Sogol Rabiee Savoji
Keyword(s):  
Decision Making ◽  
Factor Analysis ◽  
Data Analysis ◽  
Business Intelligence ◽  
Critical Success Factors ◽  
Success Factors ◽  
Successful Implementation ◽  
Domain Experts ◽  
Critical Success ◽  
Decision Making Processes

Many organizations take business intelligence (BI) systems to improve their decision-making processes. Although many organizations have adopted BI systems, not all of these implementations have been successful. This chapter seeks to identify critical success factors (CSFs) that impact on the successful implementation of BI systems in organizations. So, at first, through literature review, 26 CSFs were identified. Following that, a questionnaire was developed and then filled out by domain experts who had at least three years of experience in BI implementation projects. Robust exploratory factor analysis (EFA) was run for data analysis, which finally classified 26 CSFs into four distinct groups termed as “organizational,” “human,” “project management,” and “technical.” The results of this study provide a very useful reference for scholars and managers to identify the relevant issues of BI projects in Iran.

Integrated Data Architecture for Business

2018 ◽  
pp. 862-872
Author(s):  
Richard Kumaradjaja
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Point Of View ◽  
Future Research ◽  
Integration Framework ◽  
Architecture Framework ◽  
Data Architecture ◽  
Future Research Directions

This paper describes data integration issues in big data analytics and proposes an integrated data integration framework for big data analytics. The main focus of this article is to address the issues of data integration from the architectural point of view. Addressing the issues of data integration from the architectural point of view will lead to a better understanding of the current situation and better able to construct proposed solutions to those issues since architectural approach can give us a holistic and comprehensive view of the problems. The paper also discusses about future research directions of the proposed integrated data architecture framework.

Leveraging Business Intelligence and Data Analytics in an Integrated Digital Production Platform to Unlock Optimization Potentials

2021 ◽  
Author(s):  
Ayesha Ahmed Abdulla Salem Alsaeedi ◽  
Manar Maher Mohamed Elabrashy ◽  
Mohamed Ali Alzeyoudi ◽  
Mohamed Mubarak Albadi ◽  
Sandeep Soni ◽  
...  
Keyword(s):  
Real Time ◽  
Business Intelligence ◽  
Data Analytics ◽  
System Optimization ◽  
End Users ◽  
Production Optimization ◽  
Gas Condensate ◽  
Production Platform ◽  
Digital Production ◽  
Multiple Variables

Abstract This paper discusses business intelligence algorithms and data analytics capabilities of an integrated digital production platform implemented in a giant gas condensate field. The advanced workflow focuses on helping the user navigate through the bulk of data to identify patterns and make predictions utilizing exception-based intelligence alarming. This helps derive insightful findings and provides recommendations for users to make efficient business decisions for achieving field potential optimization objectives. An Integrated digital production platform within a giant gas condensate field is implemented with numerous production optimization workflows encompassing daily well and facility performance monitoring and surveillance. The data integration within the systems is enhanced by integration with powerful Business Intelligence (BI) tools, enabling users to create customized dashboards, KPI screens, and exception-based alarm screens. An additional integration to the production platform is carried out with data from real-time sources like PI Asset Framework and corporate databases, improving the integrated production system's daily well and facility surveillance capabilities. The advanced integration of BI tools provided users with various opportunities to identify bottlenecks, production improvement chances, and troubleshooting areas by capitalizing insights from various dashboards and business KPI screens. Further, integrating these dashboards with several corporate data sources and a real-time asset data framework enabled users to harness maximized information embedded in the bulk of data. This also enabled end-users to harness maximized system potential, with all information available under a single collaborative platform. The integration powered by various inbuilt complex algorithms extended scripting capabilities, and enhanced visualization assisted the asset in realizing business KPIs requirements. Business intelligence algorithms in user interface established a drill-down approach to utilize information associated with multiple variables on top of one another. This allowed for the quick identification of trends and patterns in data. The customization approach helped the user to draw maximum information out of data as per their engineering requirements and current practices. This advanced integration facilitated users to minimize their efforts in traditional data analysis such as gathering, mapping, filtering, and plotting. With the help of these powerful features embedded in an integrated platform, the user was able to drive more focus on optimization and minimize time and effort on system configuration. This unique integration was one of its kind. An online integrated digital production platform comprising of wells, networks, and various workflows was integrated with business intelligence tools, thereby providing end-users tremendous opportunities related to system optimization.

scholarly journals Voice Engagement Leading to Business Intelligence

2021 ◽  
Vol 12 (2) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Conceptual Framework ◽  
Business Intelligence ◽  
Research Agenda ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Future Research Agenda ◽  
Informed Decisions ◽  
Voice Data

There are multiple studies establishing the importance of Business Intelligence (BI), in the Big Data Analytics context. Voice is yet to be seen as a contributing channel. Voice enabled assistants are at the forefront of conversational AI advancement. As humans speak to devices, brands and business are investing in engagement through voice channel. This voice engagement is resulting in both intangible and tangible benefits and generating voice commerce. The resultant voice data should be integral to BI, leading to Voice BI. This paper proposes a conceptual framework from engagement to intelligence, with support of five propositions to realise voice business intelligence. Type of applications and their engagement characterisation is segregated to create better understanding using Cross-Cases Observation Technique. Along with future research agenda to strengthen the propositions, this investigation observes building voice business intelligence by tracking relevant metrics which enable informed decisions.

Review of business intelligence through data analysis

2014 ◽  
Vol 21 (2) ◽  
pp. 300-311 ◽  
Author(s):  
Belle Selene Xia ◽  
Peng Gong
Keyword(s):  
Data Analysis ◽  
Quality Management ◽  
Business Intelligence ◽  
Future Research ◽  
Research Approach ◽  
Content Type ◽  
Inductive Research ◽  
Cost And Benefit Analysis ◽  
Practical Implications

Purpose – The purpose of this paper is to explore the role of business intelligence (BI) in a consulting company. The authors propose to analyze quality through data analysis and efficiency under different business contexts. The best processes and tools in data mining are also explored. Design/methodology/approach – Management perspectives of data analysis from Florilla Consulting Company are collected using an inductive research approach. Based on a large sample of qualitative data, cost-and-benefit analysis is used to assess the BI technologies as a strategic necessity to Florilla Consulting Company. Findings – Findings classify the best processes and tools of data analysis under different business scenarios. The authors also propose a revised process and tools for Florilla Consulting Company to be further evaluated by future research. Practical implications – The insights offered in this paper derives authentic value for any consulting company that is interested to benefit from the opportunities bought by the BI technologies. Quality management also gets a new dimension when technology is integrated into business. Originality/value – This study has challenged the way quality is managed in Florilla Consulting Company. The connection of BI to quality management is explored via an empirical study of a consulting company by linking theory with practice.

scholarly journals Applications of Big Data Analytics to Control COVID-19 Pandemic

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2282
Author(s):  
Shikah J. Alsunaidi ◽  
Abdullah M. Almuhaideb ◽  
Nehad M. Ibrahim ◽  
Fatema S. Shaikh ◽  
Kawther S. Alqudaihi ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Vital Role ◽  
Future Research ◽  
Precautionary Measures ◽  
And Behavior ◽  
And Control

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

Medical Diagnosis from a Clinician’s Point of View

1978 ◽  
Vol 17 (01) ◽  
pp. 28-35
Author(s):  
F. T. De Dombal
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Computer Science ◽  
Medical Diagnosis ◽  
Information Science ◽  
Point Of View ◽  
Joint Research ◽  
The World

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

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