scholarly journals Data Visualization Method as the Facilitator for Business Intelligence

2019 ◽  
Vol 8 (6) ◽  
pp. 3925-3928 ◽  
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Business Intelligence ◽  
Visualization Method ◽  
Business Decisions ◽  
Correlation Matrices ◽  
Visual Elements ◽  
Business Analysis ◽  
Scatter Plots ◽  
Pros And Cons

Data visualization methods are used to support business analysis. This paper explores the study on the sophisticated data visualization methods for business inventiveness. These comprise Line and Bar Charts, Scatter Plots, Network Diagrams, Bubble plot, Correlation Matrices and Donut Diagram. The concepts of visual elements are explained in the context of business perception. The paper reviews the capabilities and prophecies of the visualization methods in business analysis. When more data has to be symbolized, the concentration increases and it leads to difficulty in understanding the information to be dillydallied. Data visualization methods for business decisions save the time and resources as well as provide better understanding. The study explores that there exist habituated data visualization methods that are useful in business intelligence. The methods serve as the elite outfits to epitomize Big Data effectively. These techniques are scouted through this literature review. We investigate the pros and cons of data visualization methods in business intelligence.

THE RELATIONSHIP OF SOCIOTECHNICAL AND TECHNOLOGICAL ASPECTS OF THE INTRODUCTION AND APPLICATION OF BUSINESS INTELLIGENCE SYSTEMS

2021 ◽  
Vol 5 (12) ◽  
pp. 30-35
Author(s):  
Edward N. Ozhiganov ◽  
◽  
Alexander A. Chursin ◽  
Alexey D. Linkov ◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
New Technologies ◽  
Business Analytics ◽  
Scientific Publications ◽  
Business Decisions ◽  
Business Decision ◽  
Business Intelligence Systems ◽  
Demand Fluctuations ◽  
New Research

This article describes a relation between sociotechnical and technological factors involved in launching and implementing Business Intelligence systems. Advanced BI systems include business analytics, data mining, data visualization, data tools and infrastructure, and advanced IT solutions to support business decisions based on big data. Various industries and businesses handle large amounts of data to adapt to changing markets and demand fluctuations, push new technologies, and repair ineffective strategies, etc. With an upsurge in data sizes, more and more new research papers are published today to describe BI implemen-tation, use and results. However, today most studies and scientific publications focus on Business Intelligence technological challenges, while sociotechnical aspects – that is processes involved in business decision mak-ing based on big data – are studied in much rarer cases.

Computational Business Intelligence, Big Data, and Their Role in Business Decisions in the Age of the Internet of Things

2017 ◽  
pp. 249-268 ◽  
Author(s):  
Javier Vidal-García ◽  
Marta Vidal ◽  
Rafael Hernandez Barros
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Business Intelligence ◽  
The Internet ◽  
Self Assessment ◽  
Business Decisions ◽  
Business Operations ◽  
Large Databases ◽  
The Social ◽  
The Internet Of Things

The evolution of the big data and new techniques related to the processing and analysis of large databases is revolutionizing the management of companies in the age of the Internet of Things (IoT). In this chapter, we examine the possibilities of big data to improve the services offered by companies and the customer experience and increase the efficiency of these companies. Companies must accept the challenge of self-assessment and measure the barriers that threaten to prevent them from reaching to get the maximum potential derived from big data and analytics. The combination of big data and computational business intelligence will change completely processes, logistics and distribution strategies, the choice of marketing channels and any aspect of the production and marketing of products and services. A case of GE is presented to showcase the use of the IoT and big data. All companies, regardless of size or sector, will improve their business operations due to big data generated from the social media and IoT applications and its use in computational business intelligence.

Managing extracted knowledge from big social media data for business decision making

2017 ◽  
Vol 21 (2) ◽  
pp. 275-294 ◽  
Author(s):  
Wu He ◽  
Feng-Kwei Wang ◽  
Vasudeva Akula
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Intelligence ◽  
Business Decisions ◽  
Business Decision ◽  
Social Media Data ◽  
Content Type ◽  
Media Data ◽  
Big Data Technology

Purpose This paper aims to propose a knowledge management (KM) framework for leveraging big social media data to help interested organizations integrate Big Data technology, social media and KM systems to store, share and leverage their social media data. Specifically, this research focuses on extracting valuable knowledge on social media by contextually comparing social media knowledge among competitors. Design/methodology/approach A case study was conducted to analyze nearly one million Twitter messages associated with five large companies in the retail industry (Costco, Walmart, Kmart, Kohl’s and The Home Depot) to extract and generate new knowledge and to derive business decisions from big social media data. Findings This case study confirms that this proposed framework is sensible and useful in terms of integrating Big Data technology, social media and KM in a cohesive way to design a KM system and its process. Extracted knowledge is presented visually in a variety of ways to discover business intelligence. Originality/value Practical guidance for integrating Big Data, social media and KM is scarce. This proposed framework is a pioneering effort in using Big Data technologies to extract valuable knowledge on social media and discover business intelligence by contextually comparing social media knowledge among competitors.

scholarly journals Effectiveness of Agile Implementation Methods in Business Intelligence Projects from an End-user Perspective

10.28945/3515 ◽  
2016 ◽  
Vol 19 ◽  
pp. 161-172 ◽  
Author(s):  
Jerzy Andrzej Kisielnicki ◽  
Anna Maria Misiak
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Traditional Approach ◽  
Added Value ◽  
Business Trend ◽  
End User ◽  
Global Business ◽  
Traditional Methods ◽  
User Perspective ◽  
Business Decisions

The global Business Intelligence (BI) market grew by 10% in 2013 according to the Gartner Report. Today organizations require better use of data and analytics to support their business decisions. Internet power and business trend changes have provided a broad term for data analytics – Big Data. To be able to handle it and leverage a value of having access to Big Data, organizations have no other choice than to get proper systems implemented and working. However traditional methods are not efficient for changing business needs. The long time between project start and go-live causes a gap between initial solution blueprint and actual user requirements in the end of the project. This article presents the latest market trends in BI systems implementation by comparing Agile with traditional methods. It presents a case study provided in a large telecommunications company (20K employees) and the results of a pilot research provided in the three large companies: telecommunications, digital, and insurance. Both studies prove that Agile methods might be more effective in BI projects from an end-user perspective and give first results and added value in a much shorter time compared to a traditional approach.

LiveSankey: Advanced Web Visualization in Data Intelligence Multi Domain Contexts

2020 ◽  
Author(s):  
José M. Conejero ◽  
Juan Carlos Preciado ◽  
Alvaro E. Prieto ◽  
Roberto Rodriguez-Echeverria ◽  
Fernando Sánchez-Figueroa
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Business Intelligence ◽  
Decision Makers ◽  
Need To Evaluate ◽  
Big Data Technologies ◽  
Visualization Tools ◽  
Web Visualization ◽  
Meaningful Relationships ◽  
Multiple Conditions

In the last years, the growing volumes and sources of data has made Big Data technologies to become mainstream. In that sense, techniques like Data Visualization are being used more and more to group large amounts of data in order to transform them into useful information. Nevertheless, these techniques are currently included in Business Intelligence approaches to provide companies and public organizations with helpful tools for making decisions based on evidences instead of intuition. The Sankey diagram is an example of those complex visualization tools allowing the user to graphically trace meaningful relationships in large volumes of data. However, this type of diagram is usually static so they must be continuously and manually rebuilt on top of massive multivariable environments whenever decision makers need to evaluate different options and they do not allow to establish conditions over the data shown. This paper presents LiveSankey, an approach to automatically generate dynamic Sankey Diagrams allowing users to filter the data shown. As a result, multiple conditions may be established over the data used and the corresponding diagram can be dynamically rebuilt.

Computational Business Intelligence, Big Data, and Their Role in Business Decisions in the Age of the Internet of Things

Web Services ◽  
2019 ◽  
pp. 1048-1067
Author(s):  
Javier Vidal-García ◽  
Marta Vidal ◽  
Rafael Hernández Barros
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Business Intelligence ◽  
The Internet ◽  
Self Assessment ◽  
Business Decisions ◽  
Business Operations ◽  
Large Databases ◽  
The Social ◽  
The Internet Of Things

The evolution of the big data and new techniques related to the processing and analysis of large databases is revolutionizing the management of companies in the age of the Internet of Things (IoT). In this chapter, we examine the possibilities of big data to improve the services offered by companies and the customer experience and increase the efficiency of these companies. Companies must accept the challenge of self-assessment and measure the barriers that threaten to prevent them from reaching to get the maximum potential derived from big data and analytics. The combination of big data and computational business intelligence will change completely processes, logistics and distribution strategies, the choice of marketing channels and any aspect of the production and marketing of products and services. A case of GE is presented to showcase the use of the IoT and big data. All companies, regardless of size or sector, will improve their business operations due to big data generated from the social media and IoT applications and its use in computational business intelligence.

scholarly journals EFFECTIVENESS OF AGILE COMPARED TO WATERFALL IMPLEMENTATION METHODS IN IT PROJECTS: ANALYSIS BASED ON BUSINESS INTELLIGENCE PROJECTS

2017 ◽  
Vol 9 (1) ◽  
pp. 273-286 ◽  
Author(s):  
Jerzy Kisielnicki ◽  
Anna Maria Misiak
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Traditional Approach ◽  
Added Value ◽  
Business Trend ◽  
Global Business ◽  
Traditional Methods ◽  
Business Decisions ◽  
First Results ◽  
Long Time

Abstract The global Business Intelligence (BI) market grew by 7.3% in 2016 according to the Gartner report (2017). Today, organizations require better use of data and analytics to support their business decisions. Internet power and business trend changes have provided a broad term for data analytics - Big Data. To be able to handle it and leverage a value of having access to Big Data, organizations have no other choice than to get proper systems implemented and working. However, traditional methods are not efficient for changing business needs. Long time between project start and go-live causes a gap between initial solution blueprint and actual user requirements at the end of the project. This article presents the latest market trends in BI systems implementation by comparing agile with traditional methods. It presents a case study provided in a large telecommunications company (350 BI users) and the results of a pilot research provided in the three large companies: media, digital, and insurance. Both studies prove that agile methods might be more effective in BI projects from an end-user perspective and give first results and added value in a much shorter time compared to a traditional approach.

scholarly journals A business case for a data-driven decision-making tool to support the UNBC research enterprise.

2020 ◽  
Author(s):  
◽  
Mark Barnes
Keyword(s):  
Decision Making ◽  
Data Visualization ◽  
Business Intelligence ◽  
Resource Planning ◽  
Business Case ◽  
Data Driven ◽  
Visualization Tool ◽  
Data Driven Decision Making ◽  
Business Decisions ◽  
Design Document

Business intelligence tools allows for data-driven decision-making within organizations using historical events to predict future trends, which is especially valuable when allocating operational resources. As a research-intensive Canadian university, UNBC has seen a significant increase in activities related to supporting the research enterprise, which requires additional resources (human, capital, financial etc.) in order to effectively and efficiently advance the mission of the research community. As outlined in our Annual University Accountability Report, 2018/19 was an incredibly productive year for research with more than $14 million received in support of research. The University has seen a significant increase in the number and breadth of agencies and organizations funding research at UNBC. The administration of research awards involves both pre-award and post-award processes, which requires responsible allocation of available resources to ensure a sustainable model will be developed to achieve goals outlined by the institution’s strategic priorities and build the foundation to reach our goal of a research enterprise generating $25M in annual research revenue. Therefore, using business intelligence tools to utilize historical data to predict the necessary resourcing needs of the institution will allow UNBC to make strategic investments in research and remain competitive on the provincial, national and international stage. Informed decision-making when investing resources are critical to the success of any business. The goal of my MBA project is to gather critical information to be used in the development a data visualization and forecasting tool that will allow for informed decisions for the allocation of resources necessary to support the research mission at UNBC. The objectives of the MBA project are two-fold, which include the development of the business case for the UNBC data visualization tool (DVT) and also the completion of a design document. The information gathered6 from this project will be used in the future (post-MBA) to develop a data visualization tool that will allow for the on-going monitoring of UNBC’s progress towards putting in place the appropriate resources to reach $25M in annual research revenue. Specifically, the MBA project will consist of completing a comprehensive business case outlining the “business need” and potential solutions. Secondly, the MBA project will consist of developing a “design document” for an eventual tool that will be used to visualize research funding and labor information to inform business decisions for resource planning for the UNBC research enterprise. This design support system will be used by senior leadership within UNBC to effectively and efficiently make decisions to allocate resources.

scholarly journals Effectiveness of Agile Implementation Methods in Business Intelligence Projects from End-User Perspective

10.28945/3442 ◽  
2016 ◽  
Author(s):  
Jerzy Andrzej Kisielnicki ◽  
Anna Maria Misiak
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Traditional Approach ◽  
Added Value ◽  
Business Trend ◽  
End User ◽  
Global Business ◽  
Traditional Methods ◽  
User Perspective ◽  
Business Decisions

The global Business Intelligence (BI) market grew by 10% in 2013 according to the Gartner Report. BI has been the top implementation priority for organizations for many years now. Today organizations require better use of data and analytics to support their business decisions. Internet power and business trend changes have provided a broad term - Big Data. To be able to handle it and leverage a value of having access to Big Data, organizations have no other choice than to get proper systems implemented and working. However traditional methods are not efficient for changing business needs. Long time between project start and go-live causes a gap between initial solution blueprint and actual user requirements in the end of the project. This article presents the latest market trends in BI systems implementation by comparing Agile with traditional methods. It presents a case study provided in a large telecommunications company (20K employees) and the results of a pilot research provided in the three large companies telecommunications, digital and insurance. Both studies prove that Agile methods might be more effective in BI projects from an end-user perspective and give first results and added value in a much shorter time compared to a traditional approach. Organizations often do not have a clear vision of BI requirements. Thus users ask for changes just before a BI product readiness, which Agile ensures in contradiction to traditional methods.

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