scholarly journals Building Business Analytic Tool using Dark Data and Big Data

2022 ◽  
Vol 10 (1) ◽  
pp. 72-76
Author(s):  
A. S. Oviya
Keyword(s):  
Big Data ◽  
Business Model ◽  
Business Intelligence ◽  
Business Analytics ◽  
Analytic Tool ◽  
Competitive Edge ◽  
Business Knowledge

Abstract: The data is turning into the fundamental resource in the present science and innovation. Tragically, a lot of accessible and put away information isn't utilized today. This information is known as dull information. Big data is said to offer not just phenomenal degrees of business knowledge concerning the propensities for buyers and opponents, yet in addition to proclaim an upset in the manner by which business are coordinated and run. Organizations strive to achieve a competitive edge through, big data and business analytics tool. In this paper we have discussed about how dark data is used in organizations and the technologies evolved in business model. We have explored awareness in dark data and how we can implement them in business model. Keywords: Big Data, Dark Data, Business Intelligence (BI), etc.

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.

Knowledge Management and Business Analytics

2019 ◽  
pp. 1-42
Author(s):  
Nirali Nikhilkumar Honest ◽  
Atul Patel
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Business Performance ◽  
Virtual Organization ◽  
Data Driven ◽  
Business Analytics ◽  
Broad Category ◽  
Business Operations

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

Big Data, Knowledge, and Business Intelligence

2018 ◽  
pp. 943-950
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Competitive Advantage ◽  
Intellectual Capital ◽  
Business Intelligence ◽  
Competitive Intelligence ◽  
Business Analytics ◽  
The Past ◽  
The People ◽  
Tools And Techniques

Knowledge management (KM), intellectual capital (IC), and competitive intelligence are distinct yet related fields that have endured and grown over the past two decades. KM and IC have always differentiated between the terms and concepts of data, information, knowledge, and wisdom/intelligence, suggesting value only comes from the more developed end of the range (knowledge and intelligence). But the advent of big data/business analytics has created new interest in the potential of data and information, by themselves, to create competitive advantage. This new attention provides opportunities for some exchange with more established theory. Big data gives direction for reinvigorating the more mature fields, providing new sources of inputs and new potential for analysis and use. Alternatively, big data/business analytics applications will undoubtedly run into common questions from KM/IC on appropriate tools and techniques for different environments, the best methods for handling the people issues of system adoption and use, and data/intelligence security.

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

A Service-Oriented Foundation for Big Data

2020 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Technological Characteristics ◽  
Service Oriented ◽  
Technological Level ◽  
Multi Level ◽  
Big Service

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

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