scholarly journals Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications

2019 ◽  
Vol 3 (2) ◽  
pp. 32 ◽  
Author(s):  
Ifeyinwa Angela Ajah ◽  
Henry Friday Nweke
Keyword(s):  
Big Data ◽  
Success Factors ◽  
Database Management System ◽  
Unstructured Data ◽  
Digital Data ◽  
Business Strategies ◽  
Modern World ◽  
Successful Implementation ◽  
Business Analytics ◽  
Business World

Big data and business analytics are trends that are positively impacting the business world. Past researches show that data generated in the modern world is huge and growing exponentially. These include structured and unstructured data that flood organizations daily. Unstructured data constitute the majority of the world’s digital data and these include text files, web, and social media posts, emails, images, audio, movies, etc. The unstructured data cannot be managed in the traditional relational database management system (RDBMS). Therefore, data proliferation requires a rethinking of techniques for capturing, storing, and processing the data. This is the role big data has come to play. This paper, therefore, is aimed at increasing the attention of organizations and researchers to various applications and benefits of big data technology. The paper reviews and discusses, the recent trends, opportunities and pitfalls of big data and how it has enabled organizations to create successful business strategies and remain competitive, based on available literature. Furthermore, the review presents the various applications of big data and business analytics, data sources generated in these applications and their key characteristics. Finally, the review not only outlines the challenges for successful implementation of big data projects but also highlights the current open research directions of big data analytics that require further consideration. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

scholarly journals BIG DATA BUSINESS ANALYTICS AS A STRATEGIC ASSET FOR HEALTH CARE INDUSTRY

2019 ◽  
Vol 01 (02) ◽  
pp. 12-20 ◽  
Author(s):  
Smys S ◽  
Vijesh joe C
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Customer Relationship ◽  
Business Strategies ◽  
Business Analytics ◽  
Care Industry ◽  
Strategic Assets

The big data includes the enormous flow of data from variety of applications that does not fit into the traditional data base. They deal with the storing, managing and manipulating of the data acquired from various sources at an alarming rate to gather valuable insights from it. The big data analytics is used provide with the new and better ideas that pave way to the improvising of the business strategies with its broader, deeper insights and frictionless actions that leads to an accurate and reliable systems. The paper proposes the big data analytics for the improving the strategic assets in the health care industry by providing with the better services for the patients, gaining the satisfaction of the patients and enhancing the customer relationship.

The Impact of Big Data on Security

Big Data ◽  
2016 ◽  
pp. 1495-1518
Author(s):  
Mohammad Alaa Hussain Al-Hamami
Keyword(s):  
Social Media ◽  
Big Data ◽  
Management System ◽  
Database Management ◽  
Database Systems ◽  
Structured Data ◽  
Database Management System ◽  
Unstructured Data ◽  
And Behavior ◽  
The Impact

Big Data is comprised systems, to remain competitive by techniques emerging due to Big Data. Big Data includes structured data, semi-structured and unstructured. Structured data are those data formatted for use in a database management system. Semi-structured and unstructured data include all types of unformatted data including multimedia and social media content. Among practitioners and applied researchers, the reaction to data available through blogs, Twitter, Facebook, or other social media can be described as a “data rush” promising new insights about consumers' choices and behavior and many other issues. In the past Big Data has been used just by very large organizations, governments and large enterprises that have the ability to create its own infrastructure for hosting and mining large amounts of data. This chapter will show the requirements for the Big Data environments to be protected using the same rigorous security strategies applied to traditional database systems.

scholarly journals Project management maturity in the age of big data

2014 ◽  
Vol 7 (2) ◽  
pp. 311-317 ◽  
Author(s):  
Nigel Williams ◽  
Nicole P. Ferdinand ◽  
Robin Croft
Keyword(s):  
Big Data ◽  
Project Management ◽  
Data Collection ◽  
Unstructured Data ◽  
Digital Data ◽  
Potential Contribution ◽  
Maturity Models ◽  
Research Attention ◽  
Content Type ◽  
Analytical Approaches

Purpose – While the area of project management maturity (PMM) is attracting an increased amount of research attention, the approaches to measuring maturity fit within existing social science conventions. This paper aims to examine the potential contribution of new data collection and analytical approaches to develop new insights in PMM. Design/methodology/approach – This paper takes the form of a literature review. Findings – The current trends of rapidly growing digital data collection and storage may have the potential to develop approaches to PMM assessment that overcome the limitations of existing qualitative and quantitative approaches. Research limitations/implications – Future research in PMM can employ techniques such as social network analysis and text analysis to develop insights based on the flow and content of information in organizations. Practical implications – Adoption of data analytical approaches from big data can enable the creation of new types of holistic and adaptive maturity models. Holistic maturity models provide insights based on both structured and unstructured data within organizations. Adaptive maturity models provide rapid insights based on the flow of information within an enterprise. Originality/value – The recent trend towards digitising of organizational knowledge and interactions has created the possibility to apply new analytical approaches and techniques to the understanding of PMM in firms. This paper identifies possible tools and approaches that can be applied to create new types of maturity models based on structured and unstructured data.

scholarly journals A Preliminary Systematic Performanceon Critical Success Factors Categories for Big Data Analytics

2019 ◽  
Vol 9 (1) ◽  
pp. 2320-2324 ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Critical Success Factors ◽  
Success Factors ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Successful Implementation ◽  
Critical Success ◽  
Data Driven Decisions

Big Data could be used in any industry to make effective data-driven decisions. The successful implementation of Big Data projects requires a combination of innovative technological, organizational, and processing approaches. Over the last decade, the research on Critical Success Factors (CSFs) within Big Data has developed rapidly but the number of available publications is still at a low level. Developing an understandingof the Critical Success Factors (CSFs) and their categoriesare essential to support management in making effective data-driven decisions which could increase their returns on investments.There islimited research conducted on the Critical Success Factors (CSFs) of Big DataAnalytics (BDA) development and implementation.This paper aims to provide more understanding about the availableCritical Success Factors (CSFs) categoriesfor Big Data Analytics implementation and answer the research question (RQ) “What are the existing categories of Critical Success Factors for Big Data Analytics”.Based on a preliminary Systematic Literature Review (SLR) for the available publications related to Big Data CSFs and their categories in the last twelve years (2007-2019),this paper identifiesfive categoriesfor Big Data AnalyticsCritical Success Factors(CSFs), namelyOrganization, People, Technology, Data Management, and Governance categories.

Big Data Mining

2016 ◽  
pp. 53-59 ◽  
Author(s):  
N. G. Bhuvaneswari Amma
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Social Networking ◽  
Unstructured Data ◽  
Science And Engineering ◽  
Business World ◽  
Volume Velocity ◽  
Big Data Mining ◽  
Traditional Processing ◽  
Processing Techniques

Big data is a term used to describe very large amount of structured, semi-structured and unstructured data that is difficult to process using the traditional processing techniques. It is now expanding in all science and engineering domains. The key attributes of big data are volume, velocity, variety, validity, veracity, value, and visibility. In today's world, everyone is using social networking applications like Facebook, Twitter, YouTube, etc. These applications allow the users to create the contents for free of cost and it becomes huge volume of web data. These data are important in the competitive business world for making decisions. In this context, big data mining plays a major role which is different from the traditional data mining. The process of extracting useful information from large datasets or streams of data, due to its volume, velocity, variety, validity, veracity, value and visibility is termed as Big Data Mining.

scholarly journals Information Extraction from Multifaceted Unstructured Big Data

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1398-1404
Keyword(s):  
Big Data ◽  
Information Extraction ◽  
Data Analytics ◽  
Extraction Methods ◽  
Vital Role ◽  
Unstructured Data ◽  
Digital Data ◽  
High Rate ◽  
Smart Manufacturing ◽  
Future Research

In the era of digital globalization, huge volume and variety of data are being produced at a very high rate. Every day, the world is producing around 2.5 quintillion bytes of data. According to IDC, by 2020, over 40 zettabytes of data will be generated and reproduced. Digital data have become a deluge, overwhelming in every field of information technology (IT), business, science and engineering. These fields are shifting to smart and advanced technologies such as smart manufacturing industries, data-aware medical sciences, and other smart applications. These applications are facilitating the industries in context of data-driven decision making, big data storage, and complex analysis of large data sets. Also, these applications are contributing to generate big data deluge where a variety of data necessitate the industries to use advanced IT approaches. 95% of the digital universe is unstructured data. It is rich data as it contains information that can play a vital role to improve big data analytics. The heterogeneity, complexity, lack of structured information, poor quality and scalability of unstructured data generates difficulties in adapting traditional information extraction techniques. Information extraction can play a vital role in transformation of unstructured data into useful information. A multistep pipeline with data preprocessing steps, extraction methods and representation are utmost requirement to improve the unstructured data analytics. In this regard, this paper presents a short review of information extraction process w.r.t. input data type, extraction methods with their corresponding techniques, and representation of extracted information. The issues with unstructured data and the challenges to information extraction from multifaceted unstructured big data as well as the future research directions have also been discussed

scholarly journals Role of Information Technology and Information System as an Antecedent to Successful Implementation of TQM

2020 ◽  
Vol 8 (5) ◽  
pp. 1010-1016
Keyword(s):  
Information Technology ◽  
Information System ◽  
Database Management System ◽  
Cost Ratio ◽  
Successful Implementation ◽  
Total Quality ◽  
Business World ◽  
The World ◽  
Role Of Information

Many organisations have used Information Technology (IT) and Information System (IS) for successfully implementing Total Quality Management (TQM). With the aid of IT many firms have been able to provide higher quality goods and services. Competition at the international level has expanded the importance of quality in the area of business. The world of business now faces more competition than ever with challenges and pressure growing day by day. Therefore, the focus on quality of product and services is now more paving the way for TQM practices on a large scale. As we are aware, Technology drive the world so how can the business world remain unaffected by it. Hence it is widely used for achieving the desired result. TQM is a management philosophy and IT is best explained as the telecommunication, hardware and software that helps in processing, collecting, storing and transmitting multimedia information. One of the many reasons for the expanding TQM practices is also the cost ratio, which has been possible because of computer processing. This has made the procedure economical in firms. Information System is an integral part of a firm because information is one of the main assets of a firm. Many firms depend on technology based information system for the organisation’s day-to-day activities such as decision making at a managerial level and for getting strategic advantages. It aids in decreasing wasteful spending helps in excellent and error free documentation, analysis and measuring all activities of an organization. The combination IT and TQM plays a vital role in ensuring a bug free and easy to maintain procedures and applications of the changing needs of a firm. The technologies that are used are Database management system (DBMS); Distributed data processing; Object Oriented Programs; Parallel processing; Data Warehousing; Replication; Networks; Neural Networks and Information Communication System Programs.. Basic objectives of Information Systems are to enhance the production, quality development, enhanced service delivery, reduced costs, and increasing the competitiveness of the organization's. An organization’s development expansion and growth require a combination of IT and IS and TQM go hand in hand. This study aims to present the role of Information Technology and Information System on TQM. The present study comprises of a thorough conceptual analysis of 29 review papers in order to compare the standard of literature taken from different papers like TQM practices, approach, Role of IT in TQM, Role of IS in TQM etc.

Mining Big Data: Its Current Status and Future

2017 ◽  
Vol 7 (7) ◽  
pp. 285
Author(s):  
Mayushi Chouhan ◽  
Rohit Singh Nain
Keyword(s):  
Big Data ◽  
Daily Basis ◽  
Unstructured Data ◽  
Current Status ◽  
Process Automation ◽  
Cognitive Technology ◽  
Business World ◽  
The World ◽  
Set Up ◽  
Extract Information

Organizations create 2.5 Quintilian bytes of data. So much that 90% of the data in the world today has been set up in the last two years alone. What is Big Data? Big Data is large volumes of structured and unstructured data. This data is what organizations collect on a daily basis. The amount of data is not the important part, but the information gathered from that data is the key. Collecting and analyzing Big Data gives organizations enhanced insight, decision making, and process automation. Approximately each one can agree that big data has taken the business world by storm, but what’s next?  Will data continue to grow?  What technologies will develop around it? Or will big data become a relic as quickly as the next trend — cognitive technology? Fast data? - appears on the horizon. I believe, am that big data is only going to get bigger and those companies that ignore it will be left further and further behind. This paper studies about what is big data, how does it helps organizations to extract information, its tools and technologies and its future.

scholarly journals Simulation of energy consumption in urban areas

2020 ◽  
Vol 152 ◽  
pp. 02006
Author(s):  
Nikolay Garyaev
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Energy Consumption ◽  
Urban Areas ◽  
Energy Supply ◽  
Digital Data ◽  
Successful Implementation ◽  
Digital Models ◽  
Balanced Distribution ◽  
The Way

One of the problems that may arise in the way of successful implementation of energy supply in urban areas is the difficulty of analyzing and interpreting a large amount of digital data received from various sensors. This problem may adversely affect the performance of energy organizations. The purpose of this study is to study modern tools to solve the problem of processing big data using technologies of simulation and artificial intelligence. This study is dedicated to the development of innovative digital models for the balanced distribution of energy consumption in urban areas.

scholarly journals The Big Data Era: What are the Implications for the World of Accounting?

2020 ◽  
Vol 17 (2) ◽  
pp. 248-254
Author(s):  
Rokhmat Taufiq Hidayat ◽  
Akhmad Khabibi
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Financial Accounting ◽  
Unstructured Data ◽  
Added Value ◽  
Managerial Accounting ◽  
Business World ◽  
The World ◽  
Oxford Dictionary ◽  
Definition Of

In an era where information technology is developing so rapidly as it is now, contact with technology is inevitable. One that may often be heard is the use of big data. Although the development of big data has begun long before, its growth began rapidly since the Oxford Dictionary included the definition of big data in 2013. The use of big data is thought to have a big influence on the business world, and anything that influences the business world will certainly affect the world of accounting. Does the accountant anticipate these changes? In this article, the author tries to explore what allusions might occur between the world of accounting and big data. Big data will increase the complexity of the accounting world by adding unstructured data in the accounting cycle. This presents a challenge for accountants but can also provide far greater added value if accountants are able to use it well. The results of this study indicate that there are at least 3 areas in the field of accounting that are very likely to be exposed to the use of big data, namely in the process of financial accounting, managerial accounting, and auditing

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