Toward a Conceptualization of Big Data Value Chain

2022 ◽  
pp. 319-335
Author(s):  
Rim Louati ◽  
Sonia Mekadmi
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Value Chain ◽  
Data Capture ◽  
Digital Devices ◽  
Business Executives ◽  
Data Value ◽  
Near Future ◽  
Different Sources

The generation of digital devices such as web 2.0, smartphones, social media and sensors has led to a growing rate of data creation. The volume of data available today for organizations is big. Data are produced extensively every day in many forms and from many different sources. Accordingly, firms in several industries are increasingly interested in how to leverage on these “big data” to draw valuable insights from the various kinds of data and to create business value. The aim of this chapter is to provide an integrated view of big data management. A conceptualization of big data value chain is proposed as a research model to help firms understand how to cope with challenges, risks and benefits of big data. The suggested big data value chain recognizes the interdependence between processes, from business problem identification and data capture to generation of valuable insights and decision making. This framework could provide some guidance to business executives and IT practitioners who are going to conduct big data projects in the near future.

Toward a Conceptualization of Big Data Value Chain

2019 ◽  
pp. 76-92 ◽  
Author(s):  
Rim Louati ◽  
Sonia Mekadmi
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Value Chain ◽  
Data Capture ◽  
Digital Devices ◽  
Business Executives ◽  
Data Value ◽  
Near Future ◽  
Different Sources

The generation of digital devices such as web 2.0, smartphones, social media and sensors has led to a growing rate of data creation. The volume of data available today for organizations is big. Data are produced extensively every day in many forms and from many different sources. Accordingly, firms in several industries are increasingly interested in how to leverage on these “big data” to draw valuable insights from the various kinds of data and to create business value. The aim of this chapter is to provide an integrated view of big data management. A conceptualization of big data value chain is proposed as a research model to help firms understand how to cope with challenges, risks and benefits of big data. The suggested big data value chain recognizes the interdependence between processes, from business problem identification and data capture to generation of valuable insights and decision making. This framework could provide some guidance to business executives and IT practitioners who are going to conduct big data projects in the near future.

scholarly journals Perceptions of Auditor Negligence: The Effects of Big Data Visualisations on Jurors’ Decisions

2021 ◽  
Author(s):  
◽  
Travis Christensen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Collection ◽  
Jury Verdicts ◽  
Word Clouds ◽  
Audit Litigation ◽  
Different Types ◽  
Different Sources ◽  
Bar Graphs

<p>This study analyses the effects of Big Data visualisations on jurors’ decisions in audit litigation cases. Specifically, the study investigates the effects of different types of Big Data visualisations (word clouds or bar graphs) and different sources of Big Data (emails or social media posts) on jurors’ perceptions of auditors’ work and the size of the negligence awards that jurors recommend. The study theorises that the emotions elicited and the reliability of the data used to create visualisations such as word clouds will have dramatic effects on jury verdicts in audit negligence trials. There is considerable literature to support this assertion. However, after data collection, it was discovered that jurors are not influenced by the emotions elicited by visualisations. Rather, participants were very sceptical of more novel types of visualisations, such as word clouds, but could be persuaded by the inherent emotions elicited and the reliability of the data if they found the visualisation useful.</p>

Innovation Platforms

2018 ◽  
pp. 72-95
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Business Model ◽  
Value Chain ◽  
Analytical Techniques ◽  
Human Interaction ◽  
Business Relationship ◽  
Potential Relationship ◽  
Relationship Of ◽  
Cross Fertilization

Data and analytics is the heart of a digital business platform. Today, big data (BD) becomes useful when it enriches decision making that is enhanced by application of analytical techniques and some element of human interaction. With the merging of data and information vs. knowledge and intelligence, this chapter investigates an opportunity for cross-fertilization between BD and the field of digital business with related disciplines. Primary BD and analytics platform is a set of business capabilities. This chapter aims to investigate the potential relationship of BD and analytics platform and digital business platform. In doing so, it develops a BD value chain framework, BD business model pattern (BDBMP) with related levels of BD maturity improvement. This framework could be used to find answers on the basic BD and digital business relationship questions.

Big Data in Business

2021 ◽  
pp. 235-249
Author(s):  
Farid Huseynov
Keyword(s):  
Decision Making ◽  
Big Data ◽  
New Products ◽  
Critical Role ◽  
Digital Data ◽  
Business Decisions ◽  
Business Operations ◽  
Operational Costs ◽  
Big Data Technologies ◽  
Different Sources

The term “big data” refers to the very large and diverse sets of structured, semi-structured, and unstructured digital data from different sources that accumulate and grow very rapidly on a continuous basis. Big data enables enhanced decision-making in various types of businesses. Through these technologies, businesses are able to cut operational costs, digitally transform business operations to be more efficient and effective, and make more informed business decisions. Big data technologies enable businesses to better understand their markets by uncovering hidden patterns behind consumer behaviors and introduce new products and services accordingly. This chapter shows the critical role that big data plays in businesses. Initially, in this chapter, big data and its underlying technologies are explained. Later, this chapter discusses how big data digitally transforms critical business operations for enhanced decision-making and superior customer experience. Finally, this chapter ends with the possible challenges of big data for businesses and possible solutions to these challenges.

scholarly journals From Data to Knowledge Engineering for Cybersecurity

2019 ◽  
Author(s):  
Gerardo I. Simari
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Research And Development ◽  
Knowledge Engineering ◽  
Data Sources ◽  
Daily Lives ◽  
Cyber Threats ◽  
Knowledge Based ◽  
Decision Making Processes ◽  
Near Future

Data present in a wide array of platforms that are part of today's information systems lies at the foundation of many decision making processes, as we have now come to depend on social media, videos, news, forums, chats, ads, maps, and many other data sources for our daily lives. In this article, we first discuss how such data sources are involved in threats to systems' integrity, and then how they can be leveraged along with knowledge-based tools to tackle a set of challenges in the cybersecurity domain. Finally, we present a brief discussion of our roadmap for research and development in the near future to address the set of ever-evolving cyber threats that our systems face every day.

scholarly journals A big data analysis of the relationship between future thinking and decision-making

2018 ◽  
Vol 115 (8) ◽  
pp. E1740-E1748 ◽  
Author(s):  
Robert Thorstad ◽  
Phillip Wolff
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Population Level ◽  
Future Thinking ◽  
Individual Level ◽  
The Future ◽  
The Individual ◽  
Near Future ◽  
The Relationship ◽  
Far Future

We use big data methods to investigate how decision-making might depend on future sightedness (that is, on how far into the future people’s thoughts about the future extend). In study 1, we establish a link between future thinking and decision-making at the population level in showing that US states with citizens having relatively far future sightedness, as reflected in their tweets, take fewer risks than citizens in states having relatively near future sightedness. In study 2, we analyze people’s tweets to confirm a connection between future sightedness and decision-making at the individual level in showing that people with long future sightedness are more likely to choose larger future rewards over smaller immediate rewards. In study 3, we show that risk taking decreases with increases in future sightedness as reflected in people’s tweets. The ability of future sightedness to predict decisions suggests that future sightedness is a relatively stable cognitive characteristic. This implication was supported in an analysis of tweets by over 38,000 people that showed that future sightedness has both state and trait characteristics (study 4). In study 5, we provide evidence for a potential mechanism by which future sightedness can affect decisions in showing that far future sightedness can make the future seem more connected to the present, as reflected in how people refer to the present, past, and future in their tweets over the course of several minutes. Our studies show how big data methods can be applied to naturalistic data to reveal underlying psychological properties and processes.

scholarly journals A Multi-Layer Big Data Value Chain Approach for Security Issues

2020 ◽  
Vol 175 ◽  
pp. 737-744
Author(s):  
Abou Zakaria Faroukhi ◽  
Imane El Alaoui ◽  
Youssef Gahi ◽  
Aouatif Amine
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Security Issues ◽  
Data Value

Big Data and Democracy

2020 ◽  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Data Collection ◽  
Political Campaigns ◽  
Original Research ◽  
Targeted Advertising ◽  
Echo Chambers ◽  
Political Turmoil ◽  
The Impact

This edited collection tackles subjects that have arisen as a result of new capabilities to collect, analyse and use vast quantities of data using complex algorithms. Questions tackled include what is wrong with targeted advertising in political campaigns, whether echo chambers really are a matter of genuine concern, what is the impact of data collection through social media and other platforms on questions of trust in society and is there a problem of opacity as decision-making becomes increasingly automated? The contributors consider potential solutions to these challenges and discuss whether an ethical compass is available or even feasible in an ever more digitized and monitored world. The editors bring together original research on the philosophy of big data and democracy from leading international authors, with recent examples and case references – including the 2016 Brexit Referendum, the Leveson Inquiry and the Edward Snowden leaks – and combine them in one authoritative volume at time of great political turmoil.

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