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

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 An Adaptable Big Data Value Chain Framework for End-to-End Big Data Monetization

2020 ◽  
Vol 4 (4) ◽  
pp. 34
Author(s):  
Abou Zakaria Faroukhi ◽  
Imane El Alaoui ◽  
Youssef Gahi ◽  
Aouatif Amine
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Business Models ◽  
Data Driven ◽  
Business Operations ◽  
Data Value ◽  
End To End ◽  
Almost All ◽  
Theoretical Foundations ◽  
Support Decision Making

Today, almost all active organizations manage a large amount of data from their business operations with partners, customers, and even competitors. They rely on Data Value Chain (DVC) models to handle data processes and extract hidden values to obtain reliable insights. With the advent of Big Data, operations have become increasingly more data-driven, facing new challenges related to volume, variety, and velocity, and giving birth to another type of value chain called Big Data Value Chain (BDVC). Organizations have become increasingly interested in this kind of value chain to extract confined knowledge and monetize their data assets efficiently. However, few contributions to this field have addressed the BDVC in a synoptic way by considering Big Data monetization. This paper aims to provide an exhaustive and expanded BDVC framework. This end-to-end framework allows us to handle Big Data monetization to make organizations’ processes entirely data-driven, support decision-making, and facilitate value co-creation. For this, we present a comprehensive review of existing BDVC models relying on some definitions and theoretical foundations of data monetization. Next, we expose research carried out on data monetization strategies and business models. Then, we offer a global and generic BDVC framework that supports most of the required phases to achieve data valorization. Furthermore, we present both a reduced and full monetization model to support many co-creation contexts along the BDVC.

scholarly journals Big data monetization throughout Big Data Value Chain: a comprehensive review

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Abou Zakaria Faroukhi ◽  
Imane El Alaoui ◽  
Youssef Gahi ◽  
Aouatif Amine
Keyword(s):  
Big Data ◽  
Value Creation ◽  
Value Chain ◽  
Big Data Analytics ◽  
High Volume ◽  
Value Chains ◽  
Data Generation ◽  
Comprehensive Review ◽  
Data Value ◽  
End To End

AbstractValue Chain has been considered as a key model for managing efficiently value creation processes within organizations. However, with the digitization of the end-to-end processes which began to adopt data as a main source of value, traditional value chain models have become outdated. For this, researchers have developed new value chain models, called Data Value Chains, to carry out data driven organizations. Thereafter, new data value chains called Big Data Value chain have emerged with the emergence of Big Data in order to face new data-related challenges such as high volume, velocity, and variety. These Big Data Value Chains describe the data flow within organizations which rely on Big Data to extract valuable insights. It is a set of ordered steps using Big Data Analytics tools and mainly built for going from data generation to knowledge creation. The advances in Big Data and Big Data Value Chain, using clear processes for aggregation and exploitation of data, have given rise to what is called data monetization. Data monetization concept consists of using data from an organization to generate profit. It may be selling the data directly for cash, or relying on that data to create value indirectly. It is important to mention that the concept of monetizing data is not as new as it looks, but with the era of Big Data and Big Data Value Chain it is becoming attractive. The aim of this paper is to provide a comprehensive review of value creation, data value, and Big Data value chains with their different steps. This literature has led us to construct an end-to-end exhaustive BDVC that regroup most of the addressed phases. Furthermore, we present a possible evolution of that generic BDVC to support Big Data Monetization. For this, we discuss different approaches that enable data monetization throughout data value chains. Finally, we highlight the need to adopt specific data monetization models to suit big data specificities.

scholarly journals A contextual parsing of big data values to quantity surveyors

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Zafira Nadia Maaz ◽  
Shamsulhadi Bandi ◽  
Roslan Amirudin
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Customer Value ◽  
Quantitative Research ◽  
Modern Technology ◽  
Structured Interviews ◽  
Framework Analysis ◽  
Data Value ◽  
Professional Perspective ◽  
Quantity Surveying

Big data is the new generation of technology designed for organizations to economically extract value from large volumes of a wide variety of data through high velocity capture, discovery, storage and analysis. Manifest as the frontier of 21st century technology, big data instigate superior business return. This lure businesses to zealously capitalize big data. In correspond, professionals too are charting their way to improve customer value with big data. Leading research in this area accede maximization on big data; revolutionized the norm of medical and accounting profession. Despite the substantial value, big data uptake from the quantity surveying profession recognized subtle. Contrarily, construction stakeholders swiftly embrace modern technology in their construction value chain. This invoke a change in data landscape thus, present an urgent call for professionals, especially quantity surveyors to recognize the change, embrace and reap the big data benefit. This paper aims to expand big data knowledge from the context of quantity surveying profession as an approach to soothe the big data and quantity surveying gap. This paper identifies generic big data value from professional perspective and explore big data value from the quantity surveying context. Aligning to the blurry big data paradigm in the quantity surveying context, this research adopts quantitative research with desk study on 28 papers and framework analysis through 15 semi-structured interviews with big data industry expert with quantity surveying background. This research found that big data values are consistent across profession albeit the difference on how big data is maximized. Other than that, the paucity of quantity surveying big data pursuance seen as repercussion of infancy big data state in the construction industry. However, this research insinuate quantity surveying profession are in strategic position to move forward with big data.

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