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 Les Big Data A L’emergence Des Data-Driven- Business-Models: Une Synthese De La Litterature

2018 ◽  
Vol 14 (28) ◽  
pp. 211
Author(s):  
Nabil Lazaar
Keyword(s):  
Big Data ◽  
Common Good ◽  
Statistical Power ◽  
Business Models ◽  
Research Work ◽  
Data Driven ◽  
Negative Externalities ◽  
Positive Externalities ◽  
Data Value ◽  
Using Data

This paper focuses on presenting a first step in a more in-depth literature review. In the border frame of the ‘systemic’ approach (Donnadieu & Karsky, 2002), the study focused on the exploration of ‘Big Data’ value. It follows the process of the formation of the tax decision (Bensouda, 2009) using some properties of the new network apprehension paradigm (Penard, 2014). This is in line with many approaches of identification of the taxable substance of possession and the transmission of ‘value’ (Jemmar, 2010). Also, it is captured exclusively by the ‘shareholder power’ (Vatteville, 2008), using ‘Data-Driven-Business-Models’ (Hartmann et al., 2014). This business model, however, inspired their "Digital Laborer" (Fisher & Fuchs, 2015) based on an activity whose ‘positive externalities’ (Collin & Colin, 2013) go in the form of a "Cognitive Capital" (Boutang, 2007). "Big Data" is indeed the focus of the growing attention in the literature of different disciplines. In addition, it is at the heart of controversies concerning the apprehension by various "stakeholders" who are central to the development of this concept. Based on preliminary research work, this study is aimed at understanding the different dimensions of this Value in the Moroccan context. It also aims to shed light on why its various ‘stakeholders’ are apprehensive regarding this ‘common good’, which escapes real tax measures that can mitigate the ‘negative externalities’ of this new statistical power (Rouvroy & Berns, 2010).

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 Data Innovation Spaces

2021 ◽  
pp. 211-242
Author(s):  
Daniel Alonso
Keyword(s):  
Big Data ◽  
Data Protection ◽  
Business Models ◽  
Community Building ◽  
Data Driven ◽  
It Infrastructure ◽  
Cross Border ◽  
Data Value ◽  
Data Ecosystem ◽  
Novel Applications

AbstractWithin the European Big Data Ecosystem, cross-organisational and cross-sectorial experimentation and innovation environments play a central role. European Innovation Spaces (or i-Spaces for short) are the main elements to ensure that research on big data value technologies and novel applications can be quickly tested, piloted and exploited for the benefit of all stakeholders. In particular, i-Spaces enable stakeholders to develop new businesses facilitated by advanced Big Data Value (BDV) technologies, applications and business models, bringing together all blocks, actors and functionalities expected to provide IT infrastructure, support and assistance, data protection, privacy and governance, community building and linkages with other innovation spaces, as well as incubation and accelerator services. Thereby, i-Spaces contribute to building a community, providing a catalyst for engagement and acting as incubators and accelerators of data-driven innovation, with cross-border collaborations as a key aspect to fully unleash the potential of data to support the uptake of European AI and related technologies.

scholarly journals A Roadmap to Drive Adoption of Data Ecosystems

2021 ◽  
pp. 41-62
Author(s):  
Sonja Zillner ◽  
Laure Le Bars ◽  
Nuria de Lama ◽  
Simon Scerri ◽  
Ana García Robles ◽  
...  
Keyword(s):  
Big Data ◽  
Business Models ◽  
Interdisciplinary Approach ◽  
Data Driven ◽  
Societal Benefit ◽  
European Data ◽  
Strategic Approach ◽  
Data Value ◽  
Data Ecosystem ◽  
Access To Data

AbstractTo support the adoption of big data value, it is essential to foster, strengthen, and support the development of big data value technologies, successful use cases and data-driven business models. At the same time, it is necessary to deal with many different aspects of an increasingly complex data ecosystem. Creating a productive ecosystem for big data and driving accelerated adoption requires an interdisciplinary approach addressing a wide range of challenges from access to data and infrastructure, to technical barriers, skills, and policy and regulation. In order to overcome the adoption challenges, collective action from all stakeholders in an effective, holistic and coherent manner is required. To this end, the Big Data Value Public-Private Partnership (BDV PPP) was established to develop the European data ecosystem and enable data-driven digital transformation, delivering maximum economic and societal benefit, and achieving and sustaining Europe’s leadership in the fields of big data value creation and Artificial Intelligence. This chapter describes the different steps that have been taken to address the big data value adoption challenges: first, the establishment of the BDV PPP to mobilise and create coherence with all stakeholders in the European data ecosystem; second, the introduction of five strategic mechanisms to encourage cooperation and coordination in the data ecosystem; third, a three-phase roadmap to guide the development of a healthy European data ecosystem; and fourth, a systematic and strategic approach towards actively engaging the key communities in the European Data Value Ecosystem.

Design of big data-driven framework based on manufacturing value chain

2021 ◽  
Author(s):  
Jingwen Song ◽  
Aihui Wang ◽  
Ping Liu ◽  
Daming Li ◽  
Xiaobo Han ◽  
...  
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Data Driven

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.

Data Analysis Services Related to the IoT and Big Data

Web Services ◽  
2019 ◽  
pp. 882-903
Author(s):  
Izabella V. Lokshina ◽  
Barbara J. Durkin ◽  
Cees J.M. Lanting
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Business Models ◽  
Third Party ◽  
Data Driven ◽  
Easy Access ◽  
Provision Of Services ◽  
And Control ◽  
Measurement And Control ◽  
The Internet Of Things

The Internet of Things (IoT) provides the tools for the development of a major, global data-driven ecosystem. When accessible to people and businesses, this information can make every area of life, including business, more data-driven. In this ecosystem, with its emphasis on Big Data, there has been a focus on building business models for the provision of services, the so-called Internet of Services (IoS). These models assume the existence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated by any party. Different business models may support opportunities that generate revenue and value for various types of customers. This paper contributes to the literature by considering business models and opportunities for third-party data analysis services and discusses access to information generated by third parties in relation to Big Data techniques and potential business opportunities.

A study of big data evolution and research challenges

2018 ◽  
Vol 45 (3) ◽  
pp. 322-340 ◽  
Author(s):  
Deepak Gupta ◽  
Rinkle Rani
Keyword(s):  
Big Data ◽  
Data Stream ◽  
Information Age ◽  
Tidal Wave ◽  
Digital Data ◽  
Data Driven ◽  
Research Challenges ◽  
Wide Gap ◽  
Almost All ◽  
Data Evolution

The world is already into the information age. The huge growth of digital data has overwhelmed the traditional systems and approaches. Big data is touching almost all aspects of our life and the data-driven discovery approach is an emerging paradigm for computing. The ever-growing data provides a tidal wave of opportunities and challenges in terms of data capture, storage, manipulation, management, analysis, knowledge extraction, security, privacy and visualisation. Though the promise of big data seems to be genuine, still a wide gap exists between its potential and realisation. In last few years, there is a huge surge in research efforts in academia as well as industry to have a better understanding of big data. This article discusses the following: (1) big data evolution including a bibliometric study of academic and industry publications pertaining to big data during the period 2000–2017, (2) popular open-source big data stream processing frameworks and (3) prevalent research challenges which must be addressed to realise the true potential of big data.

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