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 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 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.

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.

Data Analysis Services Related to the IoT and Big Data

2017 ◽  
Vol 9 (2) ◽  
pp. 37-56 ◽  
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.

scholarly journals Examining the Core Dilemmas Hindering Big Data-related Transformations in Public-Sector Organisations

2019 ◽  
Vol 12 (2) ◽  
pp. 131-156 ◽  
Author(s):  
Päivikki Kuoppakangas ◽  
Tony Kinder ◽  
Jari Stenvall ◽  
Ilpo Laitinen ◽  
Olli-Pekka Ruuskanen ◽  
...  
Keyword(s):  
Big Data ◽  
Risk Taking ◽  
Service Provision ◽  
Data Driven ◽  
The Core ◽  
Management Approaches ◽  
Using Data ◽  
Public Organisations ◽  
Managerial System

AbstractThis study examines public organisations planning big data-driven transformations in their service provision. Without radical structural change or managerial system changes, leaders face dilemmas: simply bolting on big data makes little difference. This study is based on a qualitative empirical case study using data collected from the cities of Helsinki and Tampere in Finland. The three core dilemma pairs detected and connected to the big data-related organisational changes are: (1) repetitive continuity vs. visionary change, (2) risk-taking vs. security-seeking and (3) technology-based development vs. human-based development. This study suggests that organisational readiness involves not only capabilities; instead, readiness involves absorbing knowledge, making decisions, handling ambiguities, managing dilemmas. Thus, big data-related transformations in public organisations require embracing the world of dilemmas, since selected and cancelled experiments may each have valuable outcomes. The capability to act on intentions is a prerequisite for readiness; however, a preparedness to detect and address dilemmas is central to big data-related transformations. Thus, the ability to make dilemma decisions is a more complicated characteristic of readiness. In conclusion, our data analysis suggests that traditional public organisational and chance management approaches produce unsolved dilemmas in big data-related organisational changes.

scholarly journals Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs

2019 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Eun Sun Kim ◽  
Yunjeong Choi ◽  
Jeongeun Byun
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Medium Size ◽  
The Government ◽  
Using Data ◽  
High Level ◽  
D Investment

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises.

IoT- and Big Data-Driven Data Analysis Services for Third Parties, Strategic Implications and Business Opportunities

2018 ◽  
Vol 9 (3) ◽  
pp. 34-52 ◽  
Author(s):  
Izabella V. Lokshina ◽  
Cees J.M. Lanting ◽  
Barbara J. Durkin
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Business Models ◽  
Management Practices ◽  
Environmental Indicators ◽  
Third Party ◽  
Data Driven ◽  
Easy Access ◽  
Business Opportunities ◽  
Provision Of Services

This article describes ubiquitous sensing devices, enabled by wireless sensor network (WSN) technologies, now cut across every area of modern day living, affecting individuals and businesses and offering the ability to obtain and measure environmental indicators. Proliferation of these devices in a communicating-actuating network creates an Internet of Things (IoT). The IoT provides the tools to establish a major, global data-driven ecosystem that also enables Big Data techniques to be used. New business models may focus on the provision of services, i.e., the Internet of Services (IoS). These models assume the presence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated. Different business models may support opportunities to create revenue and value for various types of customers. This article contributes to the literature by considering, a first, knowledge-based management practices, business models, strategic implications and business opportunities for third-party data analysis services.

Sign in / Sign up

Export Citation Format

Share Document