Big Data Driven Business Models

2015 ◽  
pp. 65-80 ◽  
Author(s):  
Vincenzo Morabito
Keyword(s):  
Big Data ◽  
Business Models ◽  
Data Driven

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 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.

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.

The IoT- and Big Data-Driven Data Analysis Services

2018 ◽  
Vol 14 (4) ◽  
pp. 88-107 ◽  
Author(s):  
Izabella V. Lokshina ◽  
Barbara J. Durkin ◽  
Cees Lanting
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Business Models ◽  
Environmental Indicators ◽  
Third Party ◽  
Data Driven ◽  
Easy Access ◽  
The Internet ◽  
Ubiquitous Sensing ◽  
Provision Of Services

Ubiquitous sensing devices, enabled by wireless sensor network (WSN) technologies, cut across every area of modern day living, affecting individuals and businesses and offering the ability to measure and understand environmental indicators. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT). The IoT provides the tools to establish a major global data-driven ecosystem with its emphasis on Big Data. Now 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 potential opportunities to create revenue and value for various types of customers. This article contributes to the literature by considering, for the first time, business models, strategic implications and business opportunities for third-party data analysis services.

Contract Law in the Age of Big Data

2020 ◽  
Vol 16 (3) ◽  
pp. 347-365
Author(s):  
Cemre Bedir
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Business Models ◽  
Data Use ◽  
Personal Data ◽  
Contract Law ◽  
Data Driven ◽  
Digital Service ◽  
Contractual Relationship

AbstractIn data-driven business models, users’ personal data is collected in order to determine the preferences of consumers and to the tailor production and advertising to these preferences. In these business models, consumers do not pay a price but provide their data, such as IP numbers, locations, and email addresses to benefit from the digital service or content. Contracts facilitate interactions between these providers and users. Their transactions are regulated by contracts in which their agreement on data use and data processing are stipulated. Data is always collected and processed through a contractual relationship and in this paper, I will argue that there are problems arising from contracts involving data to which contract law applies and that contract law can map these problems and offer insights. The scope of this study will be limited to issues where data is provided as counter-performance and where data is provided in addition to a monetary payment.

Internet of Things and Big Data-Driven Data Analysis Services for Third Parties

2019 ◽  
pp. 256-289 ◽  
Author(s):  
Izabella V. Lokshina ◽  
Barbara J. Durkin ◽  
Cees J. M. Lanting
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Business Models ◽  
Management Practices ◽  
Data Driven ◽  
The Internet ◽  
Knowledge Based ◽  
Provision Of Services ◽  
New Business

Ubiquitous sensing devices, enabled by wireless sensor network (WSN) technologies, cut across every area of modern day living, affecting individuals and businesses and offering the ability to measure and understand environmental indicators. The proliferation of these devices in a communicating-actuating network creates the internet of things (IoT). The IoT provides the tools to establish a major global data-driven ecosystem with its emphasis on big data. Now 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 creating revenue and value for different types of customers. This chapter contributes to the literature by considering, for the first time, knowledge-based management practices, business models, new ventures, and new business opportunities for third-party data analysis services.

scholarly journals Capturing value from big data – a taxonomy of data-driven business models used by start-up firms

2016 ◽  
Vol 36 (10) ◽  
pp. 1382-1406 ◽  
Author(s):  
Philipp Max Hartmann ◽  
Mohamed Zaki ◽  
Niels Feldmann ◽  
Andy Neely
Keyword(s):  
Big Data ◽  
Business Model ◽  
Business Models ◽  
Clustering Algorithms ◽  
Reference Points ◽  
Data Driven ◽  
Content Type ◽  
Start Up ◽  
Different Types ◽  
Start Ups

Purpose The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study. Design/methodology/approach To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample. Findings The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework. Practical implications A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox. Originality/value This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

Datenbasierte Produktionsoptimierung*/Data-driven production optimization: Areas of development for manufacturing companies

2018 ◽  
Vol 108 (03) ◽  
pp. 108-112
Author(s):  
D. Bauer ◽  
T. Maurer ◽  
T. Bauernhansl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Applied Research ◽  
Business Models ◽  
Big Data Analytics ◽  
Production Optimization ◽  
Data Driven ◽  
Manufacturing Companies ◽  
New Business ◽  
New Business Models

Unternehmen sehen in Big-Data-Analysen ein großes Potenzial zur Optimierung der klassischen Produktionsziele sowie zur Entwicklung neuer Geschäftsmodelle. Eine Studie des Fraunhofer IPA analysiert, welche Herausforderungen bei der Umsetzung dieser Potenziale auftreten. Darauf aufbauend werden Entwicklungsfelder für die angewandte Forschung und produzierende Unternehmen erarbeitet.   Companies expect huge benefits from big data analytics both to improve traditional production targets and to develop new business models. A study conducted by Fraunhofer IPA analyzes the upcoming challenges in exploiting these opportunities. It provides the basis for identifying areas of development for applied research and for manufacturing companies.

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