Data Ecosystem Business Models

Purpose: Organizations evolve from using and governing data internally towards the exchange of data in multi-organizational data ecosystems. The purpose of this research is to determine a business model framework for actors operating in and/or entering a data ecosystem. Methodology: To determine...

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Main Authors: Ruben D'Hauwers, Nils Walravens, Pieter Ballon
Format: Article
Language:English
Published: Aalborg University Open Publishing 2022-11-01
Series:Journal of Business Models
Online Access:https://journals.aau.dk/index.php/JOBM/article/view/6946
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author Ruben D'Hauwers
Nils Walravens
Pieter Ballon
author_facet Ruben D'Hauwers
Nils Walravens
Pieter Ballon
author_sort Ruben D'Hauwers
collection DOAJ
description Purpose: Organizations evolve from using and governing data internally towards the exchange of data in multi-organizational data ecosystems. The purpose of this research is to determine a business model framework for actors operating in and/or entering a data ecosystem. Methodology: To determine a business model framework in data ecosystems. an analysis was made based on how the research fields of “business models”, “data governance”, “data ecosystems”, “data sharing”, “business ecosystem” complement each other. A business model framework was created, which was applied to three use case studies in the field of Smart Cities and Urban Digital Twins: The Helsinki Digital Twin, the Rotterdam Digital Twin, and the Smart Retail Dashboard in Flanders. Findings: The business model of actors in a data ecosystem is determined by value and control factors. Value is determined by the capability to create value through the exchange of data in the ecosystem, and to capture value through revenue (sharing) models and cost (sharing) models. Control is determined by ecosystem control. Governance models on the ecosystem level are required to enable the collaboration and to ensure trust to allow for the willingness to share data. Additionally, data governance on an ecosystem level is required, enabling the data exchange between the actors. Research Limitations: The model was applied to three use cases in Smart Cities and Urban Digital Twins. Consequently, the data ecosystems concern a high presence of public actors, yet also includes private companies. The applicability needs to be identified in other sectors in further research. Additionally, as the scope of the study was on business models, data governance, data-sharing and data ecosystems, abstraction was made of fields of study beyond these topics. Value and practical implications: The Data Ecosystem Business Model framework can serve as a guideline for organizations entering a data ecosystem, as well as for actors aiming to establish novel data ecosystems. Additionally, the framework can serve as a high-level overview for further research into the field of business models in data ecosystems.  
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spelling doaj.art-20d68dd411f04f8cafba431291e929622024-04-03T01:45:12ZengAalborg University Open PublishingJournal of Business Models2246-24652022-11-0110210.54337/jbm.v10i2.6946Data Ecosystem Business ModelsRuben D'Hauwers0Nils WalravensPieter Ballonimec-SMIT, VUB Purpose: Organizations evolve from using and governing data internally towards the exchange of data in multi-organizational data ecosystems. The purpose of this research is to determine a business model framework for actors operating in and/or entering a data ecosystem. Methodology: To determine a business model framework in data ecosystems. an analysis was made based on how the research fields of “business models”, “data governance”, “data ecosystems”, “data sharing”, “business ecosystem” complement each other. A business model framework was created, which was applied to three use case studies in the field of Smart Cities and Urban Digital Twins: The Helsinki Digital Twin, the Rotterdam Digital Twin, and the Smart Retail Dashboard in Flanders. Findings: The business model of actors in a data ecosystem is determined by value and control factors. Value is determined by the capability to create value through the exchange of data in the ecosystem, and to capture value through revenue (sharing) models and cost (sharing) models. Control is determined by ecosystem control. Governance models on the ecosystem level are required to enable the collaboration and to ensure trust to allow for the willingness to share data. Additionally, data governance on an ecosystem level is required, enabling the data exchange between the actors. Research Limitations: The model was applied to three use cases in Smart Cities and Urban Digital Twins. Consequently, the data ecosystems concern a high presence of public actors, yet also includes private companies. The applicability needs to be identified in other sectors in further research. Additionally, as the scope of the study was on business models, data governance, data-sharing and data ecosystems, abstraction was made of fields of study beyond these topics. Value and practical implications: The Data Ecosystem Business Model framework can serve as a guideline for organizations entering a data ecosystem, as well as for actors aiming to establish novel data ecosystems. Additionally, the framework can serve as a high-level overview for further research into the field of business models in data ecosystems.   https://journals.aau.dk/index.php/JOBM/article/view/6946
spellingShingle Ruben D'Hauwers
Nils Walravens
Pieter Ballon
Data Ecosystem Business Models
Journal of Business Models
title Data Ecosystem Business Models
title_full Data Ecosystem Business Models
title_fullStr Data Ecosystem Business Models
title_full_unstemmed Data Ecosystem Business Models
title_short Data Ecosystem Business Models
title_sort data ecosystem business models
url https://journals.aau.dk/index.php/JOBM/article/view/6946
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