mathematics
Article
Techniques to Improve B2B Data Governance Using
FAIR Principles
Cristina Georgiana Calancea * and Lenut
,
a Alboaie *
Citation: Calancea, C.G.; Alboaie, L.
Techniques to Improve B2B Data
Governance Using FAIR Principles.
Mathematics 2021, 9, 1059. https://
doi.org/10.3390/math9091059
Academic Editors:
Octavian Dospinescu and Juan
Jose García-Machado
Received: 30 March 2021
Accepted: 7 May 2021
Published: 9 May 2021
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4.0/).
Faculty of Computer Science, Alexandru Ioan Cuza University, 6600 Ias
,
i, Romania
* Correspondence: gcalancea@info.uaic.ro (C.G.C.); adria@info.uaic.ro (L.A.); Tel.: +40-720-711-265 (C.G.C.)
Abstract: Sharing data along the economic supply/demand chain represents a catalyst to improve
the performance of a digitized business sector. In this context, designing automatic mechanisms for
structured data exchange, that should also ensure the proper development of B2B processes in a
regulated environment, becomes a necessity. Even though the data format used for sharing can be
modeled using the open methodology, we propose the use of FAIR principles to additionally offer
business entities a way to define commonly agreed upon supply, access and ownership procedures.
As an approach to manage the FAIR modelled metadata, we propose a series of methodologies to
follow. They were integrated in a data marketplace platform, which we developed to ensure they
are properly applied. For its design, we modelled a decentralized architecture based on our own
blockchain mechanisms. In our proposal, each business entity can host and structure its metadata
in catalog, dataset and distribution assets. In order to offer businesses full control over the data
supplied through our system, we designed and implemented a sharing mechanism based on access
policies defined by the business entity directly in our data marketplace platform. In the proposed
approach, metadata-based assets sharing can be done between two or multiple businesses, which
will be able to manually access the data in the management interface and programmatically through
an authorized data point. Business specific transactions proposed to modify the semantic model are
validated using our own blockchain based technologies. As a result, security and integrity of the
FAIR data in the collaboration process is ensured. From an architectural point of view, the lack of a
central authority to manage the vehiculated data ensures businesses have full control of the terms
and conditions under which their data is used.
Keywords: B2B data governance mechanisms; methodologies to model and manage FAIR data; de-
centralized B2B data marketplace architecture; metadata as blockchain assets; transactions controlled
semantic model; access policy based data sharing
1. Cross-Sector B2B Data Sharing and Its Impact on the Companies Ecosystem
For a long time, companies strictly relied on traditional Business-to-Business (B2B)
transactions to evolve. These transactions refer to purchasing and selling physical raw
goods, with the goal to complete the product manufacture process [1]. Usually, the obtained
product represents the base of Business-to-Consumer (B2C) transactions. In order to ensure
the success of B2C transactions, all companies involved in the B2B collaboration chain need
to have an overview of the raw good demand and their supply capacity in comparison
to similar businesses [2]. As a result, data sharing between companies has become a key
aspect in the process of growing business opportunities in the past years. B2B data sharing
refers to “making data available to or accessing data from other companies for business
purposes” [3] either for free or by making a payment to the data holder. The business
owner has the option to choose who to share the data with and under which conditions.
In [4], the author emphasizes the need to share data between well-established com-
panies in order to encourage innovation. Data sharing comes as a prerequisite since one
company alone cannot envision the complete perspective of the economic supply/demand
Mathematics 2021, 9, 1059. https://doi.org/10.3390/math9091059 https://www.mdpi.com/journal/mathematics