=Paper=
{{Paper
|id=Vol-3931/panel1
|storemode=property
|title=Challenges to Enforce Data Quality in Data Spaces
|pdfUrl=https://ceur-ws.org/Vol-3931/panel1.pdf
|volume=Vol-3931
|authors=Claudia Ayala,Besim Bilalli,Cristina Gómez,Jose-Norberto Mazón,Oscar Romero
|dblpUrl=https://dblp.org/rec/conf/dolap/AyalaB0M025
}}
==Challenges to Enforce Data Quality in Data Spaces==
https://ceur-ws.org/Vol-3931/panel1.pdf
Challenges to Enforce Data Quality in Data Spaces
Claudia P. Ayala1 , Besim Bilalli1 , Cristina Gómez1 , Jose-Norberto Mazón2,∗ and Oscar Romero1
1
Universitat Politècnica de Catalunya, BarcelonaTech
2
Universitat d’Alacant / Universidad de Alicante
Abstract
Data Spaces must preserve sovereignty and privacy while ensuring FAIR (Findable, Accessible, Interoperable and Reusable) principles.
To do so, policy-based strategies have to be developed in order to describe the agreements reached in the Data Space. In this context,
two open questions arise: how to define the right Data Space policies, as well as, how to enforce (and monitor) them. Despite the
efforts towards defining and enforcing data access and usage policies, there is no solution to operationalize the enforcement of those
considering data quality dimensions. However, data quality is becoming a hot topic due to the surge of federated learning and alternative
analytical techniques, which require all providers to guarantee a data quality threshold in order to learn robust models. Currently, we
have means to describe policies related to data quality rules (e.g., by combining standards such as ODRL and standard vocabularies) but
we are missing means to elicit these policies from data providers and enforce them while preserving the data sovereignty. In this paper,
we discuss the challenges and open questions that must be addressed in order to operationalize (and eventually, automate) data quality
in Data Spaces, which span from requirements elicitation to data validation.
Keywords
Data Spaces, Data Quality, Data Validation, Federated Data Management, Data Sharing
1. Introduction DQ) at the Data Space federated layer (i.e., at the federated
-unique- view of the data ecosystem) can be enforced at
Data Spaces are federated ecosystems in which data the providers’ data assets regardless of their heterogeneity
providers and consumers share data while preserving data and preserving data ownership and privacy. Note that this
sovereignty and privacy. Currently, the Data Mesh archi- problem has been easily tackled in centralized environments
tecture [1] is at the core of current technological solutions, by having a central authority extracting, transforming and
since it provides a domain-decentralized paradigm that suits preparing data for analysis. However, this is not possible in
the Data Space requirements [2]. Relevantly, the Data Mesh settings where data is not meant to be shared raw. For ex-
defines the Data Product concept, which provides a product- ample, the minimum number of instances and the variances
oriented view of the providers’ data assets. In short, the data of key attributes might be set as DQ criteria for all data
product is a node that encapsulates three structural compo- providers and should be automatically and locally validated
nents required to function: code for enforcing policies (i.e., by executing a software service (specific for the provider
the Data Space agreements), data (and its metadata) and infrastructure) provided by the Data Space services catalog.
infrastructure [3]. By definition, the providers’ data assets The result of the service execution should be communicated
can be heterogeneous both in the infrastructure used and to the Data Space. To our knowledge, there is no architec-
the data provided (in format and semantics). ture, framework or solution tackling this problem, despite
Behind the idea of Data Spaces is the objective of extract- the myriad of standards and definitions blooming around
ing value from data sharing. This can be achieved in many the Data Space concept (e.g., [7, 8]).
ways, but data analysis arises as prominent means to achieve We focus on how to validate DQ agreements in the Data
so, either by means of descriptive analysis (e.g., dashboard- Space and discuss the open challenges to make DQ happen
ing and OLAP) or predictive analysis (e.g., learning models). in Data Spaces to enact trustworthy federated learning.
However, how to achieve data analysis in federated envi-
ronments is an open challenge, and federated learning [4]
is currently the most widespread privacy-aware data analy- 2. Challenges and Vision
sis technique. Many efforts have been devoted to develop
robust federated learning but little attention has been paid Data Spaces require a governance model for specifying DQ
to the role of data. Yet, the impact of the data quality (DQ) agreements that stakeholders must adhere to in order to par-
from each provider on federated models learnt is huge [5, 6]. ticipate. Importantly, this governance model must also spec-
One of the biggest open problems in Data Spaces not ify DQ needs agreed among data consumers and providers
properly tackled is how the agreements reached (e.g., on when developing specific uses cases. Therefore, our view
is that the governance model for Data Spaces should dis-
DOLAP 2025: 27th International Workshop on Design, Optimization, Lan- tinguish two levels: 1) a Data Space level for agreements
guages and Analytical Processing of Big Data, co-located with EDBT/ICDT among stakeholders of the Data Space authority from data
2025, March 25, 2025, Barcelona, Spain
∗
Corresponding author.
regulations and strategic issues, and 2) a use case level for
Envelope-Open claudia.ayala@upc.edu (C. P. Ayala); besim.bilalli@upc.edu agreements among data providers and consumers to build
(B. Bilalli); cristina.gomez@upc.edu (C. Gómez); jnmazon@ua.es specific Data Products. Based on this view and to facilitate
(J. Mazón); oscar.romero@upc.edu (O. Romero) the discussion, we propose a visionary framework with a
GLOBE https://futur.upc.edu/ClaudiaPatriciaAyalaMartinez (C. P. Ayala); process for the Data Space and use case levels (see Fig. 1).
https://futur.upc.edu/BesimBilalli (B. Bilalli);
https://futur.upc.edu/CristinaGomezSeoane (C. Gómez);
Our framework follows the Open Data Product specifica-
https://s.ua.es/_MuH (J. Mazón); tion [9], thus splitting each process into two parts: one
https://futur.upc.edu/OscarRomeroMoral (O. Romero) declarative, at a higher-level of abstraction specifying what
Orcid 0000-0002-6262-3698 (C. P. Ayala); 0000-0002-0575-2389 (B. Bilalli); (analysis phase), and another one at a lower-level specifying
0000-0002-3872-0439 (C. Gómez); 0000-0001-7924-0880 (J. Mazón); how (design and implementation phases). The declarative
0000-0001-6350-8328 (O. Romero)
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License part defines the DQ dimensions and intended level. The ex-
Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Figure 1: Visionary framework for considering DQ requirements in Data Spaces.
ecutable part contains the machine-readable “as code” rules, We propose to use a rule language with well-defined seman-
provided as a service, to validate DQ dimensions. Next, we tics (e.g., ODRL), to formalize DQ rules. Several challenges
describe both processes and their main challenges. need to be tackled when performing this transformation: 1)
DQ Requirements Engineering for Data Spaces. the identification of relevant and suitable stakeholders with
Requirements engineering (RE) for complex systems in the specific knowledge for performing this activity in both
open and dynamic environments that extend beyond a sin- levels; 2) the definition of specific catalogues with reusable
gle organization is widely recognized as a challenging en- transformation patterns for translating DQ requirements
deavor [10, 11]. This is particularly true in the context of into rules, preserving their semantics; 3) the definition of the
Data Spaces, where the elicitation and management of re- artifacts needed (e.g., specialized metamodels or new ODRL
quirements must reconcile diverse perspectives, including profiles), for automating the extraction and customization
the strategic business vision, governance, compliance with of DQ rules to the specific domain and level.
laws and regulations, infrastructure, scalability demands, Implementation available as a Service of DQ Rules.
and DQ considerations. Our visionary framework proposes The inherent heterogeneity of providers in the context
applying RE practices to elicit, specify, and manage the Data of Data Spaces renders the process of translating formal
Space requirements. We advocate for the development and DQ rules into executable services a significant challenge.
use of a Catalogue of DQ Requirements at two levels: the The main goal of this activity is to avoid building and main-
Data Space level and the use case level. These catalogs pro- taining custom solutions that are tightly coupled to specific
mote knowledge sharing and requirements reuse, building a execution environments or platforms. To address this, we
robust repository of experiences and best practices. The pro- propose an agnostic solution that leverages best practices
posed process is aimed to: 1) Ensure a common understand- from software engineering, such as containerized solutions,
ing of DQ dimensions by considering established standards; ensuring portability, scalability, and interoperability. How-
2) Facilitate the elicitation of diverse DQ requirements from ever, the intrinsic characteristics of Data Spaces introduce
diverse stakeholders to enable effective data sharing; 3) Sup- several challenges that must be addressed: 1) dealing with
port the structured specification and management of DQ heterogeneity at the infrastructure level by abstracting the
requirements to ensure compliance and alignment between differences while ensuring consistent performance and secu-
the Data Space and use case levels for their subsequent oper- rity across environments; 2) allowing for dynamic and feder-
ationalization; and 4) Address trade-offs between conflicting ated execution across multiple distributed nodes, ensuring
DQ requirements. This approach aims to bridge the gap real-time validation without requiring data centralization.
between diverse stakeholder perspectives and the technical As conclusion, there is a need for further research to
requirements for robust DQ management in Data Spaces. enact DQ in Data Spaces, a must for qualitative federated
Extraction and Customization of DQ Rules. The data analysis. In this sense, we have discussed a visionary
complexity of DQ requirements and their textual or semi- framework, its main phases and challenges to be tackled.
structured formalization make their direct operationaliza-
tion challenging. With the aim of making DQ requirements
executable in an operational environment, our visionary Acknowledgments
framework proposes to transform, in a semi-automated way
This work has been partially supported by the EU-
and using specific catalogues for supporting this transfor-
HORIZON program under GA.101135513 (CYCLOPS) and
mation, DQ requirements (at Data Space and use case levels)
by CIAICO/2022/019 project from Generalitat Valenciana.
into formalized DQ rules that may be easily implemented.
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