Digital Transformation and Location Data Interoperability Skills for Small and Medium Enterprises

Abstract

In the dynamic landscape of digital transformation, data interoperability—particularly for location data—is a key enabler of operational efficiency, innovation, and collaboration for Small and Medium Enterprises (SMEs). Despite their strategic importance, SMEs face significant challenges in integrating and utilizing location data, which puts them at a disadvantage in the increasingly digital global market. As part of the European DIS4SME project, this study proposes a methodology to address these challenges, characterized by the rigorous development of a training curriculum aimed at upskilling and retraining SME owners and employees. The curriculum emphasizes practical learning through real business case studies and is aligned with European policies such as the INSPIRE Directive and the European Data Strategy. Accordingly, ten courses were designed, forming a modular and hierarchical curriculum that addresses SMEs’ diverse needs. Initial feedback from the first managers’ pilot implementation suggests that the structured training program effectively equips managers with strategic decision-making skills to address location data interoperability challenges.

1. Introduction

Digitalization is instrumental in driving productivity growth, which results in increased economic efficiency, higher competitiveness, and job creation. It fosters innovation, reduces operational costs for businesses, and enables access to new markets [1,2].

Despite its potential benefits, Small and Medium Enterprises (SMEs) often encounter obstacles when transitioning to digital platforms. Reports from the Organization for Economic Cooperation and Development (OECD) [3] underscore that smaller SMEs, particularly those with 10–49 employees, face widening gaps in digital adoption compared to larger counterparts. This trend is more pronounced in several countries (e.g., Greece, Hungary, Poland, Portugal, Turkey) where the proportion of employees with access to connected computers in small firms remains below 40%, while larger enterprises in leading countries (e.g., Denmark, Finland, Sweden) have made significant strides in digital adoption, with rates reaching approximately 80%.

Such disparities contribute to growing inequalities among SMEs and raise concerns that, without effective intervention, the gap could widen further over time. SMEs often experience knowledge gaps regarding emerging policies and technical trends in data-driven digital transformation (DT) [4], as well as challenges in fully leveraging their data assets and sharing them with collaborating companies (e.g., customers or suppliers) [5].

In this context, the concept of interoperability becomes crucial. Interoperability can be understood as the “ability of two (or more) systems or components to exchange information” [6], as well as the “ability of interaction between enterprises” [7]. Interoperability problems can significantly impact business network performance and lead to substantial economic losses across various industries [5]. Studies have shown that inadequate system interoperability results in billions of dollars in inefficiencies and wasted resources [8,9]. Improving interoperability can generate major cost savings and boost productivity across these industries [5].

To effectively navigate the ever-changing landscape of DT and fully address these challenges, companies, particularly SMEs, must acquire new competencies and skills that align with their evolving business needs and practices [10].

In line with the ongoing efforts of the European-funded DIS4SME (Data Interoperability Skills for SMEs) (https://www.dis4sme.eu/ (accessed on 16 January 2025)) project, this study aims to address the digital challenges faced by SMEs, particularly in the area of (location) data interoperability, by presenting the methodological approach used to implement training practices to improve SMEs’ skills in this area.

Positioned within the broader context of the impact of DT—with its associated opportunities and challenges—on SMEs, this paper offers a concise overview of the European data legislative landscape, tracing its evolution and evaluating its implications for DT. Additionally, it explores the role of Open Data (OD) as a catalyst for advancing innovation and promoting economic growth [11], along with the Common European Data Spaces initiative [12] as an innovative solution to facilitate data access and sharing.

Referring to the European Interoperability Framework (EIF) [13], which distinguishes four levels of interoperability—legal, organizational, semantic, and technical—this study overviews challenges and solutions associated with each of these levels. Additionally, it places special emphasis on the characterization of location data interoperability, viewed as a critical factor in unlocking the vast potential of geospatial data and its impact on citizens and businesses [14]. This discussion is contextualized by European initiatives such as the High-Value Datasets [15] and ELISE (European Location Interoperability Solutions for e-Government) (https://ec.europa.eu/isa2/actions/elise_en (accessed on 16 January 2025)) action, along with standardization efforts undertaken by international organizations such as the Open Geospatial Consortium (OGC) (https://www.ogc.org/ (accessed on 16 January 2025)).

Building on the work conducted within the DIS4SME project, this paper presents a methodological proposal to develop a customized training curriculum aimed at enhancing SMEs’ data interoperability skills, specifically focusing on the reskilling and upskilling of SME owners, managers, and technicians.

Furthermore, as a practical contribution to facilitate the replicability of this approach in other SME-oriented training contexts, this paper outlines the application of the proposed methodology. This process is based on insights gained from stakeholder and expert interviews and questionnaires, the definition of ad hoc business cases, the identification of different sets of learning outcomes, and the design and implementation of tailored, structured courses for both face-to-face and individual training activities.

This paper is structured as follows: Section 2 analyzes the current state of the European legislative landscape on data, with a particular focus on Open Data. Section 3 provides an overview of challenges and solutions related to (location) data interoperability. Section 4 introduces the DIS4SME project, detailing the proposed methodological approach for designing a comprehensive training curriculum for SMEs on geographic data interoperability. Section 5 presents the outcomes, including the courses and lessons currently implemented, while Section 6 discusses the advantages and limitations. Finally, Section 7 concludes the paper.

2. Digital Transformation Background

2.1. Legislative Landscape for Data

The European Digital Strategy, backed by the European Data Strategy [16], seeks to bolster EU competitiveness and data sovereignty. Recognizing the exponential growth of global data production, the European Data Strategy focuses on creating a unified data market to position the EU as a leader in the data economy.

Key elements like data spaces and high-value datasets, emphasized in the European Data Strategy and the Open Data Directive [17], are crucial drivers of the data economy, influencing society, businesses, and SMEs. The term “data space” encapsulates interconnected concepts initially devised for data management [18]. It has become central to managing data within a global ecosystem across diverse domains and regions. The Big Data Value Association (https://bdva.eu/ (accessed on 16 January 2025)) defines data spaces as ecosystems integrating data models, datasets, ontologies, data-sharing agreements, and management services. These are supported by governance, social interactions, and business processes, forming a framework aligned with a data engineering approach. This approach aims to optimize data storage, exchange, and the creation and dissemination of new knowledge [19].

The EC has launched the Common European Data Spaces initiative, aiming to establish a framework for data sharing across specific sectors. The European Data Strategy plans to roll out European data spaces across 14 sectors, each designed to adhere to common principles while focusing on sector-specific needs. According to the 2024 Commission Staff Working Document on Common European Data Spaces, “a common European data space brings together relevant data infrastructures and governance frameworks to facilitate data pooling, access, and sharing” [12]. The EC defines “common European data spaces” as EU-funded data spaces, each typically addressing a sector’s needs (e.g., agriculture, green deal). They are termed “common” because they are open to all EU participants, forming a unified “common European data space” [20]. The EU data portal (https://data.europa.eu/en/publications/datastories/when-open-data-meets-data-spaces (accessed on 16 January 2025)) underscores the importance of Open Data (OD) in data spaces, highlighting its role across all spaces. The rise of data spaces introduces new design principles for innovative reuse scenarios, enabling the generation of new OD from existing sources or through data processing.

The Open Data Directive on the reuse of public sector information requires datasets to be released in free and open formats [17]. It defines high-value datasets as those that provide significant societal, environmental, and economic benefits, especially due to their potential to create value-added services, applications, and jobs. These datasets are categorized into six thematic areas—geospatial, Earth observation, meteorological, statistics, companies, and mobility—with plans for expansion based on technological and market changes. High-value datasets must meet technical and legal requirements, including adopting OD licenses, ensuring updates and maintenance, providing public documentation, and ensuring machine readability. They must also be downloadable for free, available via APIs, and include detailed metadata documentation [17].

To support data sharing and industrial development, the Data Governance Act [21] and the Data Act [22] have been introduced. The Data Governance Act, effective from September 2023, aims to “increase trust in data sharing, strengthen mechanisms to increase data availability, and overcome technical obstacles to data reuse”. It establishes processes for data sharing among businesses, individuals, and the public sector. The Data Act, which came into force in January 2024, outlines the entities that can generate value from data and the conditions under which they can do so. Together, these acts facilitate secure data access, promoting its use across sectors.

The Inspire Directive (2007/2/EC) establishes a legal framework for creating a spatial data infrastructure in Europe to enhance the availability, quality, and accessibility of geospatial data. This is critical for effective environmental policies as it supports the exchange and interoperability of spatial data between EU member states. Inspire mandates that geospatial data be produced and shared in interoperable formats and accessible via web services, supporting applications in environmental monitoring, resource management, and urban planning [23]. Additionally, it promotes the creation of standard metadata to describe the data’s characteristics and usage, ensuring their reliability in scientific and administrative applications [24]. The implementation of Inspire has been essential for fostering cross-border cooperation and enhancing the ability of member states to address global environmental issues [25].

The Open Data Directive requires that geospatial, Earth observation, and environmental data comply with Inspire standards. Together, the Open Data Directive and Inspire promote the accessibility and reuse of public geospatial data, contributing to an open and collaborative geographical data environment in the EU.

2.2. Open Data Evolution

The Open Data (OD) movement has evolved significantly, transitioning from a focus on transparency and accessibility to a comprehensive framework for information sharing and collaboration across sectors [11]. Governments and public institutions have driven this evolution by releasing thousands of datasets via Open Government Data (OGD) portals like data.gov.uk and data.gov, fostering economic and social value creation [26].

Initially, OGD implementations lacked standardization, but the mid-2010s saw the development of principles, standards, and guidelines to enhance OD governance [27]. Legislative support and international collaboration reinforced this evolution, emphasizing data quality, interoperability, and accessibility for impactful reuse [27,28].

A notable example is the European data portal (https://data.europa.eu (accessed on 16 January 2025)), which consolidates OD from diverse sources, leveraging linked data principles [29] and metadata standards like DCAT-AP. The portal metadata are structured as Resource Description Framework (RDF) triples, enabling efficient access through SPARQL queries via both a graphical interface and endpoints [30].

To monitor the progress and maturity of international OGD initiatives, several indices have been introduced by organizations such as the OECD (OURdata Index) [31], the United Nations (OGDI) [32], and the World Wide Web Foundation (OD Barometer). These tools are instrumental in benchmarking countries’ OD performance, offering insights into global trends and promoting the exchange of best practices in OGD implementation [33].

Despite these efforts, challenges persist. Limited use of OGD [34,35] and the underutilization of many datasets [36,37] highlight the need for enhanced openness, interoperability, and usability [38].

Several factors hinder the use and sharing of OGD. Data and metadata quality are significant obstacles, with inadequate metadata impeding data search and retrieval [34,37,39,40]. Low levels of Open Data literacy or the ability to extract insights from data further limit reuse, emphasizing the need for lifelong learning initiatives by public administrations [41]. Misalignment between users’ needs and the datasets provided by administrations also restricts effective utilization [37,42,43,44]. Additionally, users encounter challenges in integrating datasets from various administrations, which hampers the creation of new informational assets [30].

Data interoperability challenges [30,45] significantly contribute to the underutilization of Open Data (OD). These challenges can be mitigated through the broader framework of data spaces. The International Data Spaces Association’s position paper [46] presents a roadmap for building soft infrastructures to avoid data economy fragmentation and establish a reliable OD ecosystem. Key strategies include ensuring semantic interoperability via controlled vocabularies and well-documented data models, enhancing reliability through data provenance mechanisms and facilitating data sharing with robust metadata and discovery protocols.

3. Data Interoperability

Interoperability refers to the capacity of different systems, devices, or applications to exchange and interpret data or perform tasks without manual intervention or substantial adjustments. It supports collaboration, communication, and data sharing across diverse platforms, technologies, and organizations. The scholarly literature offers various definitions for capturing its multiple dimensions and impacts [6,7,47].

The European Interoperability Framework (EIF), introduced in 2017 under the EC’s ISA and ISA² programs [48], describes interoperability as “the ability of organizations to interact toward mutually beneficial goals, involving the sharing of information and knowledge between these organizations, through the business processes they support and through the exchange of data between their ICT systems” [13].

3.1. Interoperability Layers

The EIF presents an interoperability model that applies to all digital public services, forming a cornerstone of the “interoperability-by-design paradigm”. This model consists of four layers—legal, organizational, semantic, and technical—ensuring interoperability across diverse dimensions [13].

Legal interoperability focuses on enabling organizations operating under varying legal frameworks, policies, and strategies to collaborate effectively [13]. The multilingual nature of the EU, with 23 official languages and additional indigenous ones, complicates this process, requiring precise drafting and translation of legal documents to maintain coherence, as highlighted by Santosuosso et al. [49]. Alvarez et al. further noted that “even a region defined by its unparalleled advances in legal interoperability has difficulties translating them to the digital realm” [50]. In the realm of OGD, Morando suggests that legal challenges can be mitigated through the use of standardized copyright licenses, such as Creative Commons and Open Data Commons, which have become common practice [45]. European governments also contribute with bespoke OD licenses, including the UK’s Open Government Licenses, France’s License Ouverte, and Italy’s OD License, aiding the harmonization of legal frameworks.

Organizational interoperability involves aligning the business processes, responsibilities, and expectations of public administrations to achieve mutually beneficial goals [13]. Guijarro describes it as the cooperation between various levels within public administration [51]. However, Kubicek et al. identified insufficient collaboration among public agencies as a significant challenge, primarily due to the complex organizational structure of the public sector, comprising various subsectors and hierarchical levels (local, regional/state, national). Additional barriers include political influences from elected officials and a lack of financial incentives [52]. In the context of SMEs, organizational interoperability is particularly challenging. As noted by Weichhart et al., “organizational interoperability”, defined as supporting the integration of business processes among companies across multiple supply chains, is “a near impossible task”. The authors propose a framework utilizing ”performance indicators” to facilitate cooperation among companies [53].

Semantic interoperability ensures the accurate preservation and understanding of the format and meaning of data exchanged between parties, guaranteeing that “what is sent is what is understood” [13]. According to the EIF, it operates at both the semantic and syntactic levels, especially concerning metadata management and data harmonization. Technologies such as linked data, knowledge graphs, ontologies, and vocabularies [54,55,56,57] are central to enabling machines and humans to interpret data in a standardized way [58]. The publication of dataset metadata is crucial for facilitating semantic interoperability by aiding the retrieval and integration of OGD. Syntax-level interoperability “refers to describing the exact format of the information to be exchanged in terms of grammar and format” [13], and it focuses on the data harmonization process to support the merging and integration of data based on structure or syntax [59,60].

Technical interoperability covers the infrastructure and applications enabling systems and services to interconnect, including interface specifications, data integration, secure communication protocols, and more [13]. Benson et al. [61] highlighted its context-independence and its agnostic nature regarding the meaning of exchanged information.

Various technologies enhance technical interoperability. TCP/IP separates communication link technologies from application-level communications, while HTTP and HTML isolate presentation aspects from storage and retrieval functions [62]. Open APIs foster integration across systems by enabling applications to share data, and common database standards like SQL allow consistent data access across databases. The AS4 messaging protocol supports secure and payload-agnostic exchanges of Business-to-Business documents between heterogeneous IT systems [63].

Challenges arise due to the heterogeneity of data exchange infrastructures and differing data-handling practices, complicating the establishment of cross-border connections among EU local databases [63].

3.2. Location Data Interoperability

While data interoperability is pertinent across various sectors, it holds particular significance within the DT context and, notably, in all location-related business operations. By location data, we refer to “any piece of information that has a direct or indirect reference to a specific location or geographical area, such as an address, a postcode, a building, or a census area. This term can be interchanged with spatial, geospatial, place, or geographic information” [64]. It is commonly asserted that between 50% and 80% of all public sector data have a geographic component. Consequently, the projected impact of enhanced location interoperability varies from EUR 272 billion to EUR 500 billion, contingent on its prevalence and depending on the specific scenario under consideration [14].

The significance of geospatial data is also underscored by its inclusion in both the common European data spaces and high-value datasets. The agricultural data space and the green deal data space are notable for their focus on geospatial data within the former initiative. As for the latter, the Commission is actively evaluating existing EU regulations on sharing environmental geospatial data, capitalizing on opportunities arising from legislative instruments aligned with the European Data Strategy [12]. Geospatial data play a particularly crucial role in four out of the six thematic categories of high-value datasets. These include the ’Geospatial’, ’Earth observation and environment’, ’Meteorological’, and ’Mobility’ categories. However, it is also a component of datasets related to the ’Statistics’ and ’Companies and company ownership’ thematic areas, as per the NUTS (Nomenclature of Territorial Units for Statistics) classification [65].

Given the centrality and widespread use of geospatial data, ensuring their interoperability is paramount to unlocking their full potential. According to Boguslawski et al., location interoperability “is the ability of organizations, systems, and devices to exchange and make use of location data with a coherent and consistent approach” [66].

3.2.1. Interoperability Issues in Open Government Data Integration

The integration of OGD datasets presents a significant opportunity for enhancing data analysis and decision-making processes. By combining datasets from different domains, researchers can uncover new insights and foster innovation [30]. However, integrating datasets often involves overcoming various interoperability challenges, particularly in location data representation. Some examples of these challenges are detailed below and in

Table 1. Use cases illustrating challenges associated with geospatial data interoperability.

Use Case	Challenge	Datasets/Platforms Involved	Interoperability Required
Differences in Geographical Projections: Public Transport Data	For example, open public transport data from Madrid and London use different geographical projections. To combine routes and stops for cross-border or comparative analysis, it is necessary to transform the coordinates.	⯀ Madrid uses the ETRS89 (EPSG:25830) projection for transport routes and timetables. ⯀ London uses WGS84 (EPSG:4326) for its transport data	The transformation of projections (ETRS89 to WGS84) and unification of GTFS format for comparative analysis or cross-border integration
Semantic Inconsistency in Open Air Quality Data	Air quality data at the European and municipal levels use different attributes and semantic structures, making it difficult to combine them. For example, air quality data from the European Environment Agency (EEA) and municipal data from Berlin differ in attributes and structure.	⯀ European data provided by the EEA are harmonized in CSV/JSON with common standards. ⯀ Local data, such as from Berlin, provide more detailed information in JSON/CSV formats but with specific non-standardized attributes.	Semantic harmonization through standardized schemes, such as the Air Quality Index (AQI), and temporal unification between local and regional data.
Integration of Cadastral and Satellite Data in Digital Agriculture	Integrating satellite images with cadastral data requires overcoming differences in spatial resolution and format. As a use case, Copernicus Sentinel-2 images (GeoTIFF) and Spanish cadastral data (Shapefile/GML) have different spatial resolutions.	⯀ Sentinel-2 (Copernicus) images have a resolution of 10–20 m in GeoTIFF. ⯀ Spanish Cadastral data provide detailed parcel information in Shapefile and GML.	The transformation of formats (GeoTIFF to vectors) and adjustment of spatial resolutions to combine Cadastral and satellite information.
Flood Management: Satellite Data and Local Maps	Combining satellite imagery with local risk maps presents technical difficulties due to differences in format and resolution. For example, Copernicus EMS provides maps in GeoTIFF, while flood risk maps in the Netherlands are available in GeoJSON/Shapefile. This difference requires transformation and analysis using GIS tools.	⯀ Copernicus EMS provides imagery in GeoTIFF for emergency situations. ⯀ Flood risk maps in the Netherlands are available in GeoJSON and Shapefile.	Conversion between GeoTIFF and GeoJSON, spatial alignment and analysis with GIS tools for efficient monitoring.
Integration of Urban Mobility Data between Cities	Transport data from different cities, such as Paris and Amsterdam, use different attributes and formats, making integrated comparisons and analyses difficult. This creates semantic and structural problems.	⯀ Paris publishes data in GTFS and GeoJSON. ⯀ Amsterdam provides route and stop information in GeoJSON and CSV.	The standardization of formats with GTFS and semantic alignment of attributes such as routes, names, and timetables.
IoT Sensors and Satellite Data in Agriculture	Integrating IoT sensor data (such as humidity and temperature) with satellite imagery requires compatibility of formats and temporal resolutions. One use case is that IoT moisture and temperature sensors (ThingSpeak, JSON) need to be combined with Sentinel-2 imagery (GeoTIFF). There are differences in format and time synchronization that make analysis difficult.	⯀ ThingSpeak provides real-time data from IoT sensors in JSON format. ⯀ Copernicus Sentinel-2 provides high-resolution imagery in GeoTIFF.	Format harmonization (JSON to GeoTIFF), time synchronization, and combination with GIS tools for agricultural analysis.
Copernicus Satellite Data Association	The ESA’s Copernicus program provides high-resolution imagery, but its integration with satellite imagery (Sentinel-2) has standardized metadata, while drone data, collected by local or private entities, often lack consistent metadata on sensor, resolution, or coordinate systems. This creates problems in critical applications such as natural disaster response, where incomplete or inaccurate data can lead to erroneous resource allocation decisions.		Variations in metadata quality can also hinder the effective use of data, as users may not fully understand the origin of the data, the purpose of the data, or the nature of the information. Without harmonized metadata and quality control, geospatial analyses can yield misleading results.
Incompatibility of Systems and Protocols in Urban Mapping Projects	The lack of interoperability delays integration processes and requires costly manual tasks to convert data between formats. In urban mapping projects, different administrations and contractors use different GIS tools. When exchanging data, proprietary systems do not fully support open formats without additional conversion processes.	For example, one organization uses Esri ArcGIS with proprietary formats (.mxd, .gdb), while another uses open source software such as QGIS with standard formats (GeoJSON, Shapefile).	The implementation of open standards such as the OGC: - WMS and WFS for spatial data exchange. - The use of GeoPackage as a standard and compatible format between different GIS tools. The configuration of RESTful APIs compatible with OGC standards and the use of middleware to automate data conversion.

.

Consider the case of two OGD datasets both published by the New York City data portal: the “NYC Parking Violations Issued—Fiscal Year 2024” (https://data.cityofnewyork.us/City-Government/Parking-Violations-Issued-Fiscal-Year-2024/pvqr-7yc4/data (accessed on 16 January 2025)); and the “NYC 2015 Street Tree Census—Tree Data” (https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/uvpi-gqnh/data (accessed on 16 January 2025)). Both datasets offer valuable information, but they differ significantly in how they represent location data, which poses a barrier to seamless integration (see Figure 1). The former dataset includes records of parking violations, detailing the violation code, issue date, and location. The location data in this dataset are represented using textual addresses. On the other hand, the latter dataset provides information about street trees, including their species, condition, and precise location using latitude and longitude coordinates.

Let us assume the case of a researcher who aims to analyze the correlation between parking violations and the presence of street trees in specific areas of New York City. This analysis could reveal patterns such as whether areas with more greenery experience fewer parking violations, contributing to urban planning and environmental policy decisions.

Differences in the way location data are represented complicate their integration. The primary issue is data format incompatibility. The parking violation dataset uses textual addresses, which are less precise and require additional steps to convert to exact coordinates. In contrast, the street tree census dataset uses latitude and longitude coordinates, providing precise points on the map. Furthermore, there is a spatial resolution discrepancy between the two datasets. Textual addresses can be vague or cover larger areas, leading to potential mismatches when attempting to combine datasets. In contrast, latitude and longitude coordinates are precise and necessary for accurate spatial analysis, highlighting the need for the careful handling of location data to ensure successful integration.

To cope with this issue, the textual addresses from the parking violations data need to be geocoded to obtain corresponding latitude and longitude coordinates, aligning the location data formats of both datasets. Utilizing geocoding services such as Google Maps API, OpenStreetMap Nominatim, or other geocoding tools can convert textual descriptions into coordinates. However, after geocoding, it is crucial to clean and validate the geocoded data to handle any potential errors or ambiguities resulting from the geocoding process. Ensuring the accuracy and reliability of the geocoded parking violation data before merging it with the tree dataset is necessary to maintain the integrity of the integrated dataset. From this simple example, it is clear that overcoming obstacles to data integration requires both an understanding of the problem and the technical ability to solve it.

Table 1 presents a series of use cases illustrating key challenges associated with geospatial data interoperability, a critical factor for SMEs to fully leverage digital transformation opportunities. These cases reflect real-world scenarios that expose common technical issues, such as transforming geographic projections, integrating heterogeneous data, and achieving semantic harmonization. In addition to emphasizing the technical complexity of interoperability, these examples underscore the need to develop specific practical skills aligned with European standards and cutting-edge industry tools.

3.2.2. Supporting Actions

To sustain location interoperability by demonstrating how interoperability benefits can be better understood and quantified, the ISA² (Interoperability solutions for public administrations, businesses and citizens) program promoted the ELISE initiative. ELISE provides solutions for legal, policy, organizational, semantic, and technical interoperability to streamline digital interactions across borders or sectors. It aims to facilitate data reuse and improve efficiency in digital public services, especially those involving location information and derived insights. However, as Barker et al. [64] pointed out, all interoperability layers present persistent challenges for expanding the scope and reach of location-enabled public services, both domestically and internationally. Local and regional governments have cited interoperability gaps as major obstacles encountered when investing in and executing projects aimed at leveraging location data and technology to enhance public services. To tackle the issue of location interoperability, the authors envisage that collaboration between the EC and member states is essential in defining the evolution of spatial data infrastructures.

The Open Geospatial Consortium (OGC) has been crucial in the standardization and promotion of interoperability in the geospatial sector since its founding in 1994. It is an alliance of over 500 organizations, including companies, universities, government agencies, and other institutions. Standardization is essential to integrate and share geospatial data from multiple sources used by different applications and platforms. Without common standards, integrating these data would be complex and costly, limiting the effectiveness and adoption of geospatial technologies. The OGC has created and maintained a wide range of standards, such as the Geography Markup Language (GML), which provides an XML coding model for the transport and storage of geographic information; the Web Map Service (WMS), which allows the viewing of maps via the web; the Web Feature Service (WFS), a protocol for serving geographic features across the web supporting feature query and retrieval, and OGC APIs, which facilitate access to geospatial services and data through RESTful interfaces.

These standards ensure that geospatial data are accessible, reusable, and understandable, regardless of the system or technology used, promoting technical and semantic interoperability. Knowing these standards and regulations is crucial for making geospatial interoperability possible. Therefore, improving skills in geospatial interoperability is essential not only for SMEs in the sector but also for other sectors that will use geographic information.

Figure 2 illustrates some OGC supporting actions that address interoperability issues within the geospatial domain, operating at different layers. These are crucial to ensure that geospatial data can be integrated, exchanged, and used effectively across various platforms, promoting interoperability at the legal, organizational, semantic, and technical levels. The OGC has promoted GML, CityGML, and INSPIRE conceptual models to address challenges related to semantic interoperability. Vocabularies like OGC Rainbow and Codelist, as well as metadata profiles such as ISO 19115-3 (https://www.iso.org/standard/80874.html (accessed on 16 January 2025)) and the Data Catalog Vocabulary (DCAT), ensure that data retain their meaning and context when shared, allowing different systems to interpret the information consistently.

In addition, the OGC’s technical standards guarantee efficient communication and data exchange between systems. OGC APIs offer standardized RESTful interfaces for accessing geospatial services, while formats such as GML, KML, GeoPackage, and GeoJSON facilitate data exchange across diverse platforms. Protocols like API REST, WMS, and WFS further enhance data sharing, supporting a wide array of geospatial applications.

4. Methods

4.1. DIS4SME Project Outline

Launched in January 2023 and funded under the Digital Europe Programme, DIS4SME delivers specialized training courses focused on location data interoperability across multiple sectors. The project aligns with the Digital Europe Programme’s goals by enhancing SMEs’ competencies in data interoperability and fostering their active participation in the evolving digital landscape.

DIS4SME employs a comprehensive training approach that emphasizes both upskilling and reskilling SME owners, managers, and technicians or job seekers. The latest technological and policy trends are addressed, ensuring that participants acquire new skills relevant to current industry demands while refining existing ones and closing knowledge gaps. Job seekers are also welcome to enroll, giving them the chance to improve their competitiveness in the labor market.

The DIS4SME curriculum is structured around training actions focused on real-world scenarios where location data interoperability is essential. Each introduces fundamental concepts of data interoperability and then explores specific domain challenges and opportunities in greater depth. To accommodate the varied time availabilities of workers from different organizations, the program offers a series of short online lectures, supported by tutors available for assistance upon request. Additionally, training providers in five European countries organize in-person seminars and workshops.

Micro-credentials certifying the learning outcomes of short-term learning experiences are also implemented and assigned in line with the recommendations of the European Education Area (https://education.ec.europa.eu/education-levels/higher-education (accessed on 16 January 2025)). They enhance the visibility of learners’ achievements and assist employers in identifying specific competencies, fostering a more responsive education and labor market.

DIS4SME collaborates with SMEs and Digital Innovation Hubs (https://digital-strategy.ec.europa.eu/en/activities/edihs (accessed on 16 January 2025)) to create a dynamic learning environment that ensures engagement and relevance for all participants.

4.2. A Methodology for the DIS4SME Curriculum

At the core of the curriculum definition lies the concept of learning outcomes (LOs), which, in accordance with the European Qualification Framework (EQF), are defined as “statements regarding what a learner knows, understands, and is able to perform on completion of a learning process, which are defined in terms of knowledge, skills, responsibility, and autonomy” (https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017H0615(01)&from=EN (accessed on 16 January 2025)). The adoption of LOs marks a significant shift from traditional, teacher-focused methods to a learner-centered approach, emphasizing concrete achievements over instructional processes [67]. By serving as a common framework, LOs enhance the transparency and comparability of educational qualifications across systems. Explicitly defining LOs offers key advantages for diverse stakeholders. Students benefit from clear learning expectations and better support for self-guided study, while employers gain detailed insights into graduates’ competencies, improving recruitment decisions. In quality assurance, LOs act as benchmarks for evaluating educational programs, ensuring alignment between intended and achieved outcomes.

The training curriculum is tailored to meet the specific needs of SME stakeholders, addressing their roles, responsibilities, and unique skill sets, and is organized into a hierarchical structure with three levels (see Figure 3). The foundation consists of brief lectures, which are grouped into courses, and at the top are the training actions. For managers, the focus is placed on enhancing decision-making processes and overcoming business challenges. On the other hand, technicians are equipped with proficiency in GIS software and other technical tools relevant to their industry sectors.

The methodology used for developing the curriculum follows the workflow illustrated in Figure 4 and includes the following steps:

• Identification of Learning Needs. To develop a curriculum that responds to the specific learning needs of SMEs in the digital landscape, it is crucial to understand which are the gaps in their knowledge and skill sets. This could be achieved through interviews with geospatial interoperability experts from different industry sectors, online surveys of a broad user base within the geospatial community, and the analysis of the training already available online.
• Identification of Business Case Studies (BCSs). This method involves selecting BCSs that are highly relevant to the needs of SMEs undergoing digital transformation and addressing challenges related to data interoperability. This selection process draws on the data acquired from interviews, questionnaires, and assessment of the existing training offer. These BCSs will allow learners to apply their knowledge to real-world contexts, ensuring that the training is practical and directly relevant to workplace needs.
• Definition of Learning Outcomes (LOs). This method involves LOs tailored to each target profile, such as SME managers and technicians, by addressing their specific learning needs and aligning them with relevant BCSs. Introductory LOs cover fundamental data interoperability concepts applicable across various contexts, while case-specific LOs develop skills relevant to specific real-world applications. To aid in the development of subsequent learning materials, LOs should be formulated according to Bloom’s Taxonomy [68], facilitating their categorization into cognitive levels indicative of the depth of understanding or skills to be achieved.
• Curriculum Design. This task involves organizing learning outcomes (LOs) into a structured and logical sequence to create a coherent curriculum. The curriculum aims to progressively build on learners’ understanding of concepts and skills, ensuring a step-by-step development of knowledge and competencies. Each LO serves as a foundation for subsequent ones, facilitating a systematic and effective learning journey.
• Development of Training Material. This concerns the creation of various training materials to support the achievement of the defined LOs, including slides, e-learning modules, video tutorials, webinars, workshops, and hands-on exercises. These materials should be designed to be adaptable to different delivery modalities, such as remote learning, face-to-face sessions, and blended formats. The development process leverages the expertise of training providers and a thorough review of existing online resources. Additionally, training providers have the flexibility to localize materials by adapting them to the language, local standards, and legal requirements of the member states where the training will be implemented. The methodology also includes an iterative improvement process, incorporating feedback loops to continuously refine and adapt the courses.
• Training Material Improvement. The designed curriculum is subject to evaluation to ensure its effectiveness and ongoing relevance. An Editorial Board composed of experts in location interoperability is established to peer-review the developed training materials, guaranteeing their quality and adequacy.
• Implementation. The implementation task aims to establish effective mechanisms to monitor and support participants in the planned training activities. This includes organizational support such as user registration, user management, communications, and certification. Regarding certification, micro-credentials are utilized, based on rigorous assessments of the outcomes achieved through various types of tests, ensuring the recognition of actual competencies rather than mere participation. The design and issuance of certifications adhere to well-defined criteria. The evaluation of the training activities involves collecting feedback from participants, trainers, and stakeholders through online questionnaires and feedback forms. The collected data are analyzed to assess the impact, effectiveness, relevance, and sustainability of the training actions. Additionally, quality assurance frameworks, such as EQF and the European Credit Transfer and Accumulation System, are accounted for to evaluate the training’s impact and alignment with EU standards.

5. Result: Designing the DIS4SME Curriculum

5.1. Toward Defining Learning Outcomes

As an initial step, the implementation of the curriculum design process leads to the identification of the training needs of SMEs and gaps in current educational resources and expertise in location data interoperability. A detailed survey, completed by 31 August 2023, gathered 105 responses and examined the specific skills that SMEs require in this field. This survey revealed key challenges, such as compatibility between data formats and interoperability of tools and platforms. Although respondents were generally familiar with GIS tools and web service standards, there were notable gaps in competencies related to data conversion and transformation. Respondents expressed a strong preference for short, online courses and video tutorials to improve their skills.

Additionally, 24 expert interviews were conducted to complement the survey, providing more in-depth insights into SMEs’ training needs. These results have shaped the development of targeted training programs within DIS4SME, ensuring that the curriculum addresses the precise skill gaps and challenges identified.

To avoid reinventing the wheel, an analysis of existing training offers was conducted, identifying approximately 100 courses on location data interoperability. These courses were categorized into two main areas: those focused on GIS and SDI, such as those provided by the Geospatial Knowledge Base Training Platform (https://www.geo-train.eu/ (accessed on 16 January 2025)), and those centered on open data and digital technologies, offered by Location Innovation Academy (https://academy.ogc.org/ (accessed on 16 January 2025)). These offerings were further grouped into seven thematic clusters: SDI, Legal/Governance, Interoperability, Standards, Data Sharing and Access, Digital Transformation, and Knowledge Representation. This analysis helped map the existing training offerings and highlighted gaps that DIS4SME aims to fill with new courses and materials.

The results from the questionnaire and interviews [69] led to the identification of four key BCSs, each of them referring to a strategic context linked to the use of location data interoperability: Mobile Food Marketplace, Digital Agriculture, Mobility & Transport, and Urban Planning.

An iterative agile process led to the definition of general horizontal LOs (H-LOs) for location interoperability and sector-specific LOs for each BCS. These LOs were tailored to address the distinct needs of managers and technicians. The LO definition process relied on close collaboration among partners and stakeholders. Initially, LOs were identified for horizontal and BCS domains through a bottom-up method, followed by a top-down structuring of the curriculum. The verbs of Bloom’s Taxonomy were used to define each LO.

Horizontal LOs. A total of 12 H-LOs have been defined to meet the needs of SME managers, while 16 H-LOs were identified for SME technicians. Figure 5 provides a quantitative breakdown of their distribution across the four EIF interoperability layers. For both user profiles, the majority of LOs focus on transversal topics that span multiple interoperability layers, such as the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles and concepts related to EIF. For managers, the second largest group of H-LOs (24%) concerns legal interoperability aspects. These cover EU data policies and initiatives pertinent to digital transformation and data interoperability (e.g., INSPIRE, the Open Data Directive, and the EU Data Strategy). In contrast, the technician profile includes several H-LOs focusing on the semantic (19%) and technical (13%) interoperability of location data. These outcomes cover topics like understanding ISO and DCAT metadata standards, describing UML representations of data models, and using OGC Web Services (such as OGC APIs). In addition, there is a single organizational H-LO that applies to both manager and technician profiles. It involves explaining which organizations are responsible for data interoperability at different levels (regional, national, European, global) and detailing their respective roles.

Example of LOs for a BCS. In the BCS focused on digital agriculture, a set of 22 LOs, split between 9 for managers and 13 technicians, were identified to equip participants with the necessary skills to navigate the complexities of data interoperability in the agricultural sector. For managers, the training emphasizes strategic insights on data interoperability principles that support business planning. It also covers the European Union’s regulatory concerning eco-sustainability, ethical considerations, privacy, and security. For technicians, the BCS-LOs offer a comprehensive blend of theoretical knowledge and practical skills. This training prepares them to tackle the challenges of integrating diverse data sources, such as satellite imagery, IoT data, land registries, and plant phenology data. Emphasis is placed on using GIS for agricultural monitoring and developing expertise in data models and harmonization techniques. Practical examples, including insights from vineyard cultivation, are also incorporated to illustrate real-world applications.

5.2. Courses and Lectures Design

As shown in

Table 2. Descriptions of courses for SME managers and technicians.

Course Topic	Target Profile	Lectures	Duration
Introduction to Data Interoperability	SME Managers	9	5 h
	SME Technicians	8	10 h
Mobile Food Marketplace	SME Managers	4	6.5 h
	SME Technicians	5	7 h
Digital Agriculture	SME Managers	4	5 h
	SME Technicians	6	11 h
Social Monitoring of Road Conditions	SME Managers	5	4.5 h
	SME Technicians	5	4.5 h
3D Geodata, BIM, and Digital Twins for Urban Planning	SME Managers	6	6 h
	SME Technicians	9	15 h

, based on the defined LOs, we designed a total of 10 courses: 2 introductory courses (not linked to a specific BCS) and 2 courses for each BCS (1 tailored for managers and 1 for technicians). The courses designed for managers focus on policy aspects and general concepts, while the courses for technicians emphasize the transfer of technical knowledge and skills.

The “Introduction to Data Interoperability” courses (also referred to as “horizontal” courses) are designed to provide essential knowledge and skills applicable across all BCSs. These courses are integrated into every BCS-related training action, as they address fundamental concepts and general data interoperability skills, ensuring that participants build a solid foundational understanding. The content of the horizontal course for managers is closely aligned with the overview sections of this paper (Section 2 and Section 3). It addresses critical policy issues, such as the European Data Strategy and Open Data Directive, equipping managers with the knowledge to align their operations with EU regulations and leverage these policies for competitive advantage. In addition, the course explores the evolution of OD and equips participants with the skills to effectively integrate OD into their business processes and overcome common barriers to their adoption. On the technical side, the horizontal course covers the interoperability layers discussed in Section 3, including legal, organizational, semantic, and technical aspects. It offers practical training on using key models and standards like INSPIRE and EIF, which are essential for ensuring effective data management and integration across different systems. This comprehensive approach ensures that the course not only addresses the theoretical foundations laid out in the background sections but also provides practical solutions to the interoperability challenges to be faced by SMEs.

The two horizontal courses are intended to be the first courses in each BCS-related training action, and participants should complete them before progressing to the BCS-specific courses. Both manager and technician courses will be conducted and scheduled online. After each course, trainees will receive all training materials used during the lectures, along with recordings of the lectures. This will allow them to practice using the materials and review lectures independently. Additionally, the availability of lecture recordings enables trainees to engage with the content asynchronously through self-paced learning [70,71], aligning with the overall approach of DIS4SME.

As shown in Table 2, specific courses are tailored for managers and technicians for each BCS topic. These courses equip participants with knowledge and skills that directly address the interoperability challenges faced by SMEs in their respective BCS contexts. Each course is designed based on the specific needs identified in the case studies, with the aim that the content is relevant and practical. The courses for managers focus on providing a strategic understanding of how data interoperability can influence business planning and regulatory compliance in specific sectors.

For example, the courses designed for digital agriculture focus on the following:

• Data Interoperability Principles to Support Business Planning in Digital Agriculture. This course equips managers with the knowledge and skills to enhance their business plans by understanding the impact of data interoperability on business operations and decision-making processes. It covers how to ensure compliance with relevant EU regulations and standards concerning agricultural practices and digital transformation.
• Digital Agriculture: from Data Interoperability Principles to Practical Implementations. It equips technicians with the necessary skills and knowledge to effectively integrate and utilize location data for enhancing agricultural processes, addressing interoperability challenges, and leveraging emerging technologies.

Figure 6 shows the structured approach for the two digital agriculture courses aligned with specific objectives and lectures. Figure 6a outlines the manager course made up of four lectures with different durations, which provides an in-depth understanding of data interoperability principles and their application in digital agriculture to support effective business planning. It covers foundational concepts, policy instruments, and advanced technologies driving digital agriculture. Additionally, the course includes a practical exercise to directly apply the learned principles. Figure 6b outlines the technician course, made up of six lectures with different durations. At the end of each lecture, a few quizzes are provided to participants to support their learning process and help consolidate the LOs associated with that lecture. At the end of the course, a practical exercise designed to provide hands-on experience in data interoperability solutions is foreseen. The exercise is structured to ensure learners can apply theoretical knowledge in a practical setting.

Figure 7 and Figure 8 show examples of practical exercises designed to provide hands-on experience in data interoperability solutions for managers and technicians, respectively. Each exercise is structured to ensure learners can apply theoretical knowledge in a practical setting with the guidance and evaluation of a tutor. Each exercise is characterized by the following structure:

• Demonstration by the tutor. A tutor will provide a detailed demonstration of the exercise, highlighting the main interoperability solution. This includes an explanation of the tools and techniques to be used and the expected outcomes.
• Practical exercise. After the demonstration, learners will perform the exercise in a separate session. During this session, learners will (i) follow the steps shown in the demonstration to set up their environment, (ii) apply the interoperability solution to a provided dataset, and (iii) document any issues encountered and the solutions applied.
• Testing and evaluation. Once the practical exercise is completed, learners will take a test to assess their understanding and application of the interoperability solution. Then, the tutor will evaluate the test, which also includes the assignment of any micro-credentials.

In particular, the technician workflow (Figure 8) was designed to acquire skills in key interoperability challenges commonly encountered in the acquisition, processing, and publication of data for monitoring vineyard health. It begins with data from various sources and formats, demonstrating the difficulties a technician might face in effectively managing, harmonizing, integrating, and analyzing the data. Additionally, the exercise offers insights into the use of OD, such as Copernicus data, which can be extremely valuable for companies.

5.3. Training Material Technical Platform

Training materials are available in various formats, including slide presentations, video content, and quizzes and assessments for the exercise resources. Several delivery modules are planned, including fully online self-learning (asynchronous), blended learning (a combination of online and in-person), in-person training, in-person training with supplementary online materials, and live online scheduled classes (synchronous).

The courses delivered at the European level can be accessed through the project website: https://www.dis4sme.eu/courses/ (accessed on 16 January 2025). They will be fully available by early 2025. From there, users can register to participate in various delivery modes: “online scheduled classes” (online classes with a tutor) or “online asynchronous self-learning”. These resources are hosted on the DIS4SME learning platform, which utilizes a dedicated Moodle Cloud Learning Management System. The courses are comprehensively described with detailed metadata, including information such as target audience, prerequisites for access, duration, level, learning objectives, title, a short description, expected workload, and type of materials. This structured collection of metadata ensures clear and accessible course information. Moreover, a key aspect of the training material involves the creation of baseline courses and localized courses tailored to meet the specific needs of users, especially for in-person training. These courses are managed and hosted on dedicated platforms by the training providers.

5.4. Certification

Besides the LOs formalized at the lecture level, a few LOs have also been formalized at the course level, considering the overall knowledge and skills acquired during the single lectures and learning activities that a course comprises. The achievement of the LOs at the course level is assessed through exercises to be executed at the end of each course. A positive assessment is accredited with a certificate containing a series of information aligned with the EU Council Recommendation 9237/22 [72] on a European approach to micro-credentials for lifelong learning and employability as well as the course metadata. The Recommendation seeks to support the development, implementation, and recognition of micro-credentials across institutions, businesses, sectors, and borders. Micro-credentials certify learning outcomes from short-term educational experiences, such as courses or training, and provide a flexible way to develop targeted skills and competencies for personal and professional growth. They record assessed learning outcomes based on clear, transparent criteria and address societal, cultural, and labor market needs. Owned by learners, micro-credentials are shareable and portable and can function independently or as part of larger qualifications, with quality assurance aligned to sector standards.

Annex I of the Recommendation contains the following standard metadata elements to describe a micro-credential:

• Identification of the learner;
• Title of the micro-credential;
• Country(ies)/region(s) of the issuer;
• Awarding body(ies);
• Date of issue;
• Learning outcomes;
• Notional workload needed to achieve the learning outcomes (in European Credit Transfer and Accumulation System—ECTS, wherever possible);
• Level (and cycle, if applicable) of the learning experience leading to the micro-credential (European Qualifications Framework, Qualifications Frameworks in the European Higher Education Area), if applicable;
• Type of assessment;
• Form of participation in the learning activity;
• Type of quality assurance used to underpin the micro-credential.

Annex II of the Recommendation outlines the European principles for designing and issuing micro-credentials. These 10 principles define the nature of micro-credentials and provide guidance to member states, public authorities, and providers on their development and implementation, including the systems supporting them. The principles emphasize the core attributes of the European approach to micro-credentials, fostering trust and ensuring quality. They are universally applicable and can be adapted to various areas or sectors, where relevant. Upon completing a micro-credential, learners receive a digital badge, a modern credential that showcases specific skills acquired through learning experiences. Unlike a static image, a digital badge is enriched with metadata detailing the micro-credential, as described in Annex I of the Recommendation.

In DIS4SME, although a complete system for issuing micro-credentials and delivering digital badges has not been implemented, the achievement of LOs at the course level is recognized through a certificate that includes the information specified by the Recommendation, ensuring full alignment with micro-credentials.

5.5. Quality Assessment Strategy of Learning Material

To ensure that courses meet the needs of learners and remain aligned with educational quality standards, an approach based on assessment tools and applied practices has been designed. This approach ensures that participants not only acquire relevant knowledge but also can apply it effectively in real-life contexts.

The quality of learning achievements is assessed through multiple measures beyond the mere issuance of micro-certificates. The previously described “hands-on” workshops at the end of each course are part of the quality framework. During the workshop, participants apply the principles of data interoperability to practical cases and real-life scenarios. These workshops function as performance assessments and allow verification that students have achieved the expected learning outcomes. The results from the pilot programs and subsequent implementations highlight that the training successfully addressed the skill gaps identified in SMEs. Participants demonstrated the ability to apply interoperability principles in real-world scenarios, showcasing a high level of practical understanding and skill development. An important quality assessment measure is provided by the questionnaire. Each course participant is invited to complete an anonymous questionnaire designed to evaluate the course’s content, delivery, and overall effectiveness. Participants are asked to answer 25 questions. Among these, the specific impact-related questions include the following:

• This training is very relevant to my current work duties/studies.
• What I learned in this course will be of great value for my future career/further studies.
• Due to this course, I will now have improved possibilities in finding new and better jobs.

These questionnaires allow for the collection of valuable data to measure the course’s effectiveness from the learners’ perspective. The questionnaire is available as additional material on Zenodo (https://doi.org/10.5281/ZENODO.14531783 (accessed on 16 January 2025)).

Moreover, the approach considers a systematic analysis of the feedback from the questionnaires and workshop performance to identify areas for improvement, ensuring that any recurring difficulties with concepts are addressed through additional resources or adjustments to the material, and refining practical exercises based on observed results to better meet the specific needs of learners.

5.6. Pilot and Courses Deployment

As part of the implementation process, a pilot course “Introduction to Location Data Interoperability” was held in Italy in November 2024 to test the curriculum structure, refine training materials, and gather participant feedback for improvements. The course was designed for managers and executives of SMEs with the goal of introducing the concepts and implications of geospatial data interoperability, considering the related technological and non-technological developments, and within the context of EU data policies. The course was free and took place online. It consisted of a three-hour theoretical session and a two-hour practical exercise, covering the following topics: an introduction to data interoperability, data policies and initiatives, the European framework for interoperability, data spaces, the INSPIRE Directive, data interoperability standards, and challenges and trends in geospatial data interoperability. At the end of the course, participants learned how to integrate geospatial data into their business and how to assess the impact of geospatial data interoperability on digital transformation strategies. Additionally, those who successfully completed the practical exercise received a digital certification based on the European micro-credentials system.

During the courses, specific tutoring activities were carried out to monitor progress and overcome difficulties. The course was well received by participants who appreciated the opportunity to gain skills in geospatial data interoperability, not only from a technical perspective but also from semantic, legal, and organizational viewpoints, all essential to strengthening digital transformation strategies within businesses. The evaluation questionnaire results revealed that 100% of participants found the training highly relevant to their current job duties, 89% highlighted that the knowledge gained would be valuable for their future careers, and 67% stated the course would improve their opportunities for better jobs.

Based on the results of this pilot, the finalized curriculum will be launched in early 2025, utilizing online and blended formats that combine self-learning modules with optional live workshops to enhance flexibility and effectiveness. The courses will be available in Italy, Spain, Belgium, Croatia, Slovenia, and other European countries, offered in multiple languages to ensure broad accessibility and participation.

6. Discussion

The methodology outlined in this article provides a replicable and structured approach to addressing digital skill gaps in SMEs, specifically in data interoperability. Unlike other general digital training programs, this methodology is tailored to SMEs’ specific challenges in managing location data. This focus on interoperability is crucial as SMEs increasingly need to navigate complex data environments, comply with European regulations such as INSPIRE, and integrate OD into their workflows.

A key strength of the methodology lies in its flexibility and adaptability. With a modular structure and the integration of real business case studies, the curriculum can be customized for various sectors and regions, making it a versatile tool for digital training. This aspect is particularly important for ensuring broad applicability with the engagement of a wide range of SMEs.

The quality of training material is rigorously ensured through a multifaceted approach that integrates the definition of LOs based on Bloom’s Taxonomy to establish clear and measurable objectives, expert peer review of course content to evaluate the relevance and rigor of the course content, hands-on workshops where participants apply their knowledge to practical case, and systematic participant feedback collected through detailed questionnaires. This comprehensive framework not only evaluates but also enhances the learning process, ensuring that participants achieve measurable and meaningful outcomes aligned with both academic and professional standards.

The evaluation of the curriculum through a pilot course delivery demonstrates that the methodology not only bridges knowledge gaps but also prepares SMEs to address interoperability challenges in a digitalized market. The results obtained from the pilot course and the feedback received will guide the review and optimization of the content for the next editions, ensuring accessible training for a broader audience.

However, challenges need to be considered in the future stages of implementation. One of the main limitations identified is the potential difficulty in consistently engaging SMEs throughout the training process. Given the workload of these companies’ workforce, time constraints could impact their ability to complete the entire curriculum. The inclusion of asynchronous modules partially addresses this issue, but ensuring sustained engagement remains a challenge.

Additionally, relying on participant feedback to adapt the curriculum introduces a potential delay in content refinement. Although the methodology includes continuous evaluation mechanisms, the iterative nature of this process means that the curriculum will need to be updated based on feedback collected over time, which could delay the full realization of its impact.

Furthermore, as the digital landscape evolves, the methodology will need to be reassessed to ensure its relevance to emerging technologies and new regulatory frameworks.

Finally, the scalability of this approach in non-European contexts remains untested. Although the current curriculum is designed with EU regulations in mind, further research is necessary to evaluate whether the methodology can be adapted to different regulatory and technological frameworks. Expanding its application globally could unlock new opportunities for SMEs in diverse markets, enhancing the program’s impact.

In conclusion, while the methodology demonstrates significant potential for bridging digital skill gaps in SMEs, addressing its limitations through continuous improvement and innovation will be key to maximizing its long-term value. Future research should focus on extending accessibility, improving scalability, and aligning the curriculum with technological and regulatory advancements.

7. Conclusions

This study aims to support the digital transformation of SMEs, specifically by addressing the challenges these companies face in data interoperability. It provides an overview of European policies and initiatives on data interoperability and highlights technological solutions, including OGC-supporting actions, to promote interoperability in the geospatial sector. Moreover, this paper introduces a structured, replicable methodology for developing training programs designed to close SMEs’ digital skills gaps. The key innovation of this methodology is its modular training curriculum, aligned with real-world business case studies, making it adaptable across different sectors and countries. The curriculum is structured for flexibility, addressing the challenges SMEs encounter in geospatial data management while ensuring compliance with European regulations such as the INSPIRE Directive and the European Data Strategy. The methodology has been applied within the DIS4SME project to develop a series of training courses tailored to both managers and technicians, covering cross-domain interoperability skills and specialized competencies for BCS.

This approach not only maintains relevance in the current digital economy but also sets a framework for continuous adaptation as new technologies and regulations emerge. By integrating elements from all data interoperability layers, the methodology provides SMEs with a comprehensive skill set essential for navigating increasingly complex data ecosystems. The practical implications of this curriculum are substantial. By equipping SMEs with the necessary skills to manage and integrate geospatial data, the training enhances operational efficiency, supports regulatory compliance, and improves decision-making processes. Over the long term, SMEs that adopt these competencies will be better positioned to compete in an increasingly data-driven market, creating new growth and innovation opportunities.

Future work focuses on engaging an exhaustive number of SME learners, deploying different training actions with localization actions, and systematically evaluating the curriculum’s effectiveness. As the training is implemented, gathering quantitative and qualitative data to assess its impact on SME participants will be essential. This will not only provide insights into the effectiveness of the training but also offer critical feedback for refining the curriculum to meet the evolving needs of SMEs better. Moreover, further exploration is needed to understand how this methodology can be expanded to other sectors and regions. With the growing global demand for data interoperability, this curriculum has the potential to be adapted for additional industrial sectors beyond the BCSs considered in the project.