Even though different definitions of interoperability have been given [11; 12; 13], it could be generically defined in an industrial environment as the ability of systems to understand and use the functionality of one another [14]. Interoperability in Industry 4.0 includes four levels: operational (organisational), systematic (applicable), technical, and semantic [15]. Semantic interoperability, that is, the ability to ensure information exchange between system architectures, is one of the main challenges not completely addressed by the RA [16]. One of those causes is that DT tends to be very specialised and restricted to particular domains [7; 17]. The changing nature of the sectorial context and the particular conditions and requirements of each company enforce the emergence of open platforms easily extended with additional enablers and components to implement data-driven smart applications. This facilitates a variety of sectorial and company needs and requirements and addresses specific challenges on their digital transformation path. As a result, RAs have contributed to the elimination of existing barriers to the adoption of new technologies and platforms, facilitating the incorporation of small businesses in the DT and the social revolution that transforms and integrates the different perspectives for sustainability priorities and response [18]. However, SMEs, as significant contributors to the European economy [5; 19], still require support to transform their business and successfully implement DT strategies. Heterogeneity in the systems deployed in different companies and factories requires general system connectors that simplify the interoperability requirements between factory software, data, and platform and other external system adapters [20]. Digital technologies such as the IoT have the potential to facilitate the adoption of the DT paradigm due to easy access to massive data and the transformation into useful knowledge for effective decision making. In fact, IoT technology and the standards surrounding it have the potential to enable data storage, analysis, and transformation of data created in the company and use external sources, as they have the advantage of allowing for the integration of smart things in an affordable way and the exploitation of information resources and services on the cloud [21]. Nevertheless, to our knowledge, the suitability of existing RAs on this aspect has not been investigated. The lack of a reference process for digital transformation that supports the identification, specification, and implementation of IoT solutions compatible with current custom protocols (i.e., OPC UA, MQTT, XML, etc.) hampers the adoption of the strategy-driven approaches of SMEs. Under these conditions, it is very difficult to integrate new digitalised processes that improve the workability, sustainability, and efficiency of industrial SMEs. These difficulties come from their intrinsic heterogeneity and nuances when consider their specific needs and the different support required in overcoming the barriers of potential digital transformation [22]. Therefore, there is a clear need to bridge these problems and propose a novel approach to find and understand how the RA could be customised to support the automatic translation of one generic data model, used in a variety of IoT devices, into another from specific smart factories' processes. One of the most extended open RA supporting generic data is FIWARE which offers an open framework dedicated to enhancing data interoperability and integration. By utilising open standards and common data models, it facilitates service composition and ensures the secure and efficient utilisation of shared data resources.