During the quantitative analysis of the surveys, it was observed that a few discrepancies were identified attributed to the perception of the questionnaire respondents, either from a personal capacity in their business role and their comprehension of each regulation or the perceived impact that each regulation has on their company. One of the largest discrepancies was the respondents’ perception of GDPR. When it comes to asset management and network planning, asset-related data are usually used in combination with consumer data (i.e., smart meter data). It is, therefore, apparent that GDPR might be highly impactful for the DSO who manages the smart meters, i.e., third party data, but might be of much less importance to the TSO who is concerned with proprietary data whose utilization may be internally governed following already existing rules based on regulatory reporting standards. Perception differences in the way that respondents understand the existing impact or the potential impact that a directive might have could be seen in relation to green power purchase agreements (GPPAs) between RES operators and retailers. The difference in perception on whether the Energy Communities Legislation has significant impact could be attributed to the fact that this directive currently does not affect per se the establishment of GPPAs using energy coming from PV plants owned/operated by an RES operator; however, if the Energy Communities Legislation will not explicitly facilitate the participation of energy communities in such agreements, it might restrict the scalability of such agreements, as potentially large green energy production segments such as energy communities might be excluded from appropriate regulation concerning certification of GPPAs. These discrepancies provided input to the interviews performed as part of the methodology in the form of embedding the input from this perception analysis into specific questions that aimed at capturing any barriers foreseen by the participating data value chain actors. As the initial engagement through the interviews showed that no major barriers exist, or where there are complexities, measures such as internal governance procedures and experimental agreements can be put in place to overcome these barriers, these discrepancies were attributed to the personal perception of the survey respondents as part of their business role or the specific role of their organization in relation to data-driven practices.

The socioeconomic aspects related to data-driven services were studied by means of an initial comprehensive literature review targeting the identification of such barriers through prominent literature sources. This state-of-the-art analysis was complemented by the relevant survey towards the participants which offered results that relate to the national level. When aggregating the scores from all countries, three main SE barriers were demonstrated to be the most impactful above all others. The most impactful SE barrier was related to a lack of consumer awareness for the benefits towards flexibility, opportunity, cost saving, and revenue generation. The second and third highest rated SE barriers were neglecting the value of system flexibility and the exclusion of particular societal groups, respectively.

Similarly to the regulatory domain, multiple large discrepancies were identified between the responses due to multiple organizations contributing to the averaged impact scores in the SE barrier analysis. In addition to the analysis of the barriers per demo case, the variance of scores was calculated for the demo cases where multiple organizations responded to create an average score. The most conflicting barriers are “CAPEX”, “No sponsorship for CAPEX”, “Neglecting the value of system Flexibility”, “Lack of equal opportunities in wealth”, “Converting innovation into business as usual”, and “No consideration for diversity of interests”. Following up on these conflict barriers during the interviews allowed the uresearchers to identify why there is a difference of opinion between each organization with regard to a specific use of data-driven services. The CAPEX-related conflict barriers are highly dependent on the type of data processed by each organization. As such, any innovative data-driven business models need to be customized for the type of data processes and take into account also the cost of accessing assets that are not “connected assets” and as such require further infrastructure to enable data-sharing-driven business cases. Regarding system flexibility and business-as-usual innovation, regulation is driving innovation in the flexibility of energy systems, and when existing frameworks do not promote nor facilitate such innovation, it is hard for regulated entities and aggregators to create the necessary skills and processes required to facilitate such developments.

On the organizational level, some barriers are commonly highlighted in the results across almost all stakeholder types. The analysis done per stakeholder provides information on the most impactful barriers which affect a particular SH group across multiple organizations. It would be beneficial to investigate the barriers which are deemed important from the perspective of the SHs which are integral to data-driven services. When assessing the aggregated scores from all stakeholder types, the analysis presented here suggests there are four main impactful OR barriers. These barriers were related to a lack of compatibility for multisource data, issues related to GDPR, issues related to the complexity of data, and a lack of data governance.

The analysis done per stakeholder provides information on the most impactful barriers which affect a particular SH group across multiple organizations. It is therefore beneficial to investigate the difference in perspective of the surveyed stakeholders exhibiting a conflict of opinion with regard to the impact of organizational barriers. Such conflicting barriers include “Inability to recognize data value”, “Lack of data governance”, “Data complexity” and “Closed ICT systems”. To this end, during the interviews, questions related to these conflicting barriers were included in order to potentially provide an understanding of the perspective of each key SH. The interviews indicated that perception of data governance, recognition, and complexity is highly dependent on the type of data processed by each stakeholder (such as customer data, public infrastructure data, national interconnections data, and market data). In order to be able to analyze and derive insightful value from combining multisource data, the value of data from various sources should be clearly stated and understood. The recognition of data value can be increased by ensuring data quality, accuracy, and multisource compatibility, as well as exploiting data linking of information coming from different domains.