{"id":5534,"date":"2024-07-22T15:32:58","date_gmt":"2024-07-22T13:32:58","guid":{"rendered":"https:\/\/bdva.eu\/docs\/bdva-strategic-agenda-2024\/iii-2-expanding-data-driven-ecosystems-across-sectors-and-global-value-chains\/iii-b-challenges\/"},"modified":"2024-07-22T15:33:33","modified_gmt":"2024-07-22T13:33:33","slug":"iii-b-challenges","status":"publish","type":"docs","link":"https:\/\/bdva.eu\/docs\/bdva-strategic-agenda-2024\/iii-2-expanding-data-driven-ecosystems-across-sectors-and-global-value-chains\/iii-b-challenges\/","title":{"rendered":"III.b Challenges"},"content":{"rendered":"<p>Although there has been significant progress towards raising awareness about the benefits of sharing data, breaking data silos and efforts to foster the exchange of data at different levels, several barriers still remain. Many of them come directly from the trends identified in the previous section, in the sense that although the trend is identified, it is not completely clear or fully developed how to materialize it or overcome related barriers. And many of these challenges were already identified in the mentioned BDVA position paper \u201cTowards a European Data Sharing Space: Enabling data exchange and unlocking AI potential\u201d and later referenced in the second version of the paper in 2020<a href=\"#_ftn1\" name=\"_ftnref1\"><sup>[1]<\/sup><\/a>. Based on all the above, we identify the following main challenges to overcome in the next years, so the vision of seamless connected data driven ecosystems can become a reality:<\/p>\n<ul>\n<li><strong>Interoperability<\/strong>. Even though, as mentioned in the previous section, several initiatives are addressing this aspect from different perspectives (data interoperability: technical, semantic, transport \/ legal \/ business \/ governance \/ etc \u2026) real and seamless interoperability between ecosystems is far from being a reality (much more if they belong to quite different sectors). We will probably have to wait for initiatives to be more mature and better established before they even start to think of connecting with other ones. On this regard, specific use cases that combine close domains (e.g. mobility + smart cities, transport + logistics, media + cultural heritage, \u2026) could be used as tangible examples and produce good practices (top \/ down approach) that can be later leveraged by more complex cases, that will rely more on a synergies-by-design approach (bottom \/ up).<\/li>\n<li><strong>Data provenance, traceability and lineage<\/strong>. This aspect becomes more critical and challenging as more ecosystems and stakeholders along the value chain are connected. In this sense, it is important to differentiate between provenance within the same organization (e.g. digital twin, \u2026) and between organizations, where evidence records have to be written in a machine-readable way independent on the technology of each company. It is also relevant to differentiate between pure \u201crecording of transactions\u201d from a \u201creal lineage of the data\u201d that are used (\u201cthe process of tracking the flow of data over time, providing a clear understanding of where the data originated, how it has changed, and its ultimate destination within the data pipeline\u201d). This aspect is closely linked with the business (transactions), legal (what is required to be recorded) and governance \/ negotiation process (not all cases will require the same level of traceability) in the data space \/ ecosystem.<\/li>\n<li><strong>Data security and protection through the value chain(s). <\/strong>In BDVA paper \u201cCurrent hot topics in Data Protection\u201d, many challenges on this field are already identified<a href=\"#_ftn2\" name=\"_ftnref2\"><sup>[2]<\/sup><\/a> (both technical, legal, and others). As it is mentioned therein, \u201cAI technology can provide novel instruments for intentional attacks\u201d. Besides, \u201cstrength of today\u2019s cryptographic algorithms which are usually based on hard mathematical problems will be impacted by quantum computing algorithms\u201d. Those aspects are specifically framed for data spaces in the BDVA paper \u201cLeveraging the Benefits of Combining Data Spaces and Privacy Enhancing Technologies\u201d<a href=\"#_ftn3\" name=\"_ftnref3\">[3]<\/a>, that highlights the symbiotic relationship and mutual benefits between data spaces and PET technologies. Complexity increases when this issue is extended to the whole value chain and connection between ecosystems, and different protection systems must interact (\u201cprotection interoperability\u201d).<\/li>\n<li><strong>Data quality for sharing and AI <\/strong>Data quality is an aspect widely covered in the literature and in the traditional data value chain. However, it gains a new dimension when we think of sharing of data within and between different ecosystems, that affects many blocks of those ecosystems: governance (minimum requirements for participants), business (include data quality in the definition of the data product), description of the data (what quality attributes to include), provenance (how data quality is ensured or increased in the lineage), and value creation (quality of data fits purpose of the intended applications, specific value added services for data quality validation, etc \u2026). Besides, AI applications impose new requirements for the data aimed to train their models (bias, discrimination, etc \u2026), as is specially mentioned in the AI Act: \u201cHigh quality training, validation and testing data sets require the implementation of appropriate data governance and management practices\u201d.<\/li>\n<li><strong>Scarcity of data<\/strong>. Despite the huge amount of data generated in Europe (and in the world), data is not as abundant as it might seem at first sight. First, most of this data is generated through proprietary systems, and no subject to public \/ open use. Secondly, meaningful datasets of quality that serve the purposes of intended applications (e.g. accurately capture the required trends, help us benchmark, predict, prescribe, plan, optimize, \u2026) are not so common. Finally, some datasets (e.g. personal data, \u2026) are subject to strict regulations that hinder their use. Therefore, the access to available, relevant and easy-to-use datasets becomes a huge challenge for these ecosystems.<\/li>\n<li><strong>Global schemes for trust and governance<\/strong>. Trust and governance are key pillars of digital ecosystems. Trust means that participants in the ecosystem are able to identify, authenticate and be authorized within the ecosystem to perform the corresponding operations. To this end, different mechanisms to manage the digital identity of participants and trust services that offer different functionalities can be applied. This whole trust framework is governed by an authority based on the governance rules for the ecosystem. The connection of ecosystems requires the definition of a common trust \/ governance framework, where credentials are issued at global level, and participants in one ecosystem can use them across the entire connection of ecosystems.<\/li>\n<li><strong>Legal barriers<\/strong>. First, the connection of ecosystems implies \u201clegal interoperability\u201d, that is, \u201cthat organizations operating under different legal frameworks, policies and strategies are able to work together. It implies clear agreements about how to deal with differences in legislation across borders, including the option of putting in place new legislation\u201d (from the Glossary of NIFO &#8211; National Interoperability Framework Observatory). Besides, data driven ecosystems have to consider the different pieces of regulation that affect them (DGA, DA, AI Act, ..) and be able to navigate them.<\/li>\n<li><strong>Fitting of data spaces for new AI applications and requirements<\/strong>. Even though the value that data spaces can provide to AI applications and more especially to large AI models (quality of data, trustworthiness, governed environment, etc \u2026) is clear, those specific uses still pose to data spaces specific requirements that have to be fulfilled.<\/li>\n<li><strong>Automation of contract negotiation, policies definition and management. <\/strong>As ecosystems scale-up with more participants, and connect with others with additional policies in place, the need to automatize the process in order to spare participants administrative burden becomes a must. Although there already are technologies like AI, machine learning, and blockchain that can be applied to streamline and optimize the processes of drafting, negotiating, and managing contracts and policies, their complete operationalization to solve this still remains a challenge.<\/li>\n<li><strong>Business value, adoption and engagement<\/strong>. Although most of technical elements to build a data space are available and quite mature, many organizations still struggle to see what tangible value they can get out of their participation in those ecosystems, which is hindering the real adoption and onboarding of those initiatives. Therefore, it still remains a great challenge to identify how, beyond conceptual models and pilots, data spaces (and other data driven ecosystems) are able to generate tangible value to companies in real environments, and, based on that, convince those companies to share data on those ecosystems. This challenge also includes potential barriers that mostly small actors can encounter for their onboarding and use of those ecosystems.<\/li>\n<li><strong>Lack of required skills<\/strong>. The design, implementation and deployment of data spaces require specific job profiles that are not easy to find. Those skills cover multiple aspects, from technical to business and legal. To have the right workforce that can be involved in those aspects still remain a great barrier, mostly for small organizations and public administrations.<\/li>\n<li><strong>Sustainability and scalability<\/strong>. Many running initiatives are still at an embryonic stage, and funded by public investments (or combination of private \/ public). To define a scheme that can allow the scale-up and sustainability beyond the initial investments still remains an issue for these types of ecosystems and their connection.<\/li>\n<\/ul>\n<p><a href=\"#_ftnref1\" name=\"_ftn1\">[1]<\/a> <a href=\"https:\/\/bdva.eu\/sites\/default\/files\/BDVA%20DataSharingSpaces%20PositionPaper%20V2_2020_Final.pdf\">https:\/\/bdva.eu\/sites\/default\/files\/BDVA%20DataSharingSpaces%20PositionPaper%20V2_2020_Final.pdf<\/a><\/p>\n<p><a href=\"#_ftnref2\" name=\"_ftn2\">[2]<\/a> <a href=\"https:\/\/www.bdva.eu\/sites\/default\/files\/BDVA%20DataProtection%20PositionPaper_November2022.pdf\">https:\/\/www.bdva.eu\/sites\/default\/files\/BDVA%20DataProtection%20PositionPaper_November2022.pdf<\/a><\/p>\n<p><a href=\"#_ftnref3\" name=\"_ftn3\">[3]<\/a> <a href=\"https:\/\/bdva.eu\/download\/92\/publications\/5196\/leveraging-the-benefits-of-combining-data-spaces-and-privacy-enhancing-technologies.pdf\">https:\/\/bdva.eu\/download\/92\/publications\/5196\/leveraging-the-benefits-of-combining-data-spaces-and-privacy-enhancing-technologies.pdf<\/a><\/p>\n","protected":false},"featured_media":0,"parent":5529,"menu_order":1,"comment_status":"open","ping_status":"closed","template":"","doc_tag":[],"class_list":["post-5534","docs","type-docs","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>III.b Challenges - BDV Big Data Value Association<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bdva.eu\/docs\/bdva-strategic-agenda-2024\/iii-2-expanding-data-driven-ecosystems-across-sectors-and-global-value-chains\/iii-b-challenges\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"III.b Challenges - BDV Big Data Value Association\" \/>\n<meta property=\"og:description\" content=\"Although there has been significant progress towards raising awareness about the benefits of sharing data, breaking data silos and efforts to foster the exchange of data at different levels, several barriers still remain. 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