{"id":658452,"date":"2026-09-15T09:43:58","date_gmt":"2026-09-15T07:43:58","guid":{"rendered":"https://aivancity.ai/blog/?p=658452"},"modified":"2026-09-21T15:41:38","modified_gmt":"2026-09-21T13:41:38","slug":"langle-mort-de-la-formation-en-ia-la-gestion-des-donnees","status":"publish","type":"post","link":"https://aivancity.ai/blog/langle-mort-de-la-formation-en-ia-la-gestion-des-donnees/","title":{"rendered":"L&rsquo;angle mort de la formation en IA : la gestion des données"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>By <a href=\"https://aivancity.ai/en/faculte/dr-guendalina-caldarini\">Dr. Guendalina CALDARINI</a>, Program Director, MSc in Data Management | Professor at aivancity</em> | </p>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">AI education today faces a paradox. Schools and boot camps are training graduates to refine language models, deploy generation pipelines augmented by retrieval, and build computer vision systems. Yet when asked who is responsible for the quality, consistency, and governance of the data on which these models depend, the answers quickly become hesitant. Data management—which ensures that AI systems are reliable in production—remains the least represented area in AI curricula.</p>\n\n\n\n<h2 class=\"wp-block-heading has-text-color has-link-color wp-elements-1\" style=\"color:#986e13\">How the Cycle of Enthusiasm Shaped Education</h2>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">The boom in AI training in the 2020s closely followed the hype cycle. As transformer models became accessible, training programs quickly began producing prompt engineering specialists. The arrival of large language models in enterprise software then caused a surge in demand for ML engineers capable of fine-tuning and deploying them. Training organizations therefore adopted a core curriculum that has since become standard: Python, PyTorch, LangChain, vector databases, and model evaluation. This choice made sense, as these skills are genuinely useful.</p>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">This shift has also created a structural blind spot. A model is easy to showcase and highlight in program communications. The work involved in processing the data that feeds the model attracts less attention and remains difficult to incorporate into a 12-week curriculum. Yet, in production environments, data issues derail far more AI projects than model issues do.</p>\n\n\n\n<h2 class=\"wp-block-heading has-text-color has-link-color wp-elements-2\" style=\"color:#986e13\">What Data Management Entails</h2>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">Data management is often reduced to simply “storing data.” In reality, it encompasses several interdependent areas: data quality management (ensuring completeness, consistency, and accuracy throughout the data lifecycle); metadata management (describing data so that it can be found, understood, and governed); ontology and taxonomy management (building structured representations of context); entity resolution and data alignment (identifying when different records refer to the same real-world entity); and data governance (defining rules for ownership, accountability, and lifecycle management).</p>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">Together, these fields form the foundation of reliable AI systems. They also constitute a coherent discipline—one that is still rarely taught—at the intersection of computer science, information science, and management programs.</p>\n\n\n\n<p><style>\n.aiv6-pill{display:flex;align-items:center;gap:4px;background:rgba(255,255,255,0.07);border:1px solid rgba(255,255,255,0.11);border-radius:20px;padding:4px 10px;font-size:11.5px;color:rgba(255,255,255,0.72);white-space:nowrap;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;}\n.aiv6-pill svg{width:11px;height:11px;flex-shrink:0;stroke:#3986e1;}\n.aiv6-cta:hover{background:#2f72c8 !important;}\n.aiv6-badge-inner{display:inline-flex;align-items:center;gap:5px;background:rgba(57,134,225,0.12);border:1px solid rgba(57,134,225,0.35);border-radius:20px;padding:3px 10px;}\n.aiv6-badge-dot{width:5px;height:5px;border-radius:50%;background:#3986e1;display:inline-block;flex-shrink:0;}\n@media(max-width:640px){\n  .aiv6-responsive{flex-direction:column !important;}\n  .aiv6-img-wrap{width:100% !important;height:180px !important;}\n  .aiv6-img-overlay{background:linear-gradient(to bottom,rgba(35,38,65,0) 30%,rgba(35,38,65,1) 100%) !important;}\n  .aiv6-body-wrap{padding:18px 20px 24px !important;}\n  .aiv6-logo-img{width:150px !important;}\n  .aiv6-cta{width:100% !important;text-align:center !important;display:block !important;}\n  .aiv6-badge-hide{display:none !important;}\n}\n</style>\n</p><div style=\"background:#232641;border-radius:16px;overflow:hidden;margin:40px 0;box-sizing:border-box;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;\">\n  <div class=\"aiv6-responsive\" style=\"display:flex !important;flex-direction:row !important;min-height:240px;\">\n    <div class=\"aiv6-img-wrap\" style=\"width:42% !important;flex-shrink:0 !important;position:relative;overflow:hidden;\">\n      <img decoding=\"async\" src=\"https://aivancity.ai/en/sites/default/files/2025-11/vignette-dm.jpg\" alt=\"MSc in Data Management at aivancity\" style=\"width:100% !important;height:100% !important;object-fit:cover !important;object-position:center top !important;display:block !important;filter:brightness(0.9);\">\n      <div class=\"aiv6-img-overlay\" style=\"position:absolute;inset:0;background:linear-gradient(to right,rgba(35,38,65,0) 40%,rgba(35,38,65,0.7) 75%,rgba(35,38,65,1) 100%);\"></div>\n    </div>\n    <div class=\"aiv6-body-wrap\" style=\"flex:1;padding:26px 32px 26px 22px;display:flex !important;flex-direction:column !important;justify-content:space-between;gap:14px;position:relative;z-index:1;box-sizing:border-box;\">\n      <img decoding=\"async\" class=\"aiv6-logo-img\" src=\"https://aivancity.ai/en/blog/wp-content/uploads/2026/05/BLANC-FRANCAIS-COMPLET.png\" alt=\"aivancity\" style=\"width:170px !important;height:auto !important;display:block !important;max-width:170px !important;opacity:0.95;\">\n      <div style=\"display:flex;flex-direction:column;gap:10px;flex:1;justify-content:center;\">\n        <h2 style=\"font-size:22px;font-weight:700;color:#fff;line-height:1.25;margin:0;\">MSc in <span style=\"color:#3986e1;\">Data Management</span></h2>\n        <p style=\"font-size:13px;color:rgba(255,255,255,0.58);line-height:1.55;max-width:400px;margin:0;\">Lead the strategic and operational management of data: acquisition, analysis, and governance. RNCP Level 7 degree (equivalent to a 5-year post-secondary degree).</p>\n        <div style=\"display:flex;flex-wrap:wrap;gap:6px;\">\n          <span class=\"aiv6-pill\"><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke-width=\"2\"><circle cx=\"12\" cy=\"12\" r=\"10\"></circle><polyline points=\"12 6 12 12 16 14\"></polyline></svg>1 to 2 years — 5 years of post-secondary education</span>\n          <span class=\"aiv6-pill\"><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke-width=\"2\"><path d=\"M17 21v-2a4 4 0 0 0-4-4H5a4 4 0 0 0-4 4v2\"></path><circle cx=\"9\" cy=\"7\" r=\"4\"></circle></svg>Bachelor's degree through Master's degree</span>\n          <span class=\"aiv6-pill\"><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke-width=\"2\"><polyline points=\"20 6 9 17 4 12\"></polyline></svg>Work-Study Program or Traditional Education</span>\n          <span class=\"aiv6-pill\"><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke-width=\"2\"><path d=\"M12 2C8.13 2 5 5.13 5 9c0 5.25 7 13 7 13s7-7.75 7-13c0-3.87-3.13-7-7-7z\"></path><circle cx=\"12\" cy=\"9\" r=\"2.5\"></circle></svg>Paris-Villejuif Campus</span>\n        </div>\n      </div>\n      <div style=\"display:flex;align-items:center;justify-content:space-between;flex-wrap:wrap;gap:10px;\">\n        <a class=\"aiv6-cta\" href=\"https://aivancity.ai/en/program/msc-data-management/presentation\" target=\"_blank\" rel=\"noopener noreferrer\" style=\"background:#3986e1 !important;color:#fff !important;font-size:13.5px;font-weight:600;padding:10px 24px;border-radius:8px;text-decoration:none !important;white-space:nowrap;display:inline-block !important;\">\n          Learn more about the program →\n        </a>\n        <span class=\"aiv6-badge-hide aiv6-badge-inner\">\n          <span class=\"aiv6-badge-dot\"></span>\n          <span style=\"font-size:10.5px;color:#3986e1;font-weight:600;letter-spacing:0.04em;text-transform:uppercase;\">RNCP Level 7 Certification</span>\n        </span>\n      </div>\n    </div>\n  </div>\n</div><p></p>\n\n\n\n<h2 class=\"wp-block-heading has-text-color has-link-color wp-elements-3\" style=\"color:#986e13\">Why AI Makes Data Management More Critical</h2>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">The shift to large-scale AI has exacerbated data management problems rather than solving them. Language models trained on poorly prepared data replicate those errors at scale. Retrieval-augmented generation systems are only as reliable as the knowledge bases they query. Agent-based AI systems, which act autonomously, introduce new categories of risk when the underlying data is inconsistent, undocumented, or ungoverned.</p>\n\n\n\n<figure class=\"wp-block-pullquote\"><blockquote><p><em>“In production environments, data issues derail far more AI projects than model issues. Yet most AI curricula devote only a tiny fraction of their instruction time to the discipline that would help prevent them.”</em></p></blockquote></figure>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">The European AI Act has translated this reality into legal obligations. Article 10 of the regulation establishes binding data governance requirements for high-risk AI systems. These requirements pertain to the quality, relevance, representativeness, and documentation of data. Compliance is therefore no longer merely a matter of best practices. It is a legal requirement that will apply to a significant portion of the AI systems deployed in Europe. However, most AI graduates today have little or no training on what these requirements actually entail.</p>\n\n\n\n<h2 class=\"wp-block-heading has-text-color has-link-color wp-elements-4\" style=\"color:#986e13\">The Components of a Data Management Program</h2>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">A comprehensive data management training program is structured around four interrelated areas of expertise. Technical skills include data modeling, SQL and graph query languages, ETL design, metadata standards (Dublin Core, DCAT, OWL), and data quality frameworks. Governance skills focus on GDPR compliance, stewardship models, data catalogs, audit trails, and the requirements of the European AI Act. Semantic skills include ontology design, controlled vocabularies, knowledge graph construction, and entity resolution techniques. Finally, strategic skills focus on product thinking applied to data and aligning data architecture with business objectives.</p>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">Data management is a cross-disciplinary field at the intersection of computer science, information science, law, and organizational design. This diversity explains why it is difficult to integrate into existing curricula and why dedicated programs are necessary.</p>\n\n\n\n<h2 class=\"wp-block-heading has-text-color has-link-color wp-elements-5\" style=\"color:#986e13\">The Consequences of This Blind Spot</h2>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">Every year, companies invest heavily in AI systems whose performance is limited by inconsistent, undocumented, or siloed data—rather than by the models themselves. Industry analyses consistently identify data quality as one of the main causes of AI project failure. Demand for professionals in data governance and management is rising sharply due to regulatory pressure, the enterprise-wide deployment of AI, and the proliferation of agent-based systems. However, the supply remains limited, as training has not yet caught up with market needs.</p>\n\n\n\n<p class=\"text-justify wp-block-paragraph\">Training machine learning engineers without also preparing data management specialists can compromise the reliability of the systems they build. As AI moves from pilot projects to systems that organizations rely on every day, data management will take center stage in training. Programs that incorporate it now will produce graduates capable of building reliable, large-scale AI systems that comply with the regulatory framework governing their deployment.</p>\n\n\n\n<h2 class=\"wp-block-heading has-text-color has-link-color wp-elements-6\" style=\"color:#0064c6\">Learn more </h2>\n\n\n\n<p class=\"text-justify wp-block-paragraph\"><em>Learn about the <a href=\"https://aivancity.ai/en/program/msc-data-management/presentation\" data-type=\"link\" data-id=\"https://aivancity.ai/en/program/msc-data-management/presentation\">MSc in Data Management </a>program <a href=\"https://aivancity.ai/en/program/msc-data-management/presentation\" data-type=\"link\" data-id=\"https://aivancity.ai/en/program/msc-data-management/presentation\" data-wg-splitted>at </a>aivancity and our work on data governance in the era of the European AI Act.</em></p>\n\n\n\n<p class=\"wp-block-paragraph\"></p>\n","protected":false},"excerpt":{"rendered":"<p>By Dr. Guendalina CALDARINI, Program Director, MSc in Data Management | Professor at aivancity |  AI education today faces a paradox. Schools and boot camps are training graduates who are capable of refining…</p>\n","protected":false},"author":7,"featured_media":658460,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[28],"tags":[],"class_list":["post-658452","post","type-post","status-publish","format-standard","has-post-thumbnail","category-articles"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https://yoast.com/product/yoast-seo-wordpress/ -->\n<title>The Blind Spot in AI Training: Data Management - aivancity blog</title>\n<meta name=\"description\" content=\"Quality, governance, metadata, the GDPR, and the AI Act: Why data management is becoming an essential skill for building reliable AI systems.\">\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\">\n<link rel=\"canonical\" href=\"https://aivancity.ai/en/blog/langle-mort-de-la-formation-en-ia-la-gestion-des-donnees/\">\n<meta property=\"og:locale\" content=\"fr_FR\">\n<meta property=\"og:type\" content=\"article\">\n<meta property=\"og:title\" content=\"The Blind Spot in AI Training: Data Management - 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