{"id":5509,"date":"2026-03-02T14:31:32","date_gmt":"2026-03-02T14:31:32","guid":{"rendered":"https:\/\/hamilton.global\/?p=5509"},"modified":"2026-03-03T08:36:51","modified_gmt":"2026-03-03T08:36:51","slug":"synthetic_data-in-research","status":"publish","type":"post","link":"https:\/\/hamilton.global\/ca\/synthetic_data-in-research\/","title":{"rendered":"Dades Sint\u00e8tiques i Research Augmentat amb IA"},"content":{"rendered":"<h2><strong>Dades sint\u00e8tiques, ancorades en evid\u00e8ncia humana.<\/strong><\/h2>\n<p>Accelerem la investigaci\u00f3 amb IA per generar datasets sint\u00e8tics que reprodueixen els patrons reals del mercat, amb un objectiu clar: arribar abans a conclusions accionables i reservar el fieldwork hum\u00e0 per all\u00f2 que realment ho requereix.<\/p>\n<h2><strong>Qu\u00e8 s\u00f3n<\/strong><\/h2>\n<p>Les dades sint\u00e8tiques s\u00f3n informaci\u00f3 generada mitjan\u00e7ant models dIA que replica lestructura estad\u00edstica i les relacions observades en dades de recerca real (mostra humana), sense copiar registres individuals.<\/p>\n<h2><strong>Com ho fem <\/strong><\/h2>\n<p>La nostra aproximaci\u00f3 combina dos pilars:<\/p>\n<ul>\n<li><strong>Models probabil\u00edstics<\/strong> (mostreig aleatori i modelatge de depend\u00e8ncies) per preservar relacions clau.<\/li>\n<li><strong>Models de deep learning<\/strong> (enfocament no supervisat combinant arquitectures tipus GAN\/VAE) per generar datasets sint\u00e8tics robusts.<\/li>\n<\/ul>\n<blockquote>\n<p style=\"text-align: center;\"><em><strong>No desenvolupem bases de dades \u201cfrom scratch\u201d. El nostre enfocament exigeix com a punt de partida un dataset real d&#039;origen hum\u00e0.<\/strong><\/em><\/p>\n<\/blockquote>\n<h2><strong>Com es configura <\/strong><\/h2>\n<blockquote>\n<p style=\"text-align: center;\"><strong><em>EL QUE NECESSITA A LA MESURA JUSTA<\/em><\/strong><\/p>\n<\/blockquote>\n<p>Cada projecte es defineix en tres passos:<\/p>\n<ol>\n<li><strong>Base d&#039;entrenament\/refer\u00e8ncia<\/strong>: estudis hist\u00f2rics o datasets d&#039;alta qualitat disponibles.<\/li>\n<li><strong>Definici\u00f3 del repte de negoci<\/strong>: quina decisi\u00f3 es prendr\u00e0 (i quines variables han de sostenir-la).<\/li>\n<li><strong>Regles de control (guardrails)<\/strong>: quines relacions s&#039;han de preservar (segments, \u00fas, actituds\u2026) i qu\u00e8 s&#039;exclou per evitar soroll o sensibilitat.<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h2><strong>Qualitat i credibilitat: <\/strong><\/h2>\n<p>Per garantir que el resultat \u00e9s <strong>fiable i utilitzable<\/strong>, cada model inclou un paquet de validaci\u00f3 amb:<\/p>\n<ul>\n<li><strong>Distribucions i comportament univariable<\/strong> (comportament de cada variable).<\/li>\n<li><strong>Distribucions i an\u00e0lisi bivariable<\/strong> (relacions condicionals entre variables).<\/li>\n<li><strong>Distribucions i an\u00e0lisi Multivariable <\/strong>(Homegene\u00eftats, ADM, PCA comparativa, Clustering stability (ARI), RMSE, Adversarial test)<\/li>\n<li><strong>Correlacions<\/strong> (estructura de relacions matricial).<\/li>\n<li><strong>Dist\u00e0ncies<\/strong> (proximitat sint\u00e8tica vs. Original amb Maximum Mean Discrepancy (MMD) i Jensen-Shannon distance).<\/li>\n<\/ul>\n<p><img decoding=\"async\" class=\"wp-image-5517 aligncenter\" src=\"https:\/\/hamilton.global\/wp-content\/uploads\/2026\/03\/Accuracy-Data.png\" alt=\"accuracy data\" width=\"615\" height=\"334\" srcset=\"https:\/\/hamilton.global\/wp-content\/uploads\/2026\/03\/Accuracy-Data.png 615w, https:\/\/hamilton.global\/wp-content\/uploads\/2026\/03\/Accuracy-Data-480x261.png 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) 615px, 100vw\" \/><\/p>\n<p>*La precisi\u00f3 de les dades sint\u00e8tiques no s&#039;avalua mitjan\u00e7ant una simple comparaci\u00f3 de mitjanes. S\u201fanalitza en tres nivells clau: la preservaci\u00f3 de l\u201festructura relacional, l\u201festabilitat dels segments i la robustesa predictiva en validacions creuades. Quan un model entrenat amb dades sint\u00e8tiques \u00e9s capa\u00e7 de predir amb fiabilitat dades reals, podem parlar d&#039;equival\u00e8ncia operativa.<\/p>\n<h2><strong>On <em>S\u00cd<\/em> aporta valor <\/strong><\/h2>\n<ul>\n<li><strong>Innovaci\u00f3 i concept\/claim screening<\/strong>: prioritzar idees abans d&#039;una validaci\u00f3 humana completa.<\/li>\n<li><strong>Cobertura de targets dif\u00edcils \/ mostres petites<\/strong>: \u201complir buits\u201d quan el sample hum\u00e0 no arriba o \u00e9s car.<\/li>\n<li><strong>Velocitat i agilitat<\/strong>: generar escenaris i lectures preliminars en menys temps.<\/li>\n<li><strong>Simulaci\u00f3<\/strong>: explorar situacions rares o poc freq\u00fcents que costa capturar a la realitat.<\/li>\n<\/ul>\n<h2><strong>Quan <em>NO<\/em> ho recomanem<\/strong><\/h2>\n<ul>\n<li>Quan necessites insight <strong>fresc<\/strong> i de primera m\u00e0 (crisi, canvis abruptes del mercat).<\/li>\n<li>Quan no n&#039;hi ha una <strong>base de refer\u00e8ncia<\/strong> s\u00f2lida per entrenar\/ancorar el model.<\/li>\n<li>Quan la decisi\u00f3 exigeix evid\u00e8ncia humana per motius <strong>regulatoris\/legals<\/strong>.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<blockquote>\n<p style=\"text-align: center;\">En aquests casos es pot utilitzar com a <strong>pre-an\u00e0lisi<\/strong>, per\u00f2 no com a substitut del fieldwork hum\u00e0.<\/p>\n<\/blockquote>\n<p>&nbsp;<\/p>\n<p style=\"text-align: center;\"><a href='https:\/\/hamilton.global\/ca\/contactanos\/' class='small-button smallblue' target=\"_blank\">DEMANAR M\u00c9S INFORMACI\u00d3<\/a><\/p>\n<span class=\"et_bloom_bottom_trigger\"><\/span>","protected":false},"excerpt":{"rendered":"<p>Datos sint\u00e9ticos, anclados en evidencia humana. Aceleramos la investigaci\u00f3n con IA para generar datasets sint\u00e9ticos que reproducen los patrones reales del mercado, con un objetivo claro: llegar antes a conclusiones accionables y reservar el fieldwork humano para lo que realmente lo requiere. Qu\u00e9 son Los datos sint\u00e9ticos son informaci\u00f3n generada mediante modelos de IA que [&hellip;]<\/p>","protected":false},"author":4,"featured_media":5515,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":""},"categories":[204,201],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Datos Sint\u00e9ticos y Research Aumentado con IA<\/title>\n<meta name=\"description\" content=\"La investigaci\u00f3n con IA para generar datasets sint\u00e9ticos que reproducen los patrones reales del mercado, con un objetivo claro: llegar antes a conclusiones accionables y reservar el fieldwork humano para lo que realmente lo requiere.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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