{"id":14554,"date":"2026-10-14T09:28:08","date_gmt":"2026-10-14T09:28:08","guid":{"rendered":"https:\/\/www.novotek.com\/dk\/?post_type=insights&#038;p=14554"},"modified":"2026-10-14T09:32:46","modified_gmt":"2026-10-14T09:32:46","slug":"hvordan-siemens-energy-bruger-highbyte-og-snowflake-til-at-goere-ot-data-klar-til-ai","status":"publish","type":"insights","link":"https:\/\/www.novotek.com\/dk\/insight\/hvordan-siemens-energy-bruger-highbyte-og-snowflake-til-at-goere-ot-data-klar-til-ai\/","title":{"rendered":"Hvordan Siemens Energy bruger HighByte og Snowflake til at g\u00f8re OT data klar til AI"},"content":{"rendered":"<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Kunstig intelligens i industrien handler ikke l\u00e6ngere om fremtidige muligheder. For Henrik Birk og teamet hos Siemens Energy er AI allerede en integreret del af arbejdet med at analysere produktionsdata, optimere processer og underst\u00f8tte kvalitetsforbedringer.<\/p>\n<\/div>\n\n<div class=\"container-md embed  \">\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"How Siemens Energy Uses HighByte and Snowflake to Prepare OT Data for AI\" width=\"1440\" height=\"810\" src=\"https:\/\/www.youtube.com\/embed\/Bsl8AaznWEU?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h2 class=\"wp-block-heading\">Udfordringen: Industrielle data mangler ofte kontekst<\/h2>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Mange produktionsvirksomheder har adgang til enorme m\u00e6ngder OT-data fra PLC&#8217;er, <a href=\"https:\/\/www.novotek.com\/dk\/automation\/hmi-scada\/\" data-type=\"page\" data-id=\"1045\"><strong>SCADA-systemer<\/strong><\/a>, sensorer og produktionsudstyr. Problemet er, at data ofte er spredt p\u00e5 tv\u00e6rs af systemer og lokationer, hvilket g\u00f8r det vanskeligt at skabe et samlet grundlag for analyse og AI-initiativer. <\/p>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Siemens Energy oplevede samme udfordring i arbejdet med produktionen af vindm\u00f8llevinger, hvor data skulle samles, standardiseres og g\u00f8res forst\u00e5elige p\u00e5 tv\u00e6rs af fabrikker og forretningsenheder.<\/p>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h2 class=\"wp-block-heading\">HighByte tilf\u00f8rer den n\u00f8dvendige kontekst<\/h2>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">For at l\u00f8se udfordringen anvender Siemens Energy <strong><a href=\"https:\/\/www.novotek.com\/dk\/digitalisering\/dataops\/highbyte-intelligence-hub\/\" data-type=\"page\" data-id=\"1329\">HighByte Intelligence Hub<\/a><\/strong> som det centrale lag mellem OT-systemerne og dataplatformen. Her modelleres data med en f\u00e6lles struktur og semantik, s\u00e5 information fra forskellige produktionsanl\u00e6g bliver ensartet og kan anvendes p\u00e5 tv\u00e6rs af organisationen.<\/p>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">I stedet for blot at sende r\u00e5 signaler videre til analysev\u00e6rkt\u00f8jer, tilf\u00f8rer l\u00f8sningen kontekst omkring udstyr, processer og relationer mellem data. Resultatet er, at b\u00e5de analytikere og AI-modeller arbejder med data, der allerede er organiseret og forst\u00e5elige.<\/p>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h2 class=\"wp-block-heading\">Snowflake skaber fundamentet for Analytics og AI<\/h2>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">N\u00e5r data er struktureret gennem HighByte Intelligence Hub, sendes de videre til Snowflake, som fungerer som virksomhedens centrale platform for dataanalyse og AI.<\/p>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Her udnyttes blandt andet:<\/p>\n<\/div>\n\n<div class=\"container-md list  \">\n<ul class=\"wp-block-list\"><div class=\"container-md list-item  \">\n<li>Central lagring af OT- og forretningsdata<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Avancerede analysefunktioner<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Machine Learning-modeller<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Semantic Views<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Snowflake Cortex AI-services<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Notebooks og Data Science-v\u00e6rkt\u00f8jer<\/li>\n<\/div><\/ul>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Kombinationen g\u00f8r det muligt at analysere produktionsdata p\u00e5 tv\u00e6rs af fabrikker og samtidig g\u00f8re dem tilg\u00e6ngelige for AI-baserede l\u00f8sninger.<\/p>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h2 class=\"wp-block-heading\">Fra SQL til naturligt sprog<\/h2>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Et af de mest interessante eksempler fra pr\u00e6sentationen er brugen af AI-agenter, der kan arbejde direkte p\u00e5 produktionsdata ved hj\u00e6lp af naturligt sprog.<\/p>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">I stedet for at skrive komplekse SQL-foresp\u00f8rgsler kan brugeren stille sp\u00f8rgsm\u00e5l som:<\/p>\n<\/div>\n\n<div class=\"container-md list  \">\n<ul class=\"wp-block-list\"><div class=\"container-md list-item  \">\n<li>Hvilke procesparametre p\u00e5virker kvaliteten mest?<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Hvilke produkter afviger fra normalen?<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Hvordan adskiller denne produktionsserie sig fra andre?<\/li>\n<\/div><\/ul>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">AI-platformen oms\u00e6tter derefter sp\u00f8rgsm\u00e5lene til analyser og leverer resultater baseret p\u00e5 de tilg\u00e6ngelige data. Dermed bliver avanceret analyse tilg\u00e6ngelig for langt flere medarbejdere end tidligere.<\/p>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h2 class=\"wp-block-heading\">N\u00e5r dokumentation og live-data arbejder sammen<\/h2>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">L\u00f8sningen handler ikke kun om numeriske data. AI anvendes ogs\u00e5 til at analysere arbejdsinstruktioner, procesbeskrivelser og teknisk dokumentation.<\/p>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h3 class=\"wp-block-heading\">AI forst\u00e5r produktionsprocessen<\/h3>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Ved at kombinere procesviden med aktuelle produktionsdata kan AI identificere relevante sammenh\u00e6nge og forst\u00e5 produktionsforl\u00f8bet i kontekst.<\/p>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h3 class=\"wp-block-heading\">Bedre kvalitetsforudsigelser med AI<\/h3>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Det g\u00f8r det muligt at udvikle mere pr\u00e6cise modeller til kvalitetsforudsigelse og procesoptimering, s\u00e5 potentielle problemer kan identificeres tidligere.<\/p>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h2 class=\"wp-block-heading\">AI kan kombinere dokumentation og live-data<\/h2>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">L\u00f8sningen handler ikke kun om numeriske data. AI anvendes ogs\u00e5 til at analysere arbejdsinstruktioner, procesbeskrivelser og anden dokumentation.<\/p>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">N\u00e5r AI f\u00e5r adgang til b\u00e5de procesviden og aktuelle produktionsdata, bliver det muligt at:<\/p>\n<\/div>\n\n<div class=\"container-md list  \">\n<ul class=\"wp-block-list\"><div class=\"container-md list-item  \">\n<li>Forst\u00e5 produktionsprocessen i kontekst<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Identificere relevante variabler<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Udlede sammenh\u00e6nge mellem proces og kvalitet<\/li>\n<\/div>\n\n<div class=\"container-md list-item  \">\n<li>Underst\u00f8tte udviklingen af pr\u00e6diktive modeller<\/li>\n<\/div><\/ul>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Det g\u00f8r AI til mere end blot et rapporteringsv\u00e6rkt\u00f8j. Teknologien bliver aktivt involveret i beslutningsst\u00f8tte og procesoptimering.<\/p>\n<\/div>\n\n<div class=\"container-md heading  \">\n<h2 class=\"wp-block-heading\">Fundamentet for Industrial AI<\/h2>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Erfaringerne fra Siemens Energy viser, at succesfuld industriel AI ikke starter med AI-modellen. Den starter med datakvalitet, kontekst og en arkitektur, der skaber sammenh\u00e6ng mellem OT, Analytics og AI.<\/p>\n<\/div>\n\n<div class=\"container-md paragraph  \">\n<p class=\"wp-block-paragraph\">Ved at kombinere HighByte Intelligence Hub og Snowflake har virksomheden etableret en platform, hvor data ikke blot lagres, men oms\u00e6ttes til operationel viden, avancerede analyser og AI-drevne beslutninger. Det er denne evne til at g\u00f8re OT-data AI-klare, der kan blive en afg\u00f8rende konkurrencefordel for fremtidens industrielle virksomheder.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Kunstig intelligens i industrien handler ikke l\u00e6ngere om fremtidige muligheder. For Henrik Birk og teamet hos Siemens Energy er AI allerede en integreret del af arbejdet med at analysere produktionsdata, optimere processer og underst\u00f8tte kvalitetsforbedringer.<\/p>\n","protected":false},"featured_media":0,"template":"","insight_category":[30],"class_list":["post-14554","insights","type-insights","status-publish","hentry","insight_category-artikel"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Hvordan Siemens Energy g\u00f8r OT data klar til AI<\/title>\n<meta name=\"description\" content=\"Hos Siemens Energy er AI allerede en integreret del af arbejdet med at analysere produktionsdata, optimere processer og forbedre kvaliteten.\" \/>\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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