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29, 2026","authors":[{"authorImage":{"id":"image-f4e5abd1a4","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fba90c87-8f06-45ca-8504-d29841673eb5/randy-soper.png?preferwebp=true&quality=85","lazyEnabled":true,"width":"502","height":"454",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-6a2786164b","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/randy-soper-/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Randy Soper "}},{"authorImage":{"id":"image-166589a903","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a4bcb75f-1d2d-420a-a12b-96ca6fb8bb6a/claire-walsh.png?preferwebp=true&quality=85","lazyEnabled":true,"width":"800","height":"800",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-e3c034e881","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/claire-walsh-/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Claire Walsh "}}],"tag":{"tagText":"Settore pubblico","tagColor":"#29B5E8"},"title":{"lines":["Insight da Slalom: le agenzie federali americane dovrebbero (ri)considerare Snowflake"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"blog_text":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-ee667bc5f4","text":"\u003Ch2\u003EIl nuovo calcolo decisionale federale\u003C/h2\u003E\n\u003Cp\u003EL’IT federale è cambiata, spesso in modo rapido e significativo. È l’occasione giusta per verificare se la tua organizzazione sta tenendo il passo. Probabilmente hai già definito piani solidi, tra strategia, approcci di acquisizione, governance, maturità organizzativa e architettura, ma da allora lo scenario tecnologico e le priorità dell’attuale amministrazione potrebbero essere cambiati. Questo è il momento giusto per rivedere quei piani e riallinearli alle opportunità di oggi.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ECosa non è cambiato? \u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ENegli USA, la maggior parte delle agenzie federali ha avviato il proprio percorso di modernizzazione dei dati per soddisfare i requisiti dell’Evidence Act e diventare un’agenzia basata sulle evidenze. Hanno iniziato con l’obiettivo di creare insight di business intelligence, supportare il machine learning per analisi predittiva e rilevamento delle frodi e condividere i dati in modo sicuro in tutta la forza lavoro. Lo hanno fatto per sostenere la propria missione, inclusi gli obiettivi di policy interagenzia, le esigenze dei servizi militari e gli obiettivi del DATA Act. Molte agenzie hanno iniziato a valutare o adottare cloud data platform per offrire accesso scalabile e sicuro ai dati e rispondere a queste esigenze, sviluppando strategie dati e approcci di data governance per migliorare qualità, discovery e disponibilità dei dati.  \u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ECosa è cambiato?\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EIn primo luogo, l’intelligenza artificiale continua a evolvere rapidamente. L’ambiente tecnologico necessario per supportarla richiede dati di alta qualità, accesso sicuro a foundation model di AI generativa all’avanguardia e hardware di calcolo specializzato, oltre ad ambienti e workflow in rapida evoluzione per gestire ciclo di vita, governance e controlli di capacità come generazione con retrieval augmentation (RAG) e Agentic AI. \u003C/p\u003E\n\u003Cp\u003EIn secondo luogo, le aspettative dell’amministrazione Trump nei confronti delle agenzie federali sono cambiate in modo significativo. L’amministrazione ha dato priorità alla riduzione dei costi nel governo federale, ha intrapreso azioni per ridurre il personale governativo e contrattuale in tutta l’amministrazione e si aspetta che le agenzie mantengano l’efficacia operativa in condizioni di austerità \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.nextgov.com/artificial-intelligence/2025/08/trump-administration-hopes-ai-can-mitigate-staffing-losses-federal-cio-says/407514/\"\u003Eutilizzando intelligenza artificiale e automazione\u003C/a\u003E. Inoltre, l’amministrazione ha indicato nuovi approcci di acquisizione. La \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.acquisition.gov/far-overhaul/far-part-deviation-guide/far-overhaul-part-1#FAR_1_102\"\u003Esezione 1.102(a)(3) del Revolutionary FAR Overhaul (RFO)\u003C/a\u003E indica una preferenza per le soluzioni commerciali, massimizzando l’utilizzo di prodotti commerciali prontamente disponibili rispetto alle “soluzioni uniche per il governo”.\u003C/p\u003E\n\u003Cp\u003ELe decisioni sulle piattaforme dati prese prima di questi cambiamenti derivavano dai modelli tradizionali di acquisto tecnologico del governo. Sebbene le piattaforme fossero spesso pensate per supportare ampiamente la missione, le giustificazioni dei programmi si concentravano di norma su risultati specifici e prioritari. Questa maggiore preferenza per configurazioni e integrazioni “uniche per il governo” ha migliorato in modo mirato prestazioni specifiche della missione e ha indirizzato le valutazioni tecnologiche lungo percorsi che hanno portato il governo a preferire determinate piattaforme.  \u003C/p\u003E\n\u003Cp\u003ELe agenzie hanno bisogno di piattaforme dati che supportino il successo complessivo della missione e puntino al valore di budget finale, mantenendo sicurezza dei dati, garanzia della missione e auditabilità. Se cerchi un time to value rapido con overhead operativo minimo, oltre a prestazioni e user experience pensate per business analyst e utenti, vale la pena rivalutare Snowflake. Snowflake è implementato dal 40 % delle aziende \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/news/press-releases/snowflake-reports-financial-results-for-the-fourth-quarter-and-full-year-of-fiscal-2026/\"\u003EForbes Global 2000\u003C/a\u003E (al 31 gennaio 2026). Snowflake è particolarmente forte nei servizi finanziari, che condividono con il governo l’esigenza attuale di abilitare il business in modo economicamente efficiente, con elevati livelli di garanzia e sicurezza. È utilizzato da oltre il 50 % delle aziende di servizi finanziari Fortune 500.\u003C/p\u003E\n\u003Cp\u003EVediamo dove Snowflake può fare la differenza nell’attuale contesto federale.\u003C/p\u003E\n\u003Ch2\u003EUtilizzare una strategia “and” per potenziare gli investimenti esistenti in cloud data platform\u003C/h2\u003E\n\u003Cp\u003EUn equivoco comune è che le agenzie debbano scegliere un’unica piattaforma dati. Nella realtà commerciale non è così: spesso le piattaforme dati vengono utilizzate insieme come moltiplicatori di forza. Utilizza Snowflake come data foundation governata e serving layer ad alta concorrenza per analisi dei dati di missione e intelligenza artificiale.\u003C/p\u003E\n\u003Cp\u003EIn pratica, Snowflake diventa il posto in cui le agenzie creano dataset di “verità” durevoli, auditabili e condivisibili, i data product curati che alimentano dashboard, supporto decisionale operativo e app di intelligenza artificiale. Le piattaforme specializzate si connettono quindi a quella data foundation governata per eseguire workflow avanzati, mentre gli output risultanti vengono pubblicati di nuovo in Snowflake, così possono essere riutilizzati in tutta l’enterprise invece di restare intrappolati in un altro silo.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003ESnowflake + Palantir: Palantir Foundry richiede una data foundation pulita e su scala massiva. Snowflake agisce come serving layer ad alta concorrenza e alimenta Foundry con dati governati in modo più efficace rispetto allo storage on-premise legacy.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ESnowflake + Databricks: Snowflake funge da repository “gold layer” ideale per i modelli di machine learning Databricks, rendendo gli insight ad alto valore immediatamente accessibili ai business analyst tramite dashboard SQL standard, senza dover imparare Python o Spark.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ESnowflake è anche in grado di rispondere alle esigenze di concorrenza dell’enterprise man mano che cresce l’utilizzo dell’Agentic AI. Un singolo agente AI può attivare molte query di retrieval parallele, lookup delle feature e valutazioni, mentre numerosi analisti, dashboard e app accedono agli stessi dataset governati. Snowflake lo supporta \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/warehouses-considerations\"\u003Eisolando i workload con virtual warehouse separati\u003C/a\u003E e assorbendo i picchi con warehouse multi‑cluster, che possono aggiungere cluster di calcolo quando si formano code e scalare verso il basso quando la domanda diminuisce.\u003C/p\u003E\n\u003Cp\u003ECon Snowflake come fonte dei dati di “verità” nell’enterprise, la risorsa dati agentica di riferimento e la data foundation per la resilienza alla concorrenza sono allineate.\u003C/p\u003E\n\u003Ch2\u003ELiberare i dati dai “walled garden” di ServiceNow e Salesforce \u003C/h2\u003E\n\u003Cp\u003ELe agenzie hanno investito enormi somme in piattaforme low-code come ServiceNow e Salesforce per modernizzare i workflow. Il rischio è creare nuovi silos di dati, in cui dati mission-critical restano intrappolati nei formati proprietari di questi SaaS. Attraverso pattern di ingestion standard (connettori, CDC e streaming) e Secure Data Sharing, Snowflake consente di acquisire e mantenere aggiornati i dati da piattaforme come \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/connectors/servicenow/ingestion\"\u003EServiceNow\u003C/a\u003E e Salesforce con una \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview\"\u003Ecadenza programmata o quasi in tempo reale\u003C/a\u003E, senza creare pipeline ETL fragili e personalizzate. Questo abilita una visibilità operativa trasversale ai sistemi. Immagina una dashboard che correla i dati degli incidenti IT di ServiceNow con i dati di gestione dei casi di Salesforce per prevedere le interruzioni della missione prima che si verifichino. Snowflake rende questa integrazione molto più semplice e governabile.\u003C/p\u003E\n\u003Cp\u003ELa chiave non è solo acquisire dati dalle piattaforme SaaS, ma renderli utilizzabili tra missioni diverse. Snowflake supporta pattern di ingestion incrementale per mantenere aggiornati i dati operativi, quindi abilita un livello condiviso di “data product” in cui dati su incidenti, casi, asset e identità possono essere standardizzati e collegati una sola volta, così i programmi downstream non devono ricostruire l’integrazione ogni volta. Il risultato per la missione è una correlazione più rapida tra sistemi per previsione delle interruzioni, rilevamento di frodi e rilevamento delle anomalie, oltre a reporting sulla maturità operativa, applicando al tempo stesso controlli di governance coerenti alla fonte di verità.\u003C/p\u003E\n\u003Ch2\u003EIl vantaggio “pay-for-usage” per una gestione finanziaria responsabile\u003C/h2\u003E\n\u003Cp\u003EA differenza dei database legacy, che richiedono di allocare e pagare capacità di picco 24 ore su 24, 7 giorni su 7, Snowflake separa il compute dallo storage. \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.cn/en/user-guide/warehouses-overview\"\u003EPuoi disattivare immediatamente i tuoi warehouse (compute)\u003C/a\u003E quando un task è completato. Questo si allinea perfettamente agli obiettivi dell’amministrazione per la modernizzazione tecnologica. Puoi dimostrare il ROI diretto mostrando che i costi del compute possono scalare in basso quando l’attività diminuisce, liberando fondi per l’innovazione invece che per la manutenzione. Questo modello è particolarmente efficace per la domanda federale: consente un picco di capacità per il reporting di fine mese, le operazioni di risposta o le esecuzioni di training di modelli, per poi ridimensionarsi. Invece di pagare capacità di picco tutto l’anno, le agenzie possono adeguare la taglia dei warehouse di base applicando guardrail di auto-suspend e auto-resume. Possono anche assegnare warehouse dedicati per workload, tra cui BI, pipeline, data science e intelligenza artificiale, per migliorare il controllo dei costi e la prevedibilità delle prestazioni. \u003C/p\u003E\n\u003Cp\u003EAdottare Snowflake non significa sostituire gli strumenti attuali. Significa renderli adatti al futuro. Offre la data foundation governata, sicura e scalabile che consente di avviare i progetti pilota di intelligenza artificiale, far comunicare tra loro le piattaforme SaaS e accelerare gli strumenti specializzati per la missione.\u003C/p\u003E\n\u003Ch2\u003EMaturità per l’intelligenza artificiale e Agentic AI\u003C/h2\u003E\n\u003Cp\u003ENel 2024 e nel 2025, le agenzie hanno sperimentato l’AI generativa. Nel 2026, il mandato riguarda l’Agentic AI, ossia sistemi autonomi in grado di ragionare ed eseguire task. Il più grande collo di bottiglia per l’intelligenza artificiale nel settore federale non è il modello. Sono i dati.\u003C/p\u003E\n\u003Cp\u003ECon \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/developer-guide/snowpark/index\"\u003ESnowpark\u003C/a\u003E e i servizi di intelligenza artificiale gestiti da Snowflake, come \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003ECortex\u003C/a\u003E, \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/it/blog/cortex-ai-fedramp-moderate-authorization/\"\u003Edove autorizzati\u003C/a\u003E, le agenzie possono portare il compute vicino ai dati, invece di spostare petabyte di dati sensibili verso il modello. A rafforzare questo approccio contribuiscono i controlli di governance nativi, inclusi accesso basato sui ruoli, dynamic masking e auditabilità, che aiutano i team a scalare l’accesso in sicurezza. Mantenendo i dati governati all’interno degli \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.businesswire.com/news/home/20231211980298/en/Snowflake-Achieves-FedRAMP-High-Authorization-on-AWS-GovCloud-US-West-and-US-East\"\u003Eambienti Snowflake Government (SnowGov) autorizzati per FedRAMP High\u003C/a\u003E (e \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.cn/en/user-guide/cert-dodIL5\"\u003EDoW IL5 dove applicabile\u003C/a\u003E), le agenzie possono eseguire workflow di analisi dei dati e intelligenza artificiale vicino ai dati, entro un perimetro accreditato.\u003C/p\u003E\n\u003Cp\u003EPassare da progetti pilota di intelligenza artificiale di breve durata a operazioni di intelligenza artificiale ripetibili offre vantaggi concreti: Accesso governato a dataset curati, pipeline di trasformazione ripetibili ed esecuzione controllata di workflow di feature engineering, retrieval e scoring, senza esportare dati sensibili in silos di strumenti non accreditati.\u003C/p\u003E\n\u003Ch2\u003ESlalom può aiutarti\u003C/h2\u003E\n\u003Cp\u003ESlalom ha realizzato oltre \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.slalom.com/us/en/who-we-are/partners/snowflake\"\u003E2700 progetti Snowflake di successo\u003C/a\u003E ed è \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.slalom.com/us/en/who-we-are/newsroom/snowflake-global-data-cloud-services-ai-poy\"\u003ESnowflake Global AI Data Cloud Services AI Partner of the Year 2025\u003C/a\u003E. Slalom è diversa: Offriamo \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.slalom.com/us/en/industries/public-social-impact/federal\"\u003Eai nostri clienti federali\u003C/a\u003E la stessa profonda esperienza maturata applicando Snowflake in ambienti commerciali regolamentati, combinando innovazione commerciale e rigore richiesto per dati, sicurezza e operazioni federali. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"gridClassNames":"aem-Grid aem-Grid--12 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authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-f4e5abd1a4","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fba90c87-8f06-45ca-8504-d29841673eb5/randy-soper.png?preferwebp=true&quality=85","lazyEnabled":true,"width":"502","height":"454",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-6a2786164b","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/randy-soper-/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Randy Soper "},"authorTitle":"Senior Director, Federal and AI lead at Slalom"},{"authorImage":{"id":"image-166589a903","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a4bcb75f-1d2d-420a-a12b-96ca6fb8bb6a/claire-walsh.png?preferwebp=true&quality=85","lazyEnabled":true,"width":"800","height":"800",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-e3c034e881","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/claire-walsh-/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Claire Walsh "},"authorTitle":"Senior Director at 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