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aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-278555a80c","additionalClasses":"headline-decoration","type":"heading2","lines":["Presentazione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ed6e3e80b8","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EIl data mining è una tecnica che utilizza algoritmi e principi di analisi statistica, spesso in combinazione con machine learning e analisi dei dati, per analizzare data set di grandi dimensioni e individuare schemi, anomalie e altri insight. L’ampia disponibilità di strumenti per la raccolta e l’archiviazione dei dati consente anche alle piccole organizzazioni di raccogliere e analizzare grandi quantità di dati, che si tratti di preferenze dei clienti, attività degli utenti, gestione dell’inventario o qualsiasi altra funzione aziendale.\u003C/p\u003E\n\u003Cp\u003ELe organizzazioni utilizzano il data mining per formulare previsioni efficaci, identificare i colli di bottiglia del sistema e individuare potenziali problemi prima che abbiano un impatto. Le nuove \u003Ca href=\"https://www.snowflake.com/it/product/ai/\"\u003Efunzionalità AI\u003C/a\u003E possono democratizzare l’accesso agli insight del data mining, poiché consentono agli stakeholder di chiedere informazioni sugli schemi dei dati e testare ipotesi su tali dati senza l’input diretto di un analista o di un data engineer. \u003C/p\u003E\n\u003Cp\u003EIn questo articolo discuteremo i fondamenti del data mining e descriveremo come utilizzare la tecnologia per ottenere i principali vantaggi aziendali.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__222865509":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1413360324":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__659376865":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-baac2621b1","additionalClasses":"headline-decoration","type":"heading2","lines":["Che cos’è il data mining?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-38f4bed11f","text":"\u003Cp\u003ELa raccolta dei dati avviene intorno a noi e in ogni momento, tracciando tutto, dai prodotti che acquistiamo alla nostra frequenza cardiaca durante la giornata. Le aziende raccolgono ancora più dati sulle proprie operazioni e possono trarre vantaggio da queste informazioni utilizzando tecniche di data mining. Il data mining identifica associazioni tra punti dati e/o dati storici per generare insight o prevedere il futuro. \u003C/p\u003E\n\u003Cp\u003EPrendiamo ad esempio i dati generati da una catena di alimentari, dove i dati di vendita mostrano un aumento delle vendite di gelato durante l’estate e un aumento della domanda di medicinali per il raffreddore durante l’inverno. Questi dati potrebbero non sorprenderti, ma le tecniche di data mining aiutano le organizzazioni a scoprire schemi inaspettati nascosti nei dati. Ad esempio, un’analisi del data mining potrebbe dimostrare che un aumento della domanda di determinati alimenti o integratori vitaminici è correlato a un aumento delle vendite di pannolini nove mesi dopo, il che suggerisce che questi prodotti sono popolari tra le future madri. \u003C/p\u003E\n\u003Cp\u003EL’enorme volume di dati con cui le organizzazioni lottano può rendere questo tipo di insight impossibile da rilevare senza l’aiuto di \u003Ca href=\"https://www.snowflake.com/en/product/features/end-to-end-ml-workflows/\"\u003Estrumenti di machine learning\u003C/a\u003E e dell’analisi statistica. Gli strumenti di data mining possono raggruppare i punti dati correlati e categorizzare i dati in modi imprevisti, consentendo alle organizzazioni di reagire rapidamente a cambiamenti imprevisti e prevedere le esigenze future.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f6f6a2608e","additionalClasses":"headline-decoration","type":"heading2","lines":["Perché è utile il data mining? 4 vantaggi"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-f1ac85b88e","text":"\u003Cp\u003EIl data mining e l’analisi dei dati forniscono alle organizzazioni una comprensione delle prestazioni operative, delle scelte dei clienti e degli schemi storici, consentendo loro di prendere decisioni più informate. Ecco quattro vantaggi chiave del data mining.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EMigliora il processo decisionale\u003C/h3\u003E\n\u003Cp\u003EPiuttosto che basare le scelte su presupposti o best practice di settore, il data mining offre alle organizzazioni un supporto basato sui dati, aiutandole a comprendere i vantaggi e i compromessi di ogni scelta e riducendo le congetture al momento di prendere le decisioni.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERileva frodi e anomalie\u003C/h3\u003E\n\u003Cp\u003EAnalizzando dati storici e in tempo reale, gli strumenti di data mining possono identificare schemi o altre variabili che potrebbero indicare comportamenti dannosi o a rischio. Ad esempio, l’esame degli schemi di utilizzo dei bancomat può aiutare le banche a rilevare attività correlate alla clonazione delle carte o ad altre truffe. In questo modo possono bloccare le transazioni sospette e segnalarle per le indagini.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EOttimizza i processi aziendali\u003C/h3\u003E\n\u003Cp\u003EL’analisi dei dati sull’utilizzo dei servizi, del comportamento dei flussi di acquisto e dei tempi di risposta dei ticket di assistenza può evidenziare colli di bottiglia operativi e sistemi sovraccarichi in tutta l’organizzazione. Questo può contribuire a migliorare l’allocazione delle risorse, ridurre il tempo medio di riparazione (MTTR) e ridurre la latenza del sistema.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESupporta la modellazione predittiva\u003C/h3\u003E\n\u003Cp\u003EUna delle applicazioni più potenti del data mining è la previsione, che estrapola schemi nei dati storici per prevedere i comportamenti futuri. Questo può essere utile per la logistica e la pianificazione aiutando a gestire gli inventari per garantire la disponibilità dei prodotti e nella gestione delle risorse prevedendo quanta capacità di calcolo sarà necessaria per una particolare operazione o lancio di un prodotto.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1444615495":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-cc33c60dd7","additionalClasses":"headline-decoration","type":"heading2","lines":["Le sfide del data mining"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-12d7a1b4a8","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ENonostante il suo enorme potenziale, il data mining comporta anche alcune sfide uniche che possono ridurne l’efficacia. Ecco alcuni dei maggiori problemi potenziali:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ECosti e volumi di dati elevati\u003C/h3\u003E\n\u003Cp\u003EIl data mining richiede una grande quantità di dati per essere utile, e questo può comportare carichi di archiviazione ed elaborazione su vasta scala. Ogni fase del processo di data mining, dall’\u003Ca href=\"https://www.snowflake.com/it/fundamentals/data-ingestion/\"\u003Eingestion\u003C/a\u003E all’\u003Ca href=\"https://www.snowflake.com/it/fundamentals/data-warehouse/\"\u003Earchiviazione\u003C/a\u003E fino all’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/automated-data-processing/\"\u003Eelaborazione\u003C/a\u003E, richiede risorse di calcolo e un livello elevato di investimenti che alcune organizzazioni potrebbero non essere in grado di giustificare. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EIncertezza nei risultati\u003C/h3\u003E\n\u003Cp\u003EAnche se un processo di data mining scopre uno schema o fa una previsione, non vi sono garanzie che la previsione sia corretta o che lo schema offra valore aziendale. Anche cambiamenti imprevisti nel mercato o nelle preferenze dei consumatori possono ridurre l’utilità degli insight ricavati dai dati. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EComplessità degli algoritmi\u003C/h3\u003E\n\u003Cp\u003ELe tecniche di data mining tendono a essere piuttosto complesse e richiedono test iterativi, valutazione e miglioramento continuo per adattarsi ai cambiamenti. Questo processo può essere costoso e richiedere molto lavoro, sottraendo risorse ad altre operazioni aziendali importanti. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EProblemi di qualità dei dati\u003C/h3\u003E\n\u003Cp\u003EIl data mining dipende dalla disponibilità di dati accurati e utilizzabili per fornire valore. Le inefficienze della pipeline di dati, i bias nel data set, l’inclusione inavvertita di dati sensibili e altri problemi possono creare rischi o ridurre la qualità dell’analisi.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1978652079":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-a7359d8052","additionalClasses":"headline-decoration","type":"heading2","lines":["Come funziona il data mining?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-647760c6ee","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EIl data mining non comporta un algoritmo o un software autonomo, ma piuttosto un processo di mining strategico in più fasi. Ecco come funziona:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E1. Definire gli obiettivi aziendali\u003C/h3\u003E\n\u003Cp\u003EPrima di raccogliere ed \u003Ca href=\"https://www.snowflake.com/en/fundamentals/online-analytical-processing/\"\u003Eelaborare\u003C/a\u003E qualsiasi dato, le organizzazioni devono stabilire una serie chiara di obiettivi per le proprie iniziative. Poiché la raccolta e l’archiviazione dei dati richiedono un’elevata elaborazione, è importante scegliere le fonti di dati più appropriate e complete e stabilire se ci sono abbastanza dati disponibili per ricavarne insight significativi. La scelta di obiettivi realistici aiuta anche gli analisti a scegliere il modello di data mining migliore.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E2. Raccogliere e consolidare i dati\u003C/h3\u003E\n\u003Cp\u003EOttimizzare il processo di raccolta significa impostare parametri di raccolta efficienti da applicare alle fonti di dati identificate. Raccogliere troppi dati può essere complesso, poiché l’archiviazione e l’elaborazione sono troppo complesse, ma la scarsità dei dati può limitare l’utilità del data set. È inoltre importante identificare eventuali rischi all’interno delle fonti di dati prima di anonimizzare e proteggere i dati sensibili.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E3. Pulire e preparare i dati\u003C/h3\u003E\n\u003Cp\u003ELa pulizia dei dati è una fase di elaborazione critica che rimuove gli outlier e il rumore e tiene conto di eventuali valori dei dati mancanti. Anche la standardizzazione dei formati dei dati è importante, in particolare quando si raccolgono dati da molte fonti diverse.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E4. Addestrare il modello\u003C/h3\u003E\n\u003Cp\u003EPrima di poter utilizzare i modelli per riconoscere schemi utili, potrebbe essere necessario prima addestrarli e perfezionarli. L’addestramento consiste nel regolare i pesi di diverse variabili, ad esempio assegnando più peso ai dati raccolti di recente su dati molto più vecchi o modificando le dimensioni del data set e il numero di dimensioni che si stanno analizzando.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E5. Pattern mining\u003C/h3\u003E\n\u003Cp\u003EL’implementazione di un modello addestrato per analizzare un data set grezzo di grandi dimensioni consente di identificare schemi, relazioni o trend statisticamente significativi all’interno dei dati. I dettagli di questa fase dipenderanno dai tuoi obiettivi. Per un modello predittivo, potrebbe trattarsi di analizzare le tendenze storiche per prevedere i cambiamenti nel comportamento degli utenti, mentre un modello di analisi del testo potrebbe monitorare il sentiment dei consumatori analizzando le recensioni dei clienti.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003E6. Valutare le prestazioni del modello\u003C/h3\u003E\n\u003Cp\u003EAnche se un modello di data mining raggiunge l’obiettivo desiderato, probabilmente trarrà vantaggio da un ulteriore perfezionamento, in particolare se saranno disponibili nuove fonti di dati o sarà sviluppato un modo più efficiente di analizzarli dal punto di vista computazionale.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__455068363":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-e478976a65","additionalClasses":"headline-decoration","type":"heading2","lines":["Tecniche di data mining"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8b0f98b226","text":"\u003Cp\u003EEsistono diverse tecniche di data mining, ciascuna adatta a un particolare set di obiettivi o tipo di dati. Ecco alcuni degli approcci più diffusi:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnalisi della regressione\u003C/h3\u003E\r\n\u003Cp\u003EUn’analisi di regressione esamina la relazione tra un particolare punto dati, denominato variabile dipendente, e una o più variabili indipendenti. Un esempio comune potrebbe essere l’analisi dell’elasticità dei prezzi, che misura in che modo le variazioni del prezzo di uno specifico prodotto potrebbero influenzare la domanda di tale prodotto. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAnalisi predittiva \u003C/h3\u003E\r\n\u003Cp\u003EUtilizzando dati storici, gli algoritmi predittivi creano un modello matematico che prevede possibili comportamenti futuri. Le \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/\"\u003Eaziende manifatturiere\u003C/a\u003E implementano questo modello per valutare l’utilizzo dei macchinari e identificare i componenti che potrebbero essere a rischio di guasto, inducendo una riparazione o una sostituzione proattiva. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EClassificazione\u003C/h3\u003E\r\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-classification/\"\u003Eclassificazione dei dati\u003C/a\u003E viene utilizzata per raggruppare i dati che condividono una caratteristica predefinita, ad esempio classificando alcuni tipi di comportamento degli utenti (come i messaggi email) come sospetti o non sospetti. Affinando queste classificazioni, le organizzazioni possono distribuirle per rilevare spam o attività di rete dannose. La classificazione è spesso una forma di machine learning supervisionato, il che significa che l’algoritmo viene addestrato su dati che sono già stati etichettati in base a queste caratteristiche predefinite.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EClustering\u003C/h3\u003E\r\n\u003Cp\u003EGli algoritmi di clustering creano gruppi di dati in base alle loro caratteristiche condivise anziché a classificazioni predefinite. Le organizzazioni le utilizzano per scoprire nuovi gruppi o schemi comportamentali, ad esempio per identificare un segmento di clienti che hanno preferenze di prodotto simili. Il clustering è in genere una forma di ML non supervisionato, il che significa che può essere distribuito per analizzare dati non etichettati. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003EAlberi decisionali\u003C/h3\u003E\r\n\u003Cp\u003EUn albero decisionale è una struttura visiva che suddivide un data set in base a decisioni diverse, che si dividono in più decisioni prima di concludersi con un possibile risultato o probabilità. Alcuni algoritmi diagnostici medici utilizzano questo metodo, ordinando i pazienti in base all’età, alla pressione sanguigna e alla presenza di determinati sintomi per determinare la probabilità di un particolare problema medico o malattia. \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch3\u003ERilevamento delle anomalie\u003C/h3\u003E\r\n\u003Cp\u003EIl \u003Ca href=\"https://www.snowflake.com/en/fundamentals/anomaly-detection/\"\u003Erilevamento delle anomalie\u003C/a\u003E identifica e monitora l’attività dei dati che non rientra nella norma del comportamento atteso, ad esempio una query su un database che improvvisamente inizia a utilizzare molta più potenza della CPU per l’esecuzione. L’utilizzo di queste informazioni può aiutare le organizzazioni a identificare e correggere un collo di bottiglia o un’inefficienza prima che causino problemi di prestazioni.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__222865509":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-aa84db5ce3","additionalClasses":"headline-decoration","type":"heading2","lines":["Esempi e casi d’uso del data mining"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-a20718c20a","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EI team di ogni settore si affidano a insight data-driven per migliorare i processi decisionali e la produttività. Ecco alcuni esempi di come le organizzazioni utilizzano il data mining in tutte le loro operazioni:\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ESegmentazione e targeting dei clienti\u003C/h3\u003E\n\u003Cp\u003EUtilizzando il clustering, i \u003Ca href=\"https://www.snowflake.com/it/solutions/departments/marketing/\"\u003Eteam marketing\u003C/a\u003E possono segmentare il proprio mercato indirizzabile in modo più efficiente, raggruppando i consumatori in base alle loro preferenze condivise. Questo consente loro di adattare le attività di marketing alle esigenze e alle aspettative di ogni segmento, migliorando i risultati e identificando nuove opportunità.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003ERilevamento delle frodi nel settore bancario\u003C/h3\u003E\n\u003Cp\u003EI team di sicurezza possono classificare diversi tipi di attività degli utenti, stabilendo un valore di riferimento per il comportamento atteso e segnalando \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003Epotenziali frodi\u003C/a\u003E che si discostano dalla norma, come addebiti all’estero o sulla carta di credito eccessivamente elevati. Possono anche analizzare i dati storici relativi agli incidenti di sicurezza, utilizzando il rilevamento delle anomalie per cercare schemi di dati che fanno presagire attività ostili. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EEfficienza operativa nella logistica\u003C/h3\u003E\n\u003Cp\u003EI modelli previsionali possono aiutare i team logistici a migliorare l’efficienza della supply chain prevedendo le variazioni della domanda, il che contribuisce a garantire una disponibilità uniforme dei prodotti. Possono anche analizzare complessi \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/manufacturing/improve-supply-chain-performance/\"\u003Edata set della supply chain\u003C/a\u003E per individuare modelli non visibili, come l’effetto che le condizioni meteorologiche possono avere sul prezzo di particolari materie prime. \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch3\u003EAnalisi del rischio per i pazienti in ambito sanitario\u003C/h3\u003E\n\u003Cp\u003EGli analisti sanitari utilizzano il clustering dei dati per identificare nuovi fattori di rischio, compresi quelli che potrebbero non rientrare nella diagnostica medica convenzionale. Collegando caratteristiche come la posizione, la professione o altri fattori del paziente a problemi medici specifici, il data mining può aumentare i risultati positivi e aiutare gli operatori \u003Ca href=\"https://www.snowflake.com/it/solutions/industries/healthcare-and-life-sciences/\"\u003Esanitari\u003C/a\u003E a fornire cure più specializzate. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1413360324":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__659376865":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"conclusion",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-bf053b6826","additionalClasses":"headline-decoration","type":"heading2","lines":["Conclusione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-caac8175dd","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EIl data mining è diventato una parte essenziale di molte aziende, consentendo alle organizzazioni di identificare nuove opportunità, creare prodotti migliori e aumentare l’efficienza operativa. L’ampiezza dei diversi modelli di data mining consente alle organizzazioni di estrarre informazioni utili da molti tipi diversi di dati e di identificare schemi chiave tra variabili apparentemente non correlate. Sebbene il data mining possa essere impegnativo dal punto di vista computazionale e richieda un investimento significativo, la maggior parte delle organizzazioni ritiene che questi costi siano ampiamente compensati dai suoi numerosi vantaggi analitici.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1500909857":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-35b27e3c05","additionalClasses":"headline-decoration","type":"heading2","lines":["FAQ sul data mining"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-2f9ca40714","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"Quali sono le funzioni del data mining?","richText":"\u003Cp\u003EIl data mining ha un’ampia gamma di funzioni, tra cui prevedere cambiamenti futuri in un data set, monitorare le prestazioni del sistema monitorando i KPI, scoprire relazioni tra variabili diverse e ottimizzare il processo decisionale prevedendo il risultato di scelte diverse. Le funzioni che un’organizzazione sceglie di utilizzare dipendono dai suoi obiettivi e dai tipi di dati disponibili.\u003C/p\u003E\n"},{"title":"Quali strumenti software sono disponibili per il data mining?","richText":"\u003Cp\u003EIl data mining inizia con la raccolta e la pre-elaborazione dei dati. La maggior parte delle organizzazioni utilizza uno dei numerosi strumenti open source disponibili, come Apache Spark, che aiutano a raccogliere ed elaborare grandi quantità di dati. Piattaforme di analisi come \u003Ca href=\"https://www.snowflake.com/it/product/analytics/\"\u003ESnowflake\u003C/a\u003E offrono osservabilità, gestione e visualizzazione dei dati, contribuendo a ridurre i costi di archiviazione ed elaborazione dei dati e offrendo al contempo utili integrazioni basate su ML e AI. \u003C/p\u003E\n"},{"title":"Come viene applicato il data mining nella business analytics?","richText":"\u003Cp\u003ELe aziende possono utilizzare il data mining per valutare le prestazioni dei sistemi interni e identificare nuove opportunità di ottimizzazione. Possono anche utilizzare il data mining per migliorare la propria strategia go-to-market, analizzando il comportamento dei clienti e le prestazioni di marketing, ad esempio, per trovare il messaggio più efficace e per testare nuovi approcci al marketing e alle vendite.\u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__480869401":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-3eab5a9863","additionalClasses":"headline-decoration","type":"heading2","lines":["Clienti che utilizzano Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-e95c15b515","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-09bb851fdd",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-5bf3873b20","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["AI e ML aiutano IGS Energy a rendere le previsioni meno complesse e a rilevare le anomalie in modo più efficace"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/it/customers/all-customers/case-study/igs-energy/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi i dettagli"},":type":"snowflake-site/components/card-v2","image":{"id":"image","height":"720","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--2074716c-a705-47e3-8df9-866eafb13be2/igs-energy-customer-card%25402x.jpg?quality=85&preferwebp=true","alt":"simon data logo","lazyEnabled":true,"width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ECon Snowflake, IGS Energy usa i dati per risolvere i casi d’uso di AI/ML, come modelli previsionali più economici o rilevamento delle anomalie più accurato, e realizzare la sua missione di un futuro sostenibile per tutti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["card_v2"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-b825a6d79e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-fb0e3c76a8","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["WHOOP migliora le previsioni finanziarie tramite AI/ML e ottimizza l’esperienza degli utenti"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/whoop/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi i dettagli"},":type":"snowflake-site/components/card-v2","image":{"id":"image","height":"720","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8dd7c610-8288-425f-b72e-1a1bb30f06a8/whoop-customer-card%25402x.jpg?quality=85&preferwebp=true","alt":"penske logo","lazyEnabled":true,"width":"1680",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ECon Snowflake e Apache Iceberg, WHOOP dispone di un accesso centralizzato ai dati, riduce la complessità, abbassa i costi e migliora i processi critici.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["card_v2_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["title_v2","flexible_column_cont"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__373061683":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"resources",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-bf3451f45c","type":"heading2","lines":["Risorse sul data 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