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[...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/ko/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-d74ae5a987","additionalClasses":"headline-decoration","type":"heading2","lines":["개요"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8c13d49468","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E의사 결정을 내리는 데 실시간 데이터만으로는 충분하지 않습니다. 조직은 비즈니스의 복잡한 구조를 이해하고 현재의 인사이트를 활용해 미래를 계획해야 합니다. 과거의 성과를 분석함으로써 미래의 결과를 보다 정확하게 예측할 수 있습니다. 바로 이러한 이유로 예측 분석이 중요한 역할을 합니다.\u003C/p\u003E\n\u003Cp\u003E예측 분석은 데이터 분석의 하위 집합으로, 미래를 \u003Ca href=\"https://www.snowflake.com/en/customers/all-customers/case-study/igs-energy/\"\u003E예측\u003C/a\u003E하는 데 중점을 둡니다. 이를 통해 조직의 리스크를 완화하고 개인화된 고객 여정을 설계할 수 있으며 비즈니스 전반의 전략 계획 수립에서 핵심적인 역할을 합니다. \u003C/p\u003E\n\u003Cp\u003E이 글에서는 예측 분석의 핵심 개념과 작동 방식, 그리고 예측 분석을 효과적으로 활용했을 때 비즈니스 기능 전반과 다양한 산업에서 조직에 어떤 긍정적인 영향을 미칠 수 있는지를 살펴봅니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","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"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"what","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-16116e9b81","additionalClasses":"headline-decoration","type":"heading2","lines":["예측 분석이란?"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-6b43189342","text":"\u003Cp\u003E예측 분석은 과거 데이터를 활용해 발생 가능한 시나리오를 도출하고 미래 추세를 예측함으로써 데이터 기반의 전략적 의사 결정을 지원합니다. 이를 통해 조직은 향후 예상되는 결과를 보다 명확하게 파악할 수 있습니다. \u003C/p\u003E\r\n\u003Cp\u003E예측 분석은 더 큰 데이터 분석 범주를 구성하는 요소 중 하나이며, 이 범주는 기술 분석, 진단 분석, 처방 분석이라는 세 가지 다른 분석 유형으로 이루어져 있습니다. 예측 분석은 미래에 무엇이 일어날 수 있는지에 초점을 두는 반면, 기술 분석은 실제로 무엇이 일어났는지, 진단 분석은 왜 그런 일이 발생했는지, 그리고 처방 분석은 다음에 무엇을 해야 하는지에 각각 집중합니다. \u003C/p\u003E\r\n\u003Cp\u003E예측 분석은 과거 데이터를 기반으로 수작업 프로세스와 \u003Ca href=\"https://www.snowflake.com/ko/product/ai/\"\u003EAI 및 머신러닝 도구\u003C/a\u003E를 함께 활용해 미래에 대한 가정을 도출합니다. 핵심 원칙에는 머신러닝, 예측 모델링, 데이터 마이닝과 같은 다양한 통계 기법이 포함됩니다.\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"]},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"a","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-95b0e9095b","additionalClasses":"headline-decoration","type":"heading2","lines":["예측 분석의 작동 방식"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0fb7f31443","text":"\u003Cp\u003E예측 분석은 다음에 어떤 일이 발생할지를 보다 명확하게 분석할 수 있도록 조직을 지원합니다. 이는 \u003Ca href=\"https://www.snowflake.com/ko/solutions/departments/finance/\"\u003E조직의 재무 정보\u003C/a\u003E와 고객 행동 등 과거 데이터를 미래 지향적인 예측으로 전환하는 기능을 합니다.\u003C/p\u003E\n\u003Cp\u003E예측 분석 모델링을 시작할 때 필요한 핵심 구성 요소는 다음과 같습니다. 먼저 해결하고자 하는 문제를 정의하고, 해당 문제와 관련된 데이터를 수집한 다음, 그 데이터를 의미 있고 유의미한 방식으로 정리합니다. 그 이후 조직은 예측 모델을 개발해 향후 발생 가능성이 높은 결과에 대한 인사이트를 도출할 수 있습니다. \u003C/p\u003E\n\u003Cp\u003E이 과정에서 데이터 사이언티스트는 모델을 활용해 데이터 세트 내 여러 데이터 포인트 간의 상관관계를 파악합니다. AI, 머신 러닝, 데이터 마이닝 및 통계를 활용해 과거에 발생했던 패턴과 동일한 사건이 미래에 다시 발생할 가능성을 분석하고 비즈니스 성과에 영향을 미치는 핵심 변수를 식별한 뒤 가장 가능성 높은 예측 결과를 도출합니다.\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"]},"container_copy_copy__1444615495":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-37de6756a7","additionalClasses":"headline-decoration","type":"heading2","lines":["예측 분석 모델의 유형"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-0c2f8edf9c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E예측 분석에는 다섯 가지 주요 모델 또는 방법이 포함됩니다. 분류 모델, 클러스터링 모델, 시계열 모델, 회귀 모델 및 앙상블 기법이 이에 해당합니다. 아래에서 각각을 자세히 살펴봅니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E분류 모델\u003C/h4\u003E\n\u003Cp\u003E이 모델은 과거 정보를 기반으로 데이터를 분류하고 데이터 세트 내 관계를 설명하는 역할을 합니다. 머신러닝을 활용해 데이터와 주어진 레이블 간의 관계를 학습하고, 새로운 데이터가 추가될 때 이를 분류할 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E클러스터링 모델\u003C/h4\u003E\n\u003Cp\u003E이 모델은 유사한 특성을 기준으로 데이터 포인트를 그룹화합니다. 데이터 행렬을 활용해 유사한 데이터 포인트를 연결하고, 기존 모델링 기법으로는 놓치기 쉬웠던 패턴을 발견할 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E시계열 모델\u003C/h4\u003E\n\u003Cp\u003E이 모델은 일별, 주별, 월별 또는 연간 등 일정 기간에 걸쳐 수집된 데이터 포인트를 사용합니다. 이러한 모델링 방식은 예측 분석에서 가장 널리 활용되는 접근 방식으로, 계절적 변화와 주기적인 고객 행동, 시간의 흐름에 따른 추세를 평가합니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E회귀 모델\u003C/h4\u003E\n\u003Cp\u003E이 모델은 일정 기간 동안 두 변수 간의 관계를 해석하는 데 사용됩니다. 고객 데이터를 기반으로 해당 고객이 생애 주기 동안 창출할 매출을 예측할 수 있으며 특정 행동이 전체 비즈니스 성과에 어떤 영향을 미치는지 보여줍니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E앙상블 기법 \u003C/h4\u003E\n\u003Cp\u003E앙상블 기법은 이러한 모든 모델을 결합해 보다 종합적이고 유의미한 인사이트를 도출합니다. 이를 통해 복잡성을 해소하고 비즈니스 리더의 의사 결정을 위한 통합된 정보를 제공합니다. \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"]},"container_copy_copy__1978652079":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-89d51e6e60","additionalClasses":"headline-decoration","type":"heading2","lines":["예측 분석을 사용해야 하는 이유"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-61c9b3caab","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E예측 분석은 조직이 비즈니스의 건전성을 파악하고 향후 방향성을 설정하는 데 필요한 정보를 제공하는 데 도움이 됩니다. 조직은 데이터 패턴을 검토해 미래 성과를 예측하고 더 나은 의사 결정을 내릴 수 있습니다. \u003C/p\u003E\n\u003Cp\u003E또한 예측 분석을 활용하면 고객의 니즈에 더 잘 부합하는 개인화된 경로를 설계해 고객 여정을 개선할 수 있습니다. 예측 분석은 사기와 같은 이상 징후를 감지하고 문제가 확대되기 전에 대응하는 데에도 활용할 수 있습니다. \u003C/p\u003E\n\u003Cp\u003E이를 통해 조직 전반의 운영 효율성을 향상시킬 수 있습니다. 예측 분석은 과거 성과를 기반으로 어떤 캠페인이나 프로그램을 실행할지에 대한 불확실성을 줄여줍니다.\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"]},"container_copy_copy__455068363":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-87de760d22","additionalClasses":"headline-decoration","type":"heading2","lines":["예측 분석 기법"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-aea6c90236","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E예측 분석에는 일반적으로 사용되는 여러 기법이 있습니다. 아래에서 이를 자세히 살펴봅니다.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E회귀 분석\u003C/h4\u003E\n\u003Cp\u003E이 분석 기법은 변수 간의 관계를 예측하는 데 중점을 둡니다. 데이터 세트에서 패턴과 데이터 포인트 간의 상관관계를 찾는 데 사용되며 데이터 세트 내에 특정 공식을 생성해 상관관계를 추적할 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E의사 결정 트리\u003C/h4\u003E\n\u003Cp\u003E이 기법은 변수를 기준으로 데이터를 여러 범주로 나누어 일련의 의사 결정을 매핑하는 데 유용합니다. 이를 통해 서로 다른 선택에 따라 발생할 수 있는 다양한 결과를 제시할 수 있으며, 모델 자체가 검토하고 이해하기 쉽기 때문에 의사 결정 속도를 높이는 데 도움이 됩니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E신경망\u003C/h4\u003E\n\u003Cp\u003E이 기법은 데이터 세트 내 비선형 관계를 식별하기 위해 개발되었습니다. AI와 패턴 인식 도구를 활용해 서로 관련 없어 보이는 데이터 포인트를 연결하여 인간의 사고 방식을 모방하며 텍스트, 이미지, 오디오 등 다양한 형태의 데이터를 분석할 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E시계열 예측\u003C/h4\u003E\n\u003Cp\u003E시계열 예측은 조직이 일정 기간에 걸친 데이터 포인트를 분석하는 경우가 많기 때문에 매우 중요한 기법입니다. 이러한 유형의 예측은 일별, 주별 또는 월별 데이터 반복을 중심으로 수행되며 시간에 따른 행동 추세에 중점을 두고 결과를 예측합니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E클러스터링 알고리즘\u003C/h4\u003E\n\u003Cp\u003E클러스터링 기법은 유사한 데이터 포인트를 연결합니다. 이를 통해 조직은 과거 구매 이력과 같은 데이터를 활용해 고객이 다음에 구매할 가능성이 높은 제품을 예측할 수 있습니다.\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"]},"container_copy_copy__222865509":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-1387c86ad0","additionalClasses":"headline-decoration","type":"heading2","lines":["예측 분석의 사용 사례"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-6aa49b0b0f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E다양한 산업과 비즈니스 영역 전반에서 예측 분석은 중요한 자산이 될 수 있습니다. 아래에서는 주요 산업에서의 일반적 사용 사례를 살펴봅니다. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E금융\u003C/h4\u003E\r\n\u003Cp\u003E고객의 신용 이력을 분석해 \u003Ca href=\"https://www.snowflake.com/ko/solutions/industries/financial-services/\"\u003E금융 기관\u003C/a\u003E은 대출 상환 가능성을 보다 정확하게 예측할 수 있습니다. 예측 분석은 또한 \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003E사기 위험을 완화\u003C/a\u003E하는 데에도 도움이 됩니다. 금융 기관은 트랜잭션에서 이상 징후를 탐지하고 실시간으로 우려 사항을 표시하며 적절한 가드레일을 마련할 수 있습니다.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E헬스케어 \u003C/h4\u003E\r\n\u003Cp\u003E예측 분석은 환자에게 중요한 영향을 미칠 수 있습니다. \u003Ca href=\"https://www.snowflake.com/ko/solutions/industries/healthcare-and-life-sciences/\"\u003E헬스케어 조직\u003C/a\u003E은 장기 입원 환자로부터 패혈증과 같은 중증 합병증의 발현을 시사하는 데이터 포인트를 추적해 보다 효과적으로 \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/healthcare-and-life-sciences/care-delivery/\"\u003E환자 치료를 관리\u003C/a\u003E할 수 있습니다. 조직은 환자의 건강 기록을 분석해 예방적 치료를 제공할 수 있으며, 당뇨병 가족력이 있는 경우에는 식단 개선을 권장할 수도 있습니다.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E마케팅\u003C/h4\u003E\r\n\u003Cp\u003E예측 분석은 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003E미래 마케팅 캠페인의 성공 가능성\u003C/a\u003E을 보다 정확하게 평가할 수 있습니다. \u003Ca href=\"https://www.snowflake.com/ko/solutions/departments/marketing/\"\u003E마케터\u003C/a\u003E는 과거 데이터를 활용해 타겟 고객이 어떤 유형의 캠페인에 반응하는지 파악하고 필요에 따라 전략을 조정할 수 있습니다. \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E공급망 \u003C/h4\u003E\r\n\u003Cp\u003E예측 분석은 \u003Ca href=\"https://www.snowflake.com/ko/solutions/industries/manufacturing/\"\u003E제조사\u003C/a\u003E가 재고를 보다 효과적으로 관리하고 배포하는 데 도움을 줍니다. 과거 데이터를 기반으로 공급업체는 어떤 고객이 언제 어떤 물품을 필요로 하는지 예측하고 자체 재고를 보다 효율적으로 관리할 수 있습니다. 또한 고객의 니즈와 수요에 대한 과거 데이터를 기반으로 고객에게 재주문을 선제적으로 제안할 수 있습니다.\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"]},"container_copy_copy__1413360324":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-b8a833ebb1","additionalClasses":"headline-decoration","type":"heading2","lines":["비즈니스 성공을 위한 예측 분석의 이점"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-088dd8629c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E기업은 예측 분석을 도입함으로써 다양한 이점을 얻을 수 있습니다. 주요 이점은 다음과 같습니다.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. 예측 정확도 및 계획 수립 향상\u003C/h4\u003E\n\u003Cp\u003E경쟁력을 유지하려면 조직은 결과를 예측하고 기회를 활용하며 잠재적 손실에 대비하는 미래 지향적인 접근이 필요합니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. 고객 타깃팅 및 유지율 강화\u003C/h4\u003E\n\u003Cp\u003E예측 분석을 활용하면 비즈니스 리더는 고객 행동을 보다 명확하게 파악할 수 있습니다. 조직은 고객 기반을 분석하고 고객 페르소나에 예측 분석을 적용함으로써 특정 잠재 고객에 맞춘 마케팅 캠페인을 전개하고 참여 가능성이 더 높은 영역에 대한 인사이트를 확보할 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. 운영 리스크 감소\u003C/h4\u003E\n\u003Cp\u003E조직은 예측 분석을 통해 사기 및 재무 리스크를 보다 효과적으로 감지할 수 있습니다. 구매의 성격이 일반적인 범위를 벗어날 경우 기업은 의심스럽거나 비정상적인 구매를 더 이른 시점에 플래그 처리할 수 있습니다. 또한 조직은 신용 점수와 보험 청구와 같은 데이터 포인트를 보다 쉽게 검토해 고객의 결제 불이행 가능성을 예측할 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4. ROI 및 효율성 향상\u003C/h4\u003E\n\u003Cp\u003E전반적으로 예측 분석은 조직의 효율성을 높이고 투자 수익을 증대시킵니다. 이를 통해 기업은 재고를 예측하고 내부 리소스를 보다 효율적으로 관리할 수 있으며 고객의 니즈와 선호도에 대한 데이터를 분석해 개발 중인 제품의 성과를 예측할 수 있습니다. 또한, 조직은 정기적인 장비 유지 관리를 모니터링함으로써 생산 지연을 사전에 방지할 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E5. 데이터 기반 인사이트를 통한 의사 결정 고도화 \u003C/h4\u003E\n\u003Cp\u003E예측 분석을 통해 조직은 더 나은 의사 결정을 내릴 수 있는 정보를 확보합니다. 과거 추세를 분석함으로써 미래 의사 결정을 지원하는 선제적인 접근이 가능해지며, 여기에는 특정 제품의 적정 재고 수준을 결정하는 것과 개발할 마케팅 캠페인 유형을 정하는 것, 운영 전반의 생산성을 개선하는 방법을 모색하는 것이 포함됩니다.\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_copy","text_copy"]},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"starting","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"title_v2_copy_copy":{"id":"title-v2-3145cebf5e","additionalClasses":"headline-decoration","type":"heading2","lines":["예측 분석 시작 방법"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_copy":{"id":"text-b94c00e3de","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E예측 분석을 비즈니스의 핵심 요소로 도입하는 과정은 조직에 상당한 부담이 될 수 있습니다. 아래에서는 시작을 위한 주요 단계를 살펴봅니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1. 명확한 비즈니스 문제 정의\u003C/h4\u003E\n\u003Cp\u003E우선 예측 분석을 시작할 때는 목표를 명확히 설정해야 합니다. 어떤 비즈니스 질문에 대한 답을 도출하려는지, 조직의 주요 우선순위가 무엇인지 판단한 뒤 문제의 근본 원인을 규명할 수 있도록 질문 목록을 작성해 보세요. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2. 부서 간 협업 팀 구성 \u003C/h4\u003E\n\u003Cp\u003E팀 전체가 같은 목표를 공유하도록 정렬하세요. 예측의 이점을 얻고 이를 실행에 옮길 수 있도록 비즈니스 조직과의 커뮤니케이션 채널을 구축하고 정기적인 싱크와 실행 계획을 수립하세요. 인사이트가 실행으로 연결되지 않으면 그 가치는 제한됩니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3. 접근 가능한 데이터 및 도구 선택\u003C/h4\u003E\n\u003Cp\u003E데이터를 저장하고 분석하기 위한 효과적인 기술이 필요합니다. 비즈니스 관련 부서 전반과 리더십 수준 전반의 임직원이 접근할 수 있는 도구를 갖추는 것은 투명성과 신속한 보고에 도움이 됩니다. 비기술 인력이 직접 활용하고 의미 있는 질문을 던질 수 있는 도구를 갖추면, 데이터 사이언스 팀을 통한 쿼리 실행 과정에서 발생하는 운영적 마찰을 줄이고 예측 분석 인사이트를 강화할 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4. 소규모 파일럿 프로젝트로 시작\u003C/h4\u003E\n\u003Cp\u003E작고 관리하기 쉬운 파일럿 프로젝트로 시작하면 대규모 예측 분석 프로젝트를 구현하는 여정에 도움이 됩니다. 상대적으로 작은 데이터 세트가 필요한 문제부터 시작하면 예측 분석이 제공하는 기본적인 비즈니스 임팩트를 명확하게 보여줄 수 있습니다. \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E5. ROI 측정 및 점진적 확장 \u003C/h4\u003E\n\u003Cp\u003E명확하게 정의되고 측정 가능한 프로젝트부터 시작한 뒤 프로젝트를 확장하거나 다른 관련 부서로 추가 프로젝트를 시작하기 전에 투자 대비 수익률을 계산하세요. 구현을 진행하면서 여러 비즈니스 부서 전반에 예측 분석을 연결하면 더 광범위한 워크플로를 자동화하고 분석 이니셔티브의 재무적 성과를 확대하는 데 도움이 됩니다. \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_copy_copy","text_copy_copy"]},"container_copy_copy__659376865":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"conclusion","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-0179fab09a","additionalClasses":"headline-decoration","type":"heading2","lines":["결론"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8db1b91f69","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E예측 분석은 과거 데이터를 기반으로 정보에 입각한 의사 결정을 내리고자 하는 모든 조직에 필수적인 요소입니다. 조직은 과거 성과와 연관된 패턴을 되짚어봄으로써 전략을 조정하고 보다 정보에 기반한 의사 결정을 내려 더 나은 결과를 기대할 수 있습니다.\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"]},"container_copy_copy__1500909857":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"faq","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-719c27446e","additionalClasses":"headline-decoration","type":"heading2","lines":["예측 분석 FAQ"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-d2c2d344d0","additionalClasses":"list--blue-bullets","showDivider":false,"accordionItemsList":[{"title":"대표적인 예측 분석 도구에는 무엇이 있나요?","richText":"\u003Cp\u003E예측 분석 도구에는 데이터 준비 및 모델 구축을 지원하는 종합 플랫폼이 포함되며 모델 구축을 위한 시각적 워크플로우를 제공하는 로우코드 플랫폼도 함께 제공됩니다. 이러한 도구는 일반적으로 코딩 경험이 적은 사용자에게 적합합니다. 동시에, 고급 사용자를 위해 높은 유연성을 제공하는 오픈소스 도구도 마련되어 있습니다.\u003C/p\u003E\r\n"},{"title":"ChatGPT는 예측 모델인가요?","richText":"\u003Cp\u003EChatGPT와 같은 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/large-language-model/\"\u003ELLM\u003C/a\u003E은 문맥상 가장 확률이 높은 다음 단어를 예측함으로써 사람과 유사한 응답을 자연어로 생성합니다. LLM은 예측 분석에 포함되는 데이터 준비 및 기타 작업을 지원할 수 있으며, 약간의 설정만으로 업로드한 데이터에 대해 예측 분석을 수행하도록 프롬프트를 구성할 수 있습니다. ChatGPT는 대규모 데이터를 비교적 빠르게 처리할 수 있으며, 사용자 프롬프트에 따라 주요 패턴을 식별하고 잠재적 결과에 대한 예측을 도출할 수 있습니다.\u003C/p\u003E\r\n"},{"title":"예측 분석과 머신러닝의 차이점은 무엇인가요?","richText":"\u003Cp\u003E머신러닝은 예측 분석을 지원하는 도구입니다. 머신러닝은 데이터 내의 패턴을 식별하는 데 활용되며 이러한 패턴은 예측 분석 인사이트를 도출하는 데 활용됩니다.\u003C/p\u003E\r\n"},{"title":"예측 분석 도구의 정확도는 어느 정도인가요?","richText":"\u003Cp\u003E예측 분석은 말 그대로 예측입니다. 이는 수집된 관련 데이터 포인트의 구성과 특징을 기반으로 합니다. 예측 분석은 비즈니스 의사 결정을 지원하는 데 도움이 되지만, 전체 분석 체계의 한 요소에 불과합니다. 데이터 품질이 높을수록, 그리고 분석 질문을 더 정교하게 설계할수록 예측 결과의 신뢰성은 더욱 강화됩니다.\u003C/p\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"]},"container_copy_copy__480869401":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8913936e74","additionalClasses":"headline-decoration","type":"heading2","lines":["Snowflake를 사용하는 고객 사례"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-56485a252d","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-8479652bf0","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2":{"id":"card-v2-d37c4bef5b","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["WHOOP, Snowflake 도입으로 AI/ML 기반 재무 예측 및 회원 경험 향상"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/ko/customers/all-customers/case-study/whoop/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"사례 읽기"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--879b4be4-afa2-4d82-b86c-e334c441a5bc/whoop-customer-card%25402x.jpg?preferwebp=true&quality=85","alt":"simon data logo","lazyEnabled":true,"width":"1680","height":"720",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EWHOOP 팀은 Snowflake와 Apache Iceberg를 활용해 데이터 접근을 중앙화하고 복잡성을 줄이며 비용을 절감하고 핵심 프로세스를 개선했습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-60159846db","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-2459a2dc46","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Canva, Snowflake로 고객 상호 작용 개선"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/en/customers/all-customers/video/canva-1/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"동영상 시청하기"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--6462b371-27c8-4b82-9dab-65d05acc3693/gilbert-customer-card%25402x.jpg?preferwebp=true&quality=85","alt":"penske logo","lazyEnabled":true,"width":"1680","height":"720",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ECanva의 분석 엔지니어는 Snowflake가 Canva의 성장에 발맞춰 확장성을 제공하며 분석 환경의 판도를 바꿨다고 설명합니다. 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