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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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container_949147658"],":type":"snowflake-site/components/container"},"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",":type":"snowflake-site/components/experiencefragment"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":items":{"container":{"columnClassNames":{"title_v2_copy_copy":"aem-GridColumn 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역할"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy":{"id":"text-a79cf101cf","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EMDM은 신뢰할 수 있고 정확성이 확보된 데이터의 중앙 리포지토리를 구축합니다. 기업은 MDM을 통해 잘못된 보고나 분산된 고객 인사이트 등과 같이, 사일로화되고 일관성 없는 데이터로 인해 발생하는 위험을 줄일 수 있습니다. 성공적인 MDM 전략을 수립하는 조직은 일관성 있고 신뢰할 수 있는 데이터를 생성하여, 모든 비즈니스 부서가 정보 기반 의사 결정을 내리고, 운영 효율성을 높이며, 고객 경험을 강화할 수 있도록 합니다.\u003C/p\u003E\n\u003Cp\u003E또한, MDM은 내부 팀뿐만 아니라, 필요에 따라 외부 파트너도 액세스할 수 있는 단일 데이터 뷰를 생성하여 협업을 촉진합니다. 예를 들어 물류 회사의 경우, MDM을 활용함으로써 공급망 시스템 전반에서 위치 데이터의 일관성을 유지하고, 재고 관리를 간소화하며, 정시 배송률을 높일 수 있습니다. \u003C/p\u003E\n\u003Cp\u003EMDM 전략을 수립하려면 비즈니스 요구 사항 파악, 데이터 도메인 매핑, 적절한 도구 및 기술 선택, \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/data-governance/\"\u003E데이터 거버넌스\u003C/a\u003E 정책 정의, 데이터 관리 역할 설정 등의 작업이 필요합니다. MDM은 이러한 체계적인 접근 방식에 따라 전략적 도구로써 조직의 데이터 활용 방식을 혁신할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text","title_v2_copy_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_copy_copy_":{"columnClassNames":{"text_copy_copy_29527":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_1475342575":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"importance",":items":{"title_v2":{"id":"title-v2-ebfa88dd00","type":"heading3","lines":["마스터 데이터 관리가 중요한 이유"],":type":"snowflake-site/components/title-v2"},"text_copy_1475342575":{"id":"text-c740675b8a","text":"\u003Cp\u003EMDM의 중요성은 아무리 강조해도 지나치지 않습니다. 기업은 마스터 데이터 관리를 구현함으로써 다음과 같은 몇 가지 주요 성과를 달성할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_29527":{"id":"text-56c72b661d","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E데이터의 정확성 및 일관성 향상: \u003C/b\u003E신뢰할 수 있는 정제된 데이터는 효과적인 의사 결정에서 핵심적인 역할을 합니다. 조직이 모든 시스템을 아우르는 단일 진실 공급원을 갖추면, 물류 운영에서 발생할 수 있는 상충되는 재고 수치와 같은 비용이 드는 오류를 줄일 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E데이터 분석 개선: \u003C/b\u003E신뢰할 수 있는 데이터는 더 심층적인 인사이트를 제공합니다. 단일 진실 공급원이 구축되면 전략적 의사 결정을 뒷받침하는 추세, 패턴 및 상관 관계를 더 쉽게 파악할 수 있습니다. 예를 들어, 리테일 업체는 고객 행동 및 구매 이력에 대한 분석을 통해 각 사용자에게 맞는 제품을 추천할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E운영 효율성: \u003C/b\u003E데이터 관리를 중앙 집중화하고 표준화하면 시간 소모적인 수동 작업을 해소할 수 있습니다. 이를 통해 리소스가 핵심 제품 개발에 집중되며 운영 효율성과 개발자 생산성 향상을 달성할 수 있습니다. 예를 들어, MDM을 통해 공급 및 수요 데이터를 통합하면 제조업체는 생산 일정을 개선하여 가동 중지 시간을 방지하고, 워크플로우를 더욱 원활하게 유지할 수 있습니다.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-extra-small"}},":itemsOrder":["title_v2","text_copy_1475342575","text_copy_copy_29527"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"container_copy_43816_1392604614":{"columnClassNames":{"title_v2_copy_copy_c_854165456":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527_1502676433":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527_1921551929":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527_235976618":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_793938129":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527_1599672602":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527_1805022522":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527_527539501":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_188661303":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527_1928236125":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1902378373":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1931117012":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"key-components",":items":{"title_v2":{"id":"title-v2-546d7d38ba","type":"heading3","lines":["MDM의 주요 구성 요소"],":type":"snowflake-site/components/title-v2"},"title_v2_copy_copy_c":{"id":"title-v2-5dbc217a8f","type":"heading4","lines":["데이터 거버넌스"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_29527":{"id":"text-d1bcbeee2e","text":"\u003Cp\u003EMDM과 데이터 거버넌스는 서로 연결되어 있으며 상호 보완적인 관계에 있습니다. 데이터 거버넌스는 데이터가 규제, 개인정보 보호 및 보안 기준에 부합하도록 정책과 역할을 설정합니다. MDM은 마케팅, 영업, 재무 및 운영과 같은 비즈니스 부서를 지원하기 위해 정제된 단일 정보원을 구축하는 데 중점을 둡니다. 또한, MDM은 마스터 데이터 리포지토리를 유지함으로써 효과적인 데이터 거버넌스를 지원하는 반면, 거버넌스는 데이터 표준을 적용하여 일관된 데이터 품질을 보장합니다. 즉, MDM과 데이터 거버넌스의 결합을 통해 일관성, 정확성 및 신뢰성을 향상할 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_188661303":{"id":"title-v2-5d46a24652","type":"heading4","lines":["데이터 품질"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_29527_1928236125":{"id":"text-2c46429a8e","text":"\u003Cp\u003E데이터 품질은 MDM의 핵심 요소입니다. 아무리 잘 설계된 MDM 전략이라도 신뢰할 수 없는 데이터에 의해 훼손될 수 있기 때문입니다. 정확성, 완전성 및 일관성으로 정의되는 고품질 데이터는 비즈니스 의사 결정을 뒷받침하는 신뢰할 수 있는 기반의 역할을 수행합니다.  데이터 품질을 이와 같은 수준으로 구현하고 유지하려면, 데이터 정제, 검증 및 표준화 프로세스를 포함한 여러 작업이 필요합니다. 이 과정에서 기술과 자동화가 중요한 역할을 수행하여, 프로세스를 간소화하고 수작업을 줄여 줍니다. 고급 데이터 품질 소프트웨어를 사용하면 오류, 불일치 및 중복을 해소할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1902378373":{"id":"title-v2-fe6d3e24c8","type":"heading4","lines":["데이터 통합"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_29527_1599672602":{"id":"text-8765a20ae8","text":"\u003Cp\u003E일반적으로 ETL(추출, 변환, 로드)이라고 불리는 데이터 통합은 다음 세 가지 주요 작업을 포함합니다. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_29527_1502676433":{"id":"text-145d0df7ca","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E추출: \u003C/b\u003E지정된 데이터 소스에서 데이터를 내보냅니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E변환: \u003C/b\u003E규칙 또는 데이터 형식 변경을 통해 데이터와 대상이 일치하도록 변환합니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E로드: \u003C/b\u003E변환된 데이터를 대상 데이터베이스로 가져옵니다.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_29527_527539501":{"id":"text-02f2de0dbe","text":"\u003Cp\u003E최근에는 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-extract-load-transform-elt/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EELT\u003C/a\u003E라는 용어가 더 많이 사용되는데, 이는 Snowflake와 같은 시스템에서 지원하는 것처럼 로드 중 또는 로드 후에 \u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\" target=\"_blank\"\u003E데이터 변환 작업\u003C/a\u003E이 발생하는 것을 말합니다. 이 프로세스는 여러 소스의 데이터 세트를 다양한 비즈니스 부서에서 활용 가능한 단일 리포지토리로 통합하는 데 필수적입니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_793938129":{"id":"title-v2-7ec4866f3f","type":"heading4","lines":["데이터 보안"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_29527_1805022522":{"id":"text-1b6b40b5e3","text":"\u003Cp\u003E이는 무단 액세스, 도난 또는 손상으로부터 정보를 보호하는 일련의 관행으로, 하드웨어 및 소프트웨어 애플리케이션의 보호부터 액세스 제어와 데이터 거버넌스 정책 구현까지 모든 보안 요소를 충족하도록 지원합니다. 또한, 데이터 보안은 필요한 시점에 권한을 가진 사용자가 해당 데이터를 사용할 수 있도록 하는 데 중점을 두고 있습니다. \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1931117012":{"id":"title-v2-619c916a18","type":"heading4","lines":["데이터 관리 책임"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_29527_235976618":{"id":"text-d9d08bdf1d","text":"\u003Cp\u003E데이터 관리 책임은 데이터 수명 주기 전반에 걸쳐 조직의 데이터 자산을 관리하고 감독하는 것을 말합니다. 한편, 데이터 스튜어드는 데이터 수명 주기를 관리하며, 조직 전체에서 데이터의 무결성, 정확성 및 가치를 보장합니다. 이뿐만 아니라, 규정 준수 요구 사항을 충족하고 데이터를 전략적 자산으로 활용하는 데 적극적으로 참여합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_854165456":{"id":"title-v2-c5b9b5a054","type":"heading4","lines":["데이터 분석"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_29527_1921551929":{"id":"text-9c20a0582c","text":"\u003Cp\u003E통계 분석, 머신러닝 및 예측 모델링과 같은 고급 분석 기술을 활용하여 데이터를 분석함으로써 유의미한 인사이트와 패턴을 발견할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"flexible_column_cont":{"id":"flexible-column-container-8212a58e3a","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-d833087a74",":items":{"card_v2":{"id":"card-v2-9bb4951099","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003E이 데모는 데이터 거버너와 스튜어드를 대상으로 하며, 민감 데이터 분류 및 태그 지정, 데이터 계보 시각화, 데이터 품질 모니터링, 세분화된 정책 적용 등 Snowflake Horizon의 핵심 기능을 배우고 직접 사용해 볼 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["데이터 거버너 및 스튜어드를 위한 Snowflake Horizon 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