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어떤 종속성을 만드는지, 정의를 다른 곳에서 얼마나 쉽게 재사용할 수 있는지도 검토해야 합니다. 이러한 요소는 기능 체크 표시 목록보다 \u003Ca href=\"https://www.snowflake.com/ko/analytics/business-intelligence/\"\u003E비즈니스 인텔리전스\u003C/a\u003E 환경이 시간이 지남에 따라 어떻게 운영될지를 훨씬 더 잘 보여 줍니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-are-business-intelligence-tools":{"id":"title-v2-a57724bebc","additionalClasses":"anchor-title anchor-title--what-are-business-intelligence-tools","type":"heading2","lines":["비즈니스 인텔리전스 도구의 개념"],":type":"snowflake-site/components/title-v2"},"text_what-are-business-intelligence-tools_0":{"id":"text-3e1ec47636","text":"\u003Cp\u003E비즈니스 인텔리전스 도구는 비즈니스 데이터를 쿼리, 시각화, 분석, 공유하는 데 사용하는 소프트웨어 애플리케이션입니다. 제품과 대상 사용자에 따라 대시보드, 애드혹 분석, 셀프서비스 분석, 임베디드 보고, 자연어 인터페이스 등을 포함할 수 있습니다. 대부분의 BI 도구는 비즈니스 사용자와 데이터 웨어하우스, 데이터 레이크, 데이터 플랫폼 등 기반 데이터 환경 사이에 위치합니다.\u003C/p\u003E\n\u003Cp\u003EBI 도구는 기반 데이터, 시맨틱 모델, 거버넌스 제어에 연결하는 방식에서도 차이가 있습니다. 이러한 아키텍처 선택은 여러 분석 인터페이스가 얼마나 원활하게 함께 작동하는지에 영향을 미칩니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003E주거용 청정에너지 기업 Sunrun이 Snowflake에서 분석을 중앙 집중화하고 전사적으로 셀프서비스 BI를 확장한 방법을 알아보세요.\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-are-business-intelligence-tools_0":{"id":"embed-2b13cda307","youtubeVideoId":"7gVRmTkb6b8","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":false,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_six-categories-of-business-intelligence-tools":{"id":"title-v2-37b8567548","additionalClasses":"anchor-title anchor-title--six-categories-of-business-intelligence-tools","type":"heading2","lines":["비즈니스 인텔리전스 도구의 6가지 유형"],":type":"snowflake-site/components/title-v2"},"text_six-categories-of-business-intelligence-tools_0":{"id":"text-375b27fc4b","text":"\u003Cp\u003E비즈니스 인텔리전스 도구는 지원하는 분석 작업의 유형과 주요 사용자에 따라 크게 6가지 유형으로 나눌 수 있습니다. 일부 제품은 여러 유형에 걸쳐 있으며, 대규모 조직에서는 여러 유형을 함께 사용하는 경우가 많습니다. 하지만 각 유형을 개별적으로 살펴보면 통합 BI 환경에서 각 도구가 수행하는 역할을 이해하는 데 도움이 됩니다.\u003C/p\u003E\n\u003Ch3\u003E시각화 중심 BI 도구\u003C/h3\u003E\n\u003Cp\u003E시각화 중심 도구는 인터랙티브 탐색, 대시보드, 차트 디자인에 중점을 둡니다. 질문이 자주 바뀌는 분석가와 비즈니스 팀에 적합하며, 특히 쿼리를 매번 처음부터 작성하지 않고도 여러 차원에 걸쳐 데이터를 분할하고, 이상 징후를 조사하고, 시각적 설명을 구성해야 하는 경우에 유용합니다.\u003C/p\u003E\n\u003Ch3\u003E엔터프라이즈 보고 플랫폼\u003C/h3\u003E\n\u003Cp\u003E엔터프라이즈 보고 플랫폼은 모델링되고 반복 가능한 결과물(예약 보고서, 통제된 배포, 대규모 사용자 그룹을 위한 표준화된 보고)을 우선시합니다. 이러한 환경에서는 일반적으로 탐색의 유연성보다 일관성과 운영 안정성이 더 중요합니다.\u003C/p\u003E\n\u003Ch3\u003E임베디드 분석 도구\u003C/h3\u003E\n\u003Cp\u003E임베디드 분석은 대시보드, 지표 및 기타 분석 기능을 애플리케이션이나 포털에 직접 통합하므로, 사용자는 별도의 BI 환경으로 전환하지 않고도 관련 데이터를 분석할 수 있습니다. 대표적인 예로는 고객 대상 사용량 대시보드, 파트너 보고, 비즈니스 애플리케이션에 내장된 운영 분석 등이 있습니다.\u003C/p\u003E\n\u003Ch3\u003E노트북 및 코드 중심 분석 도구\u003C/h3\u003E\n\u003Cp\u003E노트북 및 코드 중심 환경은 SQL, Python 또는 기타 프로그래밍 언어로 직접 작업하며 데이터를 탐색하고, 분석을 구축하고, 반복 가능한 워크플로우를 만드는 분석가, 데이터 과학자, 엔지니어를 위한 환경입니다. 특히 팀이 분석 로직, 재현성, 코드 기반 워크플로우와의 통합을 더 세밀하게 제어해야 할 때 유용합니다. 다만 접근성 측면에서는 한계가 있습니다. 유연성이 매우 높은 코드 중심 환경이라도 프로그래밍 워크플로우를 익히지 않고 정기적인 \u003Ca href=\"https://www.snowflake.com/ko/analytics/business-intelligence/kpi/\"\u003EKPI\u003C/a\u003E를 확인하려는 비즈니스 사용자에게는 큰 도움이 되지 않습니다.\u003C/p\u003E\n\u003Ch3\u003E스프레드시트 기반 BI 도구\u003C/h3\u003E\n\u003Cp\u003E스프레드시트 기반 도구는 거버넌스가 적용되거나 중앙에서 관리되는 데이터를 많은 재무, 운영, 비즈니스 팀에 이미 익숙한 인터페이스로 가져옵니다. 이러한 익숙함은 실질적인 가치가 있습니다. 기술적으로 가장 뛰어난 분석 시스템이라도 결국 사람들이 사용해야 가치가 있으며, 도구가 기존 워크플로우에 잘 맞을 때 도입률이 높아지는 경우가 많습니다. 그럼에도 스프레드시트 기반 분석에는 거버넌스와 정의 관리가 필요하며, 특히 로컬 수식이 다른 곳에 이미 존재하는 로직을 중복 구현하는 경우에는 더욱 그렇습니다.\u003C/p\u003E\n\u003Ch3\u003E대화형 및 에이전틱 BI 도구\u003C/h3\u003E\n\u003Cp\u003E대화형 분석은 자연어로 질문을 받아 답변, 시각화 또는 생성된 쿼리를 반환합니다. AI 기반 분석과 텍스트-SQL 변환 기술로 이 유형은 빠르게 확장되었으며, \u003Ca href=\"https://www.snowflake.com/ko/artificial-intelligence/agents/\"\u003E에이전트\u003C/a\u003E는 분석 질문에 추가 작업이나 워크플로우를 결합하는 경우가 점점 늘고 있습니다. 여기서 중요한 점은 생성된 답변의 품질이 모델에 제공된 정의, 관계, 컨텍스트의 품질을 그대로 따른다는 것입니다.\u003C/p\u003E\n\u003Cp\u003E이처럼 높은 정확성이 요구되면서 팀이 기반 데이터 모델에 기대하는 바도 달라지고 있습니다. Snowflake 분석 제품 관리 책임자 Josh Klahr는 이렇게 설명합니다. “‘대체로 정확한’ 데이터 모델은 신뢰를 빠르게 무너뜨립니다. 사용자는 헷갈리는 대시보드 컬럼 정도는 우회할 수 있지만, AI 시스템이 확신에 찬 어조로 잘못된 답을 내놓으면 분석 경험에 대한 신뢰는 급격히 떨어집니다.”\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_six-categories-of-business-intelligence-tools_0":{"id":"quote-item-bc45c759d5","alignment":"left","layout":"simple","quoteSourceName":"Josh Klahr","quoteSourceTitle":"Head of Product Management for Analytics, Snowflake","showQuoteIcon":true,"quote":"‘Mostly accurate’ data models break trust quickly. A user might be able to work around a confusing dashboard column, but when an AI system definitively delivers a wrong answer, confidence in the analytical experience drops fast.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_six-categories-of-business-intelligence-tools_1":{"id":"text-eb67a32d72","text":"\u003Cp\u003E분석가, 재무팀, 애플리케이션 사용자, 경영진이 같은 인터페이스를 필요로 하는 경우는 드물기 때문에 대규모 조직에서는 여러 BI 유형의 도구를 함께 사용하는 경우가 많습니다. 따라서 이러한 도구를 뒷받침하는 아키텍처가 더욱 중요해집니다. ‘헤드리스 BI’라고도 불리는 접근 방식은 공유 비즈니스 로직을 프레젠테이션 레이어와 분리하여 여러 BI, 분석, AI 경험에서 지표, 차원, 관계를 참조할 수 있도록 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_six-categories-of-business-intelligence-tools_0":{"id":"card-v2-4afa3b05f5","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"고객 사례 읽기"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["고객 사례: Forte Insurance"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"351","src":"/adobe/dynamicmedia/deliver/dm-aid--556e0427-7727-4e7f-9a29-43d4249b0cbb/forte-logo.jpg?quality=85&preferwebp=true","isLcpImage":false,"alt":"Forte Insurance logo","lazyEnabled":true,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EForte Insurance는 Power BI를 Snowflake와 통합하여 비즈니스 사용자에게 더 최신의 분석 인사이트를 제공하고, Power BI 보고서 새로 고침 시간을 12분에서 거의 실시간 수준으로 단축했습니다. 또한 Snowflake 결과 캐싱으로 평균 쿼리 응답 시간을 최대 60%[2025년 3월 기준] 단축했으며, 이는 BI 인터페이스 아래의 아키텍처가 성능과 확장성에 어떤 영향을 미치는지 잘 보여 줍니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_what-actually-differentiates-bi-tools-now-that-features-have-converged":{"id":"title-v2-7973d1583a","additionalClasses":"anchor-title anchor-title--what-actually-differentiates-bi-tools-now-that-features-have-converged","type":"heading2","lines":["기능이 비슷해진 지금, BI 도구를 실제로 차별화하는 요소"],":type":"snowflake-site/components/title-v2"},"text_what-actually-differentiates-bi-tools-now-that-features-have-converged_0":{"id":"text-c96cf3c6ed","text":"\u003Cp\u003E여러 도구를 사용하는 BI 환경에서는 네 가지 아키텍처 선택이 특히 광범위한 영향을 미칩니다. 바로 시맨틱 모델의 위치, 쿼리가 데이터에 도달하는 방식, 거버넌스가 적용되는 위치, 비즈니스 정의가 여러 인터페이스에 노출되는 방식입니다.\u003C/p\u003E\r\n\u003Ch3\u003E시맨틱 모델의 위치\u003C/h3\u003E\r\n\u003Cp\u003E시맨틱 모델은 물리적 데이터 구조를 비즈니스 개념으로 변환하며, 조직이 데이터를 해석하는 방식을 설명하는 지표, 차원, 관계, 계산 및 기타 규칙을 정의합니다. 시맨틱 레이어는 이러한 모델을 관리하고 분석 애플리케이션에서 사용할 수 있도록 제공하는 아키텍처 레이어입니다.\u003C/p\u003E\r\n\u003Cp\u003E일부 BI 도구는 보고 애플리케이션 내부에서 자체 시맨틱 모델을 유지 관리합니다. 여러 도구를 사용하는 환경에서 이러한 방식을 택하면 동일한 매출 지표, 고객 정의 또는 제품 계층 구조가 여러 곳에서 표현되고 유지 관리되며, 결국에는 서로 조정해야 하는 상황이 발생합니다.\u003C/p\u003E\r\n\u003Cp\u003E공유 시맨틱 레이어는 이러한 비즈니스 로직을 특정 BI 인터페이스 외부에 둡니다. 그러면 여러 대시보드, 분석 도구, AI 경험이 이곳에서 관리되는 모델을 참조할 수 있으므로, 인터페이스를 추가할 때마다 동일한 정의를 다시 만들 필요가 줄어듭니다.\u003C/p\u003E\r\n\u003Ch3\u003E쿼리가 데이터에 도달하는 방식\u003C/h3\u003E\r\n\u003Cp\u003EBI 도구는 데이터를 검색하고 처리하는 방식에서 차이가 있습니다. 직접 쿼리나 라이브 연결을 사용하면 도구가 기반 데이터 플랫폼으로 쿼리를 보내고, 쿼리는 해당 플랫폼에서 최신 데이터를 대상으로 실행됩니다. 다른 구성에서는 데이터를 BI 도구의 자체 분석 엔진으로 가져오거나 추출하므로, 별도의 새로 고침 일정과 스토리지 요구 사항을 갖는 또 하나의 복사본이 생성됩니다.\u003C/p\u003E\r\n\u003Cp\u003E직접 쿼리는 분석을 소스 데이터에 더 가깝게 유지하고 별도의 새로 고침 주기를 피할 수 있지만, 모든 워크로드에 적합한 것은 아닙니다. BI 애플리케이션의 성능 모델, 연결 요구 사항 또는 분석 기능이 추출본에 의존하는 경우에는 추출 방식이 유용할 수 있습니다. 팀은 처리가 어디에서 이루어지는지, 별도의 복사본이 생성되는지, 그로 인해 데이터 최신성, 성능, 비용 측면에서 어떤 트레이드오프가 발생하는지 파악해야 합니다.\u003C/p\u003E\r\n\u003Ch3\u003E거버넌스가 적용되는 위치\u003C/h3\u003E\r\n\u003Cp\u003E액세스 정책을 각 BI 도구에서 다시 만들지 않고 데이터 가까이에 유지하면 거버넌스를 더 쉽게 관리할 수 있습니다. 예를 들어 직접 연결을 사용하면 데이터 플랫폼이 쿼리 실행 시 행 수준 액세스, 마스킹, 역할 기반 제어를 적용한 후 해당 사용자에게 허용된 데이터만 반환할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E가능하면 BI 도구를 거쳐 기반 플랫폼까지 개별 사용자의 ID가 유지되도록 하세요. 이를 통해 기존 역할과 정책에 따라 액세스를 일관되게 관리할 수 있습니다. 공유 서비스 계정이 필요한 경우도 있습니다. 하지만 공유 서비스 계정을 사용하면 개별 사용자를 구분하기 어려워지고, 더 많은 액세스 제어 로직이 BI 애플리케이션으로 넘어갈 수 있습니다. 추출 방식에는 또 다른 고려 사항이 있습니다. 데이터가 BI 도구의 엔진으로 복사되면 해당 복사본에도 별도의 권한과 거버넌스 제어가 필요합니다.\u003C/p\u003E\r\n\u003Cp\u003EBI 도구에서 이러한 방식이 어떻게 작동하는지 평가하려면 대표 사용자 한 명을 정해 전체 과정을 처음부터 끝까지 추적해 보세요. 어떤 ID가 데이터 플랫폼에 도달하는지, 어떤 역할이 적용되는지, 행 수준 액세스와 마스킹이 어떻게 작동하는지, 데이터가 플랫폼을 벗어나면 무엇이 달라지는지 확인해야 합니다. 이렇게 하면 기능 목록을 비교할 때보다 거버넌스 모델을 훨씬 명확하게 파악할 수 있습니다.\u003C/p\u003E\r\n\u003Ch3\u003E도구 전반의 비즈니스 정의 노출 방식\u003C/h3\u003E\r\n\u003Cp\u003E다른 분석 인터페이스에서 활용할 수 없다면 비즈니스 정의를 중앙화하더라도 그 가치는 제한적입니다. 대시보드, 노트북, 애플리케이션, \u003Ca href=\"https://www.snowflake.com/ko/artificial-intelligence/\"\u003EAI 시스템\u003C/a\u003E을 함께 사용하는 환경에서는 지표, 차원, 관계 및 관련 컨텍스트가 SQL, 시맨틱 인터페이스 또는 기타 프로그래밍 방식의 액세스 경로를 통해 어떻게 노출되는지 검토해야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E이는 AI 기반 분석에서 특히 중요합니다. 모델이 명시적인 정의와 관계를 제공받으면 자연어 요청을 쿼리로 변환할 때 테이블 및 컬럼 이름에서 의미를 추론할 필요가 없습니다. 검증된 쿼리는 승인된 분석 로직을 어떻게 표현해야 하는지 보여 줌으로써 반복적이거나 복잡한 질문에 대한 지침을 제공할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003EKlahr는 이렇게 말합니다. “물리적 데이터베이스 스키마는 데이터의 구조를 설명하지만, 비즈니스가 운영되는 방식까지 담아내지는 못합니다. LLM은 테이블 스키마만으로는 수익 인식, 계절 조정, 레거시 예외 사례 같은 규칙을 추론할 수 없습니다. 이러한 로직은 명시적으로 제공되어야 합니다.”\u003C/p\u003E\r\n\u003Cp\u003E한 번 생성한 정의를 조직이 운영하는 다양한 분석 환경에서 각각 로직을 따로 재생성하지 않고도 활용할 수 있는지가 좋은 평가 기준입니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-happens-to-metric-definitions-when-bi-tools-change":{"id":"title-v2-fdeb48443f","additionalClasses":"anchor-title anchor-title--what-happens-to-metric-definitions-when-bi-tools-change","type":"heading2","lines":["BI 도구 변경이 지표 정의에 미치는 영향"],":type":"snowflake-site/components/title-v2"},"text_what-happens-to-metric-definitions-when-bi-tools-change_0":{"id":"text-49527c6a10","text":"\u003Cp\u003E성숙한 BI 환경에는 대시보드보다 훨씬 많은 요소가 포함되어 있습니다. 팀은 시간이 지남에 따라 지표, 차원, 조인, 필터, 계층 구조, 계산에 비즈니스 의미를 담게 되며, 보고 요구 사항이 변화하면서 예외와 변형을 추가하는 경우도 많습니다. 이러한 로직이 특정 BI 도구 안에 있으면 인터페이스를 변경할 때 어떤 정의를 그대로 옮길 수 있고, 어떤 정의를 변환해야 하며, 어떤 정의를 다시 구축하고 검증해야 하는지도 파악해야 합니다.\u003C/p\u003E\n\u003Ch3\u003E마이그레이션 부담을 키우는 시맨틱 락인\u003C/h3\u003E\n\u003Cp\u003E시맨틱 락인은 다른 분석 시스템이 쉽게 활용할 수 없는 독점 모델에 비즈니스 정의가 주로 표현될 때 발생합니다. 팀이 해당 모델에 더 많은 지표와 비즈니스 규칙을 구축할수록 BI 환경의 가치와 함께 종속성도 커집니다.\u003C/p\u003E\n\u003Cp\u003E기본 데이터가 동일한 플랫폼에 그대로 있더라도 이러한 정의를 다시 구축하고 검증하는 데는 상당한 노력이 필요할 수 있습니다. 새로 도입하는 BI 도구도 동일한 지표 정의, 관계, 계산 규칙을 이해해야 하며, 팀은 새로운 구현이 의도한 결과를 산출하는지 확인해야 합니다. 전환 기간에는 기존 보고서와 새 보고서가 공존할 수도 있습니다. 이로 인해 정의를 조정하는 동안 눈에 띄는 불일치가 발생할 가능성이 높아집니다.\u003C/p\u003E\n\u003Ch3\u003E실제로 도구 밖으로 옮길 수 있는 요소에 따라 달라지는 이식성\u003C/h3\u003E\n\u003Cp\u003E내보내기 옵션이 있다고 해서 시맨틱 모델의 이식성이 반드시 보장되는 것은 아닙니다. 예를 들어 생성된 SQL은 정의를 바탕으로 생성된 쿼리는 담고 있지만, 해당 쿼리를 만들어 낸 컨텍스트 일부(관계, 지표 그레인, 재사용 가능한 차원, 비즈니스 용어 및 기타 모델링 정보)는 누락됩니다.\u003C/p\u003E\n\u003Cp\u003E팀은 플랫폼이 시맨틱 수준에서 무엇을 내보낼 수 있는지 검토해야 합니다. 지표, 차원, 관계, 계산 및 관련 메타데이터는 대시보드 파일이나 생성된 쿼리와 구분하여 고려해야 합니다.\u003C/p\u003E\n\u003Cp\u003E실질적인 테스트 방법은 간단합니다. 조직이 다른 BI 인터페이스로 전환한다면 새로운 환경에서 비즈니스 로직을 얼마나 직접 활용할 수 있고, 얼마나 다시 만들어야 할까요?\u003C/p\u003E\n\u003Ch3\u003E공유 정의에도 필요한 통제된 변형\u003C/h3\u003E\n\u003Cp\u003E공유 시맨틱 레이어가 모든 비즈니스 질문에 하나의 보편적인 지표 정의를 강제한다는 의미는 아닙니다. 매출, 이탈률, 활성 고객 상태는 재무 보고, 영업 계획, 제품 분석 등 목적에 따라 다르게 계산될 수 있으며, 이러한 차이는 타당할 수 있습니다. 중요한 것은 이러한 변형이 별도의 BI 도구 안에서 제각각 생겨나도록 방치하지 않고 명시적으로 정의하는 것입니다.\u003C/p\u003E\n\u003Cp\u003E공유 시맨틱 모델에서는 표준 지표와 함께 이름이 지정된 변형을 명확한 계산 규칙, 용도, 소유권과 함께 정의할 수 있습니다. 따라서 사용자는 특정 맥락에서 어떤 정의가 적용되는지 알 수 있습니다. 이는 마이그레이션 과정에서 특히 중요합니다. 지표 변형이 시맨틱 레이어에 문서화되어 있으면 팀은 정의와 해당 컨텍스트를 함께 옮길 수 있습니다. 반면 이러한 차이가 대시보드, 워크시트 또는 독점 모델의 로컬 계산으로만 존재한다면 전환 과정에서 이를 다시 찾아내고 조정해야 합니다.\u003C/p\u003E\n\u003Ch3\u003E이식성을 높이는 개방형 시맨틱 표준\u003C/h3\u003E\n\u003Cp\u003E개방형 시맨틱 사양이 이식성 문제를 해결하기 시작했습니다. 이전 명칭이 \u003Ca href=\"https://www.snowflake.com/ko/blog/open-semantic-interchanges-specs-finalized/\"\u003EOpen Semantic Interchange\u003C/a\u003E였던 Apache Ossie(인큐베이팅) 프로젝트는 2026년 7월 Apache Incubator에 합류했습니다. 이 YAML 기반 사양은 지표, 차원, 관계 및 보다 광범위한 시맨틱 컨텍스트를 벤더 중립적인 방식으로 표현할 수 있도록 지원합니다. 참여 연합은 50개 이상의 조직으로 확대되었으며, 참조 변환기도 이미 개발 중입니다. 다만 이 프로젝트는 아직 인큐베이션 단계이므로 새롭게 부상하는 상호 운용성 이니셔티브로 보아야 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what-happens-to-metric-definitions-when-bi-tools-change_0":{"id":"text-a44eb86102","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003E자주 하는 실수\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E대시보드 내보내기를 시맨틱 이식성과 혼동하지 마세요. 시각화나 생성된 SQL을 옮긴다고 해서 그 이면의 지표, 관계, 그레인, 계산 및 비즈니스 컨텍스트가 반드시 보존되는 것은 아닙니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-to-evaluate-a-business-intelligence-tool":{"id":"title-v2-6b694ec8e9","additionalClasses":"anchor-title anchor-title--how-to-evaluate-a-business-intelligence-tool","type":"heading2","lines":["비즈니스 인텔리전스 도구 평가 방법"],":type":"snowflake-site/components/title-v2"},"text_how-to-evaluate-a-business-intelligence-tool_0":{"id":"text-dd0d74c2a6","text":"\u003Cp\u003E세련된 벤더 데모는 매력적인 결과에 이르는 가장 짧은 경로를 보여 주는 경향이 있습니다. 유용한 평가란 프로덕션 환경에서 데이터, 정의, 정책이 실제로 거치게 될 경로를 따라가는 것입니다.\u003C/p\u003E\n\u003Cp\u003E다음 항목을 순서대로 점검해 보세요.\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cstrong\u003E비즈니스 지표가 정의되는 위치 확인:\u003C/strong\u003E 매출 총이익률이나 활성 고객 수 같은 실제 측정값을 하나 선택하고 그 계산 과정을 추적합니다. 조인, 필터, 계산 및 변형이 어디에 있는지, 누가 이를 편집하는지, 다른 어떤 인터페이스가 동일한 정의에 액세스할 수 있는지 파악합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E인터페이스에서 데이터까지 쿼리 추적:\u003C/strong\u003E 도구가 소스를 실시간으로 쿼리하는지, 쿼리 푸시다운을 사용하는지, 아니면 자체 엔진에 추출본을 생성하는지 확인합니다. 추출 방식의 경우 새로 고침 주기, 스토리지, 권한, 그리고 복사본을 소스와 일치시키기 위한 운영 프로세스를 문서화합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E기존 거버넌스 정책 테스트:\u003C/strong\u003E 대표적인 행 수준 제한, 마스킹 정책 또는 제한된 컬럼을 사용하여 BI 환경에서의 동작을 검증합니다. 정책을 다시 만들어야 한다면 해당 작업을 관리 및 감사 모델에 포함합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003E실제 워크로드로 최대 동시성 테스트:\u003C/strong\u003E 평균 쿼리 성능만으로는 분석가와 비즈니스 사용자가 활발하게 작업하는 가운데 예약된 대시보드가 일제히 새로 고쳐지는 월요일 오전 시간대의 상황을 거의 파악할 수 없습니다. 개념 증명(PoC) 단계에서는 실제와 유사한 보고서 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BI 도구가 무분별하게 늘어나면 라이선스 견적에는 드러나지 않는 비용이 발생하는 경우가 많습니다.\u003C/li\u003E\n\u003C/ol\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-evaluate-a-business-intelligence-tool_0":{"id":"text-2881db9967","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003E빠른 팁\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E개념 증명 단계에서 실제 지표 하나와 대표 사용자 한 명을 정한 뒤, 정의, 쿼리 경로, 정책 적용, 이식성을 처음부터 끝까지 추적해 보세요. 일반적인 데모보다 훨씬 많은 것을 알 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-business-intelligence-tools-work-with-snowflake":{"id":"title-v2-f2a4a7fa97","additionalClasses":"anchor-title anchor-title--how-business-intelligence-tools-work-with-snowflake","type":"heading2","lines":["비즈니스 인텔리전스 도구와 Snowflake의 연동 방식"],":type":"snowflake-site/components/title-v2"},"text_how-business-intelligence-tools-work-with-snowflake_0":{"id":"text-4b60c3f934","text":"\u003Cp\u003ESnowflake는 BI 인터페이스의 하단에서 BI 도구가 사용하는 데이터, 컴퓨팅, 거버넌스 및 시맨틱 컨텍스트를 제공합니다. 팀은 Semantic Views를 사용하여 지표, 팩트, 차원, 관계, 계산 같은 비즈니스 개념을 각 보고 애플리케이션에서 개별적으로 다시 만들 필요 없이 Snowflake에서 정의할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E이러한 Semantic Views는 다양한 분석 환경을 지원합니다. 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