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aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-72b5774f05","text":"\u003Cp\u003E오늘 Snowflake는 팀이 공유 비즈니스 용어, 엔티티, 관계, 지표를 정의하고 소유하며 승인하고, 이를 구현하는 데이터와 모델에 연결할 수 있도록 지원하는 거버넌스 기반 방식인 \u003Ca target=\"_blank\" href=\"https://www.snowflake.com/ko/product/features/horizon-context/\"\u003E\u003Cb\u003EBusiness Ontology\u003C/b\u003E \u003C/a\u003E(곧 퍼블릭 프리뷰 제공)를 발표합니다. Business Ontology는 \u003Ca target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003EHorizon Catalog\u003C/a\u003E의 거버넌스 기반 컨텍스트 계층인 \u003Ca target=\"_blank\" href=\"https://www.snowflake.com/ko/product/features/horizon/\"\u003ESnowflake Horizon Context\u003Cb\u003E,\u003C/b\u003E\u003C/a\u003E의 일부로서 사람과 AI 에이전트가 동일한 승인된 정의를 사용하도록 지원합니다. 이를 통해 에이전트는 기업 데이터를 비즈니스가 이해하는 방식 그대로 해석할 수 있습니다. 팀은 영업, 재무, 마케팅처럼 도메인별 차이는 유지하면서도 각 도구마다 다시 정의할 필요 없이 에이전트와 애플리케이션 전반에서 이 지식을 재사용할 수 있습니다.\u003Cbr\u003E\r\n\u003Cbr\u003E\r\n예를 들어 매출 관련 질문에 답하는 에이전트는 재무 부서가 ‘매출’을 어떻게 정의하는지, 그리고 그것이 주문 및 고객과 어떤 관계를 갖는지 알아야 합니다. 이러한 지식은 보통 BI 모델, dbt 프로젝트, 장기 근속 직원의 경험 속에 흩어져 있으며, 팀은 새로운 에이전트를 만들 때마다 이를 다시 구축하는 경우가 많습니다. 에이전트가 이를 잘못 이해하면 사용자는 잘못된 수치를 바탕으로 의사 결정을 내리게 되고, AI 답변에 대한 신뢰도 잃게 됩니다. Business Ontology는 팀이 이러한 지식을 공식적으로 기록할 수 있는 단일 공간을 제공합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-e3d56d1977","height":"591","src":"/adobe/dynamicmedia/deliver/dm-aid--8f883669-ddb6-401c-abdf-be7f4edd6fc9/business-ontology-stack-diagram.png?quality=85&preferwebp=true","alt":"Snowflake Business Ontology connects business concepts such as customers, orders and net revenue to the tables, semantic views and services that implement them, so agents across teams work from the same model of the business.","lazyEnabled":true,"isLcpImage":true,"width":"955","title":"자료 1: Snowflake Business Ontology는 고객, 주문, 순매출과 같은 비즈니스 개념을 이를 구현하는 테이블, 시맨틱 뷰, 서비스와 연결하므로, 여러 팀의 에이전트가 동일한 비즈니스 모델을 기반으로 작업할 수 있습니다.",":type":"snowflake-site/components/image"},"blog_title":{"id":"blog-title-1e45434d64","propertiesId":"","type":"heading2","lines":["데이터 자산 전반의 지식을 통합해 신뢰할 수 있는 비즈니스 컨텍스트 구축"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1060397060":{"id":"blog-text-5d4cf891aa","text":"\u003Cp\u003EHorizon Context는 팀이 이미 사용하는 도구의 지식을 하나로 연결합니다. 기본 제공 메타데이터 커넥터는 Tableau, Power BI, dbt, SQL Server, Sigma, Omni, PostgreSQL과 같은 소스에서 스키마, 쿼리 로그, 대시보드 정의, 사용 신호를 수집합니다. 그다음 크로스 플랫폼 계보는 소스 데이터베이스에서 변환 과정을 거쳐 데이터를 표시하는 대시보드까지 데이터 흐름을 추적합니다. 그 결과 데이터 자산을 연결된 관점에서 볼 수 있으며, 팀은 Snowflake 안팎에서 데이터가 어디서 오는지, 어떻게 사용되는지 더 명확하게 이해할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E에이전트가 신뢰할 수 있는 컨텍스트를 만들기 위해 Snowflake는 각기 다른 역할을 수행하는 세 가지 컨텍스트 계층을 제공합니다. \u003Ca href=\"https://www.snowflake.com/ko/blog/enterprise-ai-agents-grounded-context/\" target=\"_blank\"\u003ESnowflake Cortex Sense\u003C/a\u003E(곧 퍼블릭 프리뷰 제공)는 컨텍스트를 자동으로 추론하고, Business Ontology는 비즈니스 의미에 거버넌스를 적용하며, \u003Ca href=\"https://docs.snowflake.com/ko/user-guide/views-semantic/overview\" target=\"_blank\"\u003ESnowflake Semantic Views\u003C/a\u003E는 지표를 실행합니다. \u003C/p\u003E\r\n\u003Cp\u003E왜 하나가 아니라 세 가지 계층이 필요할까요? 각 계층이 해결하는 문제가 다르며, 단일 계층만으로는 세 가지 역할을 모두 잘 수행할 수 없기 때문입니다. Cortex Sense는 팀이 모든 테이블과 지표를 먼저 수작업으로 정의하지 않아도 에이전트가 데이터 자산 전반을 즉시 폭넓게 이해할 수 있도록 합니다. Business Ontology는 가장 중요한 영역에 공식 기준을 마련해 사람과 에이전트가 승인된 비즈니스 의미를 기준으로 작업하게 합니다. Semantic Views는 각 지표가 매번 동일한 방식으로 계산되도록 정밀성을 제공합니다. \u003C/p\u003E\r\n\u003Cp\u003E이 계층들은 순차적으로 적용해야 하는 단계가 아닙니다. 고객은 가장 시급한 문제를 해결하는 계층부터 시작한 뒤, 성장에 맞춰 나머지 계층을 추가할 수 있습니다. 어떤 팀은 용어집 요구 사항에서 출발할 수 있고, 또 다른 팀은 실행 가능한 지표에서 시작할 수 있습니다. 어디서 시작하든 세 가지 계층은 모든 에이전트, 앱, 사람에게 일관되고 신뢰할 수 있는 하나의 비즈니스 컨텍스트를 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E각 계층을 자세히 살펴보겠습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_2052300538":{"id":"blog-title-69e85e30fe","propertiesId":"cortex-sense","type":"heading2","lines":["Cortex Sense를 통한 컨텍스트 자동화"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_64117581":{"id":"blog-text-f62090d3d8","text":"\u003Cp\u003E많은 팀에게 Cortex Sense는 백그라운드 작업의 상당 부분을 처리해 주기 때문에 자연스러운 출발점이 됩니다. Cortex Sense는 사용자가 데이터를 쿼리하고 대시보드를 만들고 파이프라인을 실행하는 과정에서 컨텍스트를 수집합니다. 일상적인 업무를 비즈니스 이해로 전환해 각 테이블과 지표의 의미, 그리고 이들이 어떻게 연결되는지 정의하는 데 드는 시간을 줄입니다.\u003C/p\u003E\r\n\u003Cp\u003ECortex Sense는 쿼리 기록, 대시보드, dbt 모델을 포함해 팀이 이미 생성하고 있는 메타데이터와 사용 패턴에서 컨텍스트를 구축하는 방식으로 이를 수행합니다. 비즈니스가 변화해도 Cortex Sense가 사용자가 선택한 주기에 맞춰 지식을 자동으로 업데이트하므로 이러한 이해도 함께 최신 상태로 유지됩니다. 팀은 비즈니스 사용 사례의 범위를 정하고 관련 기존 컨텍스트를 연결한 뒤 피드백과 평가를 통해 이를 정제할 수 있습니다. 이로써 모든 테이블을 수동으로 모델링해야 하는 부담을 줄일 수 있습니다. Cortex Sense는 상충되는 지식을 지능적으로 식별하고, 어떤 소스를 신뢰해야 하는지 팀에 확인합니다.\u003C/p\u003E\r\n\u003Cp\u003E팀이 가장 중요한 용어를 거버넌스할 준비가 되면 Cortex Sense는 제안된 정의, 관계, 데이터 링크가 포함된 초기 초안을 Business Ontology에 생성할 수 있습니다. 따라서 누구도 빈 페이지에서 시작할 필요가 없습니다. 팀은 비즈니스 사용 사례의 범위를 정하고 관련 기존 컨텍스트를 연결한 뒤 피드백과 평가를 통해 이를 정제할 수 있습니다. 이로써 모든 테이블을 수동으로 모델링해야 하는 부담을 줄일 수 있습니다. Cortex Sense는 상충되는 지식을 지능적으로 식별하고, 어떤 소스를 신뢰해야 하는지 팀에 확인합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_553216124":{"id":"blog-title-aa39744d94","propertiesId":"business-ontology","type":"heading2","lines":["Business Ontology로 비즈니스 컨텍스트 정의"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1999528628":{"id":"blog-text-36e72e8949","text":"\u003Cp\u003EBusiness Ontology는 팀이 가장 중요한 비즈니스 용어의 의미, 소유자, 관계를 기록하는 공간입니다. ‘순매출’을 예로 들어보겠습니다. 모든 시맨틱 뷰, 대시보드, 에이전트가 각각의 버전을 갖는 대신, 분석가는 이를 한 번 정의하거나 기존 용어집에서 가져온 뒤 범위를 ‘재무’로 설정하고 소유자를 지정해 승인을 받습니다. 그런 다음 이를 계산하는 시맨틱 뷰 지표에 연결합니다(자료 2 참조). 이후 에이전트와 다른 팀은 이 승인된 정의를 기준으로 작업합니다. 팀은 다음을 수행할 수 있습니다.\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E비즈니스 개념에 기반한 에이전트 구현:\u003C/b\u003E 도메인 전문가는 고객, 주문, 매출과 같은 개념과 그 관계를 정의하고, 엔지니어는 이러한 정의를 데이터에 연결합니다. 그러면 에이전트는 비즈니스 고유의 지식을 바탕으로 데이터를 해석합니다. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E시맨틱 뷰 전반의 지표 정렬: \u003C/b\u003E팀은 각자의 시맨틱 뷰에 있는 동등한 지표를 하나의 공유 비즈니스 정의에 연결해, 모든 팀에 단일 구현 방식을 강제하지 않고도 의미를 정렬할 수 있습니다. 의도적인 차이도 유지할 수 있습니다. 예를 들어 레스토랑 기업은 브랜드마다 동일 매장 매출을 다르게 정의할 수 있습니다. 각 버전은 별도로 승인되고 자체 브랜드 라벨이 지정되며, 이를 계산하는 시맨틱 뷰에 연결됩니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E모든 사용자가 올바른 정의를 찾도록 지원: \u003C/b\u003E누구나 용어를 조회해 소유자와 승인 여부를 확인하고, 해당 정의가 계산되는 위치까지 따라갈 수 있습니다. 브랜드 및 지역별 변형은 각자의 범위를 유지합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_333095280":{"id":"image-e8f2cb25bc","height":"1049","src":"/adobe/dynamicmedia/deliver/dm-aid--98b0bbf8-e01c-4435-b087-0a86bc65aae0/business-ontology-ui.png?quality=85&preferwebp=true","alt":"In Business Ontology, net revenue has one approved definition, with its synonyms, related concepts and variants, linked to the metrics that calculate it in two semantic views.","lazyEnabled":true,"isLcpImage":false,"width":"2048","title":"자료 2: Business Ontology에서 순매출은 하나의 승인된 정의로 관리되며, 동의어, 관련 개념, 변형과 함께 두 개의 시맨틱 뷰에서 이를 계산하는 메트릭과 연결됩니다.",":type":"snowflake-site/components/image"},"blog_text_1334654234":{"id":"blog-text-c0a4832ff5","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E이러한 정의는 의사 결정에 영향을 미치므로 조직은 Horizon Catalog의 거버넌스 제어를 통해 누가 정의를 생성, 편집, 승인할 수 있는지 관리합니다. AI 제안과 가져온 정의는 검토를 위한 출발점이며, 책임 있는 소유자가 무엇을 공식화할지 결정합니다. 승인된 정의가 Cortex Sense가 추론한 내용과 충돌할 경우, 에이전트는 승인된 정의를 사용하고 Cortex Sense는 그로부터 학습합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-44334bc21c","alignment":"left","layout":"card","quoteSourceName":"Joe Tobey","quoteSourceTitle":"Head of Data Product Engineering, dentsu","showQuoteIcon":true,"quote":"\"Business Ontology는 모든 정의마다 책임지는 담당자를 두고, 그 정의를 실제로 구현하는 데이터와 연결해, 어떤 에이전트든 같은 답을 내놓게 합니다. 이 제품을 초기에 도입한 입장에서, 바로 이러한 기능 덕분에 저희 데이터에 에이전트를 확신을 갖고 활용할 수 있습니다.\"","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_title_382186722":{"id":"blog-title-19011935de","propertiesId":"semantic-views","type":"heading2","lines":["Semantic Views로 정의와 로직을 한 번만 적용"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_881009154":{"id":"blog-text-d66294e365","text":"\u003Cp\u003ESemantic Views는 각 지표가 계산되는 방식을 정의하고, Business Ontology는 그 지표의 의미와 승인 주체에 거버넌스를 적용합니다. 각 뷰는 도메인의 지표, 차원, 조인을 에이전트, BI 도구, SQL이 직접 쿼리할 수 있는 Snowflake 오브젝트로 패키징합니다. 따라서 에이전트는 추측이 아니라 거버넌스가 적용된 로직을 기반으로 SQL을 생성합니다. Business Ontology는 승인된 정의를 이를 계산하는 시맨틱 뷰에 연결하지만, 계산 방식 자체를 변경하지는 않습니다. 계산 방식은 각 뷰의 소유자가 계속 관리합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_922201594":{"id":"blog-title-562e7756e7","propertiesId":"","type":"heading2","lines":["고품질 컨텍스트를 위한 개방형 설계 플랫폼"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_598255608":{"id":"blog-text-cf5f43651c","text":"\u003Cp\u003E비즈니스 지식은 비즈니스와 함께 이동할 수 있을 때 비로소 가치를 발휘합니다. Snowflake가 시맨틱 모델을 위한 벤더 중립적 개방형 표준인 \u003Ca href=\"https://www.snowflake.com/en/blog/apache-ossie-open-semantic-interchange-incubator/\" target=\"_blank\"\u003EApache Ossie(incubating)\u003C/a\u003E의 창립에 참여한 이유도 여기에 있습니다. 현재 이 표준은 Microsoft, Salesforce, dbt Labs, Qlik, ThoughtSpot을 포함한 수십 개 조직의 지지를 받고 있습니다. Semantic Views는 Ossie 정의를 가져오고 내보낼 수 있으므로, 각 도구에서 지표를 다시 구축하지 않고도 Snowflake와 다른 Ossie 호환 도구 간에 지표를 이동할 수 있습니다. Snowflake 외부의 에이전트도 Model Context Protocol을 통해 Semantic Views를 쿼리할 수 있으며, Horizon Catalog가 액세스에 거버넌스를 적용합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_189117634":{"id":"quote-item-d42d2fdca6","alignment":"left","layout":"card","quoteSourceName":"Morten Lileng","quoteSourceTitle":"Global Head of Engineering, dentsu","showQuoteIcon":true,"quote":"\"시맨틱 드리프트는 여러 파트너가 함께하는 데이터 환경에서 가장 과소평가되는 위험 중 하나입니다. dentsu의 데이터 및 기술 에이전시인 Merkle은 업계에서 가장 복잡한 축에 속하는 이러한 환경에서 일하고 있습니다. Snowflake의 Business Ontology는 인프라 수준에서 이 공백을 메워, 모든 정의에 소유자를 지정하고 모든 다운스트림 시스템이 거버넌스가 적용된 단일한 답을 얻도록 합니다.”","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_title_874620594":{"id":"blog-title-354586e4f7","propertiesId":"snowflake-business-ontology","type":"heading2","lines":["분석 기업들이 말하는 Snowflake Business Ontology"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_650815874":{"id":"blog-text-84aa1cf091","text":"\u003Cp\u003E많은 사용자가 BI 및 분석 도구를 통해 질문에 대한 답을 얻기 때문에 일관된 비즈니스 컨텍스트는 매우 중요합니다. Omni, Sigma, ThoughtSpot은 컨텍스트 과제와 이를 해결하는 Business Ontology의 역할을 다음과 같이 설명합니다.\u003C/p\u003E\r\n\u003Ch4\u003EOmni\u003C/h4\u003E\r\n\u003Cp\u003E“컨텍스트는 새로운 분석가를 온보딩하는 것과 같습니다. 언제, 왜, 어떻게 사용해야 하는지 알 수 있도록 가르쳐야 하는 모든 것이 바로 컨텍스트입니다. \u003Ca title=\"Omni\" aria-label=\"Omni\" href=\"https://omni.co/\"\u003EOmni\u003C/a\u003E는 거버넌스가 적용된 시맨틱 모델을 중심으로 구축되었으며 Snowflake Semantic Views와 유일한 양방향 통합을 제공합니다. 이제 Business Ontology는 승인된 비즈니스 정의를 포착함으로써 그 기반을 한 단계 더 확장합니다. 양사는 함께 공동 고객이 모든 사용자와 에이전트를 지원할 수 있는 신뢰할 수 있는 컨텍스트를 구축하도록 돕고 있습니다.” \u003C/p\u003E\r\n\u003Cp\u003E— Omni 공동 창립자 겸 사장 Jamie Davidson\u003C/p\u003E\r\n\u003Ch4\u003ESigma\u003C/h4\u003E\r\n\u003Cp\u003E“AI 에이전트의 신뢰성은 이를 구동하는 비즈니스 컨텍스트와 정의의 신뢰성에 달려 있습니다. Business Ontology를 통해 공동 고객은 비즈니스 개념을 한 번 정의한 뒤 \u003Ca target=\"_blank\" href=\"https://www.sigmacomputing.com/product/ai\"\u003ESigma AI \u003C/a\u003E전반에서 이를 원활하게 재사용해 거버넌스가 적용된 애플리케이션, 대시보드, 에이전트를 구축하고 Sigma에서 신뢰할 수 있는 분석을 강화할 수 있습니다.”\u003Cbr\u003E\r\n— Sigma 제품 관리 디렉터 Zalak Trivedi \u003C/p\u003E\r\n\u003Ch4\u003EThoughtSpot\u003C/h4\u003E\r\n\u003Cp\u003E“신뢰할 수 있는 AI는 신뢰할 수 있는 비즈니스 컨텍스트에 달려 있습니다. Snowflake Business Ontology는 기업이 비즈니스 용어의 의미를 관리할 수 있는 거버넌스 기반 공간을 제공합니다. ThoughtSpot은 \u003Ca target=\"_blank\" href=\"https://www.thoughtspot.com/product/agents/spotter\"\u003ESpotter\u003C/a\u003E를 통해 비즈니스 사용자로부터 포착한 지식으로 이 컨텍스트를 지속적으로 보강해 모든 사용자에게 정확하고 설명 가능한 답변과 조치를 제공합니다. 양사는 함께 고객이 에이전틱 분석을 자신 있게 확장하도록 돕고 있습니다.”\u003C/p\u003E\r\n\u003Cp\u003E— ThoughtSpot 제품 담당 수석 부사장 Francois Lopitaux\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1682682914":{"id":"blog-title-885173ec85","propertiesId":"business-ontology","type":"heading2","lines":["Business Ontology로 지식을 일관된 엔터프라이즈 자산으로 전환"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_90895576":{"id":"blog-text-d4de5b9bd3","text":"\u003Cp\u003E사람은 역할을 바꾸고, 도구는 교체되며, 데이터 모델은 변화합니다. 그 과정에서 그 이면의 지식이 사라지는 경우가 너무 많습니다. Business Ontology는 이러한 지식을 책임 있는 소유자와 각 정의가 계산되는 위치에 대한 명확한 연결을 갖춘 지속 가능한 회사 자산으로 전환합니다. 새로운 에이전트나 애플리케이션은 모두 비즈니스가 이미 알고 있는 지식에서 출발할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003EBusiness Ontology를 더 자세히 살펴보려면 \u003Ca target=\"_blank\" href=\"https://www.snowflake.com/en/webinars/virtual-hands-on-lab/get-hands-on-with-snowflake-context--how-business-ontology-gives/?utm_source=expeditionams27&utm_medium=event&utm_campaign=na-en-event&utm_content=-vhol-get-hands-on-with-snowflake-context\"\u003E핸즈온 랩을 확인하고\u003C/a\u003E 블로그 게시물 \u003Ca target=\"_blank\" href=\"http://www.snowflake.com/en/blog/business-ontology-context\" title=\"지표보다 의미가 먼저: AI 에이전트를 위한 거버넌스 기반 Business Ontology 구축 방식'\" aria-label=\"지표보다 의미가 먼저: AI 에이전트를 위한 거버넌스 기반 Business Ontology 구축 방식'\"\u003E‘지표보다 의미가 먼저입니다. AI 에이전트를 위한 거버넌스 기반 Business Ontology 구축 방식’\u003C/a\u003E도 읽어보세요.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1755654776":{"id":"blog-title-ff6f84caed","propertiesId":"","type":"heading2","lines":["자주 묻는 질문"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1329841915":{"id":"blog-text-b6c6d71f8d","text":"\u003Ch4\u003ESnowflake Business Ontology란 무엇인가요?\u003C/h4\u003E\n\u003Cp\u003EBusiness Ontology(곧 퍼블릭 프리뷰 제공)는 Snowflake Horizon Context의 비즈니스 모델링 및 거버넌스 레이어입니다. 조직은 이를 사용해 특정 데이터 모델에 종속되지 않고 비즈니스 개념, 지표, 그리고 그 관계를 정의할 수 있습니다. 각 정의에는 소유자와 승인 상태가 기록되며, 이를 구현하는 시맨틱 뷰, 테이블, 뷰에 연결됩니다. CoCo와 CoWork의 AI 에이전트에는 승인된 정의만 제공되므로, 에이전트는 경쟁하는 구현 방식 중 하나를 선택하는 대신 조직이 공식적으로 보증하는 의미를 기준으로 작업합니다.\u003C/p\u003E\n\u003Ch4\u003EAI 에이전트에 비즈니스 컨텍스트가 필요한 이유는 무엇인가요?\u003C/h4\u003E\n\u003Cp\u003E거버넌스가 적용된 비즈니스 컨텍스트는 에이전트가 비즈니스가 이해하는 방식으로 데이터를 해석하도록 도와 더 정확한 응답을 제공할 수 있게 합니다. AI 에이전트는 데이터에 액세스할 수는 있지만, 비즈니스 용어의 의미나 개념 간 관계를 자동으로 ‘이해’하지는 못합니다. 에이전트에는 컨텍스트가 필요합니다. 어떤 정의를 신뢰하고, 어떤 정책을 따르며, 어떤 기업 지식을 반영해야 하는지 알려 주는 승인된 참조 정보입니다. 이러한 컨텍스트가 없으면 에이전트는 의미와 관련 데이터 소스를 추측하게 되며, 같은 질문에도 서로 다른 답을 내놓을 수 있습니다. \u003C/p\u003E\n\u003Ch4\u003EBusiness Ontology와 Semantic Views는 어떻게 다른가요?\u003C/h4\u003E\n\u003Cp\u003EBusiness Ontology는 비즈니스 용어의 의미, 소유자, 다른 개념과의 관계를 정의합니다. Semantic Views는 각 지표의 계산 방식을 정의하고, 지표, 차원, 조인을 에이전트, BI 도구, SQL이 직접 쿼리할 수 있는 오브젝트로 패키징합니다. Business Ontology는 여러 시맨틱 뷰에 존재하는 동등한 지표를 공유되고 승인된 하나의 정의에 연결합니다.\u003C/p\u003E\n\u003Ch4\u003E비즈니스 정의를 Snowflake와 다른 도구 간에 이동할 수 있나요?\u003C/h4\u003E\n\u003Cp\u003E네. Business Ontology는 Apache Ossie (incubating)와 리소스 기술 프레임워크(RDF)를 비롯한 오픈 형식으로 비즈니스 개념, 정의, 관계를 가져오고 내보낼 수 있습니다. 이를 통해 팀은 승인된 비즈니스 지식을 다시 정의하지 않고도 다른 도구에서 재사용할 수 있습니다. Semantic Views도 Ossie 정의를 가져오고 내보낼 수 있으므로 지표를 다시 구축할 필요가 없습니다. Snowflake 외부의 에이전트도 Model Context Protocol을 통해 Semantic Views를 쿼리할 수 있으며, 이때 Horizon Catalog가 액세스에 거버넌스를 적용합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_title","blog_text_1060397060","blog_title_2052300538","blog_text_64117581","blog_title_553216124","blog_text_1999528628","image_333095280","blog_text_1334654234","quote_item","blog_title_382186722","blog_text_881009154","blog_title_922201594","blog_text_598255608","quote_item_189117634","blog_title_874620594","blog_text_650815874","blog_title_1682682914","blog_text_90895576","blog_title_1755654776","blog_text_1329841915"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 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