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Maya는 2주 전 프리미엄 스키를 구매했고, 이후 스키 부츠를 구매하기 위해 사이트를 세 차례나 다시 방문했습니다. 전환 가능성이 높게 예측되자, 회사 \u003Cb\u003EAI는 Maya가 살펴본 부츠를 20% 할인된 가격에 살 수 있는 프로모션을 발송합니다\u003C/b\u003E.\u003C/p\u003E\n\u003Cp\u003E그러나 AI가 보지 못하는 것이 있습니다. 바로 이 이벤트 시퀀스를 둘러싼 컨텍스트입니다. 사실 스키는 파손된 상태로 배송되었고, 교환이 지연되고 있으며, 품질 관련 케이스는 아직 종결되지 않았습니다. 회사 정책상 이런 상황에서는 클레임이 해결될 때까지 부츠 컬렉션에 할인을 적용해서는 안 되며, 수익성 있는 고객 유지를 우선해야 합니다.\u003C/p\u003E\n\u003Cp\u003E회사 차원에서는 이 모든 사실을 알고 있었지만, 결정을 해야하는 순간에 이러한 정보가 AI에 전달되지 않았고, 결국 AI는 잘못된 선택을 내리고 말았습니다.\u003C/p\u003E\n\u003Ch3\u003E문제는 컨텍스트였습니다\u003C/h3\u003E\n\u003Cp\u003E위 사례에서 AI의 추천은 즉각적인 전환, 즉 Maya의 다음 구매를 유도하는 데만 최적화되어 있었고, 해결되지 않은 서비스 실패, 프리미엄 라인의 가격 무결성, 불쾌한 경험을 하고 있는 고객 관계의 가치라는 주변 상황은 모두 간과했습니다.\u003C/p\u003E\n\u003Cp\u003E컨텍스트는 AI를 단순한 범용 어시스턴트에서 브랜드, 고객, 비즈니스, 그리고 달성하려는 성과에 정통한 전문가로 바꾸어 놓는 핵심 요소입니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-55e8087486","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"컨텍스트는 지속 가능한 경쟁 우위가 됩니다","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1884924545":{"id":"blog-text-410a696c2f","text":"\u003Cp\u003E\u003Cbr\u003E\n의사 결정 속도가 빨라질수록 컨텍스트의 부재는 더욱 위험해집니다. 사람의 경우에는 타이밍이 어긋난 캠페인이 발생하더라도 마케팅 담당자가 주간 리뷰에서 발견할 수 있습니다. 그러나 AI의 경우에는 누군가 패턴을 인지하기도 전에 동일한 사각지대가 수천 건의 잘못된 결정을 만들어낼 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E정작 마케팅 리더가 밤잠을 설쳐가며 고민해야 할 문제는 바로 이것인데도, AI 예산 논의는 여전히 모델 선택에만 집중되어 있습니다.\u003C/p\u003E\n\u003Ch3\u003E뛰어난 모델은 이제 누구나 사용할 수 있습니다\u003C/h3\u003E\n\u003Cp\u003E물론 품질, 속도, 전문성, 비용이 여전히 중요한 문제이지만, 대부분의 기업이 매우 뛰어난 모델에 액세스할 수 있다는 것은 엄연한 현실입니다.\u003C/p\u003E\n\u003Cp\u003E경쟁사가 빠르게 재현하기 어려운 것은 고객의 가치를 평가하고, 성장과 마진의 균형을 잡고, 브랜드를 보호하고, 수년간의 테스트 결과를 해석해 온 과정에서 축적된 비즈니스 판단력입니다.\u003C/p\u003E\n\u003Cp\u003E이것이 바로 \u003Cb\u003E컨텍스트 우위\u003C/b\u003E입니다. 기업 고유의 비즈니스 이해를 모든 의사 결정에 반영하고, 그 결과로부터 학습해 미래의 상황에 활용하는 것입니다.\u003C/p\u003E\n\u003Ch3\u003E컨텍스트를 위한 고객 360 투자\u003C/h3\u003E\n\u003Cp\u003E대부분의 마케팅 조직에게 이 여정은 기존의 고객 360 투자에서 시작해 바깥으로 확장해 나가는 과정입니다. Maya의 상황을 예로 들면, 이러한 투자는 다음과 같은 이점을 제공합니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E고객 360은 고객 진실을 지속적으로 유지합니다.\u003C/b\u003E Maya의 경우, 우수 고객이라는 지위와 최근의 스키 구매 내역을 부츠 페이지에서의 활동, 회사와의 관계, 그리고 연락할 수 있는 권한과 연결해 줍니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E컨텍스트로 강화된 고객 360은 상황을 설명합니다.\u003C/b\u003E 서비스 통화나 온라인 리뷰 같은 비정형 데이터 소스는 Maya가 불만을 느끼고 있고, 시급히 제품 교환을 필요로 하며, 이미 리조트에 도착했지만 스키를 탈 수 없는 상황임을 보여줍니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E마케팅 컨텍스트는 목표와 운영 논리를 더합니다.\u003C/b\u003E 마케팅팀은 프리미엄 부츠 캠페인이 진행 중이라는 사실을 알고 있지만, 지금은 서비스 회복이 우선순위입니다. 프로모션은 잠시 중단되어야 합니다. 여기서 성공은 Maya를 고객으로 유지하는 것이며, 이전 테스트 결과에 따르면 서비스 실패 후에는 회복을 우선하는 접근이 더 나은 성과를 내는 경향이 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E엔터프라이즈 컨텍스트는 마케팅 외부의 신뢰할 수 있는 정보를 제공합니다.\u003C/b\u003E 서비스, 상거래, 제품 시스템은 Maya의 클레임이 아직 진행 중이고, 교환 제품이 준비되어 있으며, 보증이 적용된다는 사실을 확인해 줍니다. 브랜드, 재무, 법무팀은 회사가 어떻게 대응해야 할지를 결정합니다. 여기에서 한 가지 현실이 드러납니다. 마케팅 컨텍스트든 다른 어느 한 부서의 컨텍스트든, 그것만으로는 충분하지 않다는 점입니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E연결된 엔터프라이즈 컨텍스트는 전체 상황을 활용할 수 있게 만듭니다.\u003C/b\u003E 정보는 이미 그것을 소유하고 관리하는 팀과 시스템에 그대로 남아 있습니다. 마케팅팀은 별도의 사본을 만들거나 관리할 필요 없이 필요한 시점에 관련 사실을 활용할 수 있습니다. 의사 결정의 순간에는 적정 규모의 하위 집합이 마케터, 애플리케이션 또는 에이전트가 실제로 필요로 하는 요약 정보가 됩니다. 최적의 행동 방안은 더 이상 추측이 아닙니다. 문제를 해결하고, 관계를 보호하고, 적절한 시점에 다시 고객과 소통하는 것입니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1049856448":{"id":"blog-text-2fc15432fb","text":"\u003Ch3\u003E컨텍스트를 지속 가능한 경쟁 우위로 만드는 7가지 원칙\u003C/h3\u003E\r\n\u003Cp\u003E컨텍스트가 어떻게 지속적인 우위가 되는지는 다음 일곱 가지 원칙으로 설명할 수 있습니다.\u003C/p\u003E\r\n\u003Ch4\u003E컨텍스트는 관련성을 만듭니다\u003C/h4\u003E\r\n\u003Cp\u003E사실은 시작에 불과합니다. 이 사례에서 사실은 Maya가 스키를 구매했고, 파손된 제품을 배송받았으며, 케이스가 아직 진행 중이라는 것입니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E진실, 의미, 의도, 기억\u003C/b\u003E, 그리고 \u003Cb\u003E경계\u003C/b\u003E가 함께할 때 비로소 AI가 상황을 제대로 이해할 수 있는 컨텍스트 구성 요소가 완성됩니다.\u003C/p\u003E\r\n\u003Cp\u003E비즈니스 의미는 프레임을 크로스셀링(부츠)에서 서비스 회복으로 전환합니다. 의도는 수익성 있는 고객 유지를 목표로 설정하고, 기억은 회복 우선 접근에 대한 과거 테스트에서 얻은 교훈을 불러옵니다. 그리고 경계는 관련된 노출 제한(광고/캠페인), 승인 및 동의 규칙을 적용합니다.\u003C/p\u003E\r\n\u003Ch4\u003E미활용 컨텍스트는 기회를 창출합니다\u003C/h4\u003E\r\n\u003Cp\u003E마케팅은 측정하기 가장 쉬운 신호를 최적화하는 데 집중해 왔습니다. 클릭, 구매, 캠페인 반응은 여전히 중요하지만, 어떤 일이 \u003Ci\u003E왜\u003C/i\u003E 일어났는지를 설명하는 경우는 거의 없습니다.\u003C/p\u003E\r\n\u003Cp\u003E브랜드나 제품에 대한 고객의 관점과 같은 정보는 온라인 리뷰, 채팅 기록, 소셜 게시물처럼 이른바 비정형 데이터에 담겨 있는 경우가 많습니다. 이제 조직은 트랜잭션 기록만으로는 볼 수 없는 인사이트를 이러한 데이터 세트에서 얻을 수 있게 되었습니다.\u003C/p\u003E\r\n\u003Cp\u003EAI는 그동안 활용하기 어려웠던 정보를 실행 가능한 비즈니스 신호로 전환할 기회를 열어줍니다.\u003C/p\u003E\r\n\u003Ch4\u003E올바른 컨텍스트는 정밀성을 만듭니다\u003C/h4\u003E\r\n\u003Cp\u003E단순히 모든 것에 액세스할 수 있다고 해서 컨텍스트 전략이 되는 것은 아닙니다. AI에 모든 것을 던져 넣고 좋은 결과를 기대하고 싶은 유혹에 빠지기 쉽습니다.\u003C/p\u003E\r\n\u003Cp\u003E앞서 살펴본 것처럼, 컨텍스트가 너무 부족하면 AI는 눈을 가린 채 판단하게 됩니다. 반대로 너무 많으면 오래되거나 상충되거나 불필요한 정보가 의사 결정에 부담을 주어 비용과 리스크가 커지고, 정작 중요한 신호는 찾기 어려워집니다. 긴 컨텍스트를 처리하는 언어 모델에 대한 \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://arxiv.org/abs/2307.03172\"\u003E연구\u003C/a\u003E에서도, 모델이 컨텍스트 윈도우 한계에 도달하기 전이라도 관련 정보가 긴 입력값 속에 묻혀 있으면 신뢰성 있게 활용되지 못할 수 있다는 사실이 확인되었습니다. Maya의 경우, AI에 필요한 것은 그녀의 전체 고객 이력이나 모든 서비스 녹취록 혹은 모든 브랜드 문서가 아닙니다. 서비스 케이스가 아직 진행 중이라는 사실처럼, 이 특정 결정을 실질적으로 바꾸는 몇 가지 최신 정보와 규칙이면 충분합니다.\u003C/p\u003E\r\n\u003Cp\u003E따라서 컨텍스트 계층은 \u003Cb\u003E의사 결정에 필요한 최소한으로 충분하고, 관련성 있고, 최신이며, 신뢰할 수 있고, 승인된 컨텍스트\u003C/b\u003E를 구성해야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E또한 ‘최신’이 항상 ‘실시간’을 의미하지는 않는다는 점도 중요합니다. 마케팅 수신 동의는 변경 사항을 즉시 반영해야 할 수 있지만, 승인된 브랜드 정책은 수개월 동안 유효한 기준으로 유지될 수 있습니다.\u003C/p\u003E\r\n\u003Ch4\u003E공유된 컨텍스트는 조율을  가능하게 만듭니다\u003C/h4\u003E\r\n\u003Cp\u003EMaya의 상황이 잘못된 방향으로 흘러간 것은 회사가 전체 상황을 단편적으로만 알고 있었기 때문입니다. 마케팅 부서는 그녀의 참여 활동을 보았고, 서비스팀은 파손 배송 건을 검토했으며, 브랜드 부서는 이런 상황에서는 부츠 컬렉션이 할인 대상에서 제외된다는 사실을 알고 있었습니다.\u003C/p\u003E\r\n\u003Cp\u003E각 팀은 자신의 경계 안에서는 합리적인 결정을 내릴 수 있었지만, 다른 부서의 컨텍스트를 가져오지 않고서는 어떤 시스템도 올바른 엔드투엔드 결정을 내릴 수 없었습니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E이처럼 AI는 사일로를 없애는 대신, 오히려 사일로를 자동으로 만들어 내는 경우가 많습니다\u003C/b\u003E.\u003C/p\u003E\r\n\u003Cp\u003E공유된 컨텍스트가 있으면 각 팀이 자신의 도메인에 대한 통제권을 포기하지 않고도, 시스템 차원에서 고객의 상황과 비즈니스 성과를 중심으로 조율할 수 있습니다.\u003C/p\u003E\r\n\u003Ch4\u003E거버넌스가 적용된 컨텍스트는 신뢰를 만듭니다\u003C/h4\u003E\r\n\u003Cp\u003EAI 시스템에는 권한 기반 액세스, 명확한 실행 임계값, 감사 가능한 기록을 갖춘 신뢰할 수 있는 진실 공급원이 필요합니다.\u003C/p\u003E\r\n\u003Cp\u003E이러한 통제는 AI가 행동한 뒤에 결과를 검사하는 것이 아니라, 의사 결정 이전과 도중에 사용할 수 있는 컨텍스트 자체를 형성해야 합니다. 통제가 이렇게 작동하면, 거버넌스는 사람이 모든 추천을 하나하나 검토하거나 승인하지 않아도 거버넌스를 통해 더 높은 수준의 자율 운영이 가능해집니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E거버넌스는 신뢰할 수 있는 자율성을 위한 운영 체제입니다\u003C/b\u003E.\u003C/p\u003E\r\n\u003Ch4\u003E소유된 컨텍스트는 통제력을 만듭니다\u003C/h4\u003E\r\n\u003Cp\u003E통제력이란 기술이 바뀌어도 선택권을 유지한다는 의미입니다.\u003C/p\u003E\r\n\u003Cp\u003E기업이 컨텍스트를 통제하면, 오랜 시간 축적된 비즈니스 이해를 어디로든 가져갈 수 있습니다. 모델이나 애플리케이션을 바꾼다고 해도 조직이 이미 알고 있는 것을 다시 구축할 필요가 없습니다.\u003C/p\u003E\r\n\u003Cp\u003E그러나 특정 플랫폼에 갇힌 컨텍스트는 다르게 작동합니다. 기초 데이터는 내보낼 수 있지만, 그 위에 구축된 인텔리전스는 그것을 만들어낸 플랫폼에 묶여 있을 수 있습니다. 또한 공급업체가 다른 시스템의 컨텍스트 사용 방식을 변경하면 액세스 자체가 달라질 수도 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E그곳에 학습이 많이 축적될수록, 비즈니스가 학습한 내용의 일부를 잃지 않고서는 떠나기가 어려워집니다.\u003C/p\u003E\r\n\u003Ch4\u003E컨텍스트는 우위를 만듭니다\u003C/h4\u003E\r\n\u003Cp\u003E모든 결정은 증거를 만들어냅니다. AI가 행동하고, 고객이 반응하고, 비즈니스 성과가 뒤따르고, 사람이 추천 사항을 승인하거나 거부하거나 수정할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E그리고 그 증거는 컨텍스트 기반으로 다시 돌아가야 합니다.\u003C/p\u003E\r\n\u003Cp\u003EMaya의 사례에서 유용한 정보는 단순히 프로모션이 중단되었다는 사실이 아닙니다. 기업이 학습해야 할 것은 서비스 복구가 고객 관계를 지켜냈는지, Maya가 이후 할인 없이도 부츠를 구매했는지, 그리고 사람의 개입이 결과를 개선했는지입니다.\u003C/p\u003E\r\n\u003Cp\u003E이러한 결과는 향후 유사한 의사 결정을 위한 기억이 됩니다. 시간이 지나면서 조직은 어떤 개입이 고가치 고객 관계를 지키는지, 어떤 오퍼가 추가 수요를 창출하는지, 어느 지점에서 사람의 판단이 AI의 추천을 일관되게 개선하는지 학습할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003EAI를 사용하는 것만으로는 학습 우위가 만들어지지는 않습니다. 행동과 결과, 수정 사항이 기업이 재사용할 수 있는 컨텍스트로 되돌아올 때 비로소 그 우위는 마치 복리처럼 쌓이게 됩니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E앞서 살펴본 여섯 가지 원칙은 오늘의 의사 결정을 개선합니다. 그리고 마지막 일곱 번째 원칙은 미래의 모든 의사 결정을 개선합니다\u003C/b\u003E.\u003C/p\u003E\r\n\u003Ch3\u003E엔터프라이즈 컨텍스트 계층은 어디에 구축해야 할까요?\u003C/h3\u003E\r\n\u003Cp\u003E진짜 질문은 바로 이것입니다. 선택지는 일반적으로 모델 제공업체, 마케팅 애플리케이션, 데이터 플랫폼 중 하나입니다.\u003C/p\u003E\r\n\u003Cp\u003E모델 제공업체와 마케팅 애플리케이션 모두 유용한 인텔리전스를 제공하지만, 어느 쪽도 엔터프라이즈 컨텍스트가 오래 머물 이상적인 저장소로 설계되지는 않았습니다.\u003C/p\u003E\r\n\u003Cp\u003E컨텍스트 계층을 모델 내부에 구축하면 해당 모델 제품군에 대한 종속성이 생길 수 있습니다. 그렇다고 여러 애플리케이션에 분산하면 또 다른 문제가 발생합니다. 시스템마다 자체적인 정의, 정책, 기억이 만들어지면서 어느 것도 일치하지 않게 되고, 결국 동일한 지표가 세 가지 서로 다른 결과로 이어집니다.\u003C/p\u003E\r\n\u003Cp\u003E결국 거버넌스가 적용된 무게 중심은 엔터프라이즈 기반에 두어야 합니다. 그래야 의미가 일관되게 유지되고, 정책이 단 한 번만 적용되며, 승인된 모델과 애플리케이션 전반에 걸쳐 학습이 축적됩니다.\u003C/p\u003E\r\n\u003Ch3\u003E컨텍스트를 위한 거버넌스 기반 데이터 및 AI 파운데이션으로서 Snowflake\u003C/h3\u003E\r\n\u003Cp\u003ESnowflake AI 데이터 클라우드는 세 가지 방식으로 이러한 모델을 지원합니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E의사 결정 순간에 활용할 수 있는 적합한 컨텍스트:\u003C/b\u003E 마케팅 팀은 의사 결정의 배경이 되는 비즈니스 데이터(상황을 설명하는 비정형 근거까지 포함)를 하나의 거버넌스 기반으로 통합할 수 있습니다. \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/ko/product/features/horizon-context/\"\u003ESnowflake Horizon Context\u003C/a\u003E는 AI가 이 기반을 활용할 수 있도록 지원하며, 컨텍스트의 출처를 보존하고 일관된 비즈니스 의미를 적용합니다. 이를 통해 에이전트는 추측이 아닌 신뢰할 수 있는 정의에 기반해 추론할 수 있으며, 특정 지표를 집계할 수 있는 경우와 그렇지 않은 경우까지 판단할 수 있습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E선택한 에코시스템 전반에서 재사용 가능:\u003C/b\u003E 시맨틱 뷰를 사용하면 비즈니스 의미를 한 번만 정의하고, 그 의미를 Snowflake 안팎에서 팀이 사용하는 도구로 그대로 가져갈 수 있습니다. 호환되는 모든 도구는 인터페이스마다 다시 구축할 필요 없이 동일한 거버넌스 로직을 기반으로 작동합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E학습 효과의 누적:\u003C/b\u003E Snowflake는 정의와 계보를 관리할 수 있지만, 비즈니스가 무엇을 학습해야 하는지는 결정할 수 없습니다. 마케팅 팀은 각 AI 의사결정 이후 어떤 일이 일어났는지, 마케터가 AI의 추천을 수정한 경우까지 포함해 기록하고, 그 근거를 재사용 가능한 컨텍스트로 되돌려야 합니다. Snowflake는 이러한 기억에 거버넌스가 적용된 저장소와 일관된 의미를 제공합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_2094103794":{"id":"blog-text-adc50c5c2d","text":"\u003Ch3\u003EMaya의 사례\u003C/h3\u003E\n\u003Cp\u003E앞선 사례에서 컨텍스트가 불완전할 때 AI는 Maya에게 20% 할인 오퍼를 발송했습니다. \u003C/p\u003E\n\u003Cp\u003E하지만 거버넌스가 적용되고 연결된 컨텍스트가 있었다면, 모델이 서비스 복구가 필요한 순간임을 인식해 문제를 해결하고, 고객 관계를 지키며, 적절한 시점에 다시 고객과의 접점을 만들었을 것입니다.\u003C/p\u003E\n\u003Cp\u003E그리고 Maya의 반응은 비슷한 상황에 놓인 다른 고객을 위한 컨텍스트가 됩니다.\u003C/p\u003E\n\u003Cp\u003E모델은 계속 바뀝니다. 하지만 컨텍스트는 기업의 통제 아래 머물러야 하며, 의사 결정을 거듭할수록 더 똑똑해져야 합니다.\u003C/p\u003E\n\u003Cp\u003EAI가 이미 대규모로 수행하고 있는 의사 결정 하나에서 시작해, 현재 AI가 볼 수 없는 중요한 사실을 모두 문서화해 보세요. 그것이 바로 여러분의 컨텍스트 로드맵입니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E마케팅 리더:\u003C/b\u003E \u003Ca href=\"https://www.snowflake.com/ko/solutions/departments/marketing/\"\u003ESnowflake 마케팅 AI 데이터 클라우드에 대해 자세히 알아보세요\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E데이터 및 기술 리더:\u003C/b\u003E \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/ko/blog/horizon-context-governed-context/\" target=\"_blank\"\u003ESnowflake Horizon Context를 통해 거버넌스가 적용된 비즈니스 컨텍스트를 수집, 강화, 활성화하는 방법을 확인해 보세요\u003C/a\u003E.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":itemsOrder":[],":items":{},":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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