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단축했습니다.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"}},":itemsOrder":["title_v2","text_800689963"],"appliedCssClassNames":"ui-background-05 snowflake-responsive-container-inner-padding-extra-small"},"title_v2_copy_copy":{"id":"title-v2-2fe4ae651d","type":"heading4","lines":["확장, 통합, 가속이라는 세 가지 과제"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_copy_121787673":{"id":"text-4e463d8613","text":"\u003Cp\u003E사업과 데이터 수집 규모가 기하급수적으로 커지면서 피처링은 세 가지 데이터 과제에 직면했습니다.\u003C/p\u003E\u003Cp\u003E첫째는 확장성입니다. 매일 폭증하는 여러 SNS 채널 데이터에 대응해 매번 인프라를 직접 증설하고 관리하는 방식은 한계에 이르렀습니다.\u003C/p\u003E\u003Cp\u003E둘째는 통합성입니다. 파이프라인별로 흩어진 정형·비정형·분석 데이터를 하나의 기준으로 관리할 단일 원천(SSOT, Single Source of Truth) 체계가 필요했습니다.\u003C/p\u003E\u003Cp\u003E셋째는 가속성입니다. 빠르게 변화하는 시장에서 소셜 데이터를 추출하고 분석해 서비스에 반영하는 주기(Cycle Time)를 최대한 단축해야 했습니다.\u003C/p\u003E\u003Cp\u003E피처링은 Snowflake를 단순한 저장소가 아니라 비즈니스 핵심 플랫폼으로 정의하고 도입을 결정했습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_v2_copy_copy_c":{"id":"title-v2-06badb6316","type":"heading4","lines":["Snowflake 도입 배경: 통합과 확장, 두 가지를 동시에"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy":{"id":"text-a3afc704ce","text":"\u003Cp\u003E피처링이 Snowflake를 선택한 이유는 세 가지 과제와 정확히 맞닿아 있습니다. 정형·비정형·분석 데이터를 하나의 플랫폼에서 통합해 관리할 수 있다는 점이 통합성 과제를, 컴퓨트와 스토리지가 분리된 구조로 필요한 만큼만 확장하고 사용한 만큼만 비용을 낸다는 점이 확장성 과제를 해결했습니다. 무엇보다 데이터를 외부로 옮기지 않고 이미 쌓인 데이터 위에서 바로 AI를 적용할 수 있다는 점이, 추출부터 분석, 서비스 반영까지의 주기를 단축해야 하는 가속성 과제에 결정적이었습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_v2_copy_copy_8":{"id":"title-v2-2690ce3cb5","type":"heading4","lines":["Snowflake 중심의 메달리온 아키텍처로 재편"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_527922892":{"id":"text-9e6c7ef037","text":"\u003Cp\u003E피처링은 AWS 환경에서 직접 구축해 운영하던 메달리온 아키텍처(원천 데이터부터 정제, 분석까지 단계를 거치며 데이터 품질을 높여가는 구조)를 Snowflake 중심으로 재편했습니다. 원천 데이터를 저장하는 S3 기반 브론즈 레이어는 그대로 유지하되, 데이터를 정제하고 가공하는 실버·골드 레이어를 Snowflake 내부로 이관해 통합 관리하는 구조입니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"image_351624422":{"id":"image-35bd255b11","height":"806","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--88d9ec53-52ed-4ec8-9f91-8ba6bd154c09/image4.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1776",":type":"snowflake-site/components/image"},"text_1509714807":{"id":"text-d81cffeae9","text":"\u003Cp\u003E실버 레이어에는 인플루언서 정보(SOCIAL_USER)와 게시글 메타데이터(SOCIAL_CONTENT), 특정 시점의 팔로워·좋아요·댓글 변화 추이를 보존하는 스냅샷 데이터 등 정제 테이블이 적재됩니다. 이 데이터를 바탕으로 머신러닝과 대규모 언어 모델(LLM) 분석을 거친 결과, 즉 콘텐츠 토픽 분류, 스토리텔링 분석, 타겟 오디언스 성향, 협찬 여부 판별 결과와 임베딩 데이터는 골드 레이어의 팩트 테이블과 분석 테이블에 적재되어 서비스와 분석 영역으로 유기적으로 연동됩니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"image_360707432":{"id":"image-c9236f61cb","height":"798","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--80eab7ff-04b0-4bad-8d8a-ba39bc08c827/image2.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1866",":type":"snowflake-site/components/image"},"text_copy_copy_63041":{"id":"text-d8c911b17c","text":"\u003Cp\u003E이 데이터 기반 위에서 피처링은 네 가지 방식으로 비즈니스 가치를 만들어냅니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E01 서비스 고도화:\u003C/b\u003E 주요 서비스 중 하나인 '콘텐츠 랭킹 및 브랜드 점유율 분석' 기능을 고도화한 케이스입니다. 피처링은 마케터들이 지금 뜨는 콘텐츠를 빠르게 찾을 수 있도록 매일 수백만 건의 콘텐츠를 토픽, 카테고리, 프로모션 유형별로 분류하고 집계합니다. 과거에는 대규모 집계 연산을 처리하기 위해 별도 인프라가 필요했지만, 이제는 Snowflake 위에서 정제 데이터를 결합하는 것만으로 콘텐츠 랭킹과 브랜드 점유율을 안정적으로 산출합니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E02 고객 맞춤화:\u003C/b\u003E 피처링은 엔터프라이즈 고객을 대상으로 맞춤형 '소셜 인텔리전스' 서비스를 제공하고 있습니다. 경쟁사 마케팅 동향 분석 사례의 경우, 고객의 경쟁사 키워드로 수집한 콘텐츠를 TF-IDF 및 K-Means 알고리즘으로 클러스터링한 후 반복 패턴을 추출하고, 이를 Snowflake Cortex 기반 LLM 분석과 결합하여, '뷰티 A사의 러닝×선케어 캠페인'과 같은 마케팅 시그널을 포착합니다. 사람이 일일이 콘텐츠를 훑어보지 않아도 되는 이 자동화 워크플로로 경쟁사 리서치 리드타임을 약 56% 줄였습니다. 이 구조의 핵심은 새로운 데이터를 매번 쌓지 않아도 된다는 데 있습니다. 소셜 데이터는 플랫폼에 이미 지속적으로 축적되고 있기 때문에, 관심 있는 브랜드나 카테고리, 분석 관점만 새롭게 정의하면 동일한 환경에서 다른 고객사를 위한 인사이트도 빠르게 만들어낼 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"image_1515040867":{"id":"image-91325cf7d0","height":"521","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3a18b03b-9d3a-4ec0-9445-0c6dd699cca3/image1.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"674",":type":"snowflake-site/components/image"},"text_2140612143":{"id":"text-b574622a3a","text":"\u003Cp\u003E\u003Cb\u003E03 데이터 민주화:\u003C/b\u003E 타 부서가 데이터를 확인하려면 매번 데이터 팀에 추출을 요청해야 했던 방식에서 벗어나, Streamlit in Snowflake로 셀프서비스 앱을 구축했습니다. 예를 들어, 마케팅 팀이 트렌드 뉴스레터를 발간할 때 직접 원하는 키워드 조건과 기간을 설정하면 특정 키워드의 소셜 내 언급량, 인게이지먼트 추이, 연관 해시태그 집계 결과를 데이터 팀의 개입 없이 시각화 화면으로 즉시 확인할 수 있습니다. Snowflake의 역할 기반 접근 통제(Role-Based Access Control)로 거버넌스를 유지하면서도, 누구나 필요한 데이터를 즉시 확인할 수 있는 접근성과 자율성을 동시에 확보했습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"image_1963192265":{"id":"image-1fbdf75030","height":"1140","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--28e658ef-5f11-4e4c-bf8f-cd6417de50e6/image5.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1338",":type":"snowflake-site/components/image"},"text_1730804352":{"id":"text-978aafe356","text":"\u003Cp\u003E\u003Cb\u003E04 AI 고도화\u003C/b\u003E: 피처링은 데이터 추출부터 모니터링, 배포, 상용화, 평가, Ground-Truth 구축, 모델 학습 및 개선으로 이어지는 AI 고도화 사이클 전체를 Snowflake 안에서 순환 구조로 운영하고 있으며, 이 중 핵심은 Ground Truth(정답) 데이터를 구축하고 검증하는 루프입니다. \u003C/p\u003E\r\n\u003Cp\u003E인플루언서의 사용 언어를 판별하고 라벨링하는 작업에서, 과거에는 작업자가 일일이 계정에 들어가 콘텐츠를 직접 확인해야 했습니다. 지금은 Snowflake 테이블 데이터 샘플링과 Streamlit UI를 결합해 검수 프로세스를 대폭 효율화했고, 한 화면에서 클릭 몇 번으로 빠르고 정확하게 검수할 수 있습니다. 특히 Snowflake CoCo를 활용해 라벨링·검증 환경을 신속하게 개발하고, Snowflake Cortex로 서로 다른 LLM 모델의 결과를 교차 검증하는 구조를 완성했습니다. 여러 작업자가 동시에 라벨링하더라도 작업자별 결과의 일치도를 한 화면에서 손쉽게 비교하고 확인할 수 있어 품질 검증 속도도 함께 빨라졌습니다. \u003C/p\u003E\r\n\u003Cp\u003E이를 통해 1,500건의 라벨링 작업에 소요되던 시간을 기존 160시간에서 44시간으로 71% 단축했으며, 샘플링부터 모델 평가까지 이어지는 Ground Truth 검증 루프를 빠르게 반복할 수 있는 구조를 완성했습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"image_1404371473":{"id":"image-aefd8c040c","height":"836","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fac6129e-f8f8-440d-8ceb-482e8909a7b1/image3.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1916",":type":"snowflake-site/components/image"},"title_v2_copy_copy_8_191437395":{"id":"title-v2-3df99b0499","type":"heading4","lines":["도입 효과: 셀프서비스와 AI 검증 자동화가 만든 성과"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy_63041_306225306":{"id":"text-7a9e33e7d8","text":"\u003Cp\u003E\u003Cb\u003E① 데이터 요청 84% 급감:\u003C/b\u003E 과거에는 비즈니스 조직이 데이터를 확인하려면 매번 데이터 팀에 추출을 요청해야 했습니다. 피처링은 Streamlit in Snowflake로 셀프서비스 앱을 구축해, 각 팀이 원하는 키워드와 기간을 직접 설정하고 언급량과 인게이지먼트 추이, 해시태그 집계를 시각화 화면으로 바로 확인하도록 했습니다. 그 결과 월평균 100건 이상이던 반복 요청이 20건 수준으로 줄었고, 데이터 팀은 반복적인 추출 업무에서 벗어나 고도화된 모델링에 집중하게 됐습니다.\u003C/p\u003E\u003Cp\u003E\u003Cb\u003E② 라벨링 시간 71% 단축:\u003C/b\u003E 인플루언서가 사용하는 언어를 판별하고 라벨을 다는 작업은 과거 작업자가 계정에 일일이 들어가 콘텐츠를 확인하는 수작업이었습니다. 피처링은 Snowflake CoCo로 검증 루프를 빠르게 구축하고, Snowflake Cortex로 서로 다른 LLM의 판별 결과를 교차 검증하는 체계를 완성했습니다. 데이터를 외부로 옮기지 않고 Snowflake 안에서 바로 AI를 적용한 결과, 라벨링에 걸리던 시간이 1,500건 샘플 기준 160시간에서 44시간으로 71% 감소했습니다.\u003C/p\u003E\u003Cp\u003E\u003Cb\u003E③ 리서치 리드타임 56% 감소와 운영 효율 극대화:\u003C/b\u003E 경쟁사 마케팅 동향을 분석하는 고객사 리서치 리드타임을 약 56% 단축했습니다. 운영 측면에서는 상시 가동이 필수였던 기존의 EMR 분석 클러스터를 Snowflake 기반의 온디맨드 환경으로 전환했습니다. 이를 통해 Snowflake 도입 당시 검토했던 별도 분석 인프라 구성안 대비 분석 비용을 99% 이상 절감했을 뿐만 아니라, 엔지니어들이 인프라 관리의 번거로움에서 완전히 벗어나 서비스 모델링 고도화라는 핵심 업무에 집중할 수 있게 되었습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_v2_copy_copy_8_680643117":{"id":"title-v2-74edb31111","type":"heading4","lines":["Data as a Product와 Snowflake Marketplace"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy_63041_851801357":{"id":"text-da81ff73ad","text":"\u003Cp\u003E피처링은 세 단계로 데이터 활용 범위를 넓혀가고 있습니다. 지금은 신뢰할 수 있는 SSOT 기반 위에서 AI가 데이터를 직접 탐색해 인사이트를 도출하고 분석 업무를 자율화하는 AI 기반 분석 환경('AI-Powered Analytics')으로 고도화하는 단계에 있습니다.\u003C/p\u003E\u003Cp\u003E다음 단계로는 축적된 글로벌 소셜 분석 데이터 자체를 하나의 상품(Data as a Product)으로 구성해 Snowflake Marketplace를 통해 전 세계 고객에게 제공함으로써, 소셜 데이터의 비즈니스 가치를 극대화할 계획입니다. 이와 함께 전 세계 7억 개 소셜미디어 채널을 온디맨드로 분석하는 기반을 다지며, 타겟과 크리에이터, 시장을 자동으로 분석하는 도메인 특화 멀티 에이전트와 캠페인 의사결정을 자동화하는 애드테크 AI 플랫폼으로 서비스 영역을 확장해 나갈 예정입니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"quote_item_213208411":{"id":"quote-item-e5b9ca071e","alignment":"left","layout":"simple","quoteSourceName":"","quoteSourceTitle":"피처링 Data Engine팀","showQuoteIcon":true,"quote":"데이터 기반으로 의사결정을 내리는 체제로 전환하면서, 모든 구성원이 데이터에 자유롭게 접근하게 됐습니다. 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