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인텔리전스\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container","appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-2609851079","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"callout__0":{"id":"text-97a13b1718","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003E데이터 분석의 정의\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E데이터 분석이란 통계적, 계산적, 시각적 방법을 사용해 데이터를 체계적으로 검토함으로써 분포를 설명하고, 관계를 파악하고, 가설을 검증하고, 특정 질문과 관련된 수치를 추정하는 것을 말합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-5562100171","text":"\u003Cp\u003E데이터 분석은 대개 단순한 질문에서 시작해 근거가 드러날수록 점점 구체화됩니다. 특정 지역의 매출이 감소한 이유는 무엇일까요? 감소세가 특정 제품, 고객 세그먼트 또는 기간에 집중되었을까요? 같은 시기에 다른 변화가 있었을까요?\u003C/p\u003E\n\u003Cp\u003E이러한 질문에 답하려면 조사할 대상을 정의하고, 관련 데이터를 수집 및 준비하고, 적절한 방법을 선택하고, 결과가 실제로 무엇을 의미하는지 해석해야 합니다. 이 과정은 직선적으로 진행되는 경우가 드뭅니다. 초기 결과가 다른 변수를 가리키거나, 숨은 가정을 드러내거나, 더 나은 질문을 제시하는 경우가 많기 때문입니다. Snowflake의 분석 부문 제품 관리 책임자인 \u003Ca href=\"https://www.snowflake.com/en/blog/authors/josh-klahr/\"\u003EJosh Klahr\u003C/a\u003E는 그 목표를 다음과 같이 설명합니다. “진정한 효과는 탐색과 실행 사이의 거리를 좁히는 데서 나옵니다.”\u003C/p\u003E\n\u003Cp\u003E규모가 큰 조직에서 이러한 인사이트를 안정적으로 도출하려면 그 기반이 되는 데이터가 뒷받침되어야 합니다. 분석가는 소스가 최신 상태인지, 여러 시스템 간에 레코드가 일치하는지, \u003Cem\u003E매출\u003C/em\u003E, \u003Cem\u003E활성 고객\u003C/em\u003E, \u003Cem\u003E이탈\u003C/em\u003E 같은 용어가 시스템과 도구 전반에서 일관되게 정의되어 있는지 알아야 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-data-analysis":{"id":"title-v2-499f7142f7","additionalClasses":"anchor-title anchor-title--what-is-data-analysis","type":"heading2","lines":["데이터 분석의 개념"],":type":"snowflake-site/components/title-v2"},"text_what-is-data-analysis_0":{"id":"text-3058ece9ee","text":"\u003Cp\u003E데이터 분석은 질문에 답하고 의사 결정을 지원하기 위해 데이터를 수집, 정제, 변환, 해석하는 프로세스입니다. 예를 들어 분석을 통해 고객 유지율이 하락한 이유를 설명하거나, 마진 성장에 가장 크게 기여하는 제품을 파악하거나, 온보딩 흐름의 변경이 전환율에 영향을 미쳤는지 판단할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E마지막 단계인 의사 결정 지원은 분석과 보고를 구분하는 기준이 됩니다. 보고는 일반적으로 발생한 일만 설명합니다. 반면 분석은 결과 이면의 관계, 원인 또는 시사점까지 더 깊이 파고듭니다.\u003C/p\u003E\r\n\u003Cp\u003E예를 들어 분기별 보고서는 지원 문의량이 18% 증가했다는 사실을 보여 줄 수 있습니다. 분석은 여기서 더 나아가 증가가 어디에서 발생했는지, 어떤 문제 유형이 증가에 기여했는지, 그 변화가 계절적 요인, 제품 출시 또는 기타 요인 중 무엇을 반영하는지 살펴봅니다. 이러한 결과는 팀이 다음에 어떤 조치를 취해야 할지, 혹은 조치가 필요한지 판단할 수 있는 충분한 맥락을 제공합니다.\u003C/p\u003E\r\n\u003Cp\u003E또한 이 과정은 반복적입니다. 데이터 세트를 다루는 초기 단계에서 누락된 변수, 예상치 못한 세그먼트 또는 원래 질문의 문제점이 드러나는 경우가 많습니다. 그러면 분석가는 소스 데이터로 돌아가 질문을 수정하고 다시 조사합니다.\u003C/p\u003E\r\n\u003Cp\u003E이 과정 전반에서 분석은 기반 데이터의 상태를 그대로 물려받습니다. 입력 데이터가 비즈니스를 잘못 반영하면 결과도 마찬가지로 잘못될 수밖에 없습니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003E데모를 통해 최신 AI 기반 BI와 대화형 \u003Ca href=\"https://www.snowflake.com/ko/analytics/\"\u003E분석\u003C/a\u003E 기능을 살펴보고, 심층 인사이트를 빠르게 얻는 방법을 알아보세요.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-data-analysis_0":{"id":"embed-2b45627d31","youtubeVideoId":"560rRpKij6U","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"isLcpImage":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_data-analysis-vs-data-science":{"id":"title-v2-2591484e0d","additionalClasses":"anchor-title anchor-title--data-analysis-vs-data-science","type":"heading2","lines":["데이터 분석과 데이터 과학의 차이"],":type":"snowflake-site/components/title-v2"},"text_data-analysis-vs-data-science_0":{"id":"text-5ada75fc69","text":"\u003Cp\u003E\u003Cstrong\u003E데이터 분석\u003C/strong\u003E은 정의된 질문에서 시작해 근거를 따라가며 의사 결정에 도움이 되는 결론을 도출합니다.\u003C/p\u003E\n\u003Cp\u003E\u003Cstrong\u003E데이터 과학\u003C/strong\u003E은 실험과 \u003Ca href=\"https://www.snowflake.com/ko/artificial-intelligence/machine-learning/\"\u003E머신러닝\u003C/a\u003E 영역까지 확장되는 경우가 많습니다. 예를 들어 고객 유지 데이터를 다루는 데이터 과학자는 이탈 가능성이 높은 고객을 예측하는 모델을 개발하고, 어떤 변수가 모델 성능을 개선하는지 테스트할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E구체적인 업무 분담은 조직마다 다르며, 특히 소규모 팀에서는 그 차이가 더 큽니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-data-analysis-process-step-by-step":{"id":"title-v2-d5b364fc44","additionalClasses":"anchor-title anchor-title--the-data-analysis-process-step-by-step","type":"heading2","lines":["단계별 데이터 분석 프로세스"],":type":"snowflake-site/components/title-v2"},"text_the-data-analysis-process-step-by-step_0":{"id":"text-ead162c622","text":"\u003Cp\u003E대부분의 데이터 분석은 크게 다음 6단계를 따릅니다.\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cb\u003E질문 정의\u003C/b\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E관련 데이터 수집\u003C/b\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E데이터를 정리 및 준비\u003C/b\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E데이터 분석\u003C/b\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E결과 해석\u003C/b\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E결과 공유\u003C/b\u003E\u003C/li\u003E\n\u003C/ol\u003E\n\u003Cp\u003E실제로 분석가는 이러한 단계를 오가며 작업합니다. 예를 들어 5단계에서 발견한 결과로 인해 2단계에서 누락된 데이터가 드러날 수 있고, 준비 과정에서 원래 질문의 범위를 좁혀야 한다는 사실이 밝혀질 수도 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-data-analysis-process-step-by-step_0":{"id":"text-f69fd19de6","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cb\u003E빠른 팁\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003E데이터를 수집하기 전에 분석을 통해 어떤 의사 결정을 내리려 하는지, 그리고 어떤 근거가 있으면 그 결정이 바뀔지 파악하세요. 이렇게 하면 분석에 실제로 필요한 데이터, 기간, 세부 수준을 선택하는 데 더 명확한 기준을 마련할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"image":{"id":"image-d924b490c7","alt":"데이터 분석 6단계와 반복 과정","isLcpImage":false,"lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--5f27b861-eb53-4101-bb42-a1284ff40033/data-analysis-1.%25E1%2584%258C%25E1%2585%25B5%25E1%2586%25AF%25E1%2584%2586%25E1%2585%25AE%25E1%2586%25AB-%25E1%2584%258C%25E1%2585%25A5%25E1%2586%25BC%25E1%2584%258B%25E1%2585%25B4%25E1%2584%2587%25E1%2585%25AE%25E1%2584%2590%25E1%2585%25A5-%25E1%2584%2580%25E1%2585%25A7%25E1%2586%25AF%25E1%2584%2580%25E1%2585%25AA-%25E1%2584%2580%25E1%2585%25A9%25E1%2586%25BC%25E1%2584%258B%25E1%2585%25B2%25E1%2584%2581%25E1%2585%25A1%25E1%2584%258C%25E1%2585%25B5-%25E1%2584%2583%25E1%2585%25A1%25E1%2586%25AB%25E1%2584%2580%25E1%2585%25A8%25E1%2584%2585%25E1%2585%25B3%25E1%2586%25AF-%25E1%2584%258B%25E1%2585%25A9%25E1%2584%2580%25E1%2585%25A1%25E1%2584%2586%25E1%2585%25A7-%25E1%2584%258C%25E1%2585%25B5%25E1%2586%25AB%25E1%2584%2592%25E1%2585%25A2%25E1%2586%25BC%25E1%2584%2583%25E1%2585%25AC%25E1%2584%2582%25E1%2585%25B3%25E1%2586%25AB-%25E1%2584%2583%25E1%2585%25A6%25E1%2584%258B%25E1%2585%25B5%25E1%2584%2590%25E1%2585%25A5-%25E1%2584%2587%25E1%2585%25AE%25E1%2586%25AB%25E1%2584%2589%25E1%2585%25A5%25E1%2586%25A8-6%25E1%2584%2583%25E1%2585%25A1%25E1%2586%25AB%25E1%2584%2580%25E1%2585%25A8.png?quality=85&preferwebp=true","height":"490","width":"1200",":type":"snowflake-site/components/image"},"text_the-data-analysis-process-step-by-step_1":{"id":"text-7c298c98f1","text":"\u003Ch3\u003E1. 질문 정의\u003C/h3\u003E\n\u003Cp\u003E잘 정의된 질문은 분석에 명확한 범위와 목적을 부여합니다. 예를 들어 ‘지난 분기에 중견 기업 고객의 갱신율이 하락한 이유는 무엇인가?’라는 질문은 조사할 대상 집단, 결과, 기간을 명확히 보여 줍니다.\u003C/p\u003E\n\u003Cp\u003E또한 질문은 의사 결정과 명확하게 연결되어야 합니다. 예를 들어 팀이 온보딩 흐름의 재설계 여부를 결정하려 한다면, 재설계를 통해 어떤 결과에 영향을 주려는지, 그리고 어떤 근거가 있어야 변경을 정당화할 수 있는지 알아야 합니다.\u003C/p\u003E\n\u003Cp\u003E이러한 연결이 없으면 어떤 결과가 조치나 추가 조사를 필요로 하는지 정의하지 못한 채 흥미로운 패턴만 끝없이 만들어 낼 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E2. 데이터 수집\u003C/h3\u003E\n\u003Cp\u003E질문이 명확하게 정의되면 분석가는 질문에 답하는 데 필요한 데이터를 파악합니다. 예를 들어 고객 유지 분석에서는 구독 기록, 제품 사용 이벤트, CRM 시스템의 고객 속성을 결합할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E또한 분석가는 데이터의 출처, 데이터가 포괄하는 기간, 새로 고침 빈도, 이미 적용된 변환 내용을 알아야 합니다. 이러한 세부 정보는 여러 시스템에 유사한 정보가 있을 때 특히 중요합니다. 시스템마다 같은 레이블의 필드가 있더라도 값을 채우는 규칙은 서로 다를 수 있기 때문입니다.\u003C/p\u003E\n\u003Ch3\u003E3. 데이터를 정리 및 준비\u003C/h3\u003E\n\u003Cp\u003E준비 작업은 전체 작업에서 큰 비중을 차지하는 경우가 많습니다. 분석가는 누락된 값을 해결하고, 형식을 표준화하고, 중복 레코드를 조사하고, 데이터 세트를 조인하고, 관측값을 적절한 수준으로 집계합니다. 이러한 태스크에는 생각보다 많은 분석적 판단이 필요합니다.\u003C/p\u003E\n\u003Cp\u003E고객 테이블에 동일한 조직에 대한 레코드가 세 개 있다고 가정해 보겠습니다. 이 중 어떤 레코드가 해당 고객을 나타낼까요? 가장 최근 레코드가 정확할 수도 있고, 레코드를 병합해야 할 수도 있습니다. 세 레코드를 모두 별개의 고객으로 처리하면 고객 수, 평균, 그리고 고객 규모를 기반으로 하는 모든 다운스트림 지표가 달라집니다.\u003C/p\u003E\n\u003Cp\u003ENull 값도 비슷한 문제를 일으킵니다. 빈 값은 0, 알 수 없음, 해당 없음 또는 단순 누락을 의미할 수 있습니다. 이를 잘못 채우면 분석 결과가 달라집니다.\u003C/p\u003E\n\u003Cp\u003E준비 단계는 레코드가 무엇을 나타내는지에 대한 가정이 분석가가 사용하는 데이터 세트에 반영되는 단계입니다. 따라서 데이터 정제와 데이터 랭글링은 결과의 정확성에 직접적인 영향을 미칩니다.\u003C/p\u003E\n\u003Ch3\u003E4. 데이터 분석\u003C/h3\u003E\n\u003Cp\u003E데이터가 준비되면 분석가는 질문에 맞는 분석 방법을 선택합니다. 여기에는 집계, 탐색적 데이터 분석, 회귀 분석, 코호트 분석, 통제된 실험, 기타 통계 기법이 포함됩니다.\u003C/p\u003E\n\u003Cp\u003E탐색은 대개 간단하게 시작됩니다. 분석가는 분포, 중앙값, 백분위수를 살펴보고, 집계 뷰에서는 분명하게 드러나지 않던 패턴을 시각화를 통해 확인합니다.\u003C/p\u003E\n\u003Cp\u003E예를 들어 평균 주문 금액이 6% 증가했다면, 세그먼트 수준에서 살펴봤을 때 대부분의 고객은 지출액이 그대로인 반면 소수의 대형 고객사가 증가분 전체를 견인하고 있다는 사실이 드러날 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E5. 결과 해석\u003C/h3\u003E\n\u003Cp\u003E결과 해석 단계에서는 관찰된 결과가 실제로 무엇을 뒷받침하는지 검토합니다. 분석가는 두 변수 사이에 관계가 있다고 해서 한 변수가 다른 변수의 원인이라는 사실이 입증되는 것은 아니라는 점을 기억해야 합니다. 또한 겉보기에 뚜렷한 추세도 데이터를 고객 세그먼트, 지역 또는 기간별로 나누어 보면 사라지는 경우가 있습니다.\u003C/p\u003E\n\u003Cp\u003E대안적 설명도 함께 살펴봐야 합니다. 예를 들어 가격 변경 후 이탈률이 증가했다면, 분석가는 같은 기간에 제품 장애가 발생했는지, 증가세가 새 가격의 영향을 받은 고객에게 집중되어 있는지, 또는 업스트림에서 이탈의 정의가 바뀌었는지 확인할 수 있습니다. 이러한 조사 과정에서 분석가는 범위를 좁힌 질문을 가지고 이전 단계로 되돌아가는 경우가 많습니다.\u003C/p\u003E\n\u003Ch3\u003E6. 결과 공유\u003C/h3\u003E\n\u003Cp\u003E분석의 최종 결과물은 대상자가 이를 바탕으로 행동할 수 있을 만큼 충분한 정보를 제공해야 합니다. 경영진에게는 신중하게 선별한 몇 가지 지표로 뒷받침되는 권고안이 필요할 수 있습니다. 사업 부문 리더에게는 어떤 고객, 제품 또는 프로세스가 결과를 이끌고 있는지, 그리고 이에 따라 어떤 운영상 대응이 필요한지에 대한 보다 상세한 정보가 필요할 수 있습니다. 동료 분석가에게는 결과를 재현하는 데 필요한 방법론, 가정 및 소스 데이터가 필요할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E불확실성을 축소해서 전달해서는 안 됩니다. 증거가 한 방향을 가리키더라도 중요한 교란 변수가 해결되지 않은 채 남아 있다면, 대상자는 의사 결정을 내리기 전에 이 사실을 알아야 합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-data-analysis-process-step-by-step_0":{"id":"card-v2-5276aae723","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/ko/customers/all-customers/case-study/under-armour/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"전체 활용 사례 읽기"},"text":{"id":"text","text":"\u003Cp\u003EUnder Armour는 Snowflake를 활용하여 재고, 공급망, 수요 계획, 영업 등 여러 부문의 팀이 일관된 기반 위에서 데이터를 분석할 수 있도록 지원합니다. 이전에는 일주일간의 수작업이 필요하고 매월 진행되던 제품 마진 보고가 이제는 자동화되어 매일 제공됩니다. 이후 Under Armour는 이렇게 거버넌스가 적용된 데이터 기반을 Snowflake CoWork를 통한 대화형 분석으로까지 확장했습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["고객 사례: Under Armour"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","alt":"Under Armour logo","isLcpImage":false,"lazyEnabled":true,"src":"/adobe/dynamicmedia/deliver/dm-aid--9d7e5fa7-202e-4a75-807e-77bb72b884cd/underarmour%25403x.png?quality=85&preferwebp=true","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"},"title_the-four-types-of-data-analysis":{"id":"title-v2-67f50c63dd","additionalClasses":"anchor-title anchor-title--the-four-types-of-data-analysis","type":"heading2","lines":["데이터 분석의 네 가지 유형"],":type":"snowflake-site/components/title-v2"},"text_the-four-types-of-data-analysis_0":{"id":"text-3e77e141f3","text":"\u003Cp\u003E조직이 데이터에 던지는 질문 대부분은 네 가지 분석 유형으로 다룰 수 있습니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E기술 분석\u003C/b\u003E은 무슨 일이 일어났는지 살펴봅니다. 분기별 매출, 평균 배송 시간, 채널별 웹사이트 트래픽이 대표적인 예입니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E진단 분석\u003C/b\u003E은 어떤 결과가 발생한 이유를 조사합니다. 예를 들어 매출이 감소했다면 진단 분석에서는 지역, 제품, 고객 세그먼트 또는 판매 채널을 비교하여 변화가 어디에서 시작되었는지 파악할 수 있습니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E예측 분석\u003C/b\u003E은 과거 패턴과 통계 또는 머신러닝 모델을 활용하여 다음에 일어날 가능성이 높은 일을 추정합니다. 수요 예측과 이탈 예측이 이 유형에 해당합니다.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E처방 분석\u003C/b\u003E은 예상 결과와 제약 조건을 바탕으로 실행 가능한 조치를 평가합니다. 예를 들어 물류 시스템은 수요 예측, 재고 수준, 운송 비용을 활용하여 재고를 어떻게 배분해야 할지 권고할 수 있습니다.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E이러한 분석 유형은 일반적으로 순차적으로 진행되지 않습니다. 조직은 당면한 의사 결정이나 문제에 따라 여러 유형을 함께 활용하는 경우가 많습니다. 예를 들어 리테일 기업은 기술 분석으로 전날 매출을 모니터링하고, 진단 분석으로 특정 지역의 예상치 못한 매출 감소를 조사하며, 예측 분석으로 다음 달 수요를 전망할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E각 분석의 기반이 되는 데이터 품질은 다음 분석에도 그대로 이어집니다. 기술 보고에서 고객 분류가 잘못되었다면, 이 분류로 학습한 예측 모델도 같은 문제를 그대로 물려받게 됩니다. 그 결과가 향후 조치에 영향을 미치기 시작하면, 여러 의사 결정에까지 파급 효과가 미칠 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_common-data-analysis-methods":{"id":"title-v2-46786b526f","additionalClasses":"anchor-title anchor-title--common-data-analysis-methods","type":"heading2","lines":["일반적인 데이터 분석 방법"],":type":"snowflake-site/components/title-v2"},"text_common-data-analysis-methods_0":{"id":"text-17f3b0328a","text":"\u003Cp\u003E분석 방법은 일반적으로 질문에서 출발하여 사용 가능한 데이터, 각 기법에 필요한 가정, 결과의 활용 방식에 따라 범위를 좁혀 선택합니다. 가장 일반적인 데이터 분석 방법으로는 A/B 테스트, 회귀 분석, 클러스터 분석, 코호트 분석, 시계열 분석, 감성 분석이 있습니다.\u003C/p\u003E\n\u003Ch3\u003E비교\u003C/h3\u003E\n\u003Cp\u003EA/B 테스트는 통제된 조건에서 그룹 간 결과를 비교합니다. 제품 팀과 마케팅 팀은 인터페이스, 가격, 메시지 또는 고객 경험의 변경 사항을 테스트할 때 A/B 테스트를 자주 활용합니다. 표본 크기, 무작위 배정, 다중 비교 보정은 분석가가 관찰된 차이를 얼마나 신뢰할 수 있는지에 영향을 미칩니다.\u003C/p\u003E\n\u003Ch3\u003E관계\u003C/h3\u003E\n\u003Cp\u003E회귀 분석은 변수 간의 관계를 살펴봅니다. \u003Ca href=\"https://www.snowflake.com/ko/artificial-intelligence/machine-learning/models/linear-regression/\"\u003E선형 회귀\u003C/a\u003E는 비교적 단순한 출발점이 되며, 다른 회귀 기법은 다양한 유형의 결과와 관계를 다룰 수 있습니다. 분석가는 회귀 분석을 활용하여 가격과 수요 간의 관계, 또는 제품 사용량과 고객 유지율 간의 관계를 분석할 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E그룹화\u003C/h3\u003E\n\u003Cp\u003E클러스터 분석은 유사한 특성을 공유하는 관측치를 식별합니다. 코호트 분석은 같은 달에 확보한 고객처럼 공통된 시작점이나 속성을 공유하는 그룹을 추적합니다. 예를 들어 고객 확보 코호트별로 유지율을 살펴보면 전체 평균 유지율에서는 드러나지 않는 변화를 발견할 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E시간에 따른 변화\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/fundamentals/time-series-analysis/\"\u003E시계열 분석\u003C/a\u003E은 연속된 기간에 걸쳐 기록된 관측치를 분석합니다. 시계열 분석은 예측, 추세 감지, 용량 계획에 널리 활용되며, 계절성과 같은 요인이 분석가의 패턴 해석에 영향을 미칩니다.\u003C/p\u003E\n\u003Ch3\u003E텍스트의 의미\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/fundamentals/what-is-sentiment-analysis/\"\u003E감성 분석\u003C/a\u003E 및 관련 텍스트 분석 기법은 고객 리뷰, 지원 상담 대화, 설문 응답 등 비정형 언어에서 패턴을 추출합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_common-data-analysis-methods_0":{"id":"text-568094d593","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003E자주 하는 실수\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E흔히 저지르는 실수는 기법의 가정이 데이터에 부합하는지 확인하지 않고 익숙한 방법을 습관적으로 사용하는 것입니다. 예를 들어 관측치의 독립성이나 선형 관계를 가정하는 기법은 해당 조건이 충족되지 않으면 잘못된 결과를 도출할 수 있습니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-data-quality-determines-analysis-accuracy":{"id":"title-v2-5eaa4408f1","additionalClasses":"anchor-title anchor-title--how-data-quality-determines-analysis-accuracy","type":"heading2","lines":["데이터 품질이 분석 정확도에 미치는 영향"],":type":"snowflake-site/components/title-v2"},"text_how-data-quality-determines-analysis-accuracy_0":{"id":"text-8316902e85","text":"\u003Cp\u003E방법론적으로 타당한 분석이라도 기반 데이터가 불완전하거나, 오래되었거나, 잘못 해석되었다면 잘못된 결론에 이를 수 있습니다. 분석가는 결과를 신뢰하기 전에 레코드가 올바른 엔터티와 이벤트를 나타내는지, 관련 기간을 반영하는지, 소스까지 추적할 수 있는지 확인해야 합니다.\u003C/p\u003E\n\u003Ch3\u003E데이터의 실제 상황 반영 여부 확인\u003C/h3\u003E\n\u003Cp\u003E정확성은 레코드가 기반 엔터티나 이벤트를 올바르게 나타내는지에서 출발합니다. 예를 들어 고객 ID가 잘못 매핑되면 활동이 엉뚱한 계정에 할당될 수 있으며, 업스트림 스키마가 변경되면 필드의 의미가 달라질 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E완전성과 일관성도 정확성에 영향을 미칩니다. 누락된 레코드는 비교 결과를 왜곡할 수 있으며, 소스 시스템 간에 값이 서로 충돌하면 분석가는 어떤 버전을 사용해야 할지 추측할 수밖에 없습니다.\u003C/p\u003E\n\u003Ch3\u003E질문에 맞는 데이터 최신성 확보\u003C/h3\u003E\n\u003Cp\u003E최신성은 데이터가 지원하려는 의사 결정에 충분할 만큼 최신 상태인지를 나타내며, 적절한 기준은 사용 사례에 따라 크게 달라집니다. 사기 탐지는 불과 몇 초 전에 발생한 이벤트에 의존할 수 있는 반면, 주간 인력 보고서는 해당 주 안에서만 업데이트되면 충분합니다. 예상 새로 고침 주기를 정의해 두면 분석가는 데이터 세트가 오래되었는지 구체적으로 판단할 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E리니지를 활용한 결과 출처 파악\u003C/h3\u003E\n\u003Cp\u003E리니지는 데이터가 소스에서 출발하여 변환 과정을 거쳐 다운스트림 모델, 대시보드, 보고서로 이동하는 경로를 보여줍니다. 이러한 추적 가능성 덕분에 데이터 조정을 보다 체계적으로 수행할 수 있습니다. 두 분석 결과가 일치하지 않을 때 분석가는 경로를 역으로 추적하여 서로 다른 소스에서 시작했는지, 서로 다른 변환을 적용했는지, 또는 특정 필드에서 갈라졌는지 확인할 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E데이터 변경 전 다운스트림 영향 확인\u003C/h3\u003E\n\u003Cp\u003E리니지는 반대 방향의 영향 분석도 지원합니다. 데이터 팀은 업스트림 테이블이나 컬럼을 변경하기 전에 이에 의존하는 모델, 대시보드, 보고서를 파악할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E테이블 수준 리니지는 영향을 받는 오브젝트를 폭넓게 보여주고, 컬럼 수준 리니지는 특정 필드가 어디로 흘러가는지 보여줍니다. 이러한 구분은 스키마 변경 후에도 테이블은 그대로 사용할 수 있지만, 다운스트림에서 사용하는 특정 컬럼의 의미나 형식이 달라지는 경우에 특히 중요합니다.\u003C/p\u003E\n\u003Cp\u003E분석가는 이해관계자와 분석 결과를 공유하기 전에 다음 세 가지 기본 질문에 답할 수 있어야 합니다. 데이터의 출처는 어디인가요? 데이터는 얼마나 최신 상태인가요? 이 데이터에 의존하는 다른 사람은 누구인가요?\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-semantic-layer-one-definition-of-every-metric":{"id":"title-v2-aac6bf3d7c","additionalClasses":"anchor-title anchor-title--the-semantic-layer-one-definition-of-every-metric","type":"heading2","lines":["시맨틱 레이어: 모든 메트릭에 대한 단일 정의"],":type":"snowflake-site/components/title-v2"},"text_the-semantic-layer-one-definition-of-every-metric_0":{"id":"text-7751a9f0c6","text":"\u003Cp\u003E분기별 검토 회의에서 영업 부서는 매출을 1,420만 달러(USD)로 보고하는 반면, 재무 부서는 1,370만 달러(USD)로 보고하는 상황을 생각해 보세요.\u003C/p\u003E\n\u003Cp\u003E어느 팀도 반드시 계산을 잘못한 것은 아닙니다. 한쪽 계산에서는 환불을 제외하고 다른 쪽에서는 포함했을 수 있습니다. 또는 한쪽은 주문일을 기준으로, 다른 쪽은 매출 인식일을 기준으로 계산했을 수도 있습니다. 두 쿼리 모두 올바르게 실행되었지만, 결과적으로 조직은 같은 비즈니스 용어에 두 가지 값을 갖게 됩니다.\u003C/p\u003E\n\u003Cp\u003E시맨틱 레이어는 이러한 용어에 공통된 정의를 부여합니다. 시맨틱 레이어는 메트릭, 디멘션, 관계에 대한 비즈니스 로직(매출의 의미, 활성 고객으로 간주되는 기준, 이탈률 계산 방식, 비즈니스 엔터티 간의 관계 등)을 저장하므로, 분석가와 다운스트림 도구가 동일한 규칙을 사용할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E헤드리스 BI는 시맨틱 레이어를 단일 보고 인터페이스 이상으로 확장합니다. 거버넌스가 적용된 메트릭과 디멘션을 프런트엔드와 독립적으로 사용할 수 있으면 노트북, 대시보드, 애플리케이션, AI 인터페이스가 모두 동일한 비즈니스 로직을 기반으로 작동할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003EAI가 생성하는 SQL은 비즈니스 질문을 올바른 쿼리 로직으로 변환하기 위해 컨텍스트에 의존합니다. 모델은 질문의 개념에 해당하는 테이블과 필드, 테이블 간의 관계, 쿼리에 사용할 그레인, 적용할 메트릭 정의나 필터를 알아야 합니다. 물리적 스키마는 이러한 정보의 일부를 제공하지만, 비즈니스 의미까지 담아내는 경우는 드뭅니다. 시맨틱 컨텍스트는 이러한 정의와 관계를 명시적으로 제공하므로, 모델이 쿼리를 생성할 때 선택의 범위를 보다 좁혀 줍니다.\u003C/p\u003E\n\u003Cp\u003EAI가 여러 데이터 소스에 걸쳐 추론해야 할 때 이러한 컨텍스트는 측정 가능한 차이를 만들어 낼 수 있습니다. 여러 시맨틱 뷰가 필요한 질문을 대상으로 진행한 \u003Ca href=\"https://www.snowflake.com/ko/blog/agent-context-layer-trustworthy-data-agents/\"\u003ESnowflake 내부 실험\u003C/a\u003E에서는 조인 키, 테이블 그레인, 카디널리티에 대한 정보를 명시적으로 추가하자 최종 답변 정확도가 20% 향상되었으며, 도구 호출 횟수와 전체 지연 시간도 줄어들었습니다.\u003Csup\u003E1\u003C/sup\u003E\u003C/p\u003E\n\u003Cp\u003E분석 엔지니어는 일반적으로 비즈니스 정의를 담당하는 해당 분야 전문가와 함께 시맨틱 모델 유지 관리에 참여하는 경우가 점점 늘고 있습니다. 예를 들어 재무팀이 매출의 의미를 정의하면, 데이터 팀은 그 정의를 재사용 가능한 로직으로 변환하고 다운스트림 분석이 이를 일관되게 따르도록 관리합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-ai-is-changing-data-analysis":{"id":"title-v2-9c90917adf","additionalClasses":"anchor-title anchor-title--how-ai-is-changing-data-analysis","type":"heading2","lines":["AI가 데이터 분석에 가져오는 변화"],":type":"snowflake-site/components/title-v2"},"text_how-ai-is-changing-data-analysis_0":{"id":"text-efffaaac6b","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/artificial-intelligence/generative-ai/\"\u003E생성형 AI\u003C/a\u003E는 질문을 던진 뒤 데이터를 쿼리하기까지 필요했던 수작업의 일부를 줄이고 있습니다. 자연어 인터페이스, SQL 자동 생성, 자동 요약 기능 덕분에 비기술 사용자도 데이터를 직접 탐색할 수 있습니다. 동시에 분석가는 질문 설정, 비즈니스 컨텍스트 유지, 결과 검증에 더 집중하게 됩니다.\u003C/p\u003E\n\u003Ch3\u003E자연어 쿼리를 통한 분석 접근성 확대\u003C/h3\u003E\n\u003Cp\u003E비즈니스 사용자가 모든 요청을 SQL로 변환하거나 새 대시보드를 기다리는 대신, 일상적인 언어로 직접 질문하는 경우가 늘고 있습니다. \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/natural-language-processing/\"\u003E자연어\u003C/a\u003E 인터페이스는 탐색적 분석의 접근성을 높여 줍니다. 다만 시스템은 여전히 사용자의 언어를 올바른 테이블, 관계, 필터, 지표에 매핑해야 합니다.\u003C/p\u003E\n\u003Ch3\u003EAI를 통한 쿼리 생성 및 후속 분석 가속화\u003C/h3\u003E\n\u003Cp\u003E분석가 입장에서는 SQL 자동 생성을 통해 일상적인 쿼리를 작성하고 수정하는 데 드는 시간을 줄일 수 있습니다. 또한 AI 시스템은 조사가 진행됨에 따라 결과를 요약하고, 패턴을 도출하며, 후속 질문을 지원할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E하지만 쿼리가 실제로 비즈니스 질문을 제대로 반영하는지는 분석가가 판단해야 하므로, 인간의 판단은 여전히 필수적입니다. 기술적으로 유효한 쿼리라도 잘못된 그레인을 사용하거나, 테이블을 잘못 조인하거나, 조직의 기준과 맞지 않는 지표 정의를 적용할 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E검증과 문제 정의 중심으로 바뀌는 분석가의 역할\u003C/h3\u003E\n\u003Cp\u003EAI가 기계적인 쿼리 작업을 점점 더 많이 처리하면서, 분석가는 질문 정의, 비즈니스 로직 유지, 머신이 생성한 결과물 검토에 더 많은 시간을 할애할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E검토 과정에는 다음과 같은 익숙한 분석 점검 항목이 포함됩니다. 쿼리가 올바른 그레인을 유지하고 있는가? 필터가 의도한 모집단과 일치하는가? 결과가 신뢰할 수 있는 원천 데이터와 일치하는가? 해당 패턴을 설명할 수 있는 다른 원인은 없는가?\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-analysis-at-enterprise-scale-on-snowflake":{"id":"title-v2-b3c34a4acf","additionalClasses":"anchor-title anchor-title--data-analysis-at-enterprise-scale-on-snowflake","type":"heading2","lines":["Snowflake 기반 엔터프라이즈 규모의 데이터 분석"],":type":"snowflake-site/components/title-v2"},"text_data-analysis-at-enterprise-scale-on-snowflake_0":{"id":"text-e3b406baaa","text":"\u003Cp\u003E엔터프라이즈 규모에서 분석가는 여러 소스, 변환, 비즈니스 프로세스를 거친 데이터를 다루는 경우가 많습니다. Snowflake는 거버넌스, 비즈니스 컨텍스트, AI 지원 워크플로우를 기반 데이터와 연결된 상태로 유지하면서 해당 데이터를 분석할 수 있는 공통 환경을 제공합니다.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E는 데이터 품질 모니터링과 리니지를 포함해 분석에 사용되는 데이터 전반에 거버넌스를 제공합니다. 테이블 및 컬럼 수준 리니지를 통해 팀은 분석 결과를 소스와 변환 과정을 따라 역추적하거나, 어떤 다운스트림 자산이 업스트림 필드에 의존하는지 파악할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003ESnowflake Horizon Context는 비즈니스 컨텍스트 공유를 위해 지표, 정의, 관계 및 기타 메타데이터를 AI, \u003Ca href=\"https://www.snowflake.com/ko/analytics/business-intelligence/\"\u003E비즈니스 인텔리전스\u003C/a\u003E(BI), 애플리케이션용 거버넌스 컨텍스트 레이어로 통합합니다. Semantic Views는 지표, 디멘션, 엔터티 관계 같은 비즈니스 개념을 Snowflake에 직접 저장하며, Horizon Context는 BI 시스템 및 기타 소스의 컨텍스트도 통합할 수 있습니다. 목표는 팀마다 동일한 지표의 로직을 각자 다시 만드는 일을 줄이는 것입니다.\u003C/p\u003E\n\u003Cp\u003EAI 지원 분석은 이러한 컨텍스트를 기반으로 이루어집니다. \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ECortex Agents\u003C/a\u003E는 시맨틱 뷰를 활용해 정형 데이터에 대한 자연어 질문에 답할 수 있으며, 비정형 소스 전반에서도 작동하고 다단계 분석 태스크를 지원합니다.\u003C/p\u003E\n\u003Cp\u003E비즈니스 사용자를 위해 \u003Ca href=\"https://www.snowflake.com/ko/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E는 엔터프라이즈 데이터를 탐색하고, 후속 질문을 하고, 그 결과로 생성된 차트, 테이블, 보고서를 활용할 수 있는 대화형 인터페이스를 제공합니다. CoWork(이전 명칭 Snowflake Intelligence)는 엔터프라이즈 정보 전반에 걸쳐 더 심층적인 리서치를 수행하고, 분석 결과물을 재사용 가능한 아티팩트로 보존할 수도 있습니다.\u003C/p\u003E\n\u003Cp\u003ESQL로 직접 작업하는 분석가는 Cortex AI Functions를 사용해 텍스트, 이미지, 오디오 등 비정형 또는 멀티모달 콘텐츠를 정형 데이터와 함께 분석할 수도 있습니다. 이를 통해 이전에는 별도의 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/data-processing/\"\u003E데이터 처리\u003C/a\u003E 파이프라인이 필요했던 정보까지 익숙한 분석 워크플로우를 확장할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E이러한 기능은 거버넌스가 적용된 데이터, 추적 가능한 리니지, 공유 비즈니스 정의, 그리고 동일한 기반 컨텍스트에 근거한 AI 지원 분석을 하나로 연결합니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_from-data-to-a-defensible-decision":{"id":"title-v2-27e9f444d3","additionalClasses":"anchor-title anchor-title--from-data-to-a-defensible-decision","type":"heading2","lines":["데이터에서 근거 있는 의사 결정으로"],":type":"snowflake-site/components/title-v2"},"text_from-data-to-a-defensible-decision_0":{"id":"text-d609def4bc","text":"\u003Cp\u003E데이터 분석은 조직이 질문에서 출발해 실제로 활용할 수 있는 근거에 도달하도록 돕습니다. 신뢰할 수 있는 결과를 얻으려면 잘 설정된 질문, 적절한 기법, 최신 상태이며 추적 가능한 데이터, 그리고 팀과 도구 전반에서 일관되게 유지되는 비즈니스 정의가 필요합니다. AI가 쿼리 생성과 탐색 작업을 더 많이 맡게 될수록, 이러한 기반은 어떤 답변을 신뢰할 수 있는지 판단하는 데 더 명확한 근거가 됩니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_from-data-to-a-defensible-decision_0":{"id":"text-3aef1f994a","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003E핵심 요약\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003E신뢰할 수 있는 데이터 분석은 잘 설정된 질문, 적절한 방법, 그리고 최신 상태이며 추적 가능하고 일관되게 정의된 데이터에 달려 있습니다. AI가 쿼리 생성을 더 많이 담당할수록 이러한 기반의 중요성은 줄어드는 것이 아니라 오히려 커집니다.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_from-data-to-a-defensible-decision_1":{"id":"text-d01820b58c","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003E\u003Csub\u003E1. 다중 시맨틱 뷰 쿼리 세트에서 최종 답변 정확도를 측정한 Snowflake 내부 실험 결과를 기반으로 합니다. 개선 수치는 모범 사례 기준선과 비교한 결과입니다. 전체 방법론은 \u003Ca href=\"https://www.snowflake.com/en/blog/agent-context-layer-trustworthy-data-agents/\"\u003E여기\u003C/a\u003E에서 확인하세요. 개별 결과는 다를 수 있습니다.\u003C/sub\u003E\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-data-analysis","text_what-is-data-analysis_0","yt_what-is-data-analysis_0","title_data-analysis-vs-data-science","text_data-analysis-vs-data-science_0","title_the-data-analysis-process-step-by-step","text_the-data-analysis-process-step-by-step_0","callout_the-data-analysis-process-step-by-step_0","image","text_the-data-analysis-process-step-by-step_1","card_v2_the-data-analysis-process-step-by-step_0","title_the-four-types-of-data-analysis","text_the-four-types-of-data-analysis_0","title_common-data-analysis-methods","text_common-data-analysis-methods_0","callout_common-data-analysis-methods_0","title_how-data-quality-determines-analysis-accuracy","text_how-data-quality-determines-analysis-accuracy_0","title_the-semantic-layer-one-definition-of-every-metric","text_the-semantic-layer-one-definition-of-every-metric_0","title_how-ai-is-changing-data-analysis","text_how-ai-is-changing-data-analysis_0","title_data-analysis-at-enterprise-scale-on-snowflake","text_data-analysis-at-enterprise-scale-on-snowflake_0","title_from-data-to-a-defensible-decision","text_from-data-to-a-defensible-decision_0","callout_from-data-to-a-defensible-decision_0","text_from-data-to-a-defensible-decision_1"]},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"container":{"additionalClasses":"sticky-sidebar","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-756c3f2d01",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-17dd1db0d1","additionalClasses":"eyebrow-text","text":"\u003Cp\u003E이 가이드의 주요 내용\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-afd3d9e9f2","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-data-analysis\"\u003E데이터 분석의 개념\u003C/li\u003E\u003Cli data-anchor=\"data-analysis-vs-data-science\"\u003E데이터 분석과 데이터 과학의 차이\u003C/li\u003E\u003Cli data-anchor=\"the-data-analysis-process-step-by-step\"\u003E단계별 데이터 분석 프로세스\u003C/li\u003E\u003Cli 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