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`http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/ja/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-9e5267d379","additionalClasses":"headline-decoration","type":"heading2","lines":["概要"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ae606b9231","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003Eほとんどの人は車の旅を計画する際に、GPSアプリを利用したり、地図を購入したりするはずです。初めての長距離運転で、運転が難しい地形や潜在的な危険に全く詳しくない状況を想像してみてください。頼れるのは道路標識と自分の勘だけです。同様に、企業は信頼や推測に基づいて行動することはできません。業界や規模に関係なく、あらゆる組織が意思決定、キャンペーン、ビジネス最適化の指針となるデータを必要としています。このような場合に\u003Ca href=\"https://www.snowflake.com/ja/product/analytics/\"\u003Eデータアナリティクス\u003C/a\u003Eが活躍します。\u003C/p\u003E\n\u003Cp\u003E企業はデータアナリティクスを使用して、顧客と社内チームの双方にとって、進化、成長、改善に必要な主要インテリジェンスを明らかにします。これがなければ、調査に裏付けられたスマートな行動と慎重な計画によって市場シェアを拡大している競合他社に後れを取ることになります。\u003C/p\u003E\n\u003Cp\u003Eこのページでは、ビジネス上の意思決定や分析にデータを実装する際に役立つ、さまざまなデータアナリティクスのタイプとそのメリット、一般的なユースケース、一般的なデータアナリティクスツールについて解説します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_copy__480869401":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1444615495":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__222865509":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1413360324":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__455068363":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__659376865":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1500909857":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1978652079":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"what",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-e139fdf739","additionalClasses":"headline-decoration","type":"heading2","lines":["データアナリティクスとは"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-956a964008","text":"\u003Cp\u003Eデータアナリティクスは、生データを調査、収集、分析してパターンを見つけ、結論を導くプロセスです。データアナリティクスは、情報に基づいた意思決定を促進し、組織がすべてのチームのパフォーマンスを改善できるように支援することで、ビジネスの成長を促進します。 \u003C/p\u003E\n\u003Cp\u003Eスプレッドシート、\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003Eデータウェアハウス\u003C/a\u003E、プログラミング言語など、さまざまなツールや手法を使用してデータを収集し、データからインサイトを引き出します。ソーシャルメディアのメトリクス、\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/healthcare-and-life-sciences/\"\u003E患者の健康履歴\u003C/a\u003E、在庫減少の数値など、あらゆる種類の情報を精査できます。組織の規模や、データを精査する専任のデータアナリストの有無にかかわらず、あらゆるチームが共通のデータ分析ツールを使用して必要な情報を把握できます。データアナリティクスの大きなメリットは、大量の情報の中から本質を見抜き、あらゆるデジタルノイズに埋もれていたトレンドやパターンを明らかにできることです。\u003C/p\u003E\n\u003Cp\u003Eゲーム内のプレイヤー行動を分析するゲーム会社、不正行為の兆候がないか顧客行動を精査する\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/financial-services/\"\u003E銀行\u003C/a\u003E、顧客が次の購入に踏み切るタイミングを予測する小売企業など、企業はデータアナリティクスを通じてパーソナライズされた体験の創出、リスクの軽減、ブランドロイヤルティの向上を実現できます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"a",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7cb522bbd8","additionalClasses":"headline-decoration","type":"heading2","lines":["データアナリティクスの種類"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4e5820b926","text":"\u003Cp\u003E4つのデータアナリティクスの種類は、それぞれを基盤として積み重ねることで、過去の出来事を理解する段階から、データに基づいたインサイトとレコメンデーションを通じて未来の結果に影響を与える段階へと進化します。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E1.記述的分析：過去の出来事の理解\u003C/h4\u003E\r\n\u003Cp\u003Eこの種類は、ソーシャルメディア投稿でのいいね！の増加、ゲーム内トランザクションの増加、四半期ごとの商品の売り上げの減少など、一定期間に発生した事象を把握するための履歴データを要約します。記述的分析は、最も基本的な分析レベルです。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E2.診断的分析：過去の出来事の理由の理解\u003C/h4\u003E\r\n\u003Cp\u003Eここからは、内容がさらに複雑かつ興味深くなります。診断的なアナリティクスでは、過去の事象の背後にある理由に焦点を当て、データ入力と理論化を組み合わせます。投稿の可視性を高めるために、適切なハッシュタグの組み合わせを使用したか、期間限定のゲーム内イベントがトランザクションの増加を促した可能性などを分析します。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E3.予測分析：将来の出来事の予測\u003C/h4\u003E\r\n\u003Cp\u003E内容と理由を把握できたら、ここから先の計画を立てる必要があります。\u003Ca href=\"https://www.snowflake.com/en/fundamentals/predictive-analytics-marketing/\"\u003E予測分析\u003C/a\u003Eは、次に何が起こる可能性があるかを見極めるのに役立ちます。たとえば、特定のお客様は毎月この時期に猫砂を購入する傾向があるため、近いうちに再度購入する可能性が高いと予測できます。銀行のお客様は、支払い期日どおりに支払いを行った履歴を持っているため、ローン返済についても信頼できると考えられます。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E4.処方分析：出来事への対策\u003C/h4\u003E\r\n\u003Cp\u003Eここからは、収集と分析したすべてのデータと情報を、次のアクションのためのレコメンデーションに組み込むことができます。自社のブランドでは、前月比で大幅な売上増加が見られており、前回の商品の販売分は稼働後数分で完売しました。そのため、次の販売分は、顧客満足度向上のために予約注文制にする必要があります。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1444615495":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-b37e171af9","additionalClasses":"headline-decoration","type":"heading2","lines":["データアナリティクスのメリット"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-3aa17a7d41","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E強力なデータアナリティクス戦略は、ビジネスに変革的な効果をもたらし、組織の成功している部分と改善すべき部分を可視化します。知識と実践のギャップを埋めることで、チームは効率と顧客満足度の最適化を実現できます。 \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1.情報に基づいた意思決定\u003C/h4\u003E\n\u003Cp\u003E推測や憶測は不要です。履歴データのセットで見つかった証拠やパターンに基づいてデータドリブンな意思決定を行うことで、より正確で信頼性の高い結果を得られます。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2.業務効率の向上\u003C/h4\u003E\n\u003Cp\u003E状況の把握が勝負の半分を決定すると言われています。データアナリティクスは、組織全体の業務の非効率性、ボトルネック、改善の機会にスポットライトを当てられる実用的なインサイトを提供します。これにより、企業はプロセスの合理化、コストの削減、生産性の向上、成功していない戦略の排除や改良に必要な情報を得られます。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3.リスク軽減\u003C/h4\u003E\n\u003Cp\u003E不正は進化し続け、その頻度は増え続けています。そのため、組織は悪意のある行為に対して常に注意を向ける必要があります。データアナリティクスにより、プロアクティブな脅威と異常の特定が可能になるため、組織はAIと機械学習を使用して標的を絞ったリスク緩和戦略を簡単に策定できます。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4.顧客理解の強化\u003C/h4\u003E\n\u003Cp\u003Eデータアナリティクスは、企業が顧客体験のパーソナライゼーション、製品やサービスの改善、顧客満足度とロイヤルティの向上を実現するために必要となる、重要な顧客データとインサイトを提供します。このことは、顧客維持だけでなく、新規顧客の獲得や企業の全体的な成長も改善する、大きな競争優位性につながります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1978652079":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-2ea246f622","additionalClasses":"headline-decoration","type":"heading2","lines":["データアナリティクスのステップ"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-9d4a30ed7e","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E豊富なデータは素晴らしいものですが、生データの状態から、有意義で実用的なインサイトを提供できる状態に移行するには、まずステップを踏む必要があります。一般的なデータアナリティクスプロジェクトでは、目標の定義、データの収集、データのクリーニングと準備、分析というデータアナリティクスのライフサイクル全体を通して作業を進めます。 \u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E1.目標の定義\u003C/h4\u003E\r\n\u003Cp\u003E御社のチームは、収集しているデータから何を抽出したいと考えていますか？どのようなプロセスや運用を最適化しようとしていますか？どのようなキャンペーンをサポートしていますか？明確な目標がなければ、大量のデータの収集に時間とリソースを費やしても意味がありません。達成目標の概要を整理し、データ要件とデータのグループ化と分離の方法を決定します。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E2.データの収集\u003C/h4\u003E\r\n\u003Cp\u003E求めているものが正確に把握できたので、データアナリストは目標を裏付ける関連データを収集できます。たとえば、一定期間のサイトへのトラフィック、顧客調査、ソーシャルメディアのエンゲージメント統計、トランザクションデータ、リスクスコアリングなどが考えられます。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E3.データのクリーニングと準備\u003C/h4\u003E\r\n\u003Cp\u003Eデータをスクラブして、データセットの信頼性と使い勝手に影響するエラー、不整合、外れ値がないか精査します。次に、分析しやすいように整理します。スプレッドシートは小規模なデータセットには理想的ですが、大規模なデータセットを扱う場合には遅くなり扱いにくくなります。\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/ai-for-business-intelligence/\"\u003Eビジネスインテリジェンス\u003C/a\u003E（BI）ツールはデータの可視化とダッシュボードに最適ですが、\u003Ca href=\"https://www.snowflake.com/ja/product/analytics/\"\u003Eデータベースとデータウェアハウス\u003C/a\u003Eは非常に大規模なデータセットに必要です。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E4.データの分析\u003C/h4\u003E\r\n\u003Cp\u003E準備されたクリーンなデータセットを取得できました。これで、ロジックと統計手法を使用してパターン、関係性、トレンドを発見できるようになりました。先ほど説明したデータアナリティクスの種類を思い出してください。データアナリストは、こうした手法を使用してデータの内容と理由を把握し、次のステップを計画します。 \u003C/p\u003E\r\n\u003Cp\u003EBIツールのスプレッドシートや無料バージョンは、小規模なデータセットを分析するための一般的なリソースです。通常は、専任のデータアナリストもいない小規模なチームに使用されます。エンタープライズレベルでは、クラウドサービス、ビッグデータ処理フレームワーク、およびクラウドサービスは、中程度から非常に大規模なデータセットを処理しますが、多くの場合、フル機能のBIプラットフォームと組み合わせて包括的なレポート作成と可視化を実施します。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__455068363":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-782f2a79a4","additionalClasses":"headline-decoration","type":"heading2","lines":["データアナリティクスの技術"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-059eac014b","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E大まかに言うと、データアナリティクスの技術は教師あり学習と教師なし学習のカテゴリーに分類されます。教師ありテクニックはラベル付きデータから学習して予測を行い、教師なしテクニックはラベルなしデータからパターンを見つけます。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003Eリグレッション分析\u003C/h4\u003E\n\u003Cp\u003Eこれは、1つ以上の独立変数に基づいて連続する数値を予測するために使用される、教師あり学習技術です。言い換えると、従属変数と1つ以上の独立変数の関係を、データに直線や曲線をフィッティングして理解するための統計的手法です。どの変数が影響を与えるかを判断するのに役立ちます。\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://hbr.org/2015/11/a-refresher-on-regression-analysis\"\u003Eリグレッション分析\u003C/a\u003Eの重要な質問に対する回答は「どのくらいの量または数値か」です。どの要因が最も重要で、最も重要でないかを企業が判断するのに役立ちます。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E分類分析\u003C/h4\u003E\n\u003Cp\u003E分類分析は、「どのクラスまたはカテゴリに帰属するか」という問いに回答するものであり、情報の意味を理解し、情報に基づいた意思決定を行うための強力なツールです。\u003C/p\u003E\n\u003Cp\u003Eこの教師あり学習の技術は、分類モデルをトレーニングし、新しいデータを与えてどのカテゴリに属するかを予測することによって、データをグループに分類するために使用されます。本質的に、\u003Ca href=\"https://www.indicative.com/resource/classification-analysis/\"\u003E分類分析\u003C/a\u003Eは、ラベル付けされた過去のデータを使用して、ラベル付けされていない新しいデータを正しく分類する方法をモデルに教えることを目的としています。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003Eクラスタリング分析\u003C/h4\u003E\n\u003Cp\u003E前の2つの手法とは異なり、\u003Ca href=\"https://www.qualtrics.com/experience-management/research/cluster-analysis/\"\u003Eクラスタリング分析\u003C/a\u003Eは、類似のデータポイントをクラスターにグループ化するために使用される教師なし学習手法です。事前定義されたラベルやカテゴリはありません。アルゴリズムがデータ内のグループを検出します。クラスタリング分析は、「このデータをグループ化するにはどうすればよいか」という問いに回答します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__222865509":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-373a4da50c","additionalClasses":"headline-decoration","type":"heading2","lines":["データアナリティクスのユースケース"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-be461dda7c","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E本ガイドの冒頭で触れたとおり、あらゆる業界がデータアナリティクスのメリットを享受できます。また、あらゆる規模の企業が、自社のデータを活用して最適化、進化、成長を実現することで競争優位性を高めています。\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-analytics-for-retailers/\"\u003E小売\u003C/a\u003E、ヘルスケア、金融の3つの業界について、ワークフローやプロセスにデータアナリティクスをどのように取り入れているかを見てみましょう。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Ch4\u003E小売\u003Cbr\u003E\r\n \u003C/h4\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E広告のパーソナライゼーション：小売企業は、購入履歴、デモグラフィック、行動データなどの顧客ごとに収集したデータを活用して、顧客に送信するEメールやソーシャルメディアのプロモーションをパーソナライズできます。カスタマイズされたショッピング体験は、ブランドロイヤルティとウォレットシェアの構築に役立ちます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003Eオンライン小売在庫管理：オンライン小売企業は、在庫レベルが顧客の需要に対応できるよう、顧客の過去の購買行動、在庫レベル、サードパーティデータやパブリックデータを分析しています。デジタルシェルフの在庫を維持することで、顧客維持率が高まり、顧客が欲しいものを求めて他のサイトに離脱するのを防ぎます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E実店舗での安全性：実店舗にとって、顧客や従業員に危害を加える可能性のある危険を排除することは極めて重要です。インシデントレポートや安全トレーニング記録などのさまざまなデータポイントを分析し、4種類のデータアナリティクスを活用することで、インシデントの発生を低減するための従業員安全トレーニングの改善に役立てることができます。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch4\u003Eヘルスケア\u003Cbr\u003E\r\n \u003C/h4\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E疾病の予測：予測分析では、生活習慣、遺伝学、病歴などの患者データを使用して、特定の疾患を発症するリスクが高い患者を特定します。これにより、医師はパーソナライズされた治療計画を策定し、疾病を先取りした予防医療を開始できます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003Eリソース割り当て：病院は、季節ごとの緊急治療室の入院などのデータを使用して患者の入院レベルを予測することで、患者のニーズに合わせて人員配置や供給を最適化し、待ち時間を削減して患者の満足度を向上させることができます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E不正検知：病院、薬局、保険会社は、データアナリティクスを使用して請求パターン、処方箋の使用状況、保険金請求を分析し、異常や不正なトランザクションを特定できます。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch4\u003E金融\u003Cbr\u003E\r\n \u003C/h4\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003Eリスク管理：銀行は、支出習慣、収入の安定性、支払い履歴など、さまざまな顧客データを分析して、顧客の信用度を判断できます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E顧客チャーン予測：特定のデータ（最近の銀行活動がない、手数料が高いなど）で顧客が口座を閉鎖する可能性があることを示唆している場合、銀行は顧客を維持するためのサービスやインセンティブ（預金の特別利率など）をプロアクティブに提供できます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003Eアルゴリズム取引と市場分析：高頻度の取引を行う企業は、複雑なアルゴリズムを使用して市場データを分析し、市場機会とトレンドを特定して数秒で取引を実行します。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1413360324":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"tools",":type":"snowflake-site/components/container",":items":{"title_v2_copy":{"id":"title-v2-59b133f4fb","additionalClasses":"headline-decoration","type":"heading2","lines":["一般的なデータアナリティクスツール"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy":{"id":"text-86019c2848","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003Eスプレッドシート：Microsoft Excel、Google Sheets\u003C/li\u003E\n\u003Cli\u003Eビジネスインテリジェンス（BI）ツール：Tableau、Microsoft Power BI\u003C/li\u003E\n\u003Cli\u003Eプログラミング言語：Python、R、SQL\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__659376865":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"conclusion",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-1b16664703","additionalClasses":"headline-decoration","type":"heading2","lines":["結論"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4923f75816","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003Eデータアナリティクスは、実質的にあらゆる業界の競争優位性に不可欠です。データドリブンな意思決定は、内部プロセスの最適化、顧客体験のアップグレード、ブランドロイヤルティの向上、そしてヘルスケア業界の場合は患者のウェルネスの改善を通じて、有意義な効率を生み出します。 \u003C/p\u003E\n\u003Cp\u003Eデータは驚異的なペースで増加し続けており、アナリティクスの課題は複雑化しています。同時に、AIの高度なアプリケーションを含むツールやテクニックは、データとの新たなインタラクション方法を提供し、価値の高いインサイトを引き出します。進化し続けるデータアナリティクスの分野は、デジタル時代において引き続き成功に不可欠な要素だと予測されます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1500909857":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn 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href=\"https://www.forbes.com/advisor/education/certifications/best-data-analytics-certifications/\"\u003E優れたデータアナリティクスのオンライン認定資格のリスト\u003C/a\u003Eをまとめました。Snowflakeは、Snowflakeを使用した包括的なデータ分析の原則を適用するために用いられる高度な知識とスキルを対象とした、\u003Ca href=\"https://learn.snowflake.com/en/certifications/snowpro-advanced-dataanalyst/?pdf_name=SnowProDataAnalystStudyGuide\"\u003ESnowPro® Advanced: Data Analyst Certification\u003C/a\u003Eを提供しています。\u003C/p\u003E\r\n"},{"title":"ビッグデータの分析に使用しているツールは何ですか？","richText":"\u003Cp\u003E膨大な量のデータを処理し、迅速に処理して有意義なインサイトを抽出できる専門ツールは数多く存在します。以下にそのようなツールをいくつか挙げます。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eビッグデータフレームワーク：\u003Ca href=\"https://www.snowflake.com/en/product/features/snowpark/\"\u003ESnowflake Snowpark\u003C/a\u003E、\u003Ca href=\"https://hadoop.apache.org/\"\u003EApache Hadoop\u003C/a\u003E、\u003Ca href=\"https://spark.apache.org/\"\u003EApache Spark\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003Eクラウドベースデータウェアハウス：\u003Ca href=\"https://cloud.google.com/bigquery?utm_source=google&utm_medium=cpc&utm_campaign=na-US-all-en-dr-bkws-all-all-trial-e-dr-1710134&utm_content=text-ad-none-any-DEV_c-CRE_772298885512-ADGP_Hybrid+%7C+BKWS+-+EXA+%7C+Txt-Data+Analytics-Data+Analytics-BigQuery-KWID_43700082585516178-kwd-63326440124&utm_term=KW_google%20bigquery-ST_google+bigquery&gclsrc=aw.ds&gad_source=1&gad_campaignid=22970352687&gclid=Cj0KCQjwoP_FBhDFARIsANPG24P3VZl3_a-liWj18JKg9cBt96oy0h9fSeV7h9G4GzXW5-1YxrW6tdUaAsoCEALw_wcB\"\u003EGoogle BigQuery\u003C/a\u003E、\u003Ca href=\"https://aws.amazon.com/pm/redshift/?trk=a136dbb3-ff50-468b-9a66-852dc1ca985b&sc_channel=ps&ef_id=Cj0KCQjwoP_FBhDFARIsANPG24OEawaY6lV0Vnxy6v98luBjDNhrPJ71Q1KZ_r_NcuG4ZvpzRg5ZYF8aAsHOEALw_wcB:G:s&s_kwcid=AL!4422!3!651751058853!e!!g!!amazon%20redshift!19852662155!145019245577&gad_campaignid=19852662155&gbraid=0AAAAADjHtp_w47gFWrFMuAm5btwOo9ScF&gclid=Cj0KCQjwoP_FBhDFARIsANPG24OEawaY6lV0Vnxy6v98luBjDNhrPJ71Q1KZ_r_NcuG4ZvpzRg5ZYF8aAsHOEALw_wcB\"\u003EAmazon Redshift\u003C/a\u003E、\u003Ca href=\"https://www.snowflake.com/en/product/analytics/\"\u003ESnowflake\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003EBIおよび可視化ツール：\u003Ca href=\"https://www.tableau.com/\"\u003ETableau\u003C/a\u003E、\u003Ca href=\"https://www.microsoft.com/en-us/power-platform/products/power-bi\"\u003EMicrosoft Power BI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003Eプログラミング言語とライブラリ：\u003Ca href=\"https://www.python.org/\"\u003EPython\u003C/a\u003E、\u003Ca href=\"https://www.r-project.org/\"\u003ER\u003C/a\u003E、\u003Ca href=\"https://www.coursera.org/specializations/learn-sql-basics-data-science/paidmedia?utm_medium=sem&utm_source=gg&utm_campaign=b2c_namer_learn-sql-basics-data-science_ucdavis_ftcof_specializations_px_dr_bau_gg_sem_pr-bd_us-ca_en_m_hyb_24-12_x&campaignid=20743946861&adgroupid=154886907309&device=c&keyword=sql&matchtype=b&network=g&devicemodel=&creativeid=679514232408&assetgroupid=&targetid=kwd-10206160&extensionid=&placement=&gad_source=1&gad_campaignid=20743946861&gbraid=0AAAAADdKX6azVKefwfYb2NyuzsKTgMGOJ&gclid=Cj0KCQjwoP_FBhDFARIsANPG24PZTNLiGUpmlyl3WDAV8k-_Yop5rD7fxGmgImdMCl0YROUq1DUPB04aAsUhEALw_wcB\"\u003ESQL\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n"},{"title":"データアナリティクスの例として、どのようなものがありますか？","richText":"\u003Cp\u003Eたとえば\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/data-analytics-for-retailers/\"\u003E小売\u003C/a\u003E業の顧客セグメンテーションがあります。\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/retail-consumer-goods/\"\u003E小売企業\u003C/a\u003Eは、収集した膨大な顧客データを活用して顧客をさまざまなセグメント（ハイエンドの電子機器しか購入しない顧客や、セール時にしか購入しない顧客など）に分類できます。セグメントが確立されると、小売企業はそれぞれのセグメントに、カスタマイズされた製品のレコメンデーションや早期アクセスのセールスアラートなど、ターゲットマーケティング資料を送信できます。\u003C/p\u003E\r\n"},{"title":"データアナリティクスとデータ分析の違いは何ですか？","richText":"\u003Cp\u003Eデータアナリティクスは広範かつ包括的な分野であり、データ分析はデータアナリティクスのプロセスにおける重要な要素です。データ分析の目的は、過去やこれまでに起きたことを把握し、実用的なインサイトを発見してデータドリブンな意思決定を促進することです。データアナリティクスは、データを使用して将来の行動を予測し、必要なアクションを提示します。データ分析は過去のデータから記述的なインサイトを提供しますが、データアナリティクスはこの基盤を元にして、将来を見据えた戦略を策定して対策を講じます。\u003C/p\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__480869401":{"gridClassNames":"aem-Grid aem-Grid--12 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Saves 90% in Costs and Increases Performance 10x"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/en/customers/all-customers/case-study/videoamp/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"ストーリーを読む"},"image":{"id":"image","alt":"simon data logo","lazyEnabled":true,":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-2751d6d67f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-21c24ea6a0","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003Eビルボードの象徴的な音楽チャートを支え、エンターテイメント業界で最も信頼されるデータパートナーであるLuminateは、Snowflakeを中核に据え、より革新的な製品開発ときわめて精度の高いデータ処理を実現するためにデータを統合しました。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical","title":{"id":"title","type":"heading4","lines":["Luminate Amps Entertainment Analytics with 300% Faster Data Processing and Richer 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