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Ontologyは人間とAIエージェントに同じ承認済みの定義を提供するため、エージェントはビジネスと同じ方法でエンタープライズデータを解釈できます。チームは、営業、財務、マーケティングなどのドメイン間の違いを維持しながら、ツールごとに再定義することなく、エージェントやアプリケーション全体でその知識を再利用できます。\u003Cbr\u003E\n\u003Cbr\u003E\nたとえば、収益に関する質問に答えるエージェントは、財務部門が「収益」をどのように定義しているか、またそれが注文や顧客とどのように関連しているかを知る必要があります。その知識は通常、BIモデル、dbtプロジェクト、および長年勤務している従業員の経験にまたがって存在しており、チームは新しいエージェントごとにそれを再構築することがよくあります。エージェントが間違えると、人々は間違った数値に基づいて意思決定を行い、AIの回答に対する信頼を失います。Business Ontologyは、その知識を記録するための公式な場所をチームに提供します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-5b3fca5e51","height":"591","src":"/adobe/dynamicmedia/deliver/dm-aid--31327b63-bd68-4aaf-9eff-2d703eb2229f/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":"Figure 1: 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.",":type":"snowflake-site/components/image"},"blog_title":{"id":"blog-title-dbb2b5a7b5","propertiesId":"","type":"heading2","lines":["データエステート全体の知識を統合して信頼できるビジネスコンテキストを作成"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1060397060":{"id":"blog-text-13521bb8b8","text":"\u003Cp\u003EHorizon Contextは、チームがすでに使用しているツールからの知識をまとめます。すぐに使えるメタデータコネクタは、Tableau、Power BI、dbt、SQL Server、Sigma、Omni、PostgreSQLなどのソースからスキーマ、クエリログ、ダッシュボード定義、使用状況シグナルを収集します。次に、クロスプラットフォームのリネージにより、ソースデータベースから変換を経て、それを表示するダッシュボードまでデータが追跡されます。その結果、データエステートの接続されたビューが得られ、チームはSnowflakeの社内外でデータがどこから来てどのように使用されているかをよりよく理解できるようになります。\u003C/p\u003E\n\u003Cp\u003Eエージェントが信頼できるものにするために、Snowflakeはそれぞれ異なる役割を持つ3つのコンテキストレイヤーを提供します。\u003Ca href=\"https://www.snowflake.com/en/blog/enterprise-ai-agents-grounded-context/\" target=\"_blank\"\u003ESnowflake Cortex Sense\u003C/a\u003E（まもなくパブリックプレビュー）はコンテキストを自動的に推論し、Business Ontologyはビジネス上の意味を管理し、\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\" target=\"_blank\"\u003ESnowflake Semantic Views\u003C/a\u003Eはメトリクスを実行します。 \u003C/p\u003E\n\u003Cp\u003E1つではなく3つのレイヤーがある理由について説明します。それは、それぞれが異なる問題に対処しており、1つのレイヤーですべてをうまくこなすことはできないためです。Cortex Senseを使用すると、チームが最初にすべてのテーブルとメトリクスを手動で定義しなくても、エージェントはデータエステートをすぐに幅広くカバーできるようになります。Business Ontologyは最も重要な部分に権限を追加するため、人間とエージェントは承認されたビジネス上の意味に基づいて作業できます。Semantic Viewsは精度を高めるため、各メトリクスは毎回同じ方法で計算されます。 \u003C/p\u003E\n\u003Cp\u003Eレイヤーは連続したものではありません。お客様は、最も差し迫った問題を解決するレイヤーから開始し、成長に合わせて他のレイヤーを追加できます。用語集のニーズから始めるチームもあれば、実行可能なメトリクスから始めるチームもあります。エントリーポイントに関係なく、3つのレイヤーはすべてのエージェント、アプリ、人間に一貫した信頼できるビジネスコンテキストを1つ提供します。\u003C/p\u003E\n\u003Cp\u003Eそれぞれについて詳しく見ていきましょう。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_2052300538":{"id":"blog-title-66272216df","propertiesId":"cortex-sense","type":"heading2","lines":["Cortex Senseによるコンテキストの自動化"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_64117581":{"id":"blog-text-9fb677e470","text":"\u003Cp\u003E多くのチームにとって、Cortex Senseはバックグラウンド作業の多くを処理するため、自然な出発点となります。人々がデータをクエリし、ダッシュボードを構築し、パイプラインを実行する際にコンテキストを収集し、各テーブルとメトリクスの意味やそれらの接続方法を定義する時間を短縮しながら、日常の作業をビジネスの理解に変えます。\u003C/p\u003E\n\u003Cp\u003ECortex Senseは、クエリ履歴、ダッシュボード、dbtモデルなど、チームがすでに生成しているメタデータと使用パターンからコンテキストを構築することでこれを実現します。ビジネスの変化に合わせて、Cortex Senseは選択した頻度で知識を自動的に更新するため、その理解も追随します。チームはビジネスユースケースの範囲を定め、関連する既存のコンテキストを接続し、フィードバックと評価を通じてそれを改良することで、すべてのテーブルを手動でモデル化する必要性を減らします。Cortex Senseは競合する知識をインテリジェントに特定し、どのソースを信頼すべきかをチームに尋ねます。\u003C/p\u003E\n\u003Cp\u003Eチームが最も重要な用語を管理する準備ができると、Cortex Senseは提案された定義、関係性、データへのリンクを含む初期ドラフトをBusiness Ontologyに作成できるため、白紙の状態から始める必要はありません。チームはビジネスユースケースの範囲を定め、関連する既存のコンテキストを接続し、フィードバックと評価を通じてそれを改良することで、すべてのテーブルを手動でモデル化する必要性を減らすことができます。Cortex Senseは競合する知識をインテリジェントに特定し、どのソースを信頼すべきかをチームに尋ねます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_553216124":{"id":"blog-title-3a9f129f07","propertiesId":"business-ontology","type":"heading2","lines":["Business Ontologyによるビジネスコンテキストの定義"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1999528628":{"id":"blog-text-41c844c137","text":"\u003Cp\u003EBusiness Ontologyは、チームが最も重要なビジネス用語の意味、所有者、およびそれらの関係性を記録する場所です。「純収益」を例に挙げます。すべてのセマンティックビュー、ダッシュボード、エージェントが独自のバージョンを持つ代わりに、アナリストがそれを1回定義し（または既存の用語集からインポートし）、スコープを「財務」に設定し、所有者を指定して承認を得て、それを計算するセマンティックビューのメトリクスにリンクします（図2を参照）。それ以降、エージェントや他のチームはその承認された定義に基づいて作業します。チームは以下のことができます。\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003Eビジネスコンセプトに基づくエージェントの基礎付け：\u003C/b\u003Eドメインエキスパートが顧客、注文、収益などのコンセプトとそれらの関係性を定義し、エンジニアがそれらの定義をデータに接続します。その後、エージェントはビジネス独自の知識を通じてデータを解釈します。 \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003Eセマンティックビュー全体でのメトリクスの調整：\u003C/b\u003Eチームは、独自のセマンティックビュー内の同等のメトリクスを1つの共有ビジネス定義にリンクし、すべてのチームに1つの実装を強制することなく意味を調整します。意図的な違いも維持されます。レストラン企業は、ブランドごとに既存店売上高を異なる方法で定義し、各バージョンを個別に承認し、独自のブランドのラベルを付けて、それを計算するセマンティックビューにリンクする場合があります。\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E誰もが適切な定義を見つけられるように支援：\u003C/b\u003E誰でも用語を検索し、誰が所有しているか、承認されているかどうかを確認し、それが計算されている場所までたどることができます。ブランドや地域のバリエーションは、そのスコープを維持します。\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_333095280":{"id":"image-759985a96f","height":"1049","src":"/adobe/dynamicmedia/deliver/dm-aid--a5513451-c8ca-4faf-91c0-b1d2bcd9f869/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":"Figure 2: 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.",":type":"snowflake-site/components/image"},"blog_text_1334654234":{"id":"blog-text-6e43b8ceb8","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003Eこれらの定義は意思決定を形作るため、組織はHorizonカタログのガバナンス制御を使用して、誰がそれらを作成、編集、承認できるかを制御します。AIの提案やインポートされた定義はレビューの出発点であり、責任ある所有者が何を公式にするかを決定します。承認された定義がCortex Senseの推論と競合する場合、エージェントは承認された定義を使用し、Cortex Senseはそこから学習します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-5dec198d19","alignment":"left","layout":"card","quoteSourceName":"Joe Tobey","quoteSourceTitle":"Head of Data Product Engineering, dentsu","showQuoteIcon":true,"quote":"\"ビジネスオントロジー（Business Ontology）は、あらゆる定義に対して責任を持つオーナーを定め、それを実装する実データと紐づけ、すべてのエージェントが一貫して返す「共通の正解」として確立します。初期プロダクト採用企業（アーリーアダプター）である私たちにとって、これこそが確固たる自信を持ってエージェントを自社の実データに対面させられる決め手となっています。\"","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_title_382186722":{"id":"blog-title-54937d6383","propertiesId":"semantic-views","type":"heading2","lines":["Semantic Viewsで定義とロジックを一度だけ適用"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_881009154":{"id":"blog-text-4f6b910db3","text":"\u003Cp\u003ESemantic Viewsは各メトリクスの計算方法を定義し、Business Ontologyはその意味と誰が承認したかを管理します。各ビューは、ドメインのメトリクス、ディメンション、結合を、エージェント、BIツール、SQLが直接クエリできるSnowflakeオブジェクトにパッケージ化するため、エージェントは推測するのではなく、管理されたロジックからSQLを生成します。Business Ontologyは、承認された定義をそれらを計算するセマンティックビューにリンクしますが、計算自体は変更しません。それは各ビューの所有者に委ねられます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_922201594":{"id":"blog-title-c0d6985185","propertiesId":"","type":"heading2","lines":["高品質なコンテキストには設計上オープンなプラットフォームが必要"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_598255608":{"id":"blog-text-8fda6c172c","text":"\u003Cp\u003Eビジネスの知識は、ビジネスとともに移動できて初めて報われます。そのため、Snowflakeは、セマンティックモデルのベンダー中立なオープンスタンダードである\u003Ca href=\"https://www.snowflake.com/en/blog/apache-ossie-open-semantic-interchange-incubator/\" target=\"_blank\"\u003EApache Ossie（インキュベーション中）\u003C/a\u003Eの設立を支援しました。これは現在、Microsoft、Salesforce、dbt Labs、Qlik、ThoughtSpotなど、数十の組織によって支持されています。Semantic ViewsはOssie定義をインポートおよびエクスポートできるため、メトリクスは各ツールで再構築されることなく、Snowflakeと他のOssie互換ツール間を移動できます。Snowflake外部のエージェントも、Model Context Protocolを介してSemantic Viewsをクエリでき、Horizonカタログがアクセスを管理します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_189117634":{"id":"quote-item-a7ec43ba46","alignment":"left","layout":"card","quoteSourceName":"Morten Lileng","quoteSourceTitle":"Global Head of Engineering, dentsu","showQuoteIcon":true,"quote":"\"セマンティック・ドリフト（意味や指標定義の乖離・ズレ）は、複数のパートナーが関与するデータ環境において最も過小評価されている重大なリスクの一つです。そして、電通グループのデータ＆テクノロジーエージェンシーであるMerkleにおいて、私たちは業界屈指の極めて複雑なデータエコシステムの中で業務を運用しています。Snowflakeのビジネスオントロジー（Business Ontology）は、このギャップをインフラストラクチャの階層で根本から解消し、あらゆる定義に責任あるオーナーを割り当てるとともに、下流のあらゆるシステムに対してガバナンスの効いた「単一の確固たる回答」を提供します。”","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_title_874620594":{"id":"blog-title-2a7baf2f33","propertiesId":"snowflake-business-ontology","type":"heading2","lines":["Snowflake Business Ontologyに関するアナリティクス企業のコメント"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_650815874":{"id":"blog-text-f31d56b12b","text":"\u003Cp\u003E多くの人が質問の答えを得るためにBIやアナリティクスツールを使用しているため、一貫したビジネスコンテキストが不可欠です。Omni、Sigma、ThoughtSpotがコンテキストの課題と、それに対処する上でBusiness Ontologyが果たす役割をどのように捉えているかをご紹介します。\u003C/p\u003E\n\u003Ch4\u003EOmni\u003C/h4\u003E\n\u003Cp\u003E「コンテキストは、新しいアナリストのオンボーディングのようなものだと考えてください。それは、いつ、なぜ、どのように物事を使用するかを知るために教えるすべてのことです。\u003Ca target=\"_blank\" href=\"http://omni\"\u003EOmni\u003C/a\u003Eは管理されたセマンティックモデルを中心に構築されており、Snowflake Semantic Viewsとの唯一の双方向統合を備えています。そして今回、Business Ontologyは承認されたビジネス定義をキャプチャすることで、その基盤をさらに前進させます。私たちは協力して、共通のお客様がすべてのユーザーとエージェントに役立つ信頼できるコンテキストを構築できるよう支援しています。」 \u003C/p\u003E\n\u003Cp\u003E— Omni共同創設者兼社長、Jamie Davidson氏\u003C/p\u003E\n\u003Ch4\u003ESigma\u003C/h4\u003E\n\u003Cp\u003E「AIエージェントの信頼性は、それを動かすビジネスコンテキストと定義に依存します。Business Ontologyを使用すると、共通のお客様はビジネスコンセプトを一度定義するだけで、\u003Ca target=\"_blank\" href=\"https://www.sigmacomputing.com/product/ai\"\u003ESigma AI \u003C/a\u003E全体でシームレスに再利用して、管理されたアプリケーション、ダッシュボード、エージェントを構築し、Sigmaで信頼できるアナリティクスを強化できます。」\u003Cbr\u003E\n— Sigmaプロダクトマネジメント担当ディレクター、Zalak Trivedi氏 \u003C/p\u003E\n\u003Ch4\u003EThoughtSpot\u003C/h4\u003E\n\u003Cp\u003E「信頼できるAIは、信頼できるビジネスコンテキストに依存します。Snowflake Business Ontologyは、ビジネス用語の意味を管理する場所を企業に提供し、ThoughtSpotは\u003Ca target=\"_blank\" href=\"https://www.thoughtspot.com/product/agents/spotter\"\u003ESpotter\u003C/a\u003Eを使用してビジネスユーザーからキャプチャした知識でそのコンテキストを継続的に拡張し、すべてのユーザーに正確で説明可能な回答とアクションを提供します。私たちは協力して、お客様が自信を持ってエージェント型アナリティクスを拡張できるよう支援しています。」\u003C/p\u003E\n\u003Cp\u003E— ThoughtSpot Product担当Senior Vice President、Francois Lopitaux氏\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1682682914":{"id":"blog-title-8f57bee6e5","propertiesId":"business-ontology","type":"heading2","lines":["Business Ontologyで知識を一貫したエンタープライズ資産に変換"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_90895576":{"id":"blog-text-744a0be0a5","text":"\u003Cp\u003E人々は役割を変え、ツールは置き換えられ、データモデルは変化します。その背後にある知識が途中で失われてしまうことがよくあります。Business Ontologyは、責任ある所有者と各定義が計算される場所への明確なリンクを備え、その知識を永続的な会社の資産に変えます。すべての新しいエージェントやアプリケーションは、ビジネスがすでに知っていることから始めることができます。\u003C/p\u003E\n\u003Cp\u003EBusiness Ontologyについてさらに詳しく知りたい場合は、\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\"\u003Eハンズオンラボをチェック\u003C/a\u003Eし、ブログ記事\u003Ca target=\"_blank\" href=\"http://www.snowflake.com/en/blog/business-ontology-context\"\u003E「メトリクスの前の意味：AIエージェントのための管理されたBusiness Ontologyをどのように構築したか」\u003C/a\u003Eをお読みください。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_title_1755654776":{"id":"blog-title-58a30e72fa","propertiesId":"","type":"heading2","lines":["よくある質問"],":type":"snowflake-site/components/blog/blog-title","appliedCssClassNames":"left-alignment"},"blog_text_1329841915":{"id":"blog-text-117fd79118","text":"\u003Ch4\u003ESnowflake Business Ontologyとは？\u003C/h4\u003E\n\u003Cp\u003EBusiness Ontology（まもなくパブリックプレビュー）は、Snowflake Horizon Contextのビジネスモデリングおよびガバナンスレイヤーです。組織はこれを使用して、単一のデータモデルに依存することなく、ビジネスコンセプト、メトリクス、およびそれらの関係性を定義できます。各定義には、その所有者と承認ステータスが記録され、それを実装するセマンティックビュー、テーブル、ビューにリンクされます。承認された定義のみがCoCoおよびCoWorkのAIエージェントに提供されるため、エージェントは競合する実装から選択するのではなく、組織が支持する意味に基づいて作業します。\u003C/p\u003E\n\u003Ch4\u003EAIエージェントにビジネスコンテキストが必要な理由\u003C/h4\u003E\n\u003Cp\u003E管理されたビジネスコンテキストは、エージェントがビジネスと同じ方法でデータを解釈するのに役立ち、より正確な応答を提供できるようになります。AIエージェントはデータにアクセスできますが、ビジネス用語の意味やコンセプトの関連性を自動的に「理解」するわけではありません。エージェントにはコンテキストが必要です。つまり、どの定義を信頼すべきか、どのポリシーに従うべきか、どの企業知識を組み込むべきかを示す承認済みのリファレンスです。そのコンテキストがなければ、エージェントは意味や関連するデータソースを推測し、同じ質問に対して異なる回答を返す可能性があります。 \u003C/p\u003E\n\u003Ch4\u003EBusiness OntologyとSemantic Viewsの違い\u003C/h4\u003E\n\u003Cp\u003EBusiness Ontologyは、ビジネス用語の意味、所有者、および他のコンセプトとの関連性を定義します。Semantic Viewsは各メトリクスの計算方法を定義し、メトリクス、ディメンション、結合を、エージェント、BIツール、SQLが直接クエリできるオブジェクトにパッケージ化します。Business 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