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Contextを使用すると、チームは次のことができます。\u003C/p\u003E\u003Cul\u003E\u003Cli\u003E定義、タグ、連絡先を前面に押し出す一貫したカタログエクスペリエンスにより、\u003Cstrong\u003E使用する前にデータを理解する\u003C/strong\u003E。\u003C/li\u003E\u003Cli\u003EAIユースケースごとに再構築するのではなく、レポート、データベース、パイプラインから\u003Cstrong\u003E既存のデータ知識を再利用する\u003C/strong\u003E。\u003C/li\u003E\u003Cli\u003Eサポートされているソース、変換、BIツール全体で接続されたリネージを使用して、\u003Cstrong\u003E問題のトラブルシューティングと変更の影響の評価を行う\u003C/strong\u003E。\u003C/li\u003E\u003Cli\u003E基盤となるデータに対する広範な制御を付与することなく、\u003Cstrong\u003Eビジネスエキスパートをコンテキストのキュレーションに参加させる\u003C/strong\u003E。\u003C/li\u003E\u003Cli\u003Eサポートされているダウンストリーム列に説明を伝播することで、\u003Cstrong\u003Eドキュメントのメンテナンスを削減する\u003C/strong\u003E。\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAIとアナリティクスを共有コンテキストに根付かせ\u003C/strong\u003E、各アプリケーションやプロンプトで知識を再作成する必要性を減らす。\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_1":{"id":"blog-title-dc2df53b9f","propertiesId":"ai","type":"heading2","lines":["AIの導入に合わせてビジネスの理解を拡大"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_2":{"id":"blog-text-e617df6672","text":"\u003Cp\u003Eお客様との会話の中で、AIへの熱意には実践的な期待が伴います。適切なデータを見つけ、その意味を明確にし、少数の専門家への依存を減らすことです。チームは、レポートの背後にあるソースを確認し、ドキュメントを繰り返しメンテナンスするのではなく、再利用することを重視しています。人々がデータ作業をエージェントに委任する場合、これらのニーズはさらに重要になります。\u003C/p\u003E\u003Cp\u003EAIの導入が進むにつれて、エージェントごとに個別の指示と定義のセットを維持することは、より多くの作業と不整合の機会を生み出します。共有され、ガバナンスの効いたコンテキストレイヤーは、ビジネスの理解を毎回新しい統合プロジェクトとして扱うのではなく、ユースケース全体で再利用できる基盤をチームに提供します。\u003C/p\u003E\u003Cp\u003EHorizon Contextは、その基盤の3つの部分を接続します。データシステム内の証拠、ビジネスエキスパートの知識、およびその両方を使用するツールです。目標は、コンテキストの再構築に費やす時間を減らし、ビジネス上の意思決定に適用する時間を増やすことです。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_3":{"id":"blog-title-908abbfbb2","propertiesId":"","type":"heading2","lines":["既存のデータ投資の知識を基盤にする"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_4":{"id":"blog-text-029a1ebd4e","text":"\u003Cp\u003E組織はすでに、ビジネスを運営するレポートとデータパイプラインに投資しています。それらのメタデータは、データがどのように構造化され、変換され、使用されるかについての貴重な証拠をキャプチャします。その証拠を利用できるようにすることで、AI開発者は、孤立したテーブル名や列リストよりも強力な出発点を得ることができます。\u003C/p\u003E\u003Cp\u003EHorizonカタログのメタデータコネクタは現在、データベース、BI、データパイプラインツール全体で18のソースをサポートしています。これらには、Power BI、Tableau、Looker、Sigma、Hex、dbt、SQL Server、Oracle、PostgreSQL、Databricks、BigQueryが含まれます。\u003C/p\u003E\u003Cp\u003Eコネクタとその構成に応じて、Horizon Contextはスキーマ、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。チームは、Snowflakeオブジェクトと一緒に外部アセットを参照し、そのメタデータを検索し、カタログからクロスプラットフォームのリネージを探索できます。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_5":{"id":"blog-title-de8ffb9602","propertiesId":"","type":"heading2","lines":["データの理解を前面に押し出す"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_6":{"id":"blog-text-4cabd9f83a","text":"\u003Cp\u003E再設計された\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EHorizonカタログ\u003C/a\u003Eは、データ定義、タグ、連絡先を前面に押し出す一貫したエクスペリエンスを提供します。ビジネスアナリストは、データアセットを簡単に見つけ、それらが何を意味し、どのように使用できるか、明確にするために誰に尋ねるべきかを理解できます。メタデータコネクタとともに、これによりHorizonカタログは、Snowflakeと接続されたシステム全体でデータを理解するための単一の画面になり、チームとエージェントが適切なデータを発見し、共有されたビジネスコンテキストで使用するのに役立ちます。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_7":{"id":"image-afb9746967","height":"768","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--88fc0b40-f918-433d-9bd7-1527ff8f4b66/data-catalog-interface-showing-the-customers-dataset.png?preferwebp=true&quality=85","lazyEnabled":true,"alt":"Screenshot of a data catalog interface showing the CUSTOMERS dataset overview with column details and table metadata","isLcpImage":true,"width":"1280",":type":"snowflake-site/components/image"},"blog_component_8":{"id":"image-e12a008016","height":"768","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--cb5c138e-b337-498b-b7ef-79a7143723f2/data-lineage-diagram-showing-relationships-between.png?preferwebp=true&quality=85","lazyEnabled":true,"alt":"Data lineage diagram showing relationships between CUSTOMERS dataset, source databases, tables, and downstream analytics reports","isLcpImage":false,"width":"1281",":type":"snowflake-site/components/image"},"blog_component_9":{"id":"blog-text-ebc81db6d3","text":"\u003Cp\u003E維持率レポートが予期しない低下を示した場合、チームはそれがビジネスの変化を反映しているのか、データの問題を反映しているのかを調査する必要があります。サポートされ、接続されたシステム全体で、リネージは、レポートを変換を通じてソースデータまで追跡し、どこを調査すべきかを特定するのに役立ちます。アップストリームのテーブルまたは列を変更する前に、ダウンストリームの同じ依存関係をたどって、どのレポートが影響を受ける可能性があるかを評価できます。\u003C/p\u003E\u003Cp\u003ESnowflake CoCoは、このコンテキストをAI支援ワークフローにもたらします。チームは、「この維持率データセットはどこから来たのか？」や「どのダウンストリームレポートがこの列に依存しているのか？」などの質問をすることができます。CoCoは、利用可能なリネージを使用して、問題の調査と変更の影響の評価を支援し、依存関係を手動で再構築する必要性を減らすことができます。\u003C/p\u003E\u003Cp\u003Eこれらのコネクタはメタデータを収集します。基盤となるデータセットをSnowflakeに複製する必要はありません。既存のデータ知識を発見して再利用しやすくする一方で、ガバナンスの効いたビジネス定義は、それを解釈するために必要な追加の意味を提供します。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_10":{"id":"blog-title-62b0c19608","propertiesId":"ai","type":"heading2","lines":["ビジネスの専門知識をAI基盤に取り入れる"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_11":{"id":"blog-text-53d1e4dab8","text":"\u003Cp\u003E顧客維持率を理解している人は、データベースを管理している人ではないかもしれません。しかし、顧客のステータス、除外、レポートの規則に関する彼らの知識は、データを解釈するために不可欠です。その知識のキャプチャは、エンジニアがすべての明確化をドキュメントに翻訳することに依存するべきではありません。\u003C/p\u003E\u003Cp\u003Eメタデータの可視性により、カタログの参照と基盤となるデータへのアクセスが分離されます。適切なメタデータ権限があれば、ユーザーはデータ自体をクエリする権限を受け取ることなく、カタログ情報とリネージを探索できます。\u003C/p\u003E\u003Cp\u003E新しいAPPLY COMMENT権限はまた、MODIFYやOWNERSHIPなどのより広範な権限を付与することなく、スチュワードがオブジェクトと列の説明を編集できるようにするターゲットを絞った方法を管理者に提供します。\u003C/p\u003E\u003Cp\u003Eこれにより、管理者がデータアクセスに対する制御を維持しながら、ビジネスエキスパートは、人々やAIワークフローが使用できるコンテキストの改善に直接的な役割を果たすことができます。明確化は、チャットスレッドや1つのエージェントのプロンプトにとどまる回答ではなく、データとともに再利用可能な知識になります。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_12":{"id":"blog-title-c9eefe54d4","propertiesId":"","type":"heading2","lines":["有用なコンテキストを維持するコストを削減する"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_13":{"id":"blog-text-1f17654462","text":"\u003Cp\u003EAIイニシアチブには、最初のデモのために準備されたドキュメントだけでなく、チームが維持できるコンテキストが必要です。同じ顧客識別子が複数のダウンストリームビューに表示される場合、その説明を繰り返しコピーすると、意味を追加することなく作業が追加されます。説明が欠落していたり、一貫性がなかったりすると、アナリストとAIワークフローの両方が作業するためのコンテキストが少なくなります。\u003C/p\u003E\u003Cp\u003E列の説明の伝播は、列のリネージを使用して、値が変更されずに通過するときに、サポートされているダウンストリーム列に説明を伝達します。たとえば、顧客識別子の説明は、列の名前が変更された場合でも、その列をたどってダウンストリームビューに入ることができます。\u003C/p\u003E\u003Cp\u003E値が変換、結合、または集計されると伝播は停止し、ダウンストリーム列に直接書き込まれた説明を上書きすることはありません。チームは、伝播された説明とユーザーが記述した説明を区別できます。有効にすると、所有者が意味を制御し続けながら、反復的なメンテナンスが削減されるため、専門家は判断が必要な定義に集中できます。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_14":{"id":"blog-title-83b1bdba15","propertiesId":"ai","type":"heading2","lines":["AIワークフローをビジネスの共有理解に根付かせる"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_15":{"id":"blog-text-d316058d20","text":"\u003Cp\u003EAI支援の維持率分析の場合、コンテキストはいくつかの目的を果たします。説明は、顧客フィールドが何を表しているかを説明します。リネージは、チームがデータがどこから来たのかを検査するのに役立ちます。ガバナンスの効いたメトリクス定義は、維持率をどのように計算すべきかを指定します。それぞれが、人々が評価できる回答に異なる貢献をします。\u003C/p\u003E\u003Cp\u003E人々は\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon-context/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EHorizon Context\u003C/a\u003Eでコンテキストを探索できます。Snowflake CoCoは、データディスカバリや影響分析などのタスクにカタログコンテキストを使用できます。セマンティックビューは、AIとアナリティクスのためのメトリクス、ディメンション、関係のガバナンスの効いた定義でその基盤を補完します。\u003C/p\u003E\u003Cp\u003Eビジネス上の価値は再利用です。チームは、各ダッシュボード、アプリケーション、またはプロンプトで再作成するのではなく、共有知識に基づいて構築できます。その基盤は、より一貫した解釈をサポートし、AIが生成した回答をレビューする際に人々が調べるための証拠を提供します。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_16":{"id":"blog-title-1e1f115e14","propertiesId":"","type":"heading2","lines":["ビジネス知識を活用する"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_17":{"id":"blog-text-458f070fed","text":"\u003Cp\u003Eサポートされているデータ、BI、パイプラインシステムをHorizonカタログに接続します。ビジネスエキスパートを説明のキュレーションに参加させ、Snowflake CoCoを使用してリネージを探索し、データの問題を調査し、変更の影響を評価します。\u003C/p\u003E\u003Cp\u003Eチームが今日構築する知識は、次のレポート、アプリケーション、AIワークフローのための再利用可能な基盤になる可能性があります。\u003C/p\u003E\u003Cp\u003E\u003Cstrong\u003E\u003Ca href=\"https://docs.snowflake.com/en/LIMITEDACCESS/snowflake-horizon/connectors\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eサポートされているコネクタとセットアップドキュメントを調べて、開始してください。\u003C/a\u003E\u003C/strong\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_component_0","blog_component_1","blog_component_2","blog_component_3","blog_component_4","blog_component_5","blog_component_6","blog_component_7","blog_component_8","blog_component_9","blog_component_10","blog_component_11","blog_component_12","blog_component_13","blog_component_14","blog_component_15","blog_component_16","blog_component_17"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid 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