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text-color-text-05"},"text_overview_0":{"id":"text-2345be55b9","text":"\u003Cp\u003Eコンプライアンス違反の指摘、母集団の誤りによるAIの誤学習、あるいはチーム間で定義の異なるKPIの露呈など、データに関する問題が発生した際、多くの組織はその後に突きつけられる「なぜ起きたのか」「影響範囲はどこまでか」という追及に一切答えられないのが実態です。これは、所有権が曖昧であったり、リネージが不完全、アクセス記録が存在しない、または記録を十分に遡れなかったりすることで発生します。こうした問題は、規制当局の監査や事後レビューの場で顕在化します。\u003C/p\u003E\r\n\u003Cp\u003Eデータガバナンスとは、データの所有権、所有権、分類、リネージ、監査統制をあらかじめ組み込み、そうした追及を受ける前に、いつでもエビデンスを提示できる状態を整えるための実践です。データがシステム、クラウド、パートナー、AIワークフローといった垣根を越えて移動するにつれて、ガバナンスに対する要求は高まり続けているのに対し、組織においてデータに関する質問の回答を用意することは、本来あるべき姿よりもはるかに困難なのが実情です。本ガイドでは、その状況を変える方法を説明します。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-data-governance":{"id":"title-v2-13d7d4c85c","additionalClasses":"anchor-title anchor-title--what-is-data-governance","type":"heading2","lines":["データガバナンスとは"],":type":"snowflake-site/components/title-v2"},"text_what-is-data-governance_0":{"id":"text-ad3322c6c5","text":"\u003Cp\u003Eデータガバナンスとは、組織がデータのライフサイクル全体にわたってデータをどのように管理するかを定義するポリシー、役割、プロセス、テクノロジーのシステムのことをいいます。データガバナンスにより、データの所有者、データの意味、分類方法、アクセス権限、品質の測定方法、使用状況の監査方法が確立されます。実務レベルにおいてデータガバナンスは、データの信頼性を確保したうえで保護し、かつ大規模に活用できるようにするための運用モデルです。\u003C/p\u003E\r\n\u003Cp\u003Eデータガバナンスが成熟した組織では、データに関する次の4つの問いに対して、正確かつ迅速に回答することができます。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eどのようなデータが存在し、そのデータにはどのような意味があるのか\u003C/li\u003E\r\n\u003Cli\u003Eそのデータの所有者は誰で、その使用に対して誰が責任を負うべきか\u003C/li\u003E\r\n\u003Cli\u003E誰がそのデータにアクセス、共有し、あるいはAIワークフローで使用できるか\u003C/li\u003E\r\n\u003Cli\u003Eそのデータがどのように変換、保護、使用されたかを組織が証明できるか\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003Eこれらの問いに答えるには、メタデータ、データスチュワードシップ、データ品質、プライバシー管理、コンプライアンスプロセス、そして明確な責任の所在が必要です。これらの要素が連携していなければ、組織はデータを確実に管理することはできません。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-data-governance-matters-now":{"id":"title-v2-1f35605c7e","additionalClasses":"anchor-title anchor-title--why-data-governance-matters-now","type":"heading2","lines":["今、データガバナンスが重要である理由"],":type":"snowflake-site/components/title-v2"},"text_why-data-governance-matters-now_0":{"id":"text-4c1fa762eb","text":"\u003Cp\u003E近年、データが単一のレポート環境内に収まらないようになったことで、データガバナンスの難易度は上がっています。たとえば、製品の使用状況テーブルは、アナリティクス、カスタマーサポートのワークフロー、パートナー向けのレポート、ML（機械学習）機能、エグゼクティブ向けのダッシュボードなどにデータを提供する可能性があります。1つのダッシュボードで機能する\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/data-governance/policy/\"\u003Eデータガバナンスポリシー\u003C/a\u003Eが、同じデータに触れる下流のすべてのコピー、変換、またはAIプロンプトを自動的に管理できるわけではありません。\u003C/p\u003E\r\n\u003Cp\u003Eこのような断片化は、効果的なガバナンスの実践とスケーラブルなAIの双方にとって最大の障壁の1つとなっています。SnowflakeのAIプロダクト担当VPであるBaris Gutelkinは、「データを目的ごとに異なる場所に保管していると、そのデータのガバナンスとセキュリティを確保するのは非常に困難です」と述べています。同氏は、組織全体で標準化された単一のデータ基盤に投資することで、ガバナンスとセキュリティを簡素化しながら、より強力な生成AIのユースケースを実現できると主張しています。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_why-data-governance-matters-now_0":{"id":"quote-item-b5f3b92801","alignment":"left","layout":"simple","quoteSourceName":"Baris Gutelkin","quoteSourceTitle":"Snowflake’s VP of Product, AI","showQuoteIcon":true,"quote":"When you keep your data in one place for one thing, another place for another thing, governing and securing that data becomes really difficult.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_why-data-governance-matters-now_1":{"id":"text-6873c5409b","text":"\u003Cp\u003Eそのような基盤を実際に機能させるには、データを一元化するだけでは不十分であり、一貫性をもちながら拡張可能なガバナンスが必要です。チームは、属人的な知識やバラバラの表計算シートに頼ることなく、機密性の高い列の分類、所有権の割り当て、リネージの追跡、マスキングの適用、鮮度の監視、使用状況の監査を行わなければなりません。ガバナンスが適切に行われていれば、信頼できるデータを容易に見つけ、より安全に使用できるようになります。また、データを必要とするチームに余計な摩擦が生じることもありません。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-data-governance-matters-now_0":{"id":"text-ef9321fed4","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003Eよくある落とし穴\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003E組織は、データガバナンスを日常のワークフローに組み込まれた継続的な運用ルールとしてではなく、単発のプロジェクトとして扱いがちです。これにより、所有権の曖昧さやメタデータの不完全さが生じ、データがシステムやAIのユースケース間を移動する際に管理が十分に行き渡らなくなります。その結果、問題の追跡が困難になり、監査への対応も難しくなります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_data-governance-for-ai":{"id":"title-v2-a01c9f4d07","additionalClasses":"anchor-title anchor-title--data-governance-for-ai","type":"heading2","lines":["AI向けデータガバナンス"],":type":"snowflake-site/components/title-v2"},"text_data-governance-for-ai_0":{"id":"text-237219f6fa","text":"\u003Cp\u003EAIの台頭は、データガバナンスの難易度と重要性を大きく引き上げました。ガバナンスが適用されたデータが、AIモデルや自律型AIエージェントによって自動的に取得、要約、変換され、実業務の意思決定へと直接組み込まれるためです。特に、AIエージェントがユーザーの代理として稼働する際には、具体的にどの行を参照したのか、プロンプトに何を注入したのか、その全プロセスのログは存在するのかといった、実効性を問う監査要件に回答できなければなりません。\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/ai/\" target=\"_self\"\u003EAI向けデータガバナンス\u003C/a\u003Eは、AIシステムが使用するデータに焦点を当てています。具体的には、トレーニングデータの出所、PII（個人を特定できる情報）や機密データの分類、同意および許可された使用の管理、ソースデータのバイアスと代表性、データセット、特徴量、プロンプト、出力、および下流の意思決定の間のリネージ、さらには取得、プロンプトのコンテキスト、生成された出力に対するエージェントのアクセス制御と監査ログが含まれます。\u003C/p\u003E\r\n\u003Cp\u003Eこの概念は\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\" target=\"_self\"\u003EAIガバナンス\u003C/a\u003Eと関連していますが、同じものではありません。AI向けデータガバナンスは、AIシステムが使用するデータを管理します。一方、AIガバナンスは、モデルの承認、評価、モデルカード、監視、ドリフト、人間の監督、\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/data-governance/strategy/data-risk-management/\" target=\"_self\"\u003Eリスク管理\u003C/a\u003Eなど、モデルやシステム自体を管理します。なお、これら2つのプログラムは連携している必要があります。組織が、どのデータソースからデータが供給されているのか、取得時にどの機密フィールドが表示される可能性があるのか、誰がデータの使用を承認したのか、そして出力された値がガバナンスの適用されたソースまで遡って追跡できるのかを把握していなければ、高リスクなAIワークフローを適切に管理することはできないからです。\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003E信頼性の高いAIの実現に向けて、Snowflakeがどのようにデータガバナンスを支援しているかをご覧ください：\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_data-governance-for-ai_0":{"id":"embed-92025e10cb","youtubeVideoId":"42GWL5x3Xpg","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_data-governance-principles":{"id":"title-v2-8e7a5dc472","additionalClasses":"anchor-title anchor-title--data-governance-principles","type":"heading2","lines":["データガバナンスの原則"],":type":"snowflake-site/components/title-v2"},"text_data-governance-principles_0":{"id":"text-16ddacba1e","text":"\u003Cp\u003Eデータガバナンスの原則は、プログラムがポリシー、所有権、テクノロジー、プロセスに関して行う選択の指針となります。一般的な原則には、以下のようなものがあります。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003Eアカウンタビリティの明確化：\u003C/b\u003Eすべての重要なデータアセットには、アクセスの決定、品質の問題、定義に関する相違に対して責任を負う所有者が指定されています。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E透明性：\u003C/b\u003Eユーザーは、自身が扱うデータアセットの定義、データリネージ、品質シグナル、およびポリシーのコンテキストを確認できます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eデータの品質：\u003C/b\u003Eガバナンスが適用されたデータは信頼できると盲信するのではなく、明確な期待値に照らし合わせて確認されます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eプライバシーとセキュリティ：\u003C/b\u003E機密データは、そのライフサイクル全体を通じて分類、保護、監視されます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eスチュワードシップ：\u003C/b\u003E指定されたデータスチュワードが定義を維持し、問題を解決し、ドメイン全体での責任ある使用をサポートします。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E標準化：\u003C/b\u003E用語、ポリシー、管理方法はドメイン間で一貫しており、例外があれば別途文書化され承認されます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E監査可能性：\u003C/b\u003E組織は、データがどのようにアクセス、変更、共有、使用されたかを証明できます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E倫理的な使用：\u003C/b\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/policy/data-ethics/\" target=\"_self\"\u003Eデータ倫理\u003C/a\u003Eとは、データが公正かつ非差別的で、ユーザーの期待に沿った方法で使用されることを意味し、有害な結果や意図しない結果を特定して軽減するためのメカニズムを備えていることを指します。\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003Eこれらの原則は、カタログ内の所有権フィールド、列の機密性タグ、規制対象データに適用されるマスキングポリシー、重要なレポートのリネージパス、アクセスレビューの監査ログといった、システム上の具体的なコントロールとして実装に落とし込まれます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-frameworks-and-standards":{"id":"title-v2-738e67293d","additionalClasses":"anchor-title anchor-title--data-governance-frameworks-and-standards","type":"heading2","lines":["データガバナンスのフレームワークと標準"],":type":"snowflake-site/components/title-v2"},"text_data-governance-frameworks-and-standards_0":{"id":"text-ede6456200","text":"\u003Cp\u003Eデータガバナンスのフレームワークや標準は、組織がプログラムの構造を設計し、必要な能力を定義した上で、まず何から実装すべきかという優先順位の策定を支援します。そのアプローチは多岐にわたり、データマネジメントの成熟度評価に重点を置くものから、ITガバナンス、エンタープライズアーキテクチャ、データ品質、あるいはクラウド特有のコントロールに特化したものまで、組織の目的や課題に応じて選択が可能です。\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003Eフレームワークまたは標準\u003C/th\u003E\r\n\u003Cth\u003E最適な用途\u003C/th\u003E\r\n\u003Cth\u003Eガバナンスに役立つこと\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/frameworks/dama-dmbok/\" target=\"_self\"\u003EDAMA-DMBOK\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003E広範なデータマネジメントプログラムの設計\u003C/td\u003E\r\n\u003Ctd\u003Eデータマネジメントの知識領域、役割、規範\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/frameworks/dcam/\" target=\"_self\"\u003EDCAM\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eエンタープライズデータマネジメントの成熟度\u003C/td\u003E\r\n\u003Ctd\u003E運用モデル、制御、アカウンタビリティ、および成熟度\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/frameworks/cdmc/\" target=\"_self\"\u003ECDMC\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eクラウドデータマネジメントコントロール\u003C/td\u003E\r\n\u003Ctd\u003Eクラウドおよびハイブリッドデータ環境向けのガバナンスコントロール\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/frameworks/cobit/\" target=\"_self\"\u003ECOBIT\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003EITガバナンスとの整合性\u003C/td\u003E\r\n\u003Ctd\u003Eリスク、コントロール、アカウンタビリティ、エンタープライズガバナンス\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/frameworks/togaf/\" target=\"_self\"\u003ETOGAF\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eエンタープライズアーキテクチャとの整合性\u003C/td\u003E\r\n\u003Ctd\u003Eデータアーキテクチャ、アプリケーションの依存関係、アーキテクチャガバナンス\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/data-governance/frameworks/fair-principles/\" target=\"_self\"\u003EFAIR原則\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003E科学および研究データの再利用\u003C/td\u003E\r\n\u003Ctd\u003E見つけやすさ、アクセスのしやすさ、相互運用性、再利用性\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.iso.org/standard/81745.html\" target=\"_blank\"\u003EISO 8000\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eデータ品質とマスターデータ\u003C/td\u003E\r\n\u003Ctd\u003E品質要件、データ交換、マスターデータの実践\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://datagovernance.com/the-dgi-data-governance-framework/\" target=\"_blank\"\u003EDGIデータガバナンスフレームワーク\u003C/a\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eガバナンスプログラムの設計\u003C/td\u003E\r\n\u003Ctd\u003E決定権、アカウンタビリティ、ポリシープロセス\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003E例えば、ヘルスケア組織では、DAMA-DMBOKを使用してコアデータ管理機能を定義し、CDMCを使用してクラウドコントロールをマッピングし、HIPAAのデータガバナンス要件を使用してアクセス、保持、監査の基準を定義することが考えられます。\u003C/p\u003E\r\n\u003Cp\u003Eなお、フレームワークはあくまで概念的な骨組みを示すにとどまります。プログラムを実効的に機能させるには、実際のデータ利用環境においてその方針を直接適用し得るオーナーシップ、メタデータ、分類、品質ルール、アクセスポリシー、監査プロセス、そしてこれら一連の統制を具現化するテクノロジーの実装が不可欠です。\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/frameworks/\" target=\"_self\"\u003Eデータガバナンスフレームワークの詳細はこちら →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-operating-models":{"id":"title-v2-087d8e92d2","additionalClasses":"anchor-title anchor-title--data-governance-operating-models","type":"heading2","lines":["データガバナンスの運用モデル"],":type":"snowflake-site/components/title-v2"},"text_data-governance-operating-models_0":{"id":"text-d42e69573e","text":"\u003Cp\u003Eガバナンスプログラムには、組織の働き方に適した運用モデルが必要です。多数のビジネスユニットを擁するグローバル企業では、1つのチームですべてのテーブルを管理することは困難です。一方で、完全に分散されたモデルでは、定義の不整合、ポリシーの重複、コントロールのばらつきが生じる可能性があります。\u003C/p\u003E\r\n\u003Cp\u003E多くの組織は、次の3つのモデルのいずれかを選択します。\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003Eモデル\u003C/th\u003E\r\n\u003Cth\u003E仕組み\u003C/th\u003E\r\n\u003Cth\u003E最適な用途\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E集中型\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E中央のガバナンスチームがポリシー、標準、承認を定義する\u003C/td\u003E\r\n\u003Ctd\u003E小規模なプログラム、厳格に規制されたデータ、または初期段階のガバナンス\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eフェデレーション型\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E各ドメインが共通のガバナンス標準に従いながら、ローカルでデータを所有する\u003C/td\u003E\r\n\u003Ctd\u003E強力なドメイン所有権を持つ大企業\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eハイブリッド型\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E中央チームがポリシーとプラットフォームの標準を設定し、各ドメインが日常的なスチュワードシップを担当する\u003C/td\u003E\r\n\u003Ctd\u003E最も成熟したエンタープライズプログラム\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003E多くの場合、ハイブリッド型モデルが最も実用的です。中央チームが分類基準、ポリシーテンプレート、カタログ要件、監査基準を定義します。ドメインチームは、自身のデータ製品を所有し、定義を維持し、品質問題を解決し、ローカルなコンテキストに基づいてアクセスを承認します。\u003C/p\u003E\r\n\u003Cp\u003E組織がどのモデルを採用するにしても、決定権を明確にする必要があります。例えば、2つのチームの「アクティブ顧客」の定義が異なる場合、ガバナンスモデルにおいて、誰がその不整合を解決するかを特定しておく必要があります。また、ビジネスパートナーが機密データセットへのアクセスを要求した場合、誰がその要求を承認し、どのような証跡が必要で、その決定がどのように記録されるかをモデルの中で明確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"},"card_v2_data-governance-operating-models_0":{"id":"card-v2-9145ffe70f","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003Eギルバート町は、犯罪防止、メンタルヘルス対応、道路のくぼみ補修、リサイクルなどのサービス全体の透明性を高めるため、Snowflakeを使用して一般公開向けのダッシュボードを11個立ち上げました。同町は、機密性の高い公共部門データへの信頼できるアクセスをサポートするために、役割ベースのアクセス制御、メタデータフィールド、ダイナミックデータマスキングなどのガバナンス機能やセキュリティ機能を使用しています。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},":type":"snowflake-site/components/card-v2","layoutStyle":"horizontal","image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--83f793a0-df9c-45fe-8949-f4e33942489a/gilbert%25403x.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Town of Gilbert Logo","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","title":{"id":"title","type":"heading4","lines":["カスタマーストーリー：ギルバート町"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/ja/customers/all-customers/case-study/town-of-gilbert/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"活用事例を読む"}},"title_the-core-components-of-data-governance":{"id":"title-v2-37b5ba67b3","additionalClasses":"anchor-title anchor-title--the-core-components-of-data-governance","type":"heading2","lines":["データガバナンスの主な構成要素"],":type":"snowflake-site/components/title-v2"},"text_the-core-components-of-data-governance_0":{"id":"text-bf5de60d09","text":"\u003Cp\u003Eデータガバナンスは、原則やフレームワークという設計図をもとに構築されますが、実際の運用フェーズにおいては、システムを機能させる具体的な構成要素を通じて実行に移されます。これにより、カタログ、タグ、リネージグラフ、アクセスポリシー、品質チェック、スチュワードシップワークフロー、監査ログなど、人々が毎日使用するシステムにおいてガバナンスが可視化されます。\u003C/p\u003E\r\n\u003Ch3\u003Eメタデータ管理\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/en/data-governance/metadata-management/\"\u003Eメタデータ\u003C/a\u003Eとは、データアセットが何であるか、どこから来たのか、どのように使用すべきかを人やシステムに伝えるコンテキストです。メタデータには、テーブル名、列タイプ、所有者、ビジネス定義、機密性ラベル、鮮度ターゲット、リネージパス、使用パターン、コストプロファイルなどを記述できます。\u003C/p\u003E\r\n\u003Cp\u003Eほとんどのガバナンスプログラムは、次の3つのタイプのメタデータに依存しています。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003Eビジネスメタデータ\u003C/b\u003Eは、定義、所有者、ドメイン、用語集の用語、認定ステータスをカバーしており、データアセットが適切で、使用が承認されているかどうかをチームが理解するのに役立ちます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eテクニカルメタデータ\u003C/b\u003Eは、スキーマ、データ型、変換、依存関係、リネージをカバーしており、エンジニアやアーキテクトがデータの移動や変化を理解するのに役立ちます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eオペレーショナルメタデータ\u003C/b\u003Eは、最新性、使用状況、コスト、品質結果、アクセスパターンをカバーしており、データが最新で信頼でき、適切に使用されているかどうかをチームが監視するのに役立ちます。\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/data-governance/data-governance-vs-technical-governance/\" target=\"_self\"\u003Eデータガバナンスとテクニカルガバナンスの違いをご確認ください →\u003C/a\u003E\u003C/p\u003E\r\n\u003Ch3\u003Eデータ分類\u003C/h3\u003E\r\n\u003Cp\u003Eデータ分類では、機密性、ドメイン、規制（\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/fundamentals/what-is-data-sovereignty/\" target=\"_self\"\u003Eデータ主権\u003C/a\u003Eを含む）、または許可された用途に基づいて、データにラベルを割り当てます。例えば、列にPII、保護対象医療情報、決済カードデータ、機密財務データ、または承認済みのトレーニングデータとしてタグを付けることができます。これらのラベルに基づいて、アクセスレビュー、マスキングポリシー、保持ルール、共有の承認、AIの使用制限が適用されます。\u003C/p\u003E\r\n\u003Cp\u003Eデータの分類が重要なのは、機密データが特定の安全な場所に隔離されているケースは、現実には稀であるからです。メールアドレスや顧客ID、診断コード、位置情報、取引履歴といった機密情報は、データパイプラインやダッシュボード、各アプリケーションのテーブル間を常に移動し、組織全体に拡散しています。一貫した保護を行うには、ガバナンスプログラムでまずこれらのフィールドを特定する必要があります。\u003C/p\u003E\r\n\u003Ch3\u003Eデータカタログ\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/data-catalog/\" target=\"_self\"\u003Eデータカタログ\u003C/a\u003Eは、必要なデータを誰もが迷わず検索でき、そのガバナンスルールを画面上で一目で確認できるデータアセットの検索管理ポータルです。データカタログにより、アナリスト、エンジニア、スチュワードなどのビジネスユーザーは、データアセットの検索、定義の確認、リネージの確認、所有者のチェック、品質シグナルの検査、アクセスリクエストを行うことができます。\u003C/p\u003E\r\n\u003Cp\u003E最新のデータカタログでは、承認されたデータプロダクトを見つけ出せるだけでなく、関連するポリシーの紐付け、データ鮮度のリアルタイム表示、さらには類似データセットの乱立防止までをワンストップで実現します。優れたデータカタログの価値は、クエリを作成する前に、実務上の迷いや疑問をその場で解消できる点にあります。\u003C/p\u003E\r\n\u003Cp\u003E例えば、次のような疑問に即座に答えてくれます。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eこのテーブルの意味は？\u003C/li\u003E\r\n\u003Cli\u003E所有者は誰か？\u003C/li\u003E\r\n\u003Cli\u003Eこのデータは承認されているか？\u003C/li\u003E\r\n\u003Cli\u003Eどの下流アセットがこれに依存しているか？\u003C/li\u003E\r\n\u003Cli\u003E機密データが含まれているか？\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003Eデータリネージ\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/data-governance/data-lineage/\"\u003Eデータリネージ\u003C/a\u003Eは、ソースから消費までのデータを追跡します。データリネージにより、フィールド、テーブル、またはメトリックが、取り込み、変換、モデリング、レポート作成、共有、AIワークフローをどのように移動するかを確認できます。リネージは、テーブルレベル（テーブルが他のテーブルやソースにどのように依存しているかを示す）、列レベル（特定のフィールドがどのように変換または再利用されているかを示す）、またはシステム間（データがツール、クラウド、プラットフォーム間をどのように移動するかを示す）で機能します。\u003C/p\u003E\r\n\u003Cp\u003E規制対象のデータがレポートやAIモデル、外部プロダクトへと連携されている場合、データリネージの導入が不可欠です。これにより、データはどこから来たのか、途中でどう加工されたのか、そして元データが変わった際、どこまで影響が及ぶのかを特定できるようになります。\u003C/p\u003E\r\n\u003Ch3\u003Eポリシー管理\u003C/h3\u003E\r\n\u003Cp\u003Eポリシー管理は、策定されたガバナンスルールを、システム上で執行される強制力のある統制へと落とし込むフェーズです。これには、アクセスポリシー、マスキングポリシー、行レベルの制限、保持ルール、データ共有ルール、許可された使用に関するポリシー、例外ワークフローが含まれます。\u003C/p\u003E\r\n\u003Cp\u003Eポリシーでは、誰が、どのような条件下で、どのような目的で、どのようなレビュープロセスを経て、どのデータにアクセスできるかを定義します。なお、強力なポリシー管理には例外も含まれ、例えば一部のユーザーが監査、移行、またはインシデント対応のために一時的なアクセスが必要な場合、ガバナンスでは誰が例外を承認したか、なぜ承認されたか、いつ期限切れになるかを記録しなければなりません。\u003C/p\u003E\r\n\u003Ch3\u003Eデータ品質\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/fundamentals/a-practical-guide-to-data-quality/\"\u003Eデータ品質\u003C/a\u003Eとは、そのデータがビジネスの目的に対して今すぐ信頼して使える状態にあるかを評価する基準です。具体的には、データの正確さや完全性、一貫性、最新度、一意性、そして形式の正しさを総合的に測定します。たとえば、社内の検証で一時的に使うプロダクトデータであれば、多少の誤差は許容されます。しかし、決算書を作るための売上データや、命に関わる医療の請求データとなれば、厳格な品質基準が求められます。組織やデータの目的によって、品質の合格ラインは全く異なるのです。\u003C/p\u003E\r\n\u003Cp\u003Eテーブルに所有者、用語集の定義、アクセスポリシーがあっても、そのレコードが古かったり不完全であったりすると、ユーザーはそれを信頼できません。モダンなプログラムでは、データ契約、パイプラインテスト、継続的な監視を通じて、データサイクルの早い段階で品質管理を実施します。\u003C/p\u003E\r\n\u003Ch3\u003Eデータのプライバシーとセキュリティ\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/data-governance/data-privacy/\"\u003Eデータプライバシー\u003C/a\u003E（個人情報の保護）は、個人データや機密データがどのように収集、使用、保持、共有、削除されるかを管理します。データセキュリティ（不正アクセスの防御）は、不正アクセス、悪用、または漏洩からデータがどのように保護されるかを管理します。どちらもデータの分類タグ、責任者の明確化、アクセス権の設定、監査ログの記録という共通のインフラを必要とするため、同じガバナンスフレームワークのなかでセットで運用するのが最も効率的です。\u003C/p\u003E\r\n\u003Cp\u003Eプライバシーコントロールには、同意管理、データ主体の要求ワークフロー、保持ルール、トークン化、マスキングなどが含まれます。セキュリティコントロールには、役割ベースのアクセス制御、行アクセスポリシー、暗号化、監視、インシデント対応手順などが含まれます。ガバナンスは、こうしたコントロールを実際のデータ資産と結びつけます。これにより、どのテーブルに機密データがあるか、誰にアクセス権があるか、どのルールが適用されているか、そして利用状況を後から監査できるかといった、一連の運用を可視化します。\u003C/p\u003E\r\n\u003Ch3\u003Eデータ共有とコラボレーション\u003C/h3\u003E\r\n\u003Cp\u003Eデータガバナンスは、社内システムの一画でアクセスを制限するだけのものではありません。社内の別部門はもちろん、パートナー企業や外部のデータエコシステムへと、データを安全に流通させ、再利用するための共通基盤として機能する必要があります。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eデータメッシュは、全社統一のガバナンス基準を維持したまま、データの所有権と管理権限を各ドメインへと分散、委譲します。\u003C/li\u003E\r\n\u003Cli\u003Eデータプロダクトは、単なる生データではなく、所有者、定義、品質目標、ライフサイクルをセットにして統合、提供されます。\u003C/li\u003E\r\n\u003Cli\u003Eデータ契約は、データの提供者と利用者の間で、スキーマや鮮度、品質に関する提供基準を明確に定義します。\u003C/li\u003E\r\n\u003Cli\u003Eデータクリーンルームは、元データを外部に開示することなく、複数の組織間で安全にデータを掛け合わせて共同分析できる仕組みです。\u003Cbr\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003Eデータを社内外で共有する際には、必ずいくつかの確認事項が伴います。具体的には、誰が所有者か、データの意味は何か、最新の状態か、どのポリシーが適用されるか、そして、受け取り手がその目的で利用してよいかという点です。ガバナンスの役割は、こうした確認事項をあらかじめ明文化し、システム上で確実に適用できるようにすることです。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_the-core-components-of-data-governance_0":{"id":"card-v2-0b890188e0","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EグローバルなテクノロジーリーダーであるSiemensは、Snowflakeを活用して全社規模のデータメッシュを構築しました。これにより、600以上のデータプロジェクトを稼働させ、数千のデータウェアハウスを統合し、AI、自動化、部門間コラボレーションを強力に推進するスケーラブルなAIデータクラウドを実現しています。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},":type":"snowflake-site/components/card-v2","layoutStyle":"horizontal","image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--bebf4f05-e185-42c1-a1f1-f869ef4ba166/siemens-transparent.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Siemens logo","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","title":{"id":"title","type":"heading4","lines":["カスタマーストーリー：Siemens"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/ja/customers/all-customers/case-study/siemens-1/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"活用事例を読む"}},"title_data-stewardship-and-governance-roles":{"id":"title-v2-85da8a4a0c","additionalClasses":"anchor-title anchor-title--data-stewardship-and-governance-roles","type":"heading2","lines":["データスチュワードシップとガバナンスの役割"],":type":"snowflake-site/components/title-v2"},"text_data-stewardship-and-governance-roles_0":{"id":"text-f8dda79f0f","text":"\u003Cp\u003Eデータガバナンスを機能させるには、明確な決定権を持つ担当者を指名する必要があります。\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/fundamentals/data-stewardship/\" target=\"_self\"\u003Eデータスチュワードシップ\u003C/a\u003Eは、ガバナンスの方針を現場の日常的なデータ業務へと落とし込む、運用の要となる役割です。\u003C/p\u003E\r\n\u003Cp\u003Eガバナンス体制が成熟した組織では、データスチュワードが各部門のデータ責任者やシステム管理者、セキュリティやプライバシーの担当チーム、さらにはガバナンス評議会と緊密に連携します。その上で、データの定義管理や品質監視、アクセス状況のレビューに加え、部門間で発生したルールの競合やトラブルのエスカレーションまでを担います。\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003E役割\u003C/th\u003E\r\n\u003Cth\u003Eガバナンス責任\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E最高データ責任者（CDO）\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eエンタープライズデータ戦略を設定し、ガバナンスプログラムをサポートし、データ成果に対する経営責任を担う\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eデータ所有者\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eデータドメイン、データプロダクト、メトリクス、または重要なデータセットに対するビジネス上の権限を持つ\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eデータスチュワード\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eドメインまたはアセットの定義、品質基準、メタデータ、アクセスガイダンスの維持、および問題解決を担当する\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eデータカストディアン\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eデータが保存、処理、保護、維持される技術的環境を管理する\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eデータ保護責任者\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E規制対象の個人データに対するプライバシー義務を監督する（特に法律で正式なプライバシーロールが求められる場合）\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E最高プライバシー責任者（CPO）\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003E組織全体における広範なプライバシー戦略、ポリシー、リスク管理を主導する\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eガバナンスアナリスト\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eポリシーの文書化、カタログのメンテナンス、レポート作成、問題の追跡、ガバナンスメトリクスのサポートを行う\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eガバナンス委員会\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003Eドメイン間の紛争を解決し、標準を承認し、ガバナンス業務の優先順位を決定する\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003E具体的な役割構成は組織によって異なりますが、一貫しているのは、ガバナンスにはビジネス上の権限と技術的な管理の両方が必要であるということです。大企業では、ガバナンス評議会、文書化されたエスカレーションパス、ドメインレベルのスチュワードシップを通じて、これを正式な制度にすることがよくあります。\u003C/p\u003E\r\n\u003Cp\u003Eここで重要なのが、実務における詳細な取り決めです。たとえば、2つのチーム間でメトリクスの定義に相違がある場合、プログラム側で誰が決定権を持つかを定義しておく必要があります。規制対象のフィールドに新しいマスキングポリシーが必要な場合、スチュワードはどのセキュリティまたはプライバシーの担当者を関与させるべきかを把握していなければなりません。データ品質の問題が下流のレポートに影響を与える場合、リネージによってその影響範囲を示し、スチュワードシップによって誰が修正を担当するかを決定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"},"title_data-governance-process-and-strategy":{"id":"title-v2-ca5a7c78eb","additionalClasses":"anchor-title anchor-title--data-governance-process-and-strategy","type":"heading2","lines":["データガバナンスのプロセスと戦略"],":type":"snowflake-site/components/title-v2"},"text_data-governance-process-and-strategy_0":{"id":"text-9b0bc2d84b","text":"\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/data-governance/strategy/\" target=\"_self\"\u003Eデータガバナンス戦略\u003C/a\u003Eの実践的なアプローチは通常、次の手順で行われます。\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003E優先ドメインの選択：\u003C/b\u003E顧客の360度ビュー、財務報告、規制対象データ、サプライチェーン分析、AIトレーニングデータなど、ビジネスリスクやコンプライアンス上の対応が最も急がれる領域から着手するのがします。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E重要なデータアセットのインベントリ作成：\u003C/b\u003Eそのドメインで最も重要なテーブル、ビュー、ファイル、メトリクス、レポートを特定します。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E機密データおよび規制対象データの分類：\u003C/b\u003EPII、PHI、支払いデータ、機密記録、その他の管理対象データタイプにタグを付けます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eオーナーとスチュワードの割り当て：\u003C/b\u003E定義、アクセスに関する意思決定、品質基準、問題解決の責任を持つ担当者を指名します。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eポリシーの定義：\u003C/b\u003E アクセス、マスキング、保持、共有、AIの使用、例外に関するルールを確立します。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eリネージと品質シグナルの取得：\u003C/b\u003E重要なデータフローを追跡し、鮮度、完全性、妥当性を監視できます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eアクセスと使用状況の確認：\u003C/b\u003E監査ログを使用して、誰が機密データにアクセスしたか、またポリシーが意図した通りに機能したかを検証します。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eドメインごとの規模拡大：\u003C/b\u003Eプログラムの拡大に合わせて、標準、テンプレート、これまでに得られた教訓を再利用します。\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Cp\u003E成功を測るための要素には、カタログの導入率、所有者が割り当てられている重要なデータ資産の割合、分類のカバー率、ポリシーのカバー率、品質問題の解決時間、アクセスレビューの完了率、監査指摘事項の削減などが挙げられます。\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/data-governance/strategy/best-practices/\" target=\"_self\"\u003Eデータガバナンスのベストプラクティスの詳細はこちらをご覧ください →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_data-governance-and-regulatory-compliance":{"id":"title-v2-bd5cd94ca1","additionalClasses":"anchor-title anchor-title--data-governance-and-regulatory-compliance","type":"heading2","lines":["データガバナンスと規制コンプライアンス"],":type":"snowflake-site/components/title-v2"},"text_data-governance-and-regulatory-compliance_0":{"id":"text-409722b02a","text":"\u003Cp\u003E規制当局による監査では、組織がこれらを確実に証明できるかどうかが問われます。具体的には、どのような規制対象データを保持しているか、誰がアクセスしたか、どのように保護されていたか、どれだけの期間保存されていたか、そして必要なコントロールが適用されていたかという点です。ガバナンスは、データ資産をポリシー、所有者、管理、および監査証跡に結び付けることで、\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/fundamentals/governance-risk-compliance/\" target=\"_self\"\u003EGRC\u003C/a\u003Eをサポートします。\u003C/p\u003E\r\n\u003Cp\u003E以下にいくつかの例を示します。\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003Eガバナンスの義務\u003C/th\u003E\r\n\u003Cth\u003E規制または標準の例\u003C/th\u003E\r\n\u003Cth\u003Eガバナンスによって証明できること\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E個人データと機密データの保護\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EGDPR、CCPA/CPRA、LGPD、PDPA、HIPAA\u003C/td\u003E\r\n\u003Ctd\u003Eどのような個人データが存在するか、そのデータの保存場所、アクセス権を持つユーザー、および権利要求への対応方法\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eレポートの整合性の維持\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ESOX、BCBS 239、バーゼルIII\u003C/td\u003E\r\n\u003Ctd\u003E財務データやリスクデータの定義、変換、管理、照合方法\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003E決済データの保護\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EPCI-DSS\u003C/td\u003E\r\n\u003Ctd\u003Eカード会員データの表示場所と適用される管理\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003Eオペレーショナルレジリエンスの管理\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EDORA、NIS2\u003C/td\u003E\r\n\u003Ctd\u003E重要なシステム、サードパーティ、情報通信テクノロジー（ICT）リスクの監視方法\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAI関連のデータ使用のガバナンス\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EEU AI法と新たなAI関連法\u003C/td\u003E\r\n\u003Ctd\u003EAIシステムで使用されるデータの種類、その使用が適切かどうか、および高リスクな利用の制御方法\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://artificialintelligenceact.eu/\"\u003EEU AI法\u003C/a\u003Eは、ガバナンスのタイムラインが重要である理由を示す好例です。この規制は段階的に適用され、2025年2月2日からは一般規定と禁止事項が、2025年8月2日からは汎用AIに関する規則が適用され、その後2027年8月2日にかけて拡大展開されます。AIシステムでガバナンスされたエンタープライズデータを使用する組織にとって、トレーニングデータの証明、機密データの分類、アクセス権限、および監査可能性を把握することは実務上不可欠となります。\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003Eわずか数クリックで機密データを簡単に検出し、その全体像を把握できるSnowflakeの機能（機密データモニタリングなど）について、動画でご確認ください。\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_data-governance-and-regulatory-compliance_0":{"id":"embed-5bb9eca110","youtubeVideoId":"HeZUJ1aBuz0","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_why-run-data-governance-on-snowflake":{"id":"title-v2-0edf87923d","additionalClasses":"anchor-title anchor-title--why-run-data-governance-on-snowflake","type":"heading2","lines":["Snowflakeでデータガバナンスを実行する理由"],":type":"snowflake-site/components/title-v2"},"text_why_run_data_go":{"id":"text-ea3643f7e5","text":"\u003Cp\u003Eデータの保存、処理、共有、使用が行われる場所の近くにポリシー、メタデータ、リネージ、品質モニタリング、アクセス制御を配置することで、データガバナンスの維持が容易になります。Snowflakeのガバナンス機能は、組織がデータ、アプリケーション、AIワークロードを管理するのと同じ環境に組み込まれているので、同期を維持しなければならない別のツールを介して適用する必要はありません。\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E組み込みのリネージを備えた統合カタログ：\u003C/b\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/product/features/horizon/\"\u003ESnowflake Horizonカタログ\u003C/a\u003Eは、カタログ、列レベルのリネージ、アクティブメタデータ、ポリシー適用を単一のインターフェイスで提供します。Snowflake Horizonカタログを使用すると、ガバナンスコンテキストがデータの近くに存在するため、個別のカタログツールを用意する必要性がなくなります。\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003Eコンプライアンスを意識した設計：\u003C/b\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://trust.snowflake.com/\"\u003ESnowflakeのCompliance Center\u003C/a\u003Eは、セキュリティ態勢のモニタリングに加えて、\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.hhs.gov/hipaa/index.html\"\u003EHIPAA\u003C/a\u003E、PCI DSS、SOC 2 Type II、ISO 27001、\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.fedramp.gov/\"\u003EFedRAMP\u003C/a\u003E Moderate、IRAPなどの規格をサポートする証明書を提供します。実装によっては、特定の機能やお客様による設定が必要となる場合があります。\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E機密データに対するポリシー：\u003C/b\u003Eダイナミックデータマスキング、行アクセスポリシー、タグベースのマスキング、および外部トークン化により、データレイヤーで保護管理策が適用されます。適切に設定することで、分類機能により、クエリ、アプリケーション、共有、およびAIワークロード全体でポリシーを一貫して適用できるようになります。\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E監査可能な使用状況：\u003C/b\u003Eアクセス履歴とクエリ履歴は、監査や規制審査をサポートする詳細なアクセス記録や変換記録の取得に役立ちます。適切に設定されていれば、組織は機密性の高い列に誰がいつアクセスしたかをより簡単に特定できます。\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EAIワークフロー向けのガバナンス構築：\u003C/b\u003ECortex Guardは、LLMの入出力にポリシー制御を適用して、不適切なモデルコンテキストに機密データが入り込むリスクを軽減します。データメトリック関数を使用してトレーニングデータの品質を継続的に監視できるため、AIシステムに供給されるデータが、レポートに供給されるデータと同じ基準を満たすようになります。\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E一貫して共有されたガバナンス：\u003C/b\u003Eセキュアなデータ共有、リスティング、データクリーンルームにより、セキュリティの境界を越えて生データを移動することなく、パートナーや外部コラボレーターとインサイトを共有できます。データがプラットフォームの外に出ないため、ガバナンスコントロールを一貫して適用しやすくなります。\u003C/p\u003E\r\n\u003Cp\u003Eこれらの機能が連携することで、データウェアハウスやデータレイク、Icebergなどのオープンテーブルフォーマット、さらにはアプリケーションやAIに至るまで、すべての環境を一元管理する単一のガバナンス基盤が実現します。これにより、データが新しい環境へ移動するたびに、セキュリティコントロールを何度も再構築する手間を解消できます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_build-governance-as-an-operating-discipline":{"id":"title-v2-4d9a57a2e4","additionalClasses":"anchor-title anchor-title--build-governance-as-an-operating-discipline","type":"heading2","lines":["運用規律としてのガバナンスの構築"],":type":"snowflake-site/components/title-v2"},"text_build-governance-as-an-operating-discipline_0":{"id":"text-83a21d0ded","text":"\u003Cp\u003Eガバナンスの成熟度は、できているか、いないかの二択で測れるものではありません。多くの組織では、特定の領域はカバーできていても、別の領域には管理のギャップが残っているのが実態です。そして厄介なことに、そのギャップは監査やトラブルといった最悪のタイミングで顕在化する傾向があります。たとえばコンプライアンス監査によって、一度も分類されていなかった列が発覚するかもしれません。あるいは、AIの出力結果に疑問が呈されたものの、どのデータがその出力に寄与したのかを誰も追跡できないという事態が起こる可能性もあります。\u003C/p\u003E\r\n\u003Cp\u003Eそのような事態を回避できているのは、\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/data-governance/processes/implementation/\" target=\"_self\"\u003Eガバナンス実装\u003C/a\u003Eを完了した組織ではありません。うまくこの事態を回避している組織というのは、オーナーシップ、リネージ、分類、監査コントロールをはじめから日々の運用プロセスに組み込んでいます。そのため、当局の監査や社内からの確認が入ったときには、その場ですぐに提示できる確実な証拠がすでに手元に揃っているのです。\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/en/data-governance/use-cases/\" target=\"_self\"\u003Eデータガバナンスのユースケースを見る →\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_build-governance-as-an-operating-discipline_0":{"id":"text-d5a51819ed","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003E重要なポイント\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003Eデータガバナンスは、一回限りのプロジェクトではなく、データの信頼性と安全性を大規模に維持し続けるための継続的な運用です。所有権やルールを明確にし、リネージや品質を可視化しておくことで、組織はトラブルが起きる前に、データに関する重要課題へ先手を打つことができるようになります。データとAIの利用が広がるなかで、ガバナンスを独立したコントロールレイヤーとして扱うのではなく、日常的なワークフローに組み込むことで、チームはリスクを軽減しながらより迅速に行動できるようになります。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout_what-is-data-governance_0","text_overview_0","title_what-is-data-governance","text_what-is-data-governance_0","title_why-data-governance-matters-now","text_why-data-governance-matters-now_0","quote_why-data-governance-matters-now_0","text_why-data-governance-matters-now_1","callout_why-data-governance-matters-now_0","title_data-governance-for-ai","text_data-governance-for-ai_0","yt_data-governance-for-ai_0","title_data-governance-principles","text_data-governance-principles_0","title_data-governance-frameworks-and-standards","text_data-governance-frameworks-and-standards_0","title_data-governance-operating-models","text_data-governance-operating-models_0","card_v2_data-governance-operating-models_0","title_the-core-components-of-data-governance","text_the-core-components-of-data-governance_0","card_v2_the-core-components-of-data-governance_0","title_data-stewardship-and-governance-roles","text_data-stewardship-and-governance-roles_0","title_data-governance-process-and-strategy","text_data-governance-process-and-strategy_0","title_data-governance-and-regulatory-compliance","text_data-governance-and-regulatory-compliance_0","yt_data-governance-and-regulatory-compliance_0","title_why-run-data-governance-on-snowflake","text_why_run_data_go","title_build-governance-as-an-operating-discipline","text_build-governance-as-an-operating-discipline_0","callout_build-governance-as-an-operating-discipline_0"]},"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-067c006072",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-0619bf80fe","additionalClasses":"eyebrow-text","text":"\u003Cp\u003E本ガイドの内容\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-f58da0a283","additionalClasses":"page-toc","text":"\u003Cul\u003E\n\u003Cli data-anchor=\"what-is-data-governance\"\u003Eデータガバナンスとは\u003C/li\u003E\n\u003Cli data-anchor=\"why-data-governance-matters-now\"\u003E今、データガバナンスが重要である理由\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-for-ai\"\u003EAI向けデータガバナンス\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-principles\"\u003Eデータガバナンスの原則\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-frameworks-and-standards\"\u003Eデータガバナンスのフレームワークと標準\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-operating-models\"\u003Eデータガバナンスの運用モデル\u003C/li\u003E\n\u003Cli data-anchor=\"the-core-components-of-data-governance\"\u003Eデータガバナンスの主な構成要素\u003C/li\u003E\n\u003Cli data-anchor=\"data-stewardship-and-governance-roles\"\u003Eデータスチュワードシップとガバナンスの役割\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-process-and-strategy\"\u003Eデータガバナンスのプロセスと戦略\u003C/li\u003E\n\u003Cli data-anchor=\"data-governance-and-regulatory-compliance\"\u003Eデータガバナンスと規制コンプライアンス\u003C/li\u003E\n\u003Cli data-anchor=\"why-run-data-governance-on-snowflake\"\u003ESnowflakeでデータガバナンスを実行する理由\u003C/li\u003E\n\u003Cli data-anchor=\"build-governance-as-an-operating-discipline\"\u003E運用規律としてのガバナンスの構築\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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