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[...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${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 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aem-GridColumn--default--12"},"columnCount":12,"id":"why",":items":{"title_v2":{"id":"title-v2-2e591695f5","additionalClasses":"headline-decoration","type":"heading2","lines":["データ品質が重要な理由"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-ffdfd503da","text":"\u003Cp\u003Eデータ品質は、ビジネス、従業員、顧客を支えるあらゆるデジタルプロセスが依存しているため、きわめて重要です。高品質なデータにより、企業は顧客の信頼を得ながら、より戦略的で効率的な事業運営を実現できます。高品質なデータがなければ、欠陥のあるインサイト、機会の逸失、経済的損失に苦労する可能性があります。\u003C/p\u003E\n\u003Cp\u003Eこうした損失は、多くの場合、ビジネスのさまざまな場面に潜む小さな日常的な出来事から始まります。たとえば、あなたがセールス担当者として重要なクロージングミーティングに参加しているとします。モバイルのCRMアプリを開いて、顧客に関する直近のインテリジェンスを入手します。その後の展開は、2つの道に分かれます。もしデータが最新で関連性が高く、価値のあるものであれば、顧客に強い印象を与え、成約へと導くことができるでしょう。しかし、もし古く不正確なデータに基づいて提案を行えば、準備不足という印象を顧客に与え、対話は途絶え、あらゆる販売機会を逸してしまうことになりかねません。\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",":type":"snowflake-site/components/container"},"container_copy_copy__1444615495":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"id":"understanding",":items":{"title_v2":{"id":"title-v2-daafb6c479","additionalClasses":"headline-decoration","type":"heading2","lines":["データ品質の次元の理解"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-aea41daf6b","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E組織は、以下を含む少なくとも6つのデータ品質の次元を調査してデータ品質を評価する必要があります。 \u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch4\u003E1.正確性\u003C/h4\u003E\r\n\u003Cp\u003Eデータは正しい内容になっていますか。組織は、使用するデータが実際の価値を反映したものであることを確認する必要があります。たとえば、紛れもなく正確なデータがあれば、\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/retail-consumer-goods/\"\u003E小売企業\u003C/a\u003Eは毎回迅速かつ正確に返金を処理できるため、顧客の信頼が強化されます。\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/healthcare-and-life-sciences/\"\u003E病院\u003C/a\u003Eのようなヘルスケア組織は、医師や看護師が診療現場で常に正しい情報を取得できます。また、\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/financial-services/\"\u003E銀行\u003C/a\u003Eは信用評価が信頼できる収入と返済履歴に基づいているため、自信を持って融資を承認できます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch4\u003E2.完全性\u003C/h4\u003E\r\n\u003Cp\u003E必要なデータポイントがすべて揃っていますか。必要な情報がすべて揃っていれば、企業は勘や推量に頼ることなく自信を持ってエンドツーエンドの意思決定を行えます。たとえば、乗客の記録をすべて保持している航空会社では、需要予測の精度向上、路線の最適化、高コストのオーバーブッキングの回避が可能になります。同様に、包括的な入院データを持つ病院は、患者をより効率的に適切なベッドに割り当てることで、待ち時間を削減し、全体的なケアを強化できます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch4\u003E3.一貫性\u003C/h4\u003E\r\n\u003Cp\u003Eすべてのシステム、フォーマット、ソースにわたってデータが統一されていますか。一貫したデータがあれば、組織は混乱する矛盾を回避し、すべてのチームメンバーが同じプレイブックで作業していることを把握して、自信を持って業務を進めることができます。たとえば、地方と国のデータベースで一貫したワクチン接種記録を保持している公衆衛生機関は、感染症の集団発生時の事態に誰が保護されているかを迅速に確認し、リソースを最も必要な場所に配置できます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch4\u003E4.適時性\u003C/h4\u003E\r\n\u003Cp\u003Eデータは最新の状態で利用できますか。タイムリーな情報により、組織は機会を捉え、問題に遅延なく対応することができます。たとえば、最新の価格設定やプロモーションデータを持つ営業担当者は、その場でオファーを調整して商談成立に役立てることができます。同様に、顧客の最近のやり取りにアクセスできるカスタマーサービス担当者は、問題をより迅速に解決し、顧客のニーズを予測することもできます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch4\u003E5.妥当性\u003C/h4\u003E\r\n\u003Cp\u003Eデータは定義されたルール、フォーマット、ビジネス要件に準拠していますか。妥当なデータは、正しい日付形式、標準化された製品コード、アカウント番号やIDなどの必須フィールド長など、組織のデータ品質基準に従っています。\u003C/p\u003E\r\n\u003Cp\u003E妥当なデータがあれば、システムはよりスムーズに稼働し、意思決定を信頼できます。たとえば、口座番号の形式を適用する銀行では、手動での確認なしに支払いを自動的に処理できます。検査結果に標準コードの使用を義務付けている病院では、それらの結果を患者の電子カルテへ直接反映させることが可能です。これにより、ケア提供者がより迅速に情報を参照できるようになります。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Ch4\u003E6.独自性\u003C/h4\u003E\r\n\u003Cp\u003E各レコードは区別され、不要な重複はありませんか。重複のないユニークなデータがあれば、組織は顧客、従業員、パートナー、製品など、追跡が必要なあらゆる情報について信頼できる唯一の情報源を構築できます。こうしたユニークなデータ基盤があることで、より明確なインサイト、スムーズなプロセス、パーソナライズされた顧客アウトリーチが実現します。たとえば、1人の買い物客につき1つのユニークな記録を持つ\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/retail-consumer-goods/\"\u003E小売企業\u003C/a\u003Eは、オンラインと店舗でのアクティビティをリンクして、断片的なプロファイルではなく顧客の包括的な購入履歴に基づいて商品をレコメンドできます。\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",":type":"snowflake-site/components/container"},"container_copy_copy__1978652079":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"id":"common",":items":{"title_v2":{"id":"title-v2-4e4303f12b","additionalClasses":"headline-decoration","type":"heading2","lines":["データ品質の課題"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-5b784a27c3","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E残念ながら、多くの組織がデータ品質に関する煩雑で時間のかかる課題に直面しており、それが収益に悪影響を及ぼす要因となっています。多くの場合、組織はデータを監視、管理、インベントリ、ガバナンス、クリーニングするためのツールを組み合わせて展開し、そのような問題を削減しています。\u003C/p\u003E\n\u003Cp\u003E対処すべき一般的なデータ品質の問題は、次のとおりです。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E不正確なデータ\u003C/h4\u003E\n\u003Cp\u003Eデータ入力時の人的エラー、欠陥のあるデータ収集プロセス、異なるソースからのデータの統合の難しさは、多くの場合に不正確なデータセットをもたらし、ビジネス上の意思決定や運用を妨げます。たとえば、顧客の住所がデータベースに誤って入力されると、出荷が遅延または消失し、追加コストが発生して顧客の不満につながります。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E不完全なデータ\u003C/h4\u003E\n\u003Cp\u003E重要な欠損が1つでも存在すれば、データセット全体の価値と信頼性が損なわれる可能性があります。たとえば、銀行は、収入と支出の全体像を把握できなければ、ローン申込者の信用度を評価できません。高級リゾート地を販売するマーケターも、記録から顧客が雇用されているのか失業しているのかがわからなければ、同じような状況に陥る可能性があります。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E一貫性のないデータ\u003C/h4\u003E\n\u003Cp\u003Eファイル名、日付、その他の定義データが異なる形式で保存されている場合、情報の検索や照合が困難になります。そのため、後でデータを結合または分析する際に、ビジネスエラーや誤った意思決定につながりかねません。たとえば、\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/financial-services/insurance/\"\u003E保険\u003C/a\u003Eの顧客の住所が請求システムでは更新されても保険契約データベースでは更新されないと、請求の支払いが遅延する場合があります。\u003C/p\u003E\n\u003Cp\u003Eデータの欠落は、企業の財務、業務、パブリックイメージに多大な損害ももたらします。財務モデルや予測の信頼性が低下し、自動化されたプロセスが停滞し、採用や顧客エンゲージメントのプロセスにバイアスが発生し、信頼やブランド認知が損なわれます。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E古いデータ\u003C/h4\u003E\n\u003Cp\u003E多くの場合、データの準備プロセスには非常に時間がかかるため、従業員、顧客、その他のユーザーに届く頃にはすでに古いものとなっており、複雑化につながります。たとえば、セールスチームが古いデータで運用している場合、顧客に対して守れない約束をする可能性があります。更新の滞った患者記録に依存する病院では、直近のアレルギー情報の変更を見落とすリスクがあり、ひいては診療の質や患者の安全を損なう事態を招きかねません。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E無効なデータ\u003C/h4\u003E\n\u003Cp\u003E無効な入力は時間を浪費し、不要な作業を生み出し、場合によっては規制違反を引き起こす可能性があります。たとえば、銀行のシステムにおいて、融資金額のマイナスや借り手IDの欠落などの無効な値が許容されているとします。その場合、財務リスクのエクスポージャーや資本流動性について誤った報告を行い、業界の報告基準を逸脱するおそれがあります。同様に、病院システムが標準的な医療コードなしで検査結果を受け入れているとします。その結果が患者の記録に正確に統合されず、治療が遅延し、システムへの信頼が損なわれる可能性があります。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003Eデータの重複\u003C/h4\u003E\n\u003Cp\u003E複数のユーザーやデータソースが同じ情報をシステムのさまざまな部分に入力した場合、非効率、データ整合性の欠如、不要なコストが急激に発生します。たとえば、サプライヤーレコードが重複している\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/manufacturing/\"\u003E製造企業\u003C/a\u003Eは、誤って同じ請求書を支払ったり、同じ製品を複数回注文したりして、時間、コスト、労力を浪費する場合があります。\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",":type":"snowflake-site/components/container"},"container_copy_copy__455068363":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"id":"benefits",":items":{"title_v2":{"id":"title-v2-b8b382949a","additionalClasses":"headline-decoration","type":"heading2","lines":["高品質データのメリット"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-fda4f6c0d2","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003Eこれらの課題が克服されれば、高品質なデータは組織に大きなメリットをもたらします。\u003C/p\u003E\n\u003Cp\u003E最も一般的なメリットは、次のとおりです。 \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E確実な意思決定\u003C/h4\u003E\n\u003Cp\u003E経営幹部や従業員は、信頼できるデータにすぐアクセスできれば、計画や意思決定の指針として常にデータを使用します。その信頼がなければ、無視される可能性が高くなります。たとえば、販売と在庫の状況を把握している小売企業は、在庫を過剰に販売することなく自信を持ってフラッシュプロモーションを開始できます。自社のデータを信頼できる製造企業は、同様に需要を正確に予測してコスト効率の高いジャストインタイムの生産を実行できます。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E業務効率の向上\u003C/h4\u003E\n\u003Cp\u003E優れたクリーンデータにより、チームはワークフローのボトルネックを特定し、生産性やメンテナンスの問題に迅速に対処できます。たとえば、重機企業はリースしているブルドーザーやトラクターをリアルタイムで遠隔監視し、高品質なデータを使用して顧客のためにプロアクティブなサービスを行うことで、サービス効率と機器の寿命を改善できます。 \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E顧客関係の強化\u003C/h4\u003E\n\u003Cp\u003Eセールスとマーケティングの成功は、顧客のセンチメントと行動の変化に関する豊富なインサイトにかかっています。効果的な顧客エンゲージメントと満足度向上には、データ品質の最大化が不可欠です。たとえば、消費者の60%近くが地域経済をサポートするブランドの製品を購入したいと回答する質の高いデータがあれば、企業は広告で地域的な慈善活動をアピールできます。また、「若年層は低糖質飲料を圧倒的に好む」というデータが得られれば、清涼飲料メーカーは、その層に向けた健康志向の新製品の発売やプロモーションへと動き出すかもしれません。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E信頼性の高いAIとアナリティクス\u003C/h4\u003E\n\u003Cp\u003E質の低いデータを使えば、得られるインサイトも低品質になります。高品質のデータにより、\u003Ca href=\"https://www.snowflake.com/ja/product/ai/\"\u003EAI\u003C/a\u003Eと\u003Ca href=\"https://www.snowflake.com/ja/product/analytics/\"\u003Eアナリティクスのツール\u003C/a\u003Eは、ビジネス、製品、顧客関連の重要な意思決定のための信頼できるアドバイザーになります。たとえば、物流企業は自社に高品質なデータがあることを把握できれば、自信を持ってAIやアナリティクスを使用してルートやスケジュールを最適化し、コストを削減しながら定時配送と顧客満足度を最大化できます。\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",":type":"snowflake-site/components/container"},"container_copy_copy__222865509":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"id":"howto",":items":{"title_v2":{"id":"title-v2-b548dd86ff","additionalClasses":"headline-decoration","type":"heading2","lines":["データ品質の改善方法：5つのベストプラクティス"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-2e516bdeb4","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E多くのアプローチはデータ品質を改善します。企業はさまざまなテクノロジーやプロセスを適用して、デジタル記録を効果的に管理しています。組織内のデータ品質を最大化するために使用できる、5つの重要なベストプラクティスを紹介します。  \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E1.データのプロファイリング\u003C/h4\u003E\n\u003Cp\u003Eデータ品質を改善するためには、どんなデータがあるのかを理解する必要があります。まず、データソースの品質と構造を監査して、正確性、完全性、一貫性を評価します。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E2.データ品質ルールの確立\u003C/h4\u003E\n\u003Cp\u003Eデータ品質ルールは、情報が使用に適しているかどうかを判断するための事前定義された基準です。住宅内のすべての梁、電線、配管が安全で健全であることを保証する建築基準と同様の仕組みです。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E3.データクレンジングプロセスの実装\u003C/h4\u003E\n\u003Cp\u003E整備士がエンジンをチューニングして円滑な稼働を維持するように、ツールやプロセスはデータセット内のエラー、不整合、不正確さを見つけて修正するのに役立ちます。\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E4.データ品質管理とモニタリングのツールの使用\u003C/h4\u003E\n\u003Cp\u003Eこれらのツールは、データが正確、完全、一貫しており、ビジネス利用に耐える状態であることを継続的にチェックします。組織がAIエージェントを展開する際には特に重要です。AIエージェントは、利用するデータの品質に依存します。 \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Ch4\u003E5.データ品質の文化の構築\u003C/h4\u003E\n\u003Cp\u003E組織内の誰もが、質の高いデータが不可欠である理由と、そのライフサイクルのあらゆるステージで正確性、一貫性、信頼性を維持するために必要な役割について理解する必要があります。このことは、従業員にデータ品質文化の導入を促すだけでなく、データ品質の問題を認識し、ベストプラクティスに従い、従業員が管理するデータに責任を持つための必須のトレーニングを提供することも意味します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 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