{"id":30027,"date":"2026-02-08T05:01:52","date_gmt":"2026-02-08T05:01:52","guid":{"rendered":"https:\/\/nekudos.com\/sap-data-quality-ai\/"},"modified":"2026-02-08T05:02:06","modified_gmt":"2026-02-08T05:02:06","slug":"sap-data-quality-ai","status":"publish","type":"post","link":"https:\/\/nekudos.com\/en\/sap-data-quality-ai\/","title":{"rendered":"Yapay Zeka ile SAP Veri Kalitesini S\u00fcrekli Art\u0131rman\u0131n Y\u00f6ntemi"},"content":{"rendered":"

SAP Data Quality AI ile Artar m\u0131<\/h1>\n
\n

Giri\u015f<\/strong><\/h3>\n

SAP Data Quality AI ile Artar m\u0131 sorusu, g\u00fcn\u00fcm\u00fczde veri odakl\u0131 i\u015fletmelerin en kritik g\u00fcndemlerinden birini olu\u015fturuyor. SAP ekosisteminde veri kalitesi (data quality), karar destek sistemlerinden otomasyon s\u00fcre\u00e7lerine kadar t\u00fcm operasyonel zinciri etkiler. Yapay zeka (AI) ise bu kaliteyi yaln\u0131zca do\u011frulamakla kalmaz, ayn\u0131 zamanda s\u00fcrekli olarak iyile\u015ftirir. Arama niyeti, AI\u2019nin SAP i\u00e7inde veri g\u00fcvenilirli\u011fini ve tutarl\u0131l\u0131\u011f\u0131n\u0131 nas\u0131l art\u0131rd\u0131\u011f\u0131n\u0131 teknik d\u00fczeyde anlamakt\u0131r.<\/p>\n

\n

SAP Data Quality AI ile Artar m\u0131 tan\u0131m\u0131<\/strong><\/h3>\n

SAP Data Quality AI, yapay zekan\u0131n SAP sistemlerindeki veri do\u011frulu\u011funu, tutarl\u0131l\u0131\u011f\u0131n\u0131 ve b\u00fct\u00fcnl\u00fc\u011f\u00fcn\u00fc s\u00fcrekli analiz eden ve optimize eden bir yap\u0131 olarak tan\u0131mlanabilir. Burada ama\u00e7 yaln\u0131zca hatal\u0131 verileri bulmak de\u011fil, veri ak\u0131\u015f\u0131n\u0131 \u00f6\u011frenerek gelecekteki hatalar\u0131 tahmin edebilmek ve \u00f6nleyebilmektir. Bu yakla\u015f\u0131m, klasik ETL do\u011frulama mant\u0131\u011f\u0131ndan \u00e7\u0131karak \u00f6ng\u00f6r\u00fcl\u00fc bir kalite y\u00f6netimi s\u00fcreci olu\u015fturur.<\/p>\n

\n

sap data quality ai nas\u0131l \u00e7al\u0131\u015f\u0131r<\/strong><\/h3>\n

SAP Data Quality AI, hem SAP BTP \u00fczerinde hem de harici analitik servislerle entegre bi\u00e7imde \u00e7al\u0131\u015fabilir. S\u00fcre\u00e7 genellikle veri profil \u00e7\u0131kar\u0131m\u0131, kaynak analizi, model e\u011fitimi ve kalite \u00f6l\u00e7\u00fcm\u00fc a\u015famalar\u0131ndan olu\u015fur. AI burada kalite metriklerini dinamik olarak yeniden tan\u0131mlar.<\/p>\n

Temel parametreler ve ayarlar<\/strong><\/h4>\n

Temel parametreler; veri do\u011fruluk e\u015fi\u011fi, tutarl\u0131l\u0131k oran\u0131, referans b\u00fct\u00fcnl\u00fc\u011f\u00fc ve zaman damgas\u0131 analizi gibi \u00f6l\u00e7\u00fctleri i\u00e7erir. SAP Data Intelligence veya SAP AI Core \u00fczerinden modeller bu parametrelere g\u00f6re ayarlanabilir. \u00d6rne\u011fin, bir sat\u0131\u015f verisi modelinde m\u00fc\u015fteri kimli\u011fi tutars\u0131zl\u0131klar\u0131 AI taraf\u0131ndan an\u0131nda tespit edilip d\u00fczeltme \u00f6nerileri \u00fcretilir.<\/p>\n

S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h4>\n

En s\u0131k hata, AI e\u011fitim verisinin d\u00fc\u015f\u00fck kalitede olmas\u0131 ve modelin ger\u00e7ek sistemi temsil etmemesidir. Bunun \u00f6n\u00fcne ge\u00e7mek i\u00e7in e\u011fitim setleri s\u00fcrekli g\u00fcncellenmeli, kaynak sistemlerde veri profil analizi yap\u0131lmal\u0131d\u0131r. Ayr\u0131ca kalite kurallar\u0131n\u0131n manuel olarak de\u011fil, AI temelli kurallar motoru \u00fczerinden y\u00f6netilmesi \u00f6nerilir.<\/p>\n

Ger\u00e7ek sistemlerde uygulama \u00f6rnekleri<\/strong><\/h4>\n

Ger\u00e7ek d\u00fcnyada, SAP S\/4HANA ile SAP BTP \u00fczerinde \u00e7al\u0131\u015fan bir kalite kontrol mod\u00fcl\u00fc, yapay zekan\u0131n istatistiksel modelleme g\u00fcc\u00fcyle m\u00fc\u015fteri ana veri hatalar\u0131n\u0131 y\u00fczde 80 oran\u0131nda azaltabilir. AI model, her veri g\u00fcncellemesinde do\u011fruluk skorlar\u0131n\u0131 yeniden hesaplar ve anormallikleri anl\u0131k uyar\u0131larla raporlar.<\/p>\n

\n

Teknik a\u00e7\u0131klama (derin seviye)<\/strong><\/h3>\n

AI destekli veri kalitesi iyile\u015ftirme s\u00fcreci, \u00f6ncelikle SAP BTP \u00fczerinde \u00e7al\u0131\u015fan veri boru hatlar\u0131n\u0131n izlenmesiyle ba\u015flar. Bu hatlar \u00fczerinden gelen veriler AI modellerine aktar\u0131l\u0131r. Model, NLP tabanl\u0131 veri etiketleme ve anomaly detection algoritmalar\u0131 kullanarak hatal\u0131 tutars\u0131zl\u0131klar\u0131 belirler. E\u011fitim s\u00fcreci, TensorFlow veya SAP AI Launchpad mod\u00fclleriyle y\u00fcr\u00fct\u00fcl\u00fcr. Buradaki teknik yap\u0131, veriyi statik de\u011fil dinamik bir varl\u0131k olarak ele al\u0131r ve kalite g\u00f6stergelerini s\u00fcrekli yeniden hesaplar.<\/p>\n

\n

\u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n
    \n
  • Performans:<\/strong> Hatal\u0131 veri sorgular\u0131 sistem yan\u0131t s\u00fcrelerini uzat\u0131r. <\/li>\n
  • G\u00fcvenilirlik:<\/strong> Kaliteli veri tahmin modellerinin do\u011fruluk oran\u0131n\u0131 art\u0131r\u0131r. <\/li>\n
  • Maliyet:<\/strong> Manuel kontrol maliyetlerini \u00f6nemli \u00f6l\u00e7\u00fcde d\u00fc\u015f\u00fcr\u00fcr. <\/li>\n
  • \u00d6l\u00e7ekleme:<\/strong> B\u00fcy\u00fcyen veri hacmi AI temelli kalite kontrolle y\u00f6netilebilir. <\/li>\n
  • Otomasyon:<\/strong> Onay s\u00fcre\u00e7leri AI temelli do\u011frulama ile h\u0131zlan\u0131r. <\/li>\n
  • Karar alma:<\/strong> Y\u00f6netim kararlar\u0131 y\u00fcksek do\u011frulukta veriyle beslenir. <\/li>\n
  • Operasyonel verimlilik:<\/strong> Veri hatalar\u0131n\u0131n \u00f6nceden tespiti s\u00fcre\u00e7 ak\u0131\u015f\u0131n\u0131 optimize eder.<\/li>\n<\/ul>\n
    \n

    Bu kavram NeKuDos Teknoloji i\u00e7inde nas\u0131l uygulan\u0131r<\/strong><\/h3>\n

    NeKuDos Teknoloji, SAP BTP \u00fczerinde veri hatt\u0131 mimarilerini AI destekli kalite y\u00f6netim mod\u00fclleriyle b\u00fct\u00fcnle\u015ftirir. Bu mod\u00fcller, veriyi merkezi bir kalite motorunda de\u011ferlendirir. AI algoritmalar\u0131 SAP entegrasyon katmanlar\u0131nda \u00e7al\u0131\u015farak verinin sistemden sisteme ta\u015f\u0131n\u0131rken do\u011frulu\u011funu korur. \u00d6zellikle m\u00fc\u015fteri ve tedarik\u00e7i master data y\u00f6netimi s\u00fcre\u00e7lerinde bu metodoloji uygulanarak t\u00fcm referans alanlar\u0131 s\u00fcrekli do\u011frulan\u0131r.<\/p>\n

    \n

    SAP dan\u0131\u015fmanlar\u0131, IT y\u00f6neticileri, CTOlar i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
      \n
    1. Sorun:<\/strong> Bir \u00fcretim \u015firketi SAP S\/4HANA sisteminde hatal\u0131 stok verileri nedeniyle lojistik planlamada gecikmeler ya\u015f\u0131yor. <\/li>\n
    2. Ba\u011flam:<\/strong> Veri bir\u00e7ok kaynaktan geliyor ve manuel kontrolle hatalar fark edilmiyor. <\/li>\n
    3. Kavram\u0131n uygulanmas\u0131:<\/strong> SAP Data Quality AI modeli, ge\u00e7mi\u015f stok hareketlerini analiz ederek tutars\u0131z referanslar\u0131 belirliyor. <\/li>\n
    4. Sonu\u00e7:<\/strong> Model, otomatik d\u00fczeltme \u00f6nerileriyle veri yap\u0131s\u0131n\u0131 normalize ediyor. <\/li>\n
    5. \u0130\u015f etkisi:<\/strong> Operasyonel planlama do\u011fru stok verisine dayan\u0131yor, ERP performans\u0131 art\u0131yor, hata maliyetleri d\u00fc\u015f\u00fcyor.<\/li>\n<\/ol>\n
      \n

      S\u0131k yap\u0131lan hatalar ve en iyi uygulamalar<\/strong><\/h3>\n

      Yayg\u0131n hatalardan biri, AI modellerinin yaln\u0131zca bir defa e\u011fitilip s\u00fcrekli kullan\u0131lmaya devam etmesidir. En iyi uygulama, modelin periyodik yeniden e\u011fitimi ve veri kaynaklar\u0131n\u0131n dinamik olarak izlenmesidir. Ayr\u0131ca SAP veri entegrasyon katmanlar\u0131nda do\u011frulama kurallar\u0131n\u0131n sadece ERP taraf\u0131nda de\u011fil, veri ak\u0131\u015f\u0131 ve streaming katman\u0131nda da uygulanmas\u0131 gerekir. Metrikler (accuracy, completeness, timeliness) ayr\u0131 ayr\u0131 izlenmeli ve raporlanmal\u0131d\u0131r.<\/p>\n

      \n

      Sonu\u00e7<\/strong><\/h3>\n

      SAP Data Quality AI, i\u015fletmelerin veri g\u00fcvenilirli\u011fini sadece analiz eden de\u011fil, s\u00fcrekli geli\u015ftiren bir teknolojidir. AI destekli modeller kaliteyi zaman i\u00e7inde art\u0131rarak SAP ekosisteminde karar alma ve operasyonel s\u00fcre\u00e7lere do\u011frudan etki eder. NeKuDos Teknoloji\u2019nin sundu\u011fu mimari uygulamalar, bu d\u00f6n\u00fc\u015f\u00fcm\u00fcn teknik olarak nas\u0131l ger\u00e7ekle\u015febilece\u011fini g\u00f6steren somut \u00f6rnekler sunar. Ba\u015ftan sona de\u011ferlendirildi\u011finde, veri kalitesi art\u0131k bir kontrol de\u011fil, bir \u00f6\u011frenme s\u00fcreci haline gelir.<\/p>","protected":false},"excerpt":{"rendered":"

      SAP Data Quality AI ile Artar m\u0131 Giri\u015f SAP Data Quality AI ile Artar m\u0131 sorusu, g\u00fcn\u00fcm\u00fczde veri odakl\u0131 i\u015fletmelerin en kritik g\u00fcndemlerinden birini olu\u015fturuyor. SAP ekosisteminde veri kalitesi (data quality), karar destek sistemlerinden otomasyon s\u00fcre\u00e7lerine kadar t\u00fcm operasyonel zinciri etkiler. Yapay zeka (AI) ise bu kaliteyi yaln\u0131zca do\u011frulamakla kalmaz, ayn\u0131 zamanda s\u00fcrekli olarak iyile\u015ftirir.…<\/p>","protected":false},"author":2,"featured_media":30028,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sitemap_exclude":false,"_sitemap_priority":"","_sitemap_frequency":"","footnotes":""},"categories":[122],"tags":[],"class_list":["post-30027","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sap","category-122","description-off"],"yoast_head":"\nYapay Zeka ile SAP Veri Kalitesini S\u00fcrekli Art\u0131rman\u0131n Y\u00f6ntemi - NeKuDos Teknoloji<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/nekudos.com\/en\/sap-data-quality-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Yapay Zeka ile SAP Veri Kalitesini S\u00fcrekli Art\u0131rman\u0131n Y\u00f6ntemi - NeKuDos Teknoloji\" \/>\n<meta property=\"og:description\" content=\"SAP Data Quality AI ile Artar m\u0131 Giri\u015f SAP Data Quality AI ile Artar m\u0131 sorusu, g\u00fcn\u00fcm\u00fczde veri odakl\u0131 i\u015fletmelerin en kritik g\u00fcndemlerinden birini olu\u015fturuyor. 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SAP ekosisteminde veri kalitesi (data quality), karar destek sistemlerinden otomasyon s\u00fcre\u00e7lerine kadar t\u00fcm operasyonel zinciri etkiler. 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