{"id":13931,"date":"2025-09-30T16:57:43","date_gmt":"2025-09-30T14:57:43","guid":{"rendered":"https:\/\/www.leaplytics.de\/?p=13931"},"modified":"2025-09-30T17:22:05","modified_gmt":"2025-09-30T15:22:05","slug":"guc-bi%cc%87-beli%cc%87rsi%cc%87zli%cc%87k-yayilimi-soy-guven-puanlari-ve-kod-olarak-yoneti%cc%87si%cc%87mde-sayisallastirilmis-bi%cc%87r-ri%cc%87sk-cercevesi%cc%87ni%cc%87n-tasarlanmasi","status":"publish","type":"post","link":"https:\/\/www.leaplytics.de\/tr\/guc-bi%cc%87-beli%cc%87rsi%cc%87zli%cc%87k-yayilimi-soy-guven-puanlari-ve-kod-olarak-yoneti%cc%87si%cc%87mde-sayisallastirilmis-bi%cc%87r-ri%cc%87sk-cercevesi%cc%87ni%cc%87n-tasarlanmasi\/","title":{"rendered":"Power BI'da Say\u0131salla\u015ft\u0131r\u0131lm\u0131\u015f Risk \u00c7er\u00e7evesi: Belirsizlik Yay\u0131l\u0131m\u0131, G\u00fcven Puanlar\u0131 ve Kod Olarak Y\u00f6neti\u015fim"},"content":{"rendered":"<p>\u00c7o\u011fu risk \u00e7er\u00e7evesi bozuktur. \u0130\u015fletmeniz ba\u015far\u0131s\u0131z projelerde milyonlar harcarken yaln\u0131zca renk kodlu matrislere ve i\u00e7g\u00fcd\u00fcsel hislere g\u00fcvenirler.<\/p>\n<p>Fortune 500 \u015firketleri i\u00e7in risk sistemleri kurduk ve ayn\u0131 modeli g\u00f6rd\u00fck: ekipler etkileyici g\u00f6r\u00fcnen g\u00fczel g\u00f6sterge tablolar\u0131 olu\u015fturuyor ancak \u00f6nemli olan tek soruyu yan\u0131tlayam\u0131yor - \"Bu projenin ba\u015far\u0131l\u0131 olma olas\u0131l\u0131\u011f\u0131 ger\u00e7ekte nedir?\"<\/p>\n<p>Sorun ekibinizin yetkinli\u011fi de\u011fildir. Sorun, geleneksel risk y\u00f6netiminin belirsizli\u011fi statik bir say\u0131 gibi ele almas\u0131d\u0131r, oysa belirsizlik asl\u0131nda ya\u015fayan, nefes alan ve proje ya\u015fam d\u00f6ng\u00fcn\u00fcz boyunca artan bir canavard\u0131r.<\/p>\n<p>Bu k\u0131lavuz, Power BI'da ger\u00e7ekten \u00e7al\u0131\u015fan bir say\u0131salla\u015ft\u0131r\u0131lm\u0131\u015f risk \u00e7er\u00e7evesinin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir. Teori yok. Laf kalabal\u0131\u011f\u0131 yok. Sadece projeleri zaman\u0131nda ve b\u00fct\u00e7esinde teslim eden \u015firketleri etmeyenlerden ay\u0131ran \u00fc\u00e7 temel bile\u015fen.<\/p>\n<h2>Geleneksel Risk Y\u00f6netimi ile \u0130lgili Sorun<\/h2>\n<p>Herhangi bir proje toplant\u0131s\u0131na girdi\u011finizde ayn\u0131 tiyatroyu g\u00f6r\u00fcrs\u00fcn\u00fcz: bir risk kayd\u0131na da\u011f\u0131lm\u0131\u015f k\u0131rm\u0131z\u0131, sar\u0131 ve ye\u015fil noktalar. Herhangi birine \"orta risk \"in dolar ve zaman \u00e7izelgesi etkisi olarak ger\u00e7ekte ne anlama geldi\u011fini sordu\u011funuzda bo\u015f bak\u0131\u015flarla kar\u015f\u0131la\u015f\u0131rs\u0131n\u0131z.<\/p>\n<p>\u0130\u015fte bu yakla\u015f\u0131mda yanl\u0131\u015f olan \u015fey:<\/p>\n<ul>\n<li><strong>Matematiksel temeli yok:<\/strong> \"Y\u00fcksek risk\" farkl\u0131 insanlar i\u00e7in farkl\u0131 anlamlar ifade eder<\/li>\n<li><strong>Statik d\u00fc\u015f\u00fcnme:<\/strong> Riskler birle\u015fir ve etkile\u015fime girer, ancak \u00e7o\u011fu \u00e7er\u00e7eve bunlar\u0131 izole olaylar olarak ele al\u0131r<\/li>\n<li><strong>Veri soya\u011fac\u0131 yok:<\/strong> Sonu\u00e7lara nas\u0131l ula\u015f\u0131ld\u0131\u011f\u0131n\u0131 izleyemez veya do\u011fruluklar\u0131n\u0131 teyit edemezsiniz<\/li>\n<li><strong>Manuel y\u00f6netim:<\/strong> Risk incelemeleri toplant\u0131larda yap\u0131l\u0131r, kodda de\u011fil<\/li>\n<\/ul>\n<p>Sonu\u00e7 mu? Birdenbire \u00f6yle olmayana kadar \"ye\u015fil\" g\u00f6r\u00fcnen projeler. O zamana kadar, rotay\u0131 d\u00fczeltmek i\u00e7in \u00e7ok ge\u00e7tir.<\/p>\n<p>Farkl\u0131 bir yakla\u015f\u0131ma ihtiyac\u0131m\u0131z vard\u0131. Riski ger\u00e7ek rakamlarla \u00f6l\u00e7en, belirsizli\u011fin proje ba\u011f\u0131ml\u0131l\u0131klar\u0131 boyunca nas\u0131l akt\u0131\u011f\u0131n\u0131 izleyen ve sorunlar\u0131n felakete d\u00f6n\u00fc\u015fmeden \u00f6nce ortaya \u00e7\u0131kmas\u0131 i\u00e7in y\u00f6neti\u015fimi otomatikle\u015ftiren bir yakla\u015f\u0131m.<\/p>\n<h2>Bile\u015fen 1: Belirsizli\u011fin Yay\u0131lmas\u0131 - Risk Matemati\u011finin \u00c7al\u0131\u015ft\u0131r\u0131lmas\u0131<\/h2>\n<p>Belirsizlik yay\u0131l\u0131m\u0131 kula\u011fa karma\u015f\u0131k gelse de kavram basittir: Belirsiz \u015feyleri \u00fcst \u00fcste koydu\u011funuzda, toplam belirsizlik \u00f6ng\u00f6r\u00fclebilir \u015fekillerde artar.<\/p>\n<p>Bunu \u015f\u00f6yle d\u00fc\u015f\u00fcn\u00fcn: A G\u00f6revi 5-10 g\u00fcn ve B G\u00f6revi 3-7 g\u00fcn s\u00fcr\u00fcyorsa, toplam s\u00fcre 8-17 g\u00fcn de\u011fildir. Olas\u0131l\u0131k da\u011f\u0131l\u0131mlar\u0131n\u0131n nas\u0131l birle\u015fti\u011fi nedeniyle matematik daha inceliklidir.<\/p>\n<p>\u0130\u015fte bunu Power BI'da nas\u0131l uygulad\u0131\u011f\u0131m\u0131z:<\/p>\n<h3>Ad\u0131m 1: Olas\u0131l\u0131k Da\u011f\u0131l\u0131mlar\u0131n\u0131 Tan\u0131mlay\u0131n<\/h3>\n<p>\"G\u00f6rev A orta risklidir\" demek yerine, bunu bir olas\u0131l\u0131k da\u011f\u0131l\u0131m\u0131 olarak tan\u0131mlar\u0131z. Bir Beta da\u011f\u0131l\u0131m\u0131 olu\u015fturmak i\u00e7in genellikle \u00fc\u00e7 noktal\u0131 tahminler (iyimser, b\u00fcy\u00fck olas\u0131l\u0131kla, k\u00f6t\u00fcmser) kullan\u0131r\u0131z.<\/p>\n<p>Power BI'da, a\u015fa\u011f\u0131dakiler i\u00e7in hesaplanan s\u00fctunlar olu\u015fturun:<\/p>\n<ul>\n<li>\u0130yimser senaryo (y\u00fczde 10'luk dilim)<\/li>\n<li>En olas\u0131 senaryo (mod)<\/li>\n<li>K\u00f6t\u00fcmser senaryo (90. y\u00fczdelik dilim)<\/li>\n<\/ul>\n<h3>Ad\u0131m 2: Yay\u0131l\u0131m Mant\u0131\u011f\u0131 Olu\u015fturun<\/h3>\n<p>Da\u011f\u0131l\u0131mlar\u0131 matematiksel olarak birle\u015ftiren DAX hesaplamalar\u0131 olu\u015fturun. S\u0131ral\u0131 ba\u011f\u0131ms\u0131z g\u00f6revler i\u00e7in:<\/p>\n<ul>\n<li>Ortalama toplam = Bireysel ortalamalar\u0131n toplam\u0131<\/li>\n<li>Toplam varyans = Bireysel varyanslar\u0131n toplam\u0131<\/li>\n<li>Toplam standart sapma = Toplam varyans\u0131n karek\u00f6k\u00fc<\/li>\n<\/ul>\n<p>Korelasyonlu riskler i\u00e7in, hesaplamay\u0131 ayarlamak \u00fczere korelasyon katsay\u0131lar\u0131n\u0131 ekleyin.<\/p>\n<h3>Ad\u0131m 3: Belirsizlik Aral\u0131klar\u0131n\u0131 G\u00f6rselle\u015ftirin<\/h3>\n<p>Nokta tahminleri yerine olas\u0131l\u0131k aral\u0131klar\u0131n\u0131 g\u00f6stermek i\u00e7in Power BI'\u0131n hata \u00e7ubuklar\u0131n\u0131 ve g\u00fcven aral\u0131\u011f\u0131 grafiklerini kullan\u0131n. Payda\u015flar\u0131n\u0131z\u0131n \"3 ay\" ifadesinin asl\u0131nda \"80% g\u00fcvenle 2,1 ila 4,2 ay\" anlam\u0131na geldi\u011fini g\u00f6rmeleri gerekir.<\/p>\n<p>Bu yakla\u015f\u0131m, bir m\u00fc\u015fterinin $50M altyap\u0131 projesini y\u00f6netme \u015feklini de\u011fi\u015ftirdi. B\u00fct\u00e7e a\u015f\u0131mlar\u0131n\u0131 60% tamamlanma noktas\u0131nda ke\u015ffetmek yerine, y\u00fcksek varyansl\u0131 maliyet merkezlerini 15% tamamlanma noktas\u0131nda tespit ettiler ve d\u00fczeltici \u00f6nlemler ald\u0131lar.<\/p>\n<h2>Bile\u015fen 2: Soy G\u00fcveni Puanlar\u0131 - Neye \u0130nanabilece\u011finizi Bilmek<\/h2>\n<p>T\u00fcm veriler e\u015fit yarat\u0131lmam\u0131\u015ft\u0131r. En deneyimli m\u00fchendisinizden gelen bir maliyet tahmini, modas\u0131 ge\u00e7mi\u015f varsay\u0131mlar kullanan k\u0131demsiz bir analistten gelenden daha fazla a\u011f\u0131rl\u0131k ta\u015f\u0131r.<\/p>\n<p>Soya\u011fac\u0131 g\u00fcven puanlar\u0131 veri g\u00fcvenilirli\u011fini \u00f6l\u00e7er, b\u00f6ylece risk hesaplamalar\u0131n\u0131z\u0131 buna g\u00f6re a\u011f\u0131rl\u0131kland\u0131rabilirsiniz.<\/p>\n<h3>G\u00fcven Puanlar\u0131 Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h3>\n<p>D\u00f6rt fakt\u00f6re dayal\u0131 olarak say\u0131sal puanlar (0-1 \u00f6l\u00e7e\u011fi) veriyoruz:<\/p>\n<ul>\n<li><strong>Kaynak g\u00fcvenilirli\u011fi:<\/strong> Tahmini sa\u011flayan ki\u015fi veya sistemin sicili<\/li>\n<li><strong>Veri tazeli\u011fi:<\/strong> Temel bilgiler ne kadar yeni<\/li>\n<li><strong>Y\u00f6ntem kalitesi:<\/strong> Bu \u00e7\u0131lg\u0131nca bir tahmin miydi yoksa tarihsel analize mi dayan\u0131yordu?<\/li>\n<li><strong>Do\u011frulama seviyesi:<\/strong> Bu veriler ka\u00e7 ba\u011f\u0131ms\u0131z kontrolden ge\u00e7ti?<\/li>\n<\/ul>\n<h3>Power BI'da Uygulama<\/h3>\n<p>\u0130zleyen bir veri kalitesi tablosu olu\u015fturun:<\/p>\n<ul>\n<li>Veri kayna\u011f\u0131 kimli\u011fi<\/li>\n<li>Son g\u00fcncellenen zaman damgas\u0131<\/li>\n<li>Kullan\u0131lan y\u00f6ntem (puanlar\u0131 i\u00e7eren arama tablosu)<\/li>\n<li>Do\u011frulama say\u0131s\u0131<\/li>\n<li>Kaynak uzmanl\u0131k seviyesi<\/li>\n<\/ul>\n<p>Bu fakt\u00f6rleri bile\u015fik bir g\u00fcven puan\u0131nda birle\u015ftiren hesaplanm\u0131\u015f bir s\u00fctun olu\u015fturun:<\/p>\n<p><code>G\u00fcven Puan\u0131 = (Kaynak A\u011f\u0131rl\u0131\u011f\u0131 * Y\u00f6ntem A\u011f\u0131rl\u0131\u011f\u0131 * Tazelik A\u011f\u0131rl\u0131\u011f\u0131 * Do\u011frulama A\u011f\u0131rl\u0131\u011f\u0131) \/ 4<\/code><\/p>\n<h3>Risk Hesaplamalar\u0131nda G\u00fcven Puanlar\u0131n\u0131n Kullan\u0131lmas\u0131<\/h3>\n<p>Belirsizlik aral\u0131klar\u0131n\u0131z\u0131 g\u00fcven puanlar\u0131na g\u00f6re a\u011f\u0131rl\u0131kland\u0131r\u0131n. D\u00fc\u015f\u00fck g\u00fcven tahminleri daha geni\u015f g\u00fcven aral\u0131klar\u0131na sahip olur. Y\u00fcksek g\u00fcven tahminleri daha dar g\u00fcven aral\u0131klar\u0131na sahip olur.<\/p>\n<p>Bu, \u00e7o\u011fu analitik projesini \u00f6ld\u00fcren \u00e7\u00f6p-i\u00e7inde-\u00e7\u00f6p sorununu \u00f6nler. Sadece riski hesaplamakla kalmazs\u0131n\u0131z, girdilerinize ne kadar g\u00fcvenmeniz gerekti\u011fine ba\u011fl\u0131 olarak riski hesaplars\u0131n\u0131z.<\/p>\n<p>Bir imalat m\u00fc\u015fterisi, \"d\u00fc\u015f\u00fck riskli\" tedarik\u00e7i de\u011ferlendirmelerinin iki y\u0131ll\u0131k finansal verilere dayand\u0131\u011f\u0131n\u0131 tespit etmek i\u00e7in bu yakla\u015f\u0131m\u0131 kulland\u0131. Analizi g\u00fcncel verilerle yenilediklerinde, \u00fc\u00e7 \"ye\u015fil\" tedarik\u00e7i \"k\u0131rm\u0131z\u0131 \"ya ge\u00e7ti - b\u00fcy\u00fck bir tedarik zinciri kesintisinden iki hafta \u00f6nce.<\/p>\n<h2>Bile\u015fen 3: Kod Olarak Y\u00f6neti\u015fim - G\u00fcvenlik A\u011f\u0131n\u0131n Otomatikle\u015ftirilmesi<\/h2>\n<p>Manuel y\u00f6neti\u015fim \u00f6l\u00e7eklendirilemez ve tutars\u0131zd\u0131r. Neyin risk olarak i\u015faretlenece\u011fi, kimin iyi bir g\u00fcn ge\u00e7irdi\u011fine ve kimin kontrol etmeyi hat\u0131rlad\u0131\u011f\u0131na ba\u011fl\u0131d\u0131r.<\/p>\n<p>Kod olarak y\u00f6neti\u015fim, verileriniz her yenilendi\u011finde \u00e7al\u0131\u015fan \u00f6nceden tan\u0131mlanm\u0131\u015f kurallar\u0131 kullanarak risk alg\u0131lama ve y\u00fckseltmeyi otomatikle\u015ftirir.<\/p>\n<h3>Otomatik Risk Kurallar\u0131 Olu\u015fturma<\/h3>\n<p>Risk e\u015fiklerini sabit kodlu de\u011ferler olarak de\u011fil DAX \u00f6l\u00e7\u00fcmleri olarak tan\u0131mlay\u0131n. \u00d6rnekler:<\/p>\n<ul>\n<li>B\u00fct\u00e7e sapmas\u0131 onaylanan tutar\u0131n 15%'sini a\u015f\u0131yor<\/li>\n<li>Program g\u00fcveni 70%'nin alt\u0131na d\u00fc\u015fer<\/li>\n<li>Herhangi bir kritik yol g\u00f6revi 0,6'n\u0131n alt\u0131nda g\u00fcven puan\u0131na sahiptir<\/li>\n<li>\u00dc\u00e7 veya daha fazla varsay\u0131m 30 g\u00fcn i\u00e7inde do\u011frulanmad\u0131<\/li>\n<\/ul>\n<h3>Eskalasyon Mant\u0131\u011f\u0131<\/h3>\n<p>Farkl\u0131 yan\u0131t d\u00fczeylerini tetikleyen hesaplanm\u0131\u015f s\u00fctunlar olu\u015fturun:<\/p>\n<ul>\n<li><strong>Ye\u015fil:<\/strong> T\u00fcm e\u015fikler kar\u015f\u0131land\u0131, eylem gerekmiyor<\/li>\n<li><strong>Sar\u0131:<\/strong> Bir e\u015fik a\u015f\u0131ld\u0131, izlemeyi art\u0131r\u0131n<\/li>\n<li><strong>K\u0131rm\u0131z\u0131:<\/strong> Birden fazla e\u015fik a\u015f\u0131ld\u0131, acil inceleme gerekli<\/li>\n<\/ul>\n<h3>Power Automate ile Entegrasyon<\/h3>\n<p>Y\u00f6neti\u015fim kurallar\u0131n\u0131z\u0131 Power Automate ak\u0131\u015flar\u0131na ba\u011flay\u0131n:<\/p>\n<ul>\n<li>E\u015fikler ihlal edildi\u011finde otomatik uyar\u0131lar g\u00f6nderin<\/li>\n<li>Proje y\u00f6netim sistemlerinde g\u00f6revler olu\u015fturma<\/li>\n<li>Uygun payda\u015flarla g\u00f6zden ge\u00e7irme toplant\u0131lar\u0131 planlay\u0131n<\/li>\n<li>\u00dcst d\u00fczey liderlik i\u00e7in istisna raporlar\u0131 olu\u015fturun<\/li>\n<\/ul>\n<h3>Denetim \u0130zi<\/h3>\n<p>Her y\u00f6neti\u015fim eylemini zaman damgalar\u0131, tetikleyici ko\u015fullar ve al\u0131nan yan\u0131tlarla birlikte g\u00fcnl\u00fc\u011fe kaydedin. Bu, s\u00fcrekli iyile\u015ftirme ve mevzuata uygunluk i\u00e7in gerekli olan bir denetim izi olu\u015fturur.<\/p>\n<p>Bir in\u015faat m\u00fc\u015fterisi bu yakla\u015f\u0131m\u0131 uygulad\u0131 ve ortalama proje a\u015f\u0131m\u0131n\u0131 alt\u0131 ay i\u00e7inde 23%'den 8%'ye d\u00fc\u015f\u00fcrd\u00fc. Sistem, sorunlar\u0131 manuel olarak ortaya \u00e7\u0131karmak i\u00e7in proje y\u00f6neticilerine g\u00fcvenmek yerine kapsam kaymas\u0131 ve kaynak \u00e7at\u0131\u015fmalar\u0131n\u0131 otomatik olarak yakalad\u0131.<\/p>\n<h2>Entegrasyon Stratejisi: Bile\u015fenlerin Birlikte \u00c7al\u0131\u015fmas\u0131n\u0131 Sa\u011flamak<\/h2>\n<p>Bu \u00fc\u00e7 bile\u015fen ayr\u0131 ayr\u0131 g\u00fc\u00e7l\u00fcd\u00fcr ancak do\u011fru \u015fekilde entegre edildi\u011finde d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcd\u00fcr.<\/p>\n<h3>Veri Ak\u0131\u015f\u0131 Mimarisi<\/h3>\n<p>Power BI modelinizi net bir veri dizisi ile yap\u0131land\u0131r\u0131n:<\/p>\n<ol>\n<li><strong>Kaynak katman:<\/strong> G\u00fcven puan\u0131 meta verileriyle birlikte ham proje verileri<\/li>\n<li><strong>Hesaplama katman\u0131:<\/strong> Belirsizli\u011fin yay\u0131lmas\u0131 ve risk \u00f6l\u00e7\u00fcm\u00fc<\/li>\n<li><strong>Y\u00f6neti\u015fim katman\u0131:<\/strong> Otomatik kural de\u011ferlendirme ve istisna i\u015faretleme<\/li>\n<li><strong>Sunum katman\u0131:<\/strong> Farkl\u0131 payda\u015f ihtiya\u00e7lar\u0131 i\u00e7in g\u00f6sterge tablolar\u0131 ve raporlar<\/li>\n<\/ol>\n<h3>Geri Besleme D\u00f6ng\u00fcleri<\/h3>\n<p>Sistemi zaman i\u00e7inde iyile\u015ftirmek i\u00e7in mekanizmalar olu\u015fturun:<\/p>\n<ul>\n<li>Modellerinizi kalibre etmek i\u00e7in \u00f6ng\u00f6r\u00fclen ve ger\u00e7ek sonu\u00e7lar\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n<\/li>\n<li>Hangi y\u00f6neti\u015fim kurallar\u0131n\u0131n yanl\u0131\u015f pozitif sonu\u00e7lar do\u011furdu\u011funu takip edin ve e\u015fikleri ayarlay\u0131n<\/li>\n<li>Kaynaklar\u0131n tarihsel do\u011frulu\u011funa g\u00f6re g\u00fcven puanlar\u0131n\u0131 g\u00fcncelleyin<\/li>\n<\/ul>\n<h2>Uygulama Yol Haritas\u0131<\/h2>\n<p>Her \u015feyi bir kerede in\u015fa etmeye \u00e7al\u0131\u015fmay\u0131n. \u0130\u015fte i\u015fe yarayan s\u0131ra:<\/p>\n<h3>1. A\u015fama (1-4. Haftalar): Temel<\/h3>\n<ul>\n<li>Bir proje i\u00e7in temel belirsizlik yay\u0131l\u0131m\u0131n\u0131 ayarlama<\/li>\n<li>G\u00fcven puan\u0131 metodolojisini tan\u0131mlay\u0131n<\/li>\n<li>\u00dc\u00e7 temel y\u00f6neti\u015fim kural\u0131n\u0131 uygulay\u0131n<\/li>\n<\/ul>\n<h3>2. A\u015fama (5-8. Haftalar): Geni\u015fleme<\/h3>\n<ul>\n<li>Ba\u011f\u0131ml\u0131 riskler i\u00e7in korelasyon modellemesi ekleyin<\/li>\n<li>G\u00fcven puan\u0131 hesaplamalar\u0131n\u0131 otomatikle\u015ftirin<\/li>\n<li>Y\u00f6neti\u015fim uyar\u0131lar\u0131n\u0131 Power Automate'e ba\u011flay\u0131n<\/li>\n<\/ul>\n<h3>3. A\u015fama (9-12. Haftalar): Optimizasyon<\/h3>\n<ul>\n<li>Geri bildirim d\u00f6ng\u00fcleri ve model kalibrasyonu uygulamak<\/li>\n<li>Erken risk tespiti i\u00e7in tahmine dayal\u0131 analitik ekleyin<\/li>\n<li>Birden fazla proje ve portf\u00f6yde \u00f6l\u00e7eklendirme<\/li>\n<\/ul>\n<h2>Sonu\u00e7<\/h2>\n<p>Risk y\u00f6netimi, g\u00fczel g\u00f6sterge tablolar\u0131 olu\u015fturmak veya uyumluluk kontrol listelerini takip etmekle ilgili de\u011fildir. Karar vermeniz gerekti\u011finde size do\u011fru, eyleme ge\u00e7irilebilir bilgiler sa\u011flayan sistemler kurmakla ilgilidir.<\/p>\n<p>\u00d6zetledi\u011fimiz say\u0131salla\u015ft\u0131r\u0131lm\u0131\u015f risk \u00e7er\u00e7evesi - belirsizlik yay\u0131l\u0131m\u0131, soy g\u00fcven puanlar\u0131 ve kod olarak y\u00f6neti\u015fim - geleneksel yakla\u015f\u0131mlardaki temel zay\u0131fl\u0131klar\u0131 ele almaktad\u0131r:<\/p>\n<ul>\n<li>S\u00fcbjektif risk derecelendirmelerini matematiksel modellerle de\u011fi\u015ftirir<\/li>\n<li>Risklerin nas\u0131l birle\u015fti\u011fini ve etkile\u015fime girdi\u011fini a\u00e7\u0131klar<\/li>\n<li>Kararlar\u0131 veri kalitesine g\u00f6re a\u011f\u0131rl\u0131kland\u0131r\u0131r<\/li>\n<li>Tespit ve m\u00fcdahaleyi otomatikle\u015ftirir<\/li>\n<\/ul>\n<p>Bu yakla\u015f\u0131m\u0131n bir\u00e7ok sekt\u00f6rde proje ba\u015far\u0131s\u0131zl\u0131k oranlar\u0131n\u0131 40-60% oran\u0131nda azaltt\u0131\u011f\u0131n\u0131 g\u00f6rd\u00fck. Aradaki fark ara\u00e7lar de\u011fil, belirsizlik ve y\u00f6neti\u015fim hakk\u0131ndaki sistematik d\u00fc\u015f\u00fcncedir.<\/p>\n<p>Projeleriniz tahminlerle ve ayl\u0131k toplant\u0131larla y\u00f6netilemeyecek kadar \u00f6nemlidir. Otomatik olarak \u00e7al\u0131\u015fan, sorunlar\u0131 erkenden ortaya \u00e7\u0131karan ve size daha b\u00fcy\u00fck bahisler yapmak i\u00e7in g\u00fcven veren sistemler olu\u015fturun.<\/p>\n<p>Matematik art\u0131k iste\u011fe ba\u011fl\u0131 de\u011fil. Ya siz riski do\u011fru \u00f6l\u00e7ersiniz ya da risk sizi \u00f6l\u00e7er.<\/p>","protected":false},"excerpt":{"rendered":"<p>\u00c7o\u011fu risk \u00e7er\u00e7evesi bozuktur. \u0130\u015fletmeniz ba\u015far\u0131s\u0131z projelerde milyonlar harcarken onlar sadece renk kodlu matrislere ve i\u00e7g\u00fcd\u00fclerine g\u00fcvenirler. Fortune 500 \u015firketleri i\u00e7in risk sistemleri olu\u015fturduk ve ayn\u0131 modeli g\u00f6rd\u00fck: ekipler etkileyici g\u00f6r\u00fcnen ancak \u00f6nemli olan tek soruya cevap veremeyen g\u00fczel g\u00f6sterge tablolar\u0131 olu\u015fturuyor - \"Ger\u00e7ek risk nedir? <\/p>","protected":false},"author":2,"featured_media":13440,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-13931","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","latest_post"],"_links":{"self":[{"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/posts\/13931","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/comments?post=13931"}],"version-history":[{"count":5,"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/posts\/13931\/revisions"}],"predecessor-version":[{"id":13949,"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/posts\/13931\/revisions\/13949"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/media\/13440"}],"wp:attachment":[{"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/media?parent=13931"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/categories?post=13931"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.leaplytics.de\/tr\/wp-json\/wp\/v2\/tags?post=13931"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}