Parowanie klientów przy użyciu Machine Learing w Microsoft Fabric F2

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Gourmet Foods

Employer

Job category:

Software

Expected budget:

Negotiable

Published: yesterday

29 days left

Job description

Cel:

Celem tego projektu jest stworzenie bazy sparowanych sklepu poprzez mapowanie dwóch odrębnych zestawów danych za pomocą platformy Microsoft Fabric F2.

Zestawy danych obejmują:

Zestaw danych z systemu CRM: Zawiera dane sklepów pozyskiwane z działań zarządzania relacjami z klientami.

Lista sklepów otrzymana od Sieci: Zewnętrzny zestaw danych, który obejmuje kompleksową listę sklepów

Proces integracji będzie wykorzystywał Microsoft Fabric F2 do precyzyjnego łączenia rekordów na podstawie dopasowanych pól, takich jak adres i numer identyfikacji podatkowej (NIP). Integracja ta ma na celu szybkie parowanie klientów i aktualizacje o aktualne dane sklepów w systemie CRM.

Zakres:

Źródła danych:

Zestaw danych 1 (System CRM): Zawiera wewnętrzne dane sklepów, takie jak nazwa sklepu, adres, dane kontaktowe i NIP.

Zestaw danych 2 (Lista nowoczesnego handlu): Zawiera zewnętrzne dane sklepów z segmentu nowoczesnego handlu, obejmujące szczegóły takie jak nazwa sklepu, ustandaryzowany adres i NIP.

Pola do integracji:

Adres: Główne pole do dopasowywania rekordów sklepów na podstawie lokalizacji.

NIP: Unikalny identyfikator używany do precyzyjnego dopasowania między zestawami danych.

Technologie:

Microsoft Fabric F2: Ta platforma do integracji danych, uczenia maszynowego i analizy danych w chmurze zostanie wykorzystana do:

Pobierania danych z obu zestawów danych.

Standaryzacji i oczyszczania informacji o adresach.

Dopasowywania rekordów na podstawie adresu i NIP przy użyciu algorytmów uczenia maszynowego oraz innych technik integracji danych.

Tworzenia skonsolidowanego zestawu danych do analiz i raportowania.

Required functions:

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