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      Abstract
      This thesis aims to demonstrate how efficient Key Performance Indicator (KPI) creation leads to better analysis and aids management in strategic corporate decisions. To support this goal, the data extraction and KPI definition process of the
administrative department of a major international manufacturing company in the sports sector
was analyzed.
To achieve the intended purpose, a new near real-time data pipeline ingestion was created, promoting KPI automation with a more innovative methodology. This study explores the areas of improvement on the existing method and proposes an improvement in the process.
It points out the main issues with the old method and highlights how the new process brings benefits.
The result, which involves near real-time KPIs, confirms a substantial improvement in data analysis and decision-making.
     
    
      Abstract
      This thesis aims to demonstrate how efficient Key Performance Indicator (KPI) creation leads to better analysis and aids management in strategic corporate decisions. To support this goal, the data extraction and KPI definition process of the
administrative department of a major international manufacturing company in the sports sector
was analyzed.
To achieve the intended purpose, a new near real-time data pipeline ingestion was created, promoting KPI automation with a more innovative methodology. This study explores the areas of improvement on the existing method and proposes an improvement in the process.
It points out the main issues with the old method and highlights how the new process brings benefits.
The result, which involves near real-time KPIs, confirms a substantial improvement in data analysis and decision-making.
     
  
  
    
    
      Tipologia del documento
      Tesi di laurea
(Laurea magistrale)
      
      
      
      
        
      
        
          Autore della tesi
          Caushllari, Eneriko
          
        
      
        
          Relatore della tesi
          
          
        
      
        
      
        
          Scuola
          
          
        
      
        
          Corso di studio
          
          
        
      
        
      
        
      
        
          Ordinamento Cds
          DM270
          
        
      
        
          Parole chiave
          KPI,data extraction,data pipeline ingestion,KPI analysis,Decision Making,near real-time data,Key Performance Indicators,Performance Measures
          
        
      
        
          Data di discussione della Tesi
          21 Marzo 2024
          
        
      
      URI
      
      
     
   
  
    Altri metadati
    
      Tipologia del documento
      Tesi di laurea
(NON SPECIFICATO)
      
      
      
      
        
      
        
          Autore della tesi
          Caushllari, Eneriko
          
        
      
        
          Relatore della tesi
          
          
        
      
        
      
        
          Scuola
          
          
        
      
        
          Corso di studio
          
          
        
      
        
      
        
      
        
          Ordinamento Cds
          DM270
          
        
      
        
          Parole chiave
          KPI,data extraction,data pipeline ingestion,KPI analysis,Decision Making,near real-time data,Key Performance Indicators,Performance Measures
          
        
      
        
          Data di discussione della Tesi
          21 Marzo 2024
          
        
      
      URI
      
      
     
   
  
  
  
  
  
  
    
      Gestione del documento: 
      
        