In recent years, data literacy has become critical in areas such as marketing, sales, and more generally in businesses. These companies rely on software such as Customer Relationship Management (CRM) systems to derive useful information from the vast amount of data collected. However, lack of data quality undermines the effectiveness of this approach, as it directly impacts overall business performance. This thesis investigates the various issues and challenges related to data quality in CRM systems, focusing particularly on datasets with attributes such as first name, last name, and e-mail address. In addition, an algorithm for cleaning such datasets is proposed.
In recent years, data literacy has become critical in areas such as marketing, sales, and more generally in businesses. These companies rely on software such as Customer Relationship Management (CRM) systems to derive useful information from the vast amount of data collected. However, lack of data quality undermines the effectiveness of this approach, as it directly impacts overall business performance. This thesis investigates the various issues and challenges related to data quality in CRM systems, focusing particularly on datasets with attributes such as first name, last name, and e-mail address. In addition, an algorithm for cleaning such datasets is proposed.
Improving Data Quality in Customer Relationship Management Systems: a method for cleaning personal information
LEONELLI, ELENA
2021/2022
Abstract
In recent years, data literacy has become critical in areas such as marketing, sales, and more generally in businesses. These companies rely on software such as Customer Relationship Management (CRM) systems to derive useful information from the vast amount of data collected. However, lack of data quality undermines the effectiveness of this approach, as it directly impacts overall business performance. This thesis investigates the various issues and challenges related to data quality in CRM systems, focusing particularly on datasets with attributes such as first name, last name, and e-mail address. In addition, an algorithm for cleaning such datasets is proposed.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/45817