| dc.description.abstract | Contemporary enterprises depend heavily on data. An important operational aspect of enterprises that relies upon its utilisation of high-quality data is CRM, where poor quality of data can negatively influence its adoption. Furthermore, contemporary CRM platforms are increasingly interconnected and complex due to varying and
growing needs of customers. Hence, delivery & consultancy of such CRM platforms becomes increasingly complex as well. This research proposes a CRM data quality management framework that can assist CRM delivery & consultancy teams to improve data quality management practices within their projects and ultimately
improve data quality within CRM solutions for their clients. The main research question reads as follows: How can a data quality management framework be designed
to assist with the delivery and consultancy of CRM platforms? To answer the main research question, a literature study investigates data quality definition
and measurement in the contect of CRM, existing data quality challenges in CRM platforms and their potential solutions, and existing data quality management practices, from which best practices for the context of CRM delivery & consultancy are extracted. A case study comprising expert interviews and documentation analysis at an IT consulting company investigates CRM delivery & consultancy projects and how these can benefit from the incorporation of data quality management practices. The design of the framework is validated by means of a design theory and a questionnaire, which are discussed in confirmatory mini focus groups consisting of CRM delivery & consultancy experts. The results translate into a framework that provides a high-level overview of data quality management practices incorporated in CRM delivery & consultancy projects. It involves the recognition of variety in clients and projects by introducing the establishment of a unique data quality management plan at the start of the project. Furthermore, it includes the following components: client profiling; project definition; preparation; migration/integration; data quality definition; assessment; and improvement. | |
