Beyond the numbers : exploring customer data quality through multi-dimensional analysis
Pumpanen, Sanna (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025112630031
https://urn.fi/URN:NBN:fi:amk-2025112630031
Tiivistelmä
In today’s data-driven world, quality is everything—yet it is often overlooked. The thesis analyses the quality of customer data within the case company, aiming to identify key issues and uncover opportunities for improvement. Through a detailed examination of data structures and practices, the study provides insights into the factors that affect data reliability and offers recommendations to enhance overall data quality.
The analysis identified key challenges, including the presence of duplicate entries in customer data, which compromise consistency and reliability. To address these issues, the thesis outlines practical recommendations and a proposed implementation plan. These measures are intended to enhance data quality and contribute to a more structured and trustworthy approach to customer data management within the organization.
Beyond the technical findings, the thesis highlights the broader organizational implications of customer data quality. Inconsistent and unreliable data not only hinders operational efficiency but also erodes trust in the data. By investing in training and fostering cross-functional collaboration, the organization can move toward a more resilient and insight-driven approach. The proposed roadmap emphasizes incremental changes, stakeholder engagement, and measurable outcomes. Ultimately, improving data quality is not just a technical fix—it’s a cultural transformation that supports smarter, faster, and more confident decision-making across the organisation.
The analysis identified key challenges, including the presence of duplicate entries in customer data, which compromise consistency and reliability. To address these issues, the thesis outlines practical recommendations and a proposed implementation plan. These measures are intended to enhance data quality and contribute to a more structured and trustworthy approach to customer data management within the organization.
Beyond the technical findings, the thesis highlights the broader organizational implications of customer data quality. Inconsistent and unreliable data not only hinders operational efficiency but also erodes trust in the data. By investing in training and fostering cross-functional collaboration, the organization can move toward a more resilient and insight-driven approach. The proposed roadmap emphasizes incremental changes, stakeholder engagement, and measurable outcomes. Ultimately, improving data quality is not just a technical fix—it’s a cultural transformation that supports smarter, faster, and more confident decision-making across the organisation.