Tiedonhallinta ja tekninen velka : ohjelmistoalustan arviointityökalu

This thesis examines how data management practices influence the accumulation and mitigation of technical debt, with a particular focus on metadata debt within the data layer of enterprise systems. As modern architecture grows in complexity, poorly maintained metadata, inconsistent data models and insufficient governance create hidden forms of debt that hinder change management, reduce system reliability and impair long-term scalability. Building on existing research on data management practices and technical debt, the thesis develops a practical platform assessment toolkit designed to evaluate data-layer technical debt through structured metrics and a scoring model. The model incorporates components such as metadata completeness, accuracy, timeliness, consistency and ownership and integrates insights from frameworks including DAMA-DMBOK and ITIL to contextualize how governance and system development processes influence debt formation. The resulting toolkit aims to offer organizations a repeatable method to identify weaknesses in their metadata landscape.