3 posts
A lack of data governance quietly drives AI projects into a dead end: ownership gaps, data quality problems, source ambiguity. A field observation with symptoms and minimum safeguards.
What is data quality? Data quality is the sum of dimensions — accuracy, completeness, consistency, timeliness, uniqueness, and validity — that determine data's fitness for its intended use. This guide: a clear definition, the six quality dimensions, measurement metrics, the data cleaning process, the impact on AI and RAG projects, common mistakes, and FAQs.
What is data governance? Data governance is the set of policies and processes that define the ownership, quality, security, and usage rules of data in an organization. This guide: a clear definition, the difference from data management, core components, the KVKK dimension, its role in AI projects, implementation steps, and FAQs.