Rich metadata provides the context and understanding that an LLM needs to effectively use data to generate accurate and personalized responses. However, if your data catalog is poorly maintained, your metadata will be stale, and your AI initiatives will be ineffective.
AI data quality can be negatively impacted by privacy measures, such as data masking and access controls, which can hinder the retention of your data’s referential consistency.
Referential consistency refers to the accuracy of cambodia rcs data relationships between different data points. When anonymization techniques, like static or dynamic data masking, disrupt these relationships, your data quality suffers. Masked data is less reliable and meaningful for both your LLM and your user.
Essentially, the very measures designed to protect data privacy can inadvertently undermine the quality of the data itself and prevent generative AI from extracting valuable insights. For this reason, your AI data governance solution should assure referential consistency.
The quality vs. strategy dilemma
Traditionally, data quality initiatives have often been lone efforts, disconnected from core business objectives and strategies. Such isolation makes it difficult to measure the impact of data quality improvements and secure the AI investments you seek. As a result, AI struggles to gain the attention it deserves.