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From Garbage-In-Garbage-Out to Reliable Intelligence Executive Summary Traditional data quality (DQ) focused on structured, static rules (completeness, uniqueness). In the AI era, quality is contextual, probabilistic, and generative . Poor data quality is the #1 reason AI projects fail to scale. This report outlines the shift from DQ for BI to DQ for LLMs and predictive models. 1. The Core Shift: Why Old Rules Break | Traditional DQ (for Dashboards/Reports) | AI-Ready DQ (for Models/Agents) | | --- | --- | | Fixed schema, known fields | Unstructured text, embeddings, vectors | | Missing values = null | Missing context = hallucination | | Duplicates = exact match | Duplicates = semantic near-duplicates | | Accuracy vs. source of truth | Accuracy vs. real-world consistency | | Measured at rest | Measured in motion & inference |
From Garbage-In-Garbage-Out to Reliable Intelligence Executive Summary Traditional data quality (DQ) focused on structured, static rules (completeness, uniqueness). In the AI era, quality is contextual, probabilistic, and generative . Poor data quality is the #1 reason AI projects fail to scale. This report outlines the shift from DQ for BI to DQ for LLMs and predictive models. 1. The Core Shift: Why Old Rules Break | Traditional DQ (for Dashboards/Reports) | AI-Ready DQ (for Models/Agents) | | --- | --- | | Fixed schema, known fields | Unstructured text, embeddings, vectors | | Missing values = null | Missing context = hallucination | | Duplicates = exact match | Duplicates = semantic near-duplicates | | Accuracy vs. source of truth | Accuracy vs. real-world consistency | | Measured at rest | Measured in motion & inference |
