Enterprise data quality: the hidden constraint on AI
Enterprise data quality is the combined set of practices, tools, and controls that keep business information accurate, complete, timely, and trustworthy enough to support reliable AI systems and high‑stakes decisions at scale. As enterprises rush to embed AI into everyday workflows, many discover that their biggest obstacle is not model performance but the data itself. Unstructured data cleanup has become a critical first step, because redundant, obsolete, and trivial content silently pollutes knowledge bases and internal search indices long before any model is trained. When large language models must search across millions of unmanaged files, hallucinations and off‑base answers become far more likely, yet teams often misread these symptoms as model flaws. The result is a growing shift in AI data governance: organizations are prioritizing data infrastructure that can identify garbage, control lineage, and document every AI‑assisted decision.
Data ROT: how unstructured junk sabotages AI projects
Unstructured data cleanup is moving to the center of enterprise AI planning because most file systems are saturated with what Clario calls data ROT: redundant, obsolete, and trivial files. These include near‑duplicate documents, legacy formats no one can open, and long‑abandoned content from former employees. One early Clario customer discovered that more than 20% of 5.5 million files fell into this category, and much of it traced back to only four departed staff members. Industry estimates suggest more than a third of stored enterprise data is garbage, and Gartner projects that 60% of AI projects will be abandoned due to poor data quality. When this clutter is indexed into retrieval‑augmented generation systems, models must sift through outdated policies and discontinued product documentation, wasting compute tokens and increasing the chance of misleading answers. The problem is less model failure and more polluted inputs.
Outcome-based unstructured data cleanup as new infrastructure
Platforms like Clario treat unstructured data cleanup as a repeatable infrastructure layer rather than a one‑off migration project. Clario connects to content systems such as Google Drive, SharePoint, OneDrive, Box, and Confluence, then scans file metadata—checksums, names, access timestamps, and format support—without opening the files themselves. It flags likely garbage and triggers workflows in Slack or Teams so file owners can keep, archive, or delete items, and the platform learns from every decision. Clario only gets paid when customers act on a flagged file, creating an outcome‑based model tied to actual garbage removed. This emphasis on precision over recall aims to win trust by first eliminating obvious junk, like knowledge base articles for discontinued product lines or personal media stashed in corporate storage. In practice, such systems are becoming part of the core data infrastructure that keeps enterprise data quality high enough for AI.
From model monitoring to AI decision systems of record
As AI systems move into production workflows, enterprise AI infrastructure is expanding beyond deployment and monitoring to include AI data governance and decision audit layers. Obligra’s Verify illustrates this shift. Instead of focusing on model metrics, Verify acts as a system of record for AI‑assisted operational decisions. It captures prompts, responses, workflow context, timestamps, retrieval identifiers, environment details, and supporting evidence for each decision. When a claim denial, routing choice, or support answer is later questioned, teams can reconstruct exactly what information the AI saw and what it produced. This goes further than standard logs, which only confirm an event occurred. By preserving the full decision context, Verify supports compliance review, legal inquiries, risk management, and executive oversight. Organizations are effectively building systems of record for AI decisions, ensuring traceable data lineage alongside unstructured data cleanup so that both inputs and outcomes remain auditable.
