Data classification & cataloging
Data classification means labeling your information by type, sensitivity, and currency — so both people and AI systems know what they're working with. Is this document a draft or final? Is this data confidential, internal, or public? Is this record current or archived? Without classification, your AI agent treats a three-year-old draft proposal the same as yesterday's signed contract. Classification is the metadata that makes automation trustworthy.
Go deeper
Your HVAC company's AI dispatch system just recommended a technician for a job based on a certification record that expired eight months ago. The record was still in the system — nobody flagged it as expired, nobody classified it by currency. The AI saw 'certified' and matched the tech to the job. A customer got an underqualified technician, and you got a liability exposure. Classification isn't bureaucracy. It's the metadata that tells AI (and people) whether a piece of information is current, accurate, and appropriate to act on.
The trap most companies fall into is thinking classification is a one-time project. You classify everything, declare victory, and move on. But data changes state constantly — certifications expire, contracts get amended, policies get updated, employees change roles. Classification has to be maintained, ideally as an automated process that flags when a record's status may have changed rather than relying on someone to remember.
Questions to ask
- Which categories of data in our systems have a shelf life — certifications, contracts, pricing, policies — and do we have automated expiration or review triggers?
- If our AI systems act on stale or misclassified data, what's the worst-case business impact?
- Can we start with the highest-risk data categories and classify those first, rather than trying to catalog everything at once?