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The decision or task is clearly defined
The team can describe what the data needs to help a person or system do.
Data readiness
Review the data before implementation
Clarify sources, ownership, sensitivity, quality, access and evaluation needs before connecting data to a system.
Prepare the conversation
Topics to discuss
Source systems are known
The origin of relevant records, files, APIs or documents is identified.
Data ownership is known
Someone can approve access, definitions, retention and corrections.
Sensitivity is classified
Personal, confidential, regulated or secret fields are identified before external processing.
Representative quality has been inspected
Missing values, duplicates, stale records and inconsistent formats are understood.
Access boundaries are defined
Users and services have an explicit reason and permission to access the data they need.
Ground truth or review criteria exist
There is a way to judge whether extraction, classification, retrieval or generation is useful.
Retention/removal expectations are known
The implementation knows what should be kept, for how long, and what should be removed.
Important transformations are traceable
It is possible to understand where derived data came from and what changed it.
A non-AI/manual path exists where needed
Critical workflows can continue or escalate when data or model output is unavailable.
Evaluation cases reflect real usage
Tests include common, difficult and edge-case examples instead of only ideal samples.
Monitoring and correction ownership is known
Someone is responsible for data drift, source changes and recurring quality issues after launch.
Contact me directly
Use WhatsApp, phone or email to discuss these topics and agree the next step. Keep passwords, API keys and recovery codes out of your message.