A Data Readiness Checklist Before Starting an AI Project
Most AI projects that stall do so because of data. Twelve questions to answer before committing budget, and what to do when the answers are uncomfortable.
Insight topic
Data Engineering insights
Pipelines, data quality and the data foundations that AI depends on. Articles in this topic are written for leaders and practitioners who need practical, evidence-based guidance.
1 article in Data Engineering
Topic guide
Understanding data engineering
Every AI system depends on data that is available, permitted, accurate and timely. This topic covers data readiness, pipelines, quality and governance as the foundation for analytics and AI.
FAQ
Key questions about data engineering
What is data readiness for AI?
The condition in which data for a use case exists, can be lawfully accessed, is of sufficient quality, represents real conditions and has a clear owner.
Why assess data with samples?
Descriptions of data are usually more optimistic than the data itself. Profiling real samples reveals gaps within days.
What if data is not ready?
Options include narrowing scope, fixing quality at source, labelling a focused sample, collecting new data or building data foundations before returning to the use case.
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Where these ideas are put into practice.
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