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What data does AI actually require?

ai data requirements data for ai training ai training data needs machine learning data requirements what data does ai need

Quick Answer

AI requires data that meets six quality dimensions: accuracy (correct values), completeness (no missing critical fields), timeliness (fresh, not stale), relevance (directly related to the problem), consistency (uniform formats across sources), and sufficient volume (the '10x rule' — ten data points per model parameter). The data must be properly labelled, representative of real-world conditions, and free from bias. Poor data quality is the #1 reason AI projects fail.

Full Answer

AI has specific, non-negotiable data requirements that most businesses don't meet. Understanding these requirements before attempting AI deployment prevents the 80% failure rate. Five critical needs: raw event streams, multi-year historical depth, complete capture, unified datasets, and consistent schemas. Requirement #1: Raw Event Streams What AI needs: Individual user actions with complete context: What platforms provide: Aggregated dashboard summaries:

  • GA4: "1,247 purchases, $186,329 revenue, avg order $149.34"
  • Facebook: "834 conversions attributed to ads"
  • Summary statistics ≠ training data Why raw events matter: AI learns from individual examples. Training recommendation engine:
  • Needs: "User bought SKU-123 + SKU-456 together"
  • Doesn't need: "Average order contained 2.3 products" Platform aggregates destroy the granularity AI requires. Requirement #2: Historical Depth (2-3 Years) What AI needs: Thousands of examples spanning:
  • Seasonal cycles: Black Friday 2023 vs 2024 vs 2025 (patterns across years)
  • Customer lifecycles: Acquisition → Month 6 →...

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