Choose Table Columns

Select columns that meet all three of the following criteria.

• Clean: Remove the following types of unreliable data:

  • Fake or synthetic data
  • Predictions from other ML models
  • Columns whose definition has changed over time, especially where the same attribute ID may point to different things at different times
  • Data that is otherwise unreliable or frequently inaccurate

• Relevant and Mutually Exclusive: The larger the graph size (the sum across all tables in a graph), the higher the compute cost. Remove columns that provide duplicate or similar information, irrelevant information, or other extraneous data.

• Complete: Cover the full history across the timeframe in question. For example, include the complete record of purchases or interactions rather than a user’s first or last purchase, or a subscriber’s most recent interaction. If this results in an oversized dataset, provide Kumo with a compressed version that indicates changes in aggregate metrics over time (for example, per day, week, or month).

See Data Preparation Best Practices and Column Preprocessing for the complete workflow.