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Workspace Best Practices

Effective workspace collaboration requires clear structure and consistent practices.
Keep structure simple:
  • Agree on folder and naming conventions early
  • Group files by project or use case
  • Keep folder structures shallow when possible
  • Use clear, descriptive file names
Use tags consistently:
  • Agree on tag meanings as a team
  • Use tags for cross-cutting categories
  • Mark priority or status with tags
  • Build shared tagging systems
Tags work best when the team agrees on their meaning.
Rely on AI for synthesis:
  • Use summaries and queries to surface insights
  • Generate cross-file summaries instead of manual review
  • Let AI handle search instead of over-organizing
  • Combine multiple files for analysis
Review access regularly:
  • Remove outdated members
  • Adjust permissions as projects evolve
  • Grant minimum necessary permissions
  • Update roles as needed
Regular access reviews keep workspaces secure and relevant.

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Data Analysis Best Practices

Before running analysis or building visualizations, understand the structure and contents of your dataset.
Use these queries to understand the shape of your data:
  • “How many rows are in this data?”
  • “What columns are available?”
  • “Show the schema”
These commands help you confirm what fields exist and how the dataset is organized.
To inspect actual values, request samples:
  • “Show the first 10 rows”
  • “Show a random sample of 20 rows”
  • “Show rows with the highest values”
Previews help you understand formatting, value ranges, and potential anomalies before analysis.
Assess data completeness and consistency:
  • “Are there missing values?”
  • “How many duplicate rows exist?”
  • “Which columns contain null values?”
  • “Show unique values in the Status column”
These checks help you identify issues that may affect analysis results.