Welcome to documentation - the unsung hero of data analysis! Without proper documentation, your brilliant insights become mysteries that nobody (including future you) can understand or trust.
Think of documentation as leaving breadcrumbs for anyone who needs to follow your analytical journey.
Engagement Message
What's one time you've struggled to understand your own work from months ago?
Here's what happens without documentation: your manager asks "How did you calculate this?" and you can't remember. Your teammate needs to update your analysis but doesn't know which data sources you used.
Documentation prevents these business disasters.
Engagement Message
How might poor documentation affect a team project you're working on?
Document three key elements: data sources (where did this come from?), cleaning steps (what did you fix?), and analytical decisions (why did you choose this approach?).
Each element answers crucial questions for reproducibility.
Engagement Message
Which of these three elements is most critical for someone verifying your analysis?
For data sources, record the specific file names, database tables, date ranges, and any filters applied. "Customer data from marketing team" isn't enough - you need "customers_2024.csv, exported 1/15/2024, filtered for active accounts."
Specificity prevents confusion later.
Engagement Message
What's one detail about data sources that people often forget to document?
Document every cleaning step you performed. "Removed outliers" becomes "Removed 12 customers with spending >$10,000 (clear data entry errors based on investigation)."
Include your reasoning for each decision.
Engagement Message
In one sentence, why is explaining your reasoning more important than just listing steps?
