Dates: Pick one format (e.g., YYYY-MM-DD) and stick to it.
Text: Use consistent capitalization (e.g., "New York" not "new york" or "NEW YORK").
Categories: For categorical data (e.g., "Product Type," "Feedback Category"), create a fixed list of accepted values and don't deviate. Avoid "Apparel," "Clothes," "Clothing" for the same thing.
Units: Always specify and standardize units (e.g., all prices in USD, all weights in kg).
4. Separate Data Points into Atomic Units
Don't combine multiple pieces of information in one field if they might need to be analyzed separately.
Bad: "John Doe, New York"
Good: "Customer_Name: John Doe", "City: New York" This allows you to filter, sort, and group by list to data individual elements later.
5. Leverage Unique Identifiers (IDs)
If your data records don't naturally have a unique ID, create one.
Purpose: Allows you to uniquely identify each row or record.
Benefit: Essential for linking related data across different tables/lists (e.g., linking a customer's feedback to their specific order using an Order_ID).
6. Clean and Validate Relentlessly
Data cleaning is not a one-time event; it's an ongoing process.
Correct Typos: Spell-check and fix errors.
Handle Missing Values: Decide how to treat blanks (e.g., NULL, "N/A", or impute based on context). Document your approach.
Validate Data Types: Ensure numbers are numbers, dates are dates, etc.
7. Choose the Right Tool for the Job
Your choice of tool depends on the complexity and volume of your list.