The "Dumb" Look: You can't filter by a specific part of the data. Want to see all customers from New York City vs. New York State? If you put "New York, NY" and "Albany, NY" in one "Location" column, you're stuck. You'll constantly be asked for basic insights you can't provide.
Examples of the Mistake:
Customer: John Doe (from NYC) in one column instead of Customer_Name: John Doe, City: New York, State: NY.
Product: Widget-Blue-Small instead of Product_Name: Widget, Color: Blue, Size: Small.
Date/Time: 2025-05-27 10:08:20 in one column when you might need to analyze by date or time independently.
Why it's Bad: It prevents granular analysis. You can't easily count, sum, or filter by the individual components within that field. It shows a lack of foresight about future analytical needs.
The "Hero" Fix: Always break down information into its smallest meaningful components, each in its own dedicated column. Think about what the smallest unit you might ever want to filter or group by is.
Mistake 3: Lack of Unique Identifiers or Keys
If your data doesn't have a way to uniquely identify each record, or to link related records across list to data different lists, you're creating isolated islands of
The "Dumb" Look: You can't tell if "John Smith" in one list is the same "John Smith" in another list, or if it's a different person with the same name. You can't reliably update or reference specific records. Your data becomes isolated and untrustworthy.
Examples of the Mistake:
Having a customer list where "John Doe" appears three times, and you have no Customer_ID to differentiate them if they are indeed three different people.
Having an "Orders" list and a "Products" list, but no Product_ID in the Orders list to link them, so you can't tell which product was in which order without complex, error-prone text matching.
Not using an Order_ID for customer feedback, making it impossible to link a specific complaint back to the original purchase.
Why it's Bad: It breaks the relational aspect of data. You can't merge information from different sources, track changes to specific items, or ensure data integrity. It implies a fundamental misunderstanding of how databases and data relationships work.
The "Hero" Fix: Assign a unique ID to every primary entity (e.g., Customer_ID, Product_ID, Order_ID). Use these IDs as "keys" to link related data across different lists or tables. This is foundational for building a robust data model.
Avoiding these three mistakes will dramatically improve the quality of your "LIST TO DATA" work, making your data more reliable, your analysis more accurate, and your contributions more impactful.