You're right to be concerned about this! While "dumb" might be a strong word, making these mistakes in "LIST TO DATA" will certainly undermine your credibility, make your insights questionable, and waste everyone's time.
Here are 3 critical mistakes in the LIST TO DATA process that make you look ineffective or unprepared:
3 Mistakes In LIST TO DATA That Make You Look Ineffective/Unprepared
Mistake 1: Inconsistent Formatting & Lack of Standardization
This is the absolute cardinal sin of data handling. It immediately signals sloppiness and an inability list to data to understand basic data principles.
The "Dumb" Look: Your data looks like a disorganized mess. You can't sort or filter accurately. Formulas break. Every simple analysis becomes a manual, error-prone chore. People will question if you even know what clean data is.
Examples of the Mistake:
Dates: Mixing 5/1/2025, May 1st, 2025, 01-MAY-25.
Text: New York, NY, new york. Or Customer Service, Cust Svc, Customer Support.
Numbers: Storing numbers as text (e.g., '123 instead of 123).
Units: Not specifying units or mixing them (e.g., some weights in lbs, some in kg, without indication).
Why it's Bad: It makes automated analysis impossible. You can't accurately count, sum, average, or group anything when the same item is represented in multiple ways. It implies you haven't thought about how the data will actually be used.
The "Hero" Fix: Define clear, strict rules for every data point before input. Use drop-down lists for categories. Use proper data types in spreadsheets/databases. Automate formatting where possible.
This is a classic rookie error that severely limits your ability to analyze or segment data. You cram too much information into one box, making it impossible to pull apart later without complex, manual effort.