This role-play tests how the candidate handles a common real-world scenario where supplier data is messy and incomplete. It screens for initiative, data standardization skills, and the ability to communicate clearly about what they can fix versus what requires client input.
Immediately acknowledge the inconsistencies, then describe a systematic cleaning process: identify unique values, create standardization rules, fill missing fields using available resources, and flag anything that needs client confirmation. End by explaining that you would never upload unclean data.
Start by thanking the owner for sending the file, then say plainly that you will not upload it as is because inconsistent and incomplete data will look unprofessional on the storefront and could hurt search rankings. Walk through your first pass: open the CSV in a spreadsheet tool, sort by the size column, and list every unique value so you can see all the variations at once. Explain that you would create a simple mapping rule, for example standardizing everything to S, M, L, or numeric measurements, and use find and replace or a formula to apply it consistently. For the missing material field, say you would check the product title, description, or any other column for clues, and if the supplier has a catalog or past orders, pull the material from there. Then be honest about what you cannot solve: if a row has no material anywhere and no way to infer it, you would flag those specific product IDs and ask the owner whether to mark them as "material varies" or request the data from the supplier. End by repeating that you would only upload the cleaned file after showing the owner a summary of the changes, so nothing goes live without their approval. This shows you take ownership while respecting that some decisions are the owner's to make.
A typical Filipino mistake is to say 'Sir, dapat po ayusin natin itong file, maraming mali eh' and wait for the owner to fix everything. Instead, take ownership of cleaning the file yourself while clearly communicating which specific missing data points need the owner's input.
Situation
In my previous internship at a local clothing brand, I received a spreadsheet of 120 new products where the size column mixed full words, abbreviations, and combined sizes.
Task
I had to clean and standardize the data so that all products had consistent size formatting and complete material information before they went live on the store.
Action
I first opened the file and used filters to identify all unique size values. I created a standardization rule that converted 'small' to 'S', 'medium' to 'M', and 's/m' to 'S/M'. For missing material fields, I cross-referenced the supplier's product catalog and filled in the data myself. I then flagged any product that still lacked material information and asked the store owner for confirmation before proceeding.
Result
I cleaned all 120 rows within one afternoon. The store launched the new products with uniform size labels, and the owner only had to provide material details for three items I had flagged.
Standardizing data formats before upload saves hours of correction later.
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