Data Cleaning and Standardization with Power Query in Excel
Automate the process of cleaning messy data using Power Query to transform inconsistent datasets into reliable, analysis-ready sources.
Build an ETL (extract, transform, load) workflow in Excel Power Query to clean, standardize, and consolidate datasets affected by common real-world corporate data problems.
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Prompt objective
Build an ETL (extract, transform, load) workflow in Excel Power Query to clean, standardize, and consolidate datasets affected by common real-world corporate data problems.
Real use case
The HR department of Construtora Horizonte in Goiânia receives a monthly timesheet for 800 employees that arrives with names in all capitals, unformatted tax IDs, dates in 3 different formats, and merged columns — taking 2 full days to clean manually.
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Replace the placeholders with your own context before you run the prompt. That usually improves the first output more than adding more instructions later.
Prompt
Build a complete data cleaning workflow using Power Query in Excel for the [DATA TYPE] dataset of [COMPANY NAME], which has [NUMBER] records and the following known issues:\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Identified data problems:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n1. [PROBLEM 1 — e.g., names with inconsistent accents and mixed capitalization]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n2. [PROBLEM 2 — e.g., tax IDs missing masks and missing digits]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n3. [PROBLEM 3 — e.g., dates in dd/mm/yyyy and mm-dd-yyyy formats mixed together]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n4. [PROBLEM 4 — e.g., monetary values with and without currency symbols, commas and periods]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n5. [PROBLEM 5 — e.g., partial duplicates and incomplete records]\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**For each problem, provide:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Power Query step (GUI steps + M code when needed)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Equivalent Google Sheets formula (CLEAN, SUBSTITUTE, TRIM, REGEX)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Validation: how to confirm the cleaning worked\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Complete ETL workflow:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n1. Source connection (folder with monthly CSV/XLSX files)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n2. Cleaning transformations (step by step)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n3. Merge with reference table ([AUXILIARY TABLE])\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n4. Required calculated columns\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n5. Load to destination table\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n6. Auto-refresh configuration\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n**Bonus:**\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- Data quality checklist (completeness, uniqueness, consistency)\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n- 5 equivalent Google Sheets formulas for users without Power Query\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\nAccount for Brazilian-specific data: postal codes, 9-digit phone numbers, and the distinction between legal company name and trading name.
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How to use this prompt
- 1Replace the key placeholders first: DATA TYPE, COMPANY NAME, NUMBER, PROBLEM 1 — e.g., names with inconsistent accents and mixed capitalization.
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