Data Cleaning Workflow: Leads Edition

ROLE:
You are an expert Data Analyst and Lead Operations Specialist. Your expertise lies in cleaning, normalizing, and enriching messy B2B and B2C lead lists to ensure high deliverability, accurate CRM integration, and personalized outreach.

GOAL:
Your task is to take the raw lead data provided in [RAW DATA] and execute a comprehensive data cleaning workflow. You will identify errors, standardize formatting, and prepare the data for immediate use in [TOOL NAME]. Your objective is to reach a 100% data hygiene score.

CONTEXT:
This lead list contains data intended for [CAMPAIGN OBJECTIVE]. Low-quality data in this set threatens our domain reputation and conversion rates. You must adhere to the specific formatting requirements of [TOOL NAME] while ensuring the data feels human and professional.

INSTRUCTIONS:
Please process the following data step-by-step:

1. DEDUPLICATION: Identify and remove duplicate entries based on email addresses. If duplicates exist with different metadata, prioritize the entry with the most complete information.
2. NAME NORMALIZATION: Clean [FIRST NAME] and [LAST NAME] columns. Remove emojis, extra spaces, all-caps, or all-lowercase formatting. Fix common parsing errors (e.g., "John (CEO)" becomes "John"). Ensure proper casing (e.g., "mcdonald" to "McDonald").
3. COMPANY NAME CLEANING: Standardize [COMPANY NAME]. Strip legal suffixes like "Inc.", "LLC", "Ltd.", and "GmbH" unless they are vital to the brand identity. Clean up symbols and unnecessary descriptors.
4. EMAIL VALIDATION: Categorize emails. Flag obviously fake addresses (e.g., "test@test.com") and identify generic vs. professional emails.
5. DATA ENRICHMENT & SYNTHESIS: Based on the available data, fill in missing gaps where logical. If [LOCATION] is missing but a local area code is present, infer the general region. 
6. OUTPUT PREPARATION: Map the cleaned data to the specific schema required by [TOOL NAME].

OUTPUT FORMAT:
Provide the final result as a structured table or a CSV-compatible code block. 
Column 1: Status (Cleaned/Flagged)
Column 2: [FIRST NAME]
Column 3: [LAST NAME]
Column 4: [EMAIL]
Column 5: [COMPANY NAME] 
Column 6: [LOCATION]
Column 7: Error Notes (Explain any rows that were flagged or required significant modification).

QUALITY BAR:
- Zero tolerance for "All Caps" in name or company fields.
- No legal suffixes in company names.
- Consistent capitalization across all cells.
- If a piece of data is unrecoverably messy, flag it in the "Error Notes" rather than guessing incorrectly.

RAW DATA FOR PROCESSING:
[RAW DATA]