Prompt lesson · 22 prompts
Data Cleansing prompts for Data Entry Specialists
22 ready-to-use prompts from our AI for Data Entry Specialists course. Copy one, fill in the {{placeholders}}, and paste it into ChatGPT, Claude, Gemini or any other AI.
Correct Data Inconsistencies
Use this when you need to identify and resolve conflicting or duplicate information in a dataset.
Role You are a data quality specialist who identifies and corrects inconsistencies such as conflicting information, duplicates, and mismatched values.
Context you provide
- {{dataset}}: The dataset to review.
- {{inconsistency_types}}: Specific types of inconsistencies to look for (e.g., mismatched addresses, duplicate entries, pricing discrepancies).
Instructions
- If the dataset or inconsistency types are not specified, ask for them.
- Review the dataset for the specified inconsistencies, including duplicates, conflicting values, and formatting issues.
- For each inconsistency, provide a clear explanation and suggest a correction.
- If a correction is ambiguous, flag it and ask for guidance.
- Provide a summary of all identified issues and recommended actions.
Output format Provide a report with:
- List of inconsistencies found, with examples.
- Recommended corrections for each.
- Any issues that require human decision.
- A summary of the overall data quality.
Guardrails
- Do not automatically change data without user approval; suggest corrections.
- Do not assume which value is correct; present options.
- Stay within the scope of the specified inconsistencies.
Example Dataset: "customer_database.csv" with mismatched addresses and duplicate entries.
Open this prompt Analysis · Intermediate
Correct Spelling and Grammar in Data
Use this when you need to identify and correct spelling and grammar errors in text-based datasets to ensure accuracy and professionalism.
Role You are a meticulous data entry specialist with expertise in text processing. Your goal is to identify and correct spelling and grammar errors in my dataset while preserving the original meaning.
Context you provide
- {{dataset}}: The dataset or text content to correct.
- {{text_type}}: The type of text (e.g., customer feedback, product reviews) to tailor corrections appropriately.
Instructions
- If I haven't provided the dataset or text type, ask for them before starting.
- Review the text for spelling and grammar errors, including punctuation and capitalization.
- Correct errors while maintaining the original tone and intent.
- Provide a summary of the types of errors found and corrections made.
- Suggest common patterns to watch for in future data entry.
Output format Present the corrected text, followed by a brief 'Error Summary' section listing common errors and corrections. Use a table if helpful.
Guardrails
- Do not alter the meaning or style of the original text.
- Flag any ambiguous corrections you made.
- Stay focused on spelling and grammar; do not rewrite content for style.
Example Dataset: 'customer_feedback.csv', text_type: 'customer feedback forms'.
Open this prompt Writing · Beginner
Data Deduplication Processing
Use this when you need to identify and remove duplicate records in a dataset to ensure data integrity and accuracy.
Open this prompt Analysis · Beginner
Data Error Correction and Validation
Use this when you need to identify and correct inaccuracies, inconsistencies, or duplicates in a dataset to maintain data quality.
Open this prompt Analysis · Intermediate
Data Formatting Requirements Analysis
Use this when you need to clarify data formatting rules, identify key fields, and outline validation steps before analysis or reporting.
Open this prompt Analysis · Beginner
Data Quality Assessment
Use this when you need to evaluate a dataset for missing values, outliers, distribution issues, and receive recommendations for data cleaning.
Open this prompt Analysis · Beginner
Data Standardization Script
Use this when you need to standardize data formats (like dates, phone numbers, addresses) across a dataset to ensure consistency.
Open this prompt Coding · Intermediate
Data Validation and Accuracy Check
Use this when you need to verify the accuracy and consistency of newly entered data against existing records.
Open this prompt Analysis · Beginner
Duplicate Record Removal Plan
Use this when you need a step-by-step plan to identify and remove duplicate records in a dataset while preserving the most relevant data.
Open this prompt Planning · Intermediate
Enrich Dataset with Additional Information
Use this when you need to add relevant supplementary information to an existing dataset to make it more comprehensive.
Open this prompt Creating · Intermediate
Ensure Data Compliance
Use this when you need to verify that data handling meets regulatory standards like GDPR, HIPAA, or PCI DSS.
Open this prompt Analysis · Advanced
Fill Missing Data Values
Use this when you need to predict and fill missing values in a dataset based on existing patterns.
Open this prompt Analysis · Intermediate
Identify and Handle Outliers
Use this when you need to detect and manage outliers in a dataset to improve analysis accuracy.
Open this prompt Analysis · Intermediate
Identify and Remove Irrelevant Data
Use this when you need to clean a dataset by identifying and removing outdated or irrelevant entries.
Open this prompt Analysis · Beginner
Normalize Data for Analysis
Use this when you need to standardize raw data from multiple sources into a consistent format for analysis.
Open this prompt Analysis · Intermediate
Normalize Data to Standard Format
Use this when you need to convert data into a consistent format for easier analysis and comparison.
Open this prompt Analysis · Intermediate
Profile Data for Quality Issues
Use this when you need to analyze a dataset's content and structure to identify anomalies, missing values, and inconsistencies.
Open this prompt Analysis · Intermediate
Remove Duplicate Entries
Use this when you need to clean a dataset by identifying and removing duplicate records to ensure accuracy.
Open this prompt Analysis · Beginner
Remove Special Characters and Symbols
Use this when you need to clean a dataset by removing special characters and symbols that may affect data integrity.
Open this prompt Analysis · Beginner
Standardize Data Formats
Use this when you need to clean and standardize inconsistent data formats in your database or spreadsheets.
Open this prompt Analysis · Beginner
Standardize Naming Conventions
Use this when you need to ensure consistent naming conventions across your dataset for better data management and analysis.
Open this prompt Analysis · Beginner
Validate and Verify Data
Use this when you need to check the accuracy of data entries against predefined criteria or policies.
Open this prompt Analysis · Beginner