How do I use the "Generate Comprehensive Data Quality Report" prompt?

Copy the prompt template, fill in the variables (DATASET_NAME, RECORD_COUNT, COLUMN_COUNT, DATA_SOURCE, TIME_PERIOD, KEY_COLUMNS, DATA_ISSUES, BUSINESS_CONTEXT), and paste it into ChatGPT, Claude, or Gemini.

What AI tools work with this data prompt?

This prompt works with chatgpt, claude, gemini. Simply copy and paste the template into any of these AI assistants.

Tips for getting the best results?

Provide specific numbers and percentages rather than vague descriptions of data issues Include both technical data quality metrics and business impact explanations for stakeholder buy-in Prioritize recommendations by implementation difficulty and business value Use industry-standard data quality dimensions (completeness, accuracy, consistency, timeliness, validity) Include examples of actual data issues found to make the report more concrete and actionable

data intermediate

Generate Comprehensive Data Quality Report

Create detailed data quality reports with automated issue detection, recommendations, and executive summaries using this AI prompt.

Works with: chatgptclaudegemini

Prompt Template

You are a senior data analyst tasked with creating a comprehensive data quality report. Analyze the provided dataset information and generate a professional report that identifies issues, provides recommendations, and presents findings in a business-ready format. **Dataset Information:** - Dataset Name: [DATASET_NAME] - Number of Records: [RECORD_COUNT] - Number of Columns: [COLUMN_COUNT] - Data Source: [DATA_SOURCE] - Collection Period: [TIME_PERIOD] - Key Columns: [KEY_COLUMNS] **Data Issues Identified:** [DATA_ISSUES] **Business Context:** [BUSINESS_CONTEXT] Create a structured report with the following sections: 1. **Executive Summary** (3-4 sentences highlighting overall data quality score and critical findings) 2. **Data Quality Metrics** - Completeness percentage - Accuracy assessment - Consistency score - Timeliness evaluation - Validity rating 3. **Critical Issues Analysis** - High-priority problems requiring immediate attention - Impact on business operations - Root cause analysis where applicable 4. **Detailed Findings** - Column-by-column quality assessment - Pattern anomalies - Data distribution insights 5. **Recommendations** - Immediate fixes (quick wins) - Long-term improvements - Process enhancements - Data governance suggestions 6. **Risk Assessment** - Business risks from current data quality issues - Compliance implications - Decision-making impact Format the report professionally with clear headings, bullet points, and actionable insights. Include specific examples and quantify issues where possible.

Variables to Customize

[DATASET_NAME]

Name of the dataset being analyzed

Example: Customer Transaction Database

[RECORD_COUNT]

Total number of records in the dataset

Example: 1,250,000

[COLUMN_COUNT]

Total number of columns/fields

Example: 23

[DATA_SOURCE]

Origin of the data

Example: E-commerce platform API and CRM system

[TIME_PERIOD]

Date range of data collection

Example: January 2024 - March 2024

[KEY_COLUMNS]

Most important columns for business operations

Example: customer_id, transaction_amount, order_date, product_category, email_address

[DATA_ISSUES]

List of identified data quality problems

Example: 15% missing email addresses, 3,200 duplicate customer records, 45 invalid zip codes, inconsistent date formats in order_date column

[BUSINESS_CONTEXT]

How this data is used in business operations

Example: Used for customer segmentation, marketing campaigns, revenue reporting, and inventory management decisions

Example Output

# Data Quality Report: Customer Transaction Database ## Executive Summary The Customer Transaction Database shows 78% overall data quality with critical issues in customer contact information and data consistency. While transaction data integrity is strong (95%), missing email addresses and duplicate records pose significant risks to marketing operations and customer analytics. ## Data Quality Metrics - **Completeness**: 82% (15% missing emails impact marketing reach) - **Accuracy**: 89% (3,200 duplicate records identified) - **Consistency**: 74% (date format inconsistencies) - **Timeliness**: 96% (real-time API integration) - **Validity**: 91% (45 invalid geographic codes) ## Critical Issues Analysis **High Priority:** - Missing email addresses (187,500 records): Directly impacts email marketing ROI - Duplicate customer records: Creates inflated customer counts and skewed analytics - Inconsistent date formats: Prevents accurate time-series analysis ## Recommendations **Immediate Actions:** 1. Implement email validation at point of entry 2. Run deduplication process using customer_id and phone number matching 3. Standardize date format to ISO 8601 across all systems **Long-term Improvements:** - Deploy data quality monitoring dashboard - Establish data governance policies - Implement automated data validation rules ## Risk Assessment **Business Impact**: Current issues may reduce marketing campaign effectiveness by 15% and compromise customer lifetime value calculations.

Pro Tips for Best Results

Provide specific numbers and percentages rather than vague descriptions of data issues
Include both technical data quality metrics and business impact explanations for stakeholder buy-in
Prioritize recommendations by implementation difficulty and business value
Use industry-standard data quality dimensions (completeness, accuracy, consistency, timeliness, validity)
Include examples of actual data issues found to make the report more concrete and actionable