Dataset Cleaning Tips
Cleaning Multi-Source Datasets for Consistency
This prompt helps data science teams clean datasets collected from multiple sources by resolving inconsistencies, normalizing data formats, and addressing schema mismatches. It ensures the dataset is cohesive and ready for integration or analysis.
Responsible:
Data Science
Accountable, Informed or Consulted:
Data Science, Engineering
THE PREP
Creating effective prompts involves tailoring them with detailed, relevant information and uploading documents that provide the best context. Prompts act as a framework to guide the response, but specificity and customization ensure the most accurate and helpful results. Use these prep tips to get the most out of this prompt:
Gather datasets from all sources and review schemas, field names, and formats.
Identify trusted data sources or rules for prioritizing conflicting data.
Define the desired unified schema and data quality standards for the combined dataset.
THE PROMPT
Help create a cleaning plan for preparing a multi-source dataset from [specific sources, e.g., CRM, third-party APIs, and user logs]. Focus on:
Schema Alignment: Recommending standardization methods, such as, ‘Unify field names, data types, and column orders across all sources to ensure schema consistency.’
Data Format Normalization: Suggesting formatting strategies, like, ‘Standardize date-time formats, currency types, and measurement units to ensure compatibility during analysis.’
Deduplication Across Sources: Including strategies for resolving redundancies, such as, ‘Identify and remove duplicate records that appear across sources by matching unique identifiers or fuzzy attributes.’
Handling Conflicting Data: Proposing conflict resolution methods, such as, ‘Prioritize data from trusted sources or apply aggregation rules when discrepancies exist between records.’
Source Tracking: Recommending validation steps, such as, ‘Maintain metadata about the origin of each record to enable traceability and auditing after integration.’
Provide a detailed plan for cleaning and unifying multi-source datasets to improve their reliability and usability. If additional details about the data sources or integration goals are needed, ask clarifying questions to refine the cleaning plan.
Bonus Add-On Prompts
Propose strategies for handling data encoding differences (e.g., UTF-8 vs. ASCII) in multi-source datasets.
Suggest methods for visualizing overlaps and conflicts between sources during cleaning.
Highlight techniques for automating multi-source cleaning workflows using tools like Apache Spark or DataWrangler.
Use AI responsibly by verifying its outputs, as it may occasionally generate inaccurate or incomplete information. Treat AI as a tool to support your decision-making, ensuring human oversight and professional judgment for critical or sensitive use cases.
SUGGESTIONS TO IMPROVE
Focus on cleaning multi-source datasets for specific use cases, such as marketing analytics or financial forecasting.
Include tips for merging datasets with sparse or partially overlapping attributes.
Propose ways to document data lineage and transformations for multi-source cleaning projects.
Highlight tools like Talend or KNIME for managing complex multi-source workflows.
Add suggestions for validating the final dataset with downstream applications or models.
WHEN TO USE
During data integration projects involving multiple sources.
To prepare datasets for unified reporting, dashboards, or machine learning.
When addressing discrepancies between datasets collected from different platforms.
WHEN NOT TO USE
For single-source datasets with minimal inconsistencies.
If data sources are unreliable or poorly documented.