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Maximizing Your Data’s Potential: Best Practices for Streamlining Data Enrichment
- Data enrichment refers to the process of enhancing your first-party data by adding supplemental information from external, often third-party, sources.
- The process of combining proprietary data with external data sources can be slow and costly.
- Common data enrichment challenges include data quality, coverage, delivery, cost, and formatting.
- To streamline data enrichment, third-party data should be relevant, consistent, accessible, and trustworthy.
- Four key criteria to look for when evaluating datasets include relevance, consistency, accessibility, and trustworthiness.
- Trusted third-party data sources that meet criteria outlined can supplement existing data with additional attributes that meet business needs.
- Precisely offers thousands of data attributes and hundreds of datasets, organized into six categories.
- Travelers, an industry-leading insurance company, was able to use the PreciselyID to streamline data enrichment, saving valuable time and money.
- Enriched data helps businesses personalize marketing efforts, predict consumer behavior, and optimize inventory management.
- Data enrichment enhances patient records by incorporating social determinants of health, leading to better outcomes and more targeted treatments.
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