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Data Enrichment

TL;DR

Section titled “TL;DR”
  • Add supplementary attributes or derived information to existing datasets to make them more useful for analysis.
  • Sources include external datasets (e.g., third-party demographic or market data) and extraction from unstructured text using NLP.
  • Common benefits are improved analysis accuracy, clearer relationships among variables, and detection of new patterns or trends.

Definition

Section titled “Definition”

Data enrichment is the process of adding additional data to existing sets of information in order to enhance their value and utility.

Explanation

Section titled “Explanation”

Data enrichment enhances an existing dataset by supplementing it with extra information or by extracting additional attributes from unstructured content. This can improve the accuracy of analyses, deepen the understanding of complex relationships within the data, and help identify new patterns and trends. Enrichment can come from external sources or from applying techniques such as natural language processing (NLP) to unstructured data, enabling more detailed analysis and better-informed decisions.

Examples

Section titled “Examples”

External data sources

Section titled “External data sources”

A company may have a large dataset containing information about its customers, including their names, addresses, and purchase history. By adding data from external sources, such as demographic information or purchasing habits from third-party market research firms, the company can gain a more comprehensive understanding of its customer base and target its marketing efforts more effectively.

NLP on unstructured data

Section titled “NLP on unstructured data”

A dataset containing customer reviews of a product may not contain explicit information about the features of the product or the satisfaction level of the reviewers. By using NLP techniques, such as sentiment analysis or topic modeling, the dataset can be enriched with this additional information, allowing for more detailed analysis and insights.

Use cases

Section titled “Use cases”
  • Improving the accuracy of analysis
  • Enhancing the understanding of complex relationships
  • Identifying new patterns and trends within the data
Section titled “Related terms”
  • Natural language processing (NLP)
  • Sentiment analysis
  • Topic modeling
  • Third-party market research firms