What are unstructured data examples in ESG context?

What are unstructured data examples in ESG context?

• A. Stock values
• B. Water-use quantities
• C. News articles, call transcripts, reports
• D. Relational database metrics

Answer: (C)

Unstructured data are information that don’t fit neatly into a predefined table or database schema, often appearing as free text or media. In ESG, this includes text-heavy sources like news articles about a company’s ESG performance, call transcripts from earnings or investor meetings where sustainability topics come up, and narrative reports such as annual reports or sustainability reports. These differ from structured data, like stock values or water-use quantities, which are numeric and stored in tables with defined fields. Relational database metrics are also structured, organized into rows and columns. Unstructured ESG data are valuable because NLP and text analytics can extract sentiment, topics, and risk signals to complement the quantitative metrics, giving a fuller picture of ESG performance.

Unstructured data are information that don’t fit neatly into a predefined table or database schema, often appearing as free text or media. In ESG, this includes text-heavy sources like news articles about a company’s ESG performance, call transcripts from earnings or investor meetings where sustainability topics come up, and narrative reports such as annual reports or sustainability reports. These differ from structured data, like stock values or water-use quantities, which are numeric and stored in tables with defined fields. Relational database metrics are also structured, organized into rows and columns. Unstructured ESG data are valuable because NLP and text analytics can extract sentiment, topics, and risk signals to complement the quantitative metrics, giving a fuller picture of ESG performance.