Web-scraped data is stored in various formats like CSV or JSON and analyzed with Business Intelligence tools. Analysts may scrape data like product prices or reviews and visualize it using platforms such as Tableau or Power BI. Modern solutions facilitate this process by structuring data for analytics systems, though query formulation and visualization still require manual effort. Large Language Models are introducing a new method for deriving insights through natural language prompts, enabling non-technical users to access information more intuitively without the need for complex dashboards or queries.
Web-scraped data has traditionally been stored in files or databases and analyzed using Business Intelligence tools. teams might scrape product prices or customer reviews and then create dashboards for insights.
Modern web scraping solutions output data in structured formats like CSV or JSON to facilitate the analytics pipeline. This allows analysts to slice data for insights but requires manual effort.
Large Language Models are changing the paradigm of data analysis by allowing organizations to derive insights from data through natural language prompts rather than static dashboards.
Using AI assistants, users can directly ask questions about the data, making information access faster and more intuitive for non-technical users without the overhead of building complex queries.
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