💹 Financial Data

Quant and corporate finance teams increasingly rely on non-traditional data: supply chain signals, job postings, government datasets, and investor relations updates. Yet each source carries its own authentication, throttling, and legal considerations. Modern scraping vendors provide the guardrails—proxy pools with consent records, audit logs, and takedown workflows—so compliance officers remain confident while analysts access the feeds they need.

A typical financial data pipeline starts with discovery and classification: mapping which public pages, portals, or APIs contain value for a research hypothesis. Scrapers then normalise the output into machine-readable formats—JSON, CSV, or warehouse tables—while enrichment steps align records to securities, industries, or tickers. Versioned delivery into data lakes or notebooks allows researchers to backtest signals and track revisions over time.

Because regulators scrutinise how alternative data is sourced, mature teams partner with providers that deliver compliance documentation, opt-out handling, and region-specific guidance. Combining self-serve extraction for exploratory work with managed datasets for production risk models keeps pipelines nimble without sacrificing governance.