Machine-Readable Data
Machine-readable data is structured in a way that software can understand directly. Instead of being locked inside text or PDFs, the data is labeled, organized, and standardized.
In SEC filings, machine-readable data is commonly provided through formats like XBRL and XML. These formats allow financial figures, dates, and identifiers to be extracted precisely.
This approach transforms filings from static documents into usable datasets. It enables automation, large-scale analysis, and real-time monitoring of disclosures.
Why It Matters
Machine-readable data makes financial information faster to access and easier to analyze. It supports accuracy, scalability, and transparency in modern markets.
How does machine-readable data differ from human-readable filings?
Human-readable filings are designed for people to read, often as text or PDFs. Machine-readable data is designed for software, using structure and tags instead of layout. Computers can process machine-readable data instantly. This removes the need for manual data extraction.
Why is machine-readable data critical for financial analytics?
Analytics depend on clean, structured inputs. Machine-readable data provides consistent formats that models and tools can rely on. It enables comparison across thousands of filings. Without it, large-scale analysis would be slow and error-prone.
How does the SEC use machine-readable data?
The SEC uses machine-readable data to monitor markets and enforce reporting rules. Structured data helps regulators detect trends, inconsistencies, and potential risks. It also improves public access to financial information. This strengthens oversight and transparency.
Real-World Example
A data platform tracks revenue changes across all public companies. Machine-readable SEC data allows the platform to update its models automatically as new filings are released.
Machine-Readable Data and FinFeedAPI
All FinFeedAPI products deliver machine-readable data by design. Whether accessing SEC filings, stock data, currency rates, prediction markets, or bulk datasets, users receive structured formats ready for automation and analysis. This consistency allows teams to build reliable workflows across different financial data sources.