Making Alternative Data Research-Ready

The promise of alternative data is compelling: information sources outside the traditional market data ecosystem — satellite imagery, credit card transactions, job postings, patent filings — can provide an informational edge that conventional data cannot. The reality, however, is that alternative data arrives in formats that are anything but analysis-ready.

Consider satellite imagery of retail parking lots, a popular alternative data source for predicting quarterly revenue. The raw data is a collection of geospatial images captured at irregular intervals, with varying cloud cover, resolution, and lighting conditions. Turning this into a time series of "cars in the parking lot of Store X on Date Y" requires image preprocessing, object detection, geospatial matching to store locations, temporal interpolation, and quality-control filtering. Each step introduces potential errors and biases that, if unaddressed, will corrupt downstream analysis.

ShoalFlow's alternative data pipeline handles this complexity through a layered architecture. The ingestion layer connects to vendor APIs and raw data feeds, normalizing formats and applying basic quality checks. The enrichment layer runs domain-specific processing — computer vision models for imagery, NLP for text, entity resolution for transactional data — and outputs structured records tagged with metadata. The alignment layer joins these records to a master timeline, handles point-in-time correctness to prevent lookahead bias, and publishes the result as a clean, query-ready dataset. Researchers interact with the final output through SQL or DataFrame APIs; the messy pipeline that produced it is entirely abstracted away.