OMNISIGHT FIELD GUIDE
What Is Alternative Data? A Source-Aware Guide
A practical framework for evaluating nontraditional data without confusing collection volume, source events, and real-world impact.
Alternative data is evidence outside the usual report
Alternative data is information that helps explain activity before, between, or beyond conventional financial statements and official summary statistics. Examples include public job postings, electricity prices, procurement notices, regulatory dockets, research metadata, satellite-derived observations, and software activity.
The label does not make a dataset useful. Value comes from preserving when a fact was observable, where it came from, what changed, and how confidently it can be linked to a real entity or event. A large feed with weak timestamps and unclear provenance can be less useful than a small, well-documented event log.
Three layers that should never be mixed
A collection record says that a monitored system returned data. A source event says that a specific real-world item appeared or changed. An interpretation explains why a group of events might matter. Treating these as the same thing creates false headlines and unstable models.
OmniSight stores observed time and ingested time separately, identifies historical backfills, and withholds partial-day comparisons. This prevents a newly downloaded old record from being presented as new activity and reduces future-reference leakage in model training.
- Collection volume: how much data the connector returned
- Current event: a specific item first observable inside the current window
- Historical backfill: an old event indexed today
- Interpretation: a reviewed claim supported by linked evidence
What makes an alternative-data claim credible
A credible claim uses exact item URLs, independent upstream hosts, explicit freshness and revision states, reproducible arithmetic, and a counterpoint. Two connectors that ultimately repeat the same publisher are one source, not two.
The safest question is not whether a chart looks unusual. Ask whether the underlying event is specific, material to the stated topic, independently corroborated, and still current. If any answer is no, the correct output is monitoring context rather than a public claim.