Introduction
Getting data into your spreadsheet, list, or database is the visible half of an import. The invisible half is knowing whether what arrived is correct. Economic data is unusually good at looking fine while being wrong: a source returns an empty array instead of an error, a series gets revised after the fact, a value lands in the wrong column, or a number arrives in a different unit than last month. None of these throw an exception. They just sit there, plausibly, until a chart looks strange or a stakeholder asks a question you cannot answer.
This guide is a short validation checklist to run after every load. It is deliberately lightweight — four checks you can implement in any tool — because validation only helps if it is simple enough that you actually keep it on.
Check 1: Completeness
The first question is whether you got data at all, and whether you got the rows you expected. A request that succeeds with a 200 status can still return an empty result set when a source is mid-update or a series ID has quietly changed. Append-on-empty logic then writes nothing, the import reports success, and your series silently stops advancing.
Guard against this with a minimum-rows expectation. If a daily series should add roughly one observation per run and a run adds zero across several days, treat that as a failure, not a no-op. If you import a fixed set of indicators, count them after loading and confirm the count matches your configuration. A load that came back with three of your five tracked series is a problem even though nothing errored.
Check 2: Range and type
The second check is whether the values are physically sensible. Economic indicators have natural bounds, and a value outside them almost always signals a parsing or unit problem rather than a real economic event.
An unemployment rate of 250 is not a recession — it is a percentage that got multiplied or a wrong column. A negative CPI index, a GDP figure three orders of magnitude off last quarter, a date in the year 1900: these are the fingerprints of a shape that changed upstream. Define a plausible range for each series and flag anything outside it for review rather than loading it blindly. Check types too — a value that arrived as text instead of a number will quietly break every calculation downstream.
💡 Tip: The most common silent corruption is a unit or scale change at the source — a series that reported in thousands switches to millions, or percent switches to a ratio. A range check catches the obvious version; comparing each new value to the recent trend catches the subtle one. A single point that jumps 100x from its neighbors deserves a look before you trust it.
Check 3: Freshness
The third check is whether the data is actually new. This overlaps with the monitoring habit from the reliability guide, and it belongs in validation too because stale-but-present data is a distinct failure from missing data. An import can run flawlessly, write the same last-known value every day, and look perfectly healthy while the underlying series has been frozen for a month.
Record the date of the newest observation per series and compare it against that series’ known update cadence. Daily series should advance most business days; monthly series like CPI or the unemployment rate advance once a month on a published schedule. When a series falls behind its cadence, surface it. The goal is to distinguish “the source has not released new data yet” (fine) from “our import stopped collecting it” (not fine) — and only the freshness check, run against an expected cadence, can tell those apart.
Check 4: Revisions
The fourth check is specific to economic data and frequently overlooked: published figures change after the fact. GDP, employment, and many other series are released as estimates and then revised — sometimes more than once, sometimes months later. An import that assumes a past value is fixed will hold a stale estimate while the official number has moved underneath it.
Decide explicitly how you handle this. The simplest robust approach is to re-fetch a trailing window — say the last few periods — on each run and upsert rather than insert, so revisions overwrite the earlier estimate keyed on (series_id, observation_date). If you need to preserve the history of revisions themselves, store a “vintage” or “as-of” date alongside each value so you can see both what was reported and when. Either way, the mistake to avoid is treating the first number you ever saw as permanent truth. In economic data, it usually was not.
📌 Note: Revisions are not errors in the source — they are how official statistics work. Building revision handling into the import from the start is far easier than discovering months later that your historical series disagrees with the agency’s published figures.
Putting it together
These four checks compose into a small validation step that runs right after the load and before anyone trusts the result: did we get the expected rows (completeness), are the values sane (range and type), is the data actually new (freshness), and have past values been allowed to update (revisions). A row that fails completeness or range should be quarantined and reported rather than published. A freshness or revision miss should raise the same kind of alert as a failed fetch. The work is modest, and it converts an import from “data showed up” into “data we can stand behind.”
Conclusion
An import you can trust is an import you have validated. Loading is mechanical; trust comes from the four checks that run afterward — completeness so empty responses cannot masquerade as success, range and type so corrupted values cannot slip through, freshness so a frozen series cannot look healthy, and revisions so official updates are not missed. Keep the checks simple enough to leave on permanently, route their failures to the same place your reliability alerts go, and the numbers you publish will hold up to the one test that matters: someone asking, “are you sure this is right?”