A quarterly content audit checklist works because most of what it checks drifts slowly. Titles, meta descriptions, internal links, the date in the byline: a pass every 13 weeks catches all of them before any real damage lands. Two other things live inside those same posts. The statistic you published as fact ages every time its source releases a new reading, and the page you cited can drop the figure or go dark on any day of the gap. Those two keep a schedule you never agreed to.
I downloaded six of the most-shared content audit checklists before building this one. All six inspect the wrapper around the post. The question of whether the number inside is still true never appears on any of them.
What a Quarterly Content Audit Checklist Misses
A quarterly content audit checklist reviews every published post on a roughly 13-week cycle for title and meta accuracy, link health, honest update dates, rendering, and search intent drift. A data accuracy content audit adds two rows those templates skip: the age of the data inside each post, and whether the cited source still states the claim.
The two added rows come first here, because they're the ones that actually rot.
Check the age of every cited number
We scanned 5,034 data claims to measure how they age, and the decay concentrates with time.
In posts under a year old, 2.0% of cited stats have aged past their intent. In the two-to-three-year band, the share increases to 10.3%, and that band is where most of a healthy catalog already sits. In the state of content decay study, one in five posts that made a data claim was leaning on a number two or more years old. When you order your inventory for this pass, sort by data age instead of traffic; the refresh-vs-claim gap study measures how far visible refresh dates run ahead of the claims underneath them.
Check: for every post older than a year, find the load-bearing statistic and confirm it still matches the latest reading from its original source.
Check the source still states the claim
A link scan tells you the URL resolves. It says nothing about whether the page behind it still states the figure you put under your name. Most borrowed claims can't survive that question: only 30% carry an external link a reader could follow at all, and just 17.2% of citations reach a primary source instead of a secondary page repeating the same figure. When a source has dropped the number, you're asserting it on your own authority without knowing it.
Check: for every borrowed statistic, follow the citation and confirm the source still shows that exact figure.
Neither row can be run by eye. Nothing on the page tells you whether the 34% you cited in 2024 is still 34% at the source, or whether the page you linked still loads.
The workload is just as lumpy. In the corpus behind the chart above, one post in four cited no data at all, while the median data-citing post carried four checkable claims and one in ten carried 16 or more. From the outside you cannot tell which is which, so a hand pass spends its first hour just finding out how much work each post is.
Paste the post's URL into the scanner below and both rows finish themselves.
The Content Health Scanner fetches the live page, extracts its data claims, and reports the age of each figure, which cited sources still load and still state the number, which sources resolve but have gone abandoned, and which figures sit on the page with no source at all. It runs without an account and returns results in under a minute.
Work the Wrapper Rows Fast
The rest of the checklist is the part every template already covers. These rows are real and they belong in a quarterly pass, and because a person can finish each one by reading the page, they go fast:
- Title and meta accuracy. Confirm the title tag and meta description still describe what the post says and still match the query it ranks for.
- Internal links. Click through them; fix any pointing at moved or retired pages.
- Broken links. Run a link scan and clear the dead outbound URLs. Remember what the scan proves: the URL is alive. Whether the page still backs your claim is the row above.
- Last-updated honesty. Make sure the visible updated date reflects a real change to the post.
- Render and format. Open the post on a phone, confirm images load and embeds display.
- Intent drift. Confirm the post still answers the question a reader arrives with today.
All six pass or fail in a reading pass. A 2024 figure that expired in 2025 sails through every one of them.
Here is the full sheet, data rows on top:
How Often Should You Run a Content Audit
The genre consensus says every three months, and for the wrapper rows that holds up. Sites that publish rarely can audit quarterly or even twice a year without much risk. Before you commit to an interval, though, be honest about the one you've actually kept.
Whatever you answered is the width of your blind spot. The wrapper rows hold still inside that window. The data and source rows keep moving through it while the calendar waits.
A fixed interval is a sampling rate, and a sampling rate decides what you can miss. The wrapper rows change slowly enough that a quarter still catches them. The data and source rows move faster than that, on a clock the calendar never syncs to. The interval you settle on is the sampling window you read those two rows through, and a number can turn wrong anywhere inside it.
Two Rows That Never Belonged on a Quarter
You've now worked a full quarterly content audit checklist, and two of its rows have a problem the other six don't share. A source can change its figure or drop offline on day three of a 13-week gap, and the sheet won't hear about it until week 13. For those 10 weeks, a post you certified healthy is publishing a number its own source no longer states.
Data age and source liveness belong on a watch, not a calendar. Monitored Pages tracks the external URLs you cite and flags the affected posts the day one of them changes. Claims turns each statistic on a page into a tracked entity with its own status, so a number going stale reaches you as an event instead of waiting for your next pass.
Whether to keep running these two rows by hand every quarter is a real argument, and manual audits miss source decay takes it up directly. If you want the mechanics of watching the gap between passes, they're laid out in how to detect when published data goes stale.
What the Last Audit Certified
Somewhere in your catalog a figure crossed into wrong between two passes, and the last audit marked that post healthy because every row a person can read off the page came back clean. Move the two rows it couldn't read onto an always-on watch, and the checklist stops vouching for numbers it never saw.