Open the oldest post on your blog that still ranks. Somewhere in its body sits a statistic with a year attached, and that year is further away today than it was when you hit publish.
We scanned 5,034 data claims across 961 SaaS blog posts from 46 domains to measure exactly that distance. One in five of the posts that make data claims carry at least one number two or more years out of date, and the share keeps growing the longer a post goes without a real review.
Old Data Grows With Post Age
Every dated claim in the scan got sorted by the age of the data it cites. A claim pointing at a 2026 figure is fresh. A 2025 figure is aging. Anything from 2024 or earlier counts as aged, old enough that a careful reader would want it rechecked before repeating it.
One in five of the posts that cite data already carry at least one aged figure. And the aged share inside a post tracks the post's own age:
In posts under a year old, 2.0% of cited stats are two or more years out of date. By the time a post is two to three years old, the figure is 10.3%, about five times higher. The mechanism is passive: the citations sit still while the calendar moves, so an evergreen post accumulates aged data simply by continuing to exist.
Risk Doubles at the One Year Mark
Group the same claims by post instead and the curve grows a corner. Under 12 months, 10.0% of posts carry data two or more years old. Past 12 months the share jumps to around 24% and stays there: 23.2% at 12 to 18 months, 25.1% at 18 to 24 months, 23.9% at 24 to 36 months.
Read the two curves together. The count of affected posts doubles at the one-year line and then plateaus, while the amount of aged data inside each affected post keeps increasing. A post crosses its first birthday and its odds of carrying years-old figures more than double, on a day when nothing on the page changed and no reminder fired anywhere.
Any post past 12 months deserves an audit, and the hidden cost of outdated charts compounds with every month past that line.
Borrowed Statistics Age Fastest
Sort the aged claims by where their data came from and one pattern repeats across domains: the "according to a 2023 report" citation is the likeliest to be carrying years-old data. Numbers a publisher measured themselves age the least, because a team that ran the survey once tends to run it again.
The mechanics explain the gap. You control a first-party number and can refresh it on your own schedule. A borrowed number stays frozen at whatever year you copied it from, and it keeps aging while your attention moves to the next post. So when you open an old post to update it, start with the borrowed citations. They age the most and they hide the best, because the sentence around an aged statistic reads exactly as well as it did the day you wrote it.
Old Data Versus Wrong Data
Checking each aged claim against its live source produced the study's most important result. For most of the aged numbers, the source offered no evidence the figure had changed. The data was old, and old was the whole finding. A measurable slice, though, has finished the trip: 163 claims, about 3% of the total, are stale as presented, a benchmark measured somewhere between 2019 and 2023 offered to a 2026 reader as a description of right now.
That majority is why this is a maintenance problem rather than a fact-checking scandal. A 2023 statistic in a post that ranks in 2026 is a figure your reader takes as current while the world has had three years to move. Nobody debunked it. Everybody left it. The job is flagging the years-old numbers for a human before they drift the rest of the way from old into wrong.
A date-stamp audit can't see any of this. The last-updated field records that someone touched the page, and the 2023 figure in paragraph nine can survive a dozen of those touches without anyone rereading the sentence it lives in.
Source Links Are Dying Too
Every claim and citation was already sitting in a database, so the same scan checked the links behind them. Among borrowed claims, only 30% carry an external link in the claim's own paragraph; the other 70% name a source with no way to check it, or cite nothing at all. And of the verified links that do exist, roughly one in five (20%) turns out to be dead, gated, or broken. The full breakdown is in the source-verification study.
Stack that on the aging curve and the failure becomes a system. A post borrows a figure from 2023. The figure ages. The link behind it rots. The paragraph keeps reading fine, the post keeps ranking and converting, and a normal refresh cycle visits neither problem.
What Claim Maintenance Costs
Put a price on the fix. The cost model assumes 0.5 hours per claim to locate the data point, check it against a current source, and update it. Borrowed claims with no link cost more, because the auditor has to relocate the original source before checking anything.
What Moves the Number
Blog size sets the floor. Average post age sets the multiplier. Audit frequency swings the total more than either, and it's the one variable most teams have never written down.
Take the typical case: a 50-post SaaS blog, average post age 18 months, audited once a year at $75 per hour. That's roughly $2,000 per year in claim-maintenance debt. I've never seen that line in a content budget. Teams track whether a post got touched, and the data inside it goes uncounted.
Whether this model fits your team comes down to what your update cycle actually reviews.
Where your answer lands decides how much of that $2,000 buys real corrections.
The cost model above assumes every claim is found during the audit. That assumption depends entirely on how deep the review goes. If the update cycle stops at structural edits and never reaches the data layer, the budget spent on refreshes produces zero claim corrections. The data inside a post ages on its own clock whether or not the intro gets refreshed, so an update that never reaches the data layer leaves the aging in place.
LiquiChart's content debt estimator runs the same cost model on your numbers.
If the result is worth a conversation, generate a shareable link and send it to whoever owns the content budget.
Methodology
The dataset: 961 posts collected from 46 SaaS domains, every data claim extracted from each, 5,034 claims in total.
Each claim that references a specific year got a computed age: a current-year figure counts as fresh, a one-year-old figure as aging, and a figure two or more years old as aged. Claims anchored to a fixed event or a historical subject are exempt, since a stat about the 2020 election or a 2018 funding round was never supposed to change. The Content Health Scanner writes the same deterministic freshness tier for every claim, so scanning a post from the study reproduces its result.
Two questions stay deliberately separate. "Is this data old?" is a calendar fact, and it's what this study reports. "Is this number now wrong?" demands positive evidence the figure changed, and that flag only fires when the live source contradicts the cited value. Keeping the first claim from inflating into the second is the discipline the whole study rests on.
Source verification ran as a separate pass: HTTP requests against every unique external citation, classified by reachability, with publish and modified dates pulled from the reachable ones.
The Debt Is Already on the Books
Content debt hides from the places budgets get set and surfaces everywhere else: the ad hoc fix request, the late-night edit before a sales call, the discovery that your oldest posts cite data from years you wouldn't put in a slide deck today.
The scale is knowable now. About three in four SaaS blog posts contain data claims, and the average post carries several. Past 24 months, about one in ten of a post's cited stats has aged two or more years out of date, alongside borrowed citations that were old on arrival and source links partway to a 404.
Page-level refreshes leave all of it in place, because the unit that ages is the individual claim. Catching it takes claim-level detection running on living content infrastructure, and that shift is what living content makes: maintenance targets the claim.
Pick your highest-traffic evergreen post and see how much of its data has aged.
Every quarter the budget skips claim-level maintenance, another cohort of posts crosses the one-year line and starts serving numbers readers will take as current. The debt accrues either way. Pricing it early is the cheap option.