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50 sites. Daily collection. 3-4 people touching the data.
That's the scenario. And 800-1,300 hours per year is what it actually costs in maintenance nobody's tracking.
Smaller operation? Your number is lower — maybe 200-300 hours. Larger, or scraping sites with aggressive anti-bot? It's higher. Sometimes 2-3x higher.
But the pattern is the same: teams often underestimate their scraping maintenance by 4-6x. The time is real. It's just invisible.
We call this Hidden Labor. Here's where it goes.
Monday, 8:15 AM. Your engineer checks Slack before coffee. Three alerts overnight:
He spends 55 minutes on Nordstrom. They changed how the page is structured. Again. Another 35 minutes on ASOS — connection issue, needs rotating. 20 more minutes adjusting Zalando's scheduling to reduce timeouts.
By 9:45, he hasn't started his actual planned work.
Then a Slack from your analyst: "Shopbop prices look weird. All about 40% higher than last week."
Turns out Shopbop added a new "RRP" field above the actual price. The scraper grabbed the wrong number. Three days of data are wrong. Another 45 minutes to fix, plus re-pulling the missing data.
None of this is in anyone's sprint. None of it shows up in project tracking. It's just... Monday.
This is Hidden Labor — the untracked work that accumulates around any data collection system. After over 20 years of building scraping infrastructure — and taking over operations from dozens of in-house teams — we've learned something uncomfortable: the gap between what companies think they spend and what they actually spend is often 4-6x.
(How do we know? We ask teams to estimate before we start. Then we do two-week time audits across engineering, analyst, and business users. The gap is consistent enough that we stopped being surprised.)
A team that estimates "5-10 hours a month" is typically spending 40-60 hours. A team that says "our engineer handles it, maybe a few hours a week" is usually looking at 15-20 hours weekly — they just don't see it.
Hidden labor doesn't show up for three reasons:
That number isn't invented. Here's the breakdown for a typical mid-size operation — 50 sites, daily collection, mixed difficulty, 3-4 people involved:
| Category | Hours/Week | Hours/Year |
|---|---|---|
| Fixing broken scrapers | 6-8 | 364 |
| Data validation | 4-5 | 234 |
| Silent data changes | 2-3 | 130 |
| Connection/infrastructure | 4-5 | 234 |
| Request fulfillment | 3-4 | 182 |
| Firefighting (critical periods) | 1 | 52 |
| Coordination overhead | 2.5-3 | 143 |
| Total (fully loaded) | ~25 | ~1,300 |
That table shows fully loaded time — including context switching, meetings, and coordination. Pure task time (just hands-on-keyboard work) runs lower. Here's the distinction:
Most teams, when they actually track everything, land between 800-1,500 hours annually for a 50-site operation. The range depends on how you count — but either way, it's far more than the "few hours a week" most teams estimate.
Your numbers will vary based on three things:
But the pattern holds: when you actually add it up across everyone who touches the system, it's far more than anyone expected.
After working with dozens of companies on their competitive intelligence, we've identified seven categories where time disappears. Most teams experience all of them.
The obvious one — but even here, teams undercount.
A scraper breaks. Someone notices (eventually). Someone else investigates. They figure out what changed. They update the code. They re-run the job. They verify the output.
That's not one task. That's six. And each one takes time:
That's a 1-2% weekly break rate. It's not a bug — it's the baseline reality of web scraping. Sites change constantly. Scrapers break constantly.
If you're running 50 scrapers in-house, expect 2-3 to break every week — higher than our rate because in-house teams typically have less monitoring, fewer automatic retries, and less battle-tested systems. Running 150? Expect 8-12.
Different from a broken scraper: the scraper ran successfully, but is the data correct?
Someone has to check. They scan for obvious errors — missing fields, weird values, counts that don't match yesterday. They spot-check records against the source. They flag anomalies.
This happens every time data lands. And it's almost never tracked as "maintenance."
The math: 50 sites × 7 days = 350 runs per week. If even 10% need a human glance (3 minutes each) and 3% need real investigation (15 minutes each), that's about 105 minutes of glancing plus 2.5 hours of investigating. Every week. Just on validation.
Different from broken scrapers: the scraper doesn't crash, but the data quietly becomes wrong.
A price field that used to be clean now includes shipping. A product status that was binary is now a dropdown with six options. A category structure that was two levels deep is now three.
These changes don't trigger errors. They just corrupt your data — until someone notices and has to fix both the scraper and clean up historical data.
Why this is insidious: You often don't catch these silent changes until you've already made decisions based on bad data. The cost isn't just the fix — it's the decisions that were wrong.
We see 1-2 of these incidents per month on a 50-site operation. Each one takes 3-5 hours to fully resolve (detect, diagnose, fix, re-pull old data, communicate).
Different from fixing broken scrapers: this is ongoing maintenance, not incident response.
If you're scraping at any real scale, you're managing proxies (the connections scrapers use to avoid getting blocked). Monitoring success rates. Rotating pools. Replacing flagged IPs. Upgrading when sites get more aggressive.
Plus the infrastructure: servers, containers, scheduling systems, monitoring dashboards, cost tracking.
Weekly tasks that add up:
This work is often invisible because it's done by an engineer who has "other responsibilities." Scraper infrastructure is just one thing on their plate. But it still takes 4–5 hours weekly.
The scraper is built. The pipeline is running. But then:
Each request feels small. Each one requires: understanding the requirement, checking feasibility, modifying code, testing, deploying, validating output.
Reality check: Adding a "simple" new field takes 1-2 hours. Adding a new site takes 3-5 hours (longer if it has anti-bot protection). Changing frequency often requires infrastructure adjustments.
Most teams get 3-4 requests per month. That's 8-15 hours monthly that doesn't feel like "maintenance" but absolutely is.
Different from regular fixes: this is about timing and urgency, not just fixing.
Black Friday. Prime Day. A major competitor's product launch. A pricing decision that needs to happen today.
These are the moments when data gaps hurt most — and when teams scramble hardest.
The scraper that was "good enough" yesterday is suddenly a crisis. Engineers get pulled off other projects. Analysts run manual checks. Stakeholders ask questions every hour.
The hidden cost: Firefighting isn't just the hours spent. It's the stress. The other projects that slip. The weekend work. The technical debt from "just get it working" fixes.
We estimate 3-4 critical periods per year, each adding 15-20 extra hours. That's 60-80 hours annually of pure scramble mode.
Someone has to be the person who knows how the system works.
Who fields questions when data looks weird. Who explains to new team members how to interpret the outputs. Who documents (or more likely, keeps in their head) how everything fits together. Who sits in meetings about data quality, pipeline changes, stakeholder requests.
What we found in time audits: A senior engineer spent 2.5-3 hours per week just on coordination — meetings, Slack questions, code reviews, helping teammates. None of it felt like "scraper maintenance." All of it happened because the scraping system existed.
This "glue work" is invisible. But if the person doing it leaves, you suddenly realize how much they were holding together.
Hidden labor compounds. That's what makes it dangerous.
Scenario 1: A small data quality issue doesn't get caught. Decisions get made on bad data. Someone eventually notices. Now you're not just fixing the scraper — you're re-running analysis, updating reports, explaining to stakeholders why the numbers changed.
Two hours of work becomes twenty.
Scenario 2: An engineer leaves. Their replacement spends weeks figuring out how the system works. During that time, maintenance falls behind. Small issues become big issues. The backlog grows.
Scenario 3: A site adds aggressive anti-bot protection. Your success rate drops from 95% to 60%. You don't have time to properly fix it, so you just re-run failed jobs and accept the gaps.
Now everyone downstream is working with partial data — but they don't know it's partial.
Each of these scenarios is common. Each one multiplies the hidden labor. And they compound on each other.
Let me show you what this looks like in practice — at a company where the hidden labor was massive but completely invisible.
A luxury fashion marketplace — one of the world's largest — needed to track assortment data across their seller network. Which brands does each seller carry? What categories are they strong in? Where are the gaps?
The setup: 20 account sales people, each responsible for monitoring their assigned sellers.
The "simple" task: Check what products each seller has listed.
The reality: Each account manager was spending 6-8 hours per week on manual data collection. Not analysis. Not seller conversations. Just getting the data. Visiting seller sites, counting products, copying into spreadsheets.
That's 120-160 hours per week across the team. On data collection alone.
And here's the painful part: even with all that effort, they were only achieving about 10% coverage. They simply couldn't check enough sites, frequently enough, to get a complete picture.
Why it stayed hidden: Those 6-8 hours per person felt like "part of the job." It wasn't tracked as data collection. The hidden labor was invisible because it was distributed across 20 people, each absorbing their share.
We've been running this for them for over two years. The hidden labor didn't disappear — it just became our job instead of theirs.
Most teams are sitting on a similar story. They just haven't done the math.
Here's a simple audit you can run to surface that hidden work:
The gap is almost always larger than expected.
Once you see your actual hidden labor, you have to ask: is this the best use of these people's time?
Your analysts were hired to analyze — not to check if data arrived correctly.
Your engineers were hired to build products — not to babysit scrapers.
Your sales team was hired to sell — not to manually count products on competitor websites.
Every hour spent on hidden labor is an hour not spent on work that actually moves your business forward.
For some companies, the math still works out. If you're scraping 5-10 simple sites with no anti-bot protection, the hidden labor might genuinely be minimal. Keep doing it in-house.
But if you're scraping 50+ sites, dealing with anti-bot protection, running daily jobs, serving multiple stakeholders — the hidden labor is real, it's substantial, and it's worth quantifying honestly.
At PWS, we absorb the hidden labor. All of it.
We've been building scraping infrastructure for over 20 years — evolving from manual scripts to a fully automated system that runs with minimal human intervention. When sites change, our system detects it and adapts — often before you'd notice anything was wrong. When requests fail, smart retry logic handles it automatically. When data looks unusual, anomaly detection flags it before it reaches your team.
You get clean data. We handle the chaos underneath.
The hidden labor doesn't disappear — it just becomes our job instead of yours.
The numbers: 2,500+ scrapers running daily. 30-35 need manual fixes each week — about 1-2% break rate. Average turnaround on fixes: typically 4 hours. The other failures? Auto-recovery handles them before they reach your dashboard.
That's not because we're smarter. It's because we've spent two decades building systems specifically to handle the hidden labor that buries in-house teams. Every failure mode you'll encounter — we've seen it hundreds of times.
The Math Most Clients Do
If hidden labor costs 1,000+ hours annually, and even half of that is senior engineer time at $75-100/hour loaded, that's $37,000–50,000 in hidden cost. Our typical engagement for a 50-site operation runs well below that — and you get better data with zero maintenance burden.
The difference: our cost is visible on an invoice. Yours is buried across people who should be doing other things. See our pricing
You Stay in Control
You control what data you need. We handle how it gets collected.
When requirements change, our turnaround is typically 4 hours for routine requests, same-day for urgent ones. You're not locked into a rigid system — you're offloading the maintenance while keeping the flexibility.
Hidden labor is real. It's larger than you think. And it's eating time from people who should be doing higher-value work.
The first step is just seeing it clearly. Run the audit. Do the math. Then decide if it's worth it.
Most companies, once they actually see the number, realize it isn't.