On paper, cheap data looks like a win.
Lower cost per record. Bigger volumes. Faster turnaround. A spreadsheet full of opportunity, delivered neatly to your inbox.
Until it hits a live campaign.
That’s usually the moment when expectations meet reality. Bounce rates start creeping up. Open rates don’t quite land where they should. Call connect rates disappoint. Sales teams start asking awkward questions. And suddenly, that “cost-effective” dataset is consuming time, budget and goodwill.
This isn’t a scare story. It’s a pattern we see repeatedly when organisations come to us after a poor experience elsewhere.
Cheap data isn’t cheap. The costs just don’t show up on the invoice.
The Price You See vs the Cost You Pay
When people talk about the cost of data, they almost always mean one thing – price per record.
It’s an easy metric to compare. It looks objective. And in isolation, it feels like a sensible way to buy.
The problem is that marketing data doesn’t exist in a vacuum. It isn’t a static asset you purchase once and forget about. It either works for your campaigns or quietly undermines them, often without anyone noticing straight away.
The real cost of data tends to appear later, in places like:
- Wasted media spend
- Sales time lost chasing dead leads
- Deliverability and reputation issues
- Brand damage caused by poor targeting
- Internal friction when performance doesn’t stack up
None of those line items appear on the original quote. All of them affect results.
What “Cheap Data” Usually Means in Practice
Low-cost data has to cut corners somewhere. There’s no magic behind it.
In our experience, cheap data typically means one or more compromises are being made behind the scenes – even if the dataset itself looks perfectly fine when you first receive it.
Validation Is Optional (or an Upsell)
Phone numbers haven’t been HLR checked. Emails haven’t been properly verified. Addresses haven’t been screened or suppressed.
Instead, validation is either skipped entirely or positioned as an optional extra.
The result is simple: you pay for records that may never connect, never deliver, or never reach a real person. And you don’t find out until your campaign is already live.
Freshness Is Overstated
Data decays quickly. People move house. Change jobs. Switch numbers. Abandon inboxes.
Cheaper datasets are often refreshed less frequently, even if they carry reassuring “last updated” labels. A timestamp tells you very little about how aggressively decay is managed or how much unusable data has already crept back in.
Compliance Is Treated as a Grey Area
Proper compliance takes effort. Managing consent, legitimate interest, TPS screening and suppression files isn’t free.
When data is priced aggressively, compliance is often reduced to a vague assurance rather than a clearly defined process. That shifts risk from the supplier to the buyer – whether they realise it or not.
Volume Is Prioritised Over Accuracy
Large counts look good in proposals. They feel reassuring. They promise scale.
But volume without accuracy simply means more waste at scale. More records doesn’t equal more opportunity if a significant percentage of them are unusable.
Where the Hidden Costs Actually Show Up
The biggest issue with cheap data isn’t that it never works. It’s that when it doesn’t, the cost lands somewhere else.
Media Spend That Delivers Nothing
Emails sent to dead inboxes. Mail sent to old addresses. Paid media layered on top of poor targeting.
You still pay for delivery, even if nobody meaningful ever sees the message.
Sales Time Burn
Sales teams tend to feel the impact first.
Low connect rates. Wrong contacts. Conversations that go nowhere. It’s frustrating, but it’s also expensive. Time spent chasing bad data is time not spent speaking to people who could actually buy.
Deliverability Damage
Email platforms are unforgiving. High bounce rates and low engagement quickly affect sender reputation, which means even your good data starts performing worse over time.
Repairing deliverability is far harder – and far more expensive – than protecting it upfront.
Subtle Brand Damage
Consumers notice when marketing feels sloppy or irrelevant. Wrong names. Old details. Messages that clearly miss the mark.
That damage isn’t always loud, but it sticks.
Why Validation isn’t a “Nice to Have”
One of the clearest warning signs we see is data suppliers charging extra for validation.
That usually tells you validation isn’t fundamental to the process. It’s an add-on, not a baseline.
At TDP, validation happens before anything leaves the building. Every single time.
That includes HLR checks on phone numbers, email verification, postal screening, and TPS and suppression checks. Not because it sounds impressive, but because it’s basic data hygiene.
Validation doesn’t make data perfect. What it does is remove a large amount of avoidable waste before campaigns ever go live.
The False Economy of Fixing Data Afterwards
A common pattern we see goes something like this:
First, data is purchased cheaply.
Then, the campaign underperforms.
Next, validation and cleaning are applied retrospectively.
Finally, the client pays again to fix what should have been right in the first place.
That isn’t optimisation. It’s paying twice.
Good data work happens before campaigns launch, not as damage control once results disappoint.
Why Cheap Data Often Looks Fine in a Spreadsheet
Here’s the uncomfortable truth.
Cheap data usually looks fine when you first receive it. Fields are populated. Counts match the brief. Everything appears orderly.
Spreadsheets don’t show decay. They don’t reveal compliance gaps. They don’t tell you whether a phone will ring or an email will land.
Performance only reveals itself in the real world, once a campaign is live. By that point, the spend is committed and the opportunity cost is already ticking.
What “Good Value” Data Actually Looks Like
Good data isn’t about being the cheapest option on the table. It’s about being usable.
In practice, that means:
- Clear, transparent sourcing
- Validation included as standard
- Realistic counts rather than inflated promises
- Compliance explained in plain English
- Suppliers who are open about what’s removed, not just what’s included
Sometimes it also means hearing “less than you hoped”. We’d always rather supply a smaller, cleaner dataset that performs than a larger one that quietly drains budget.
The Most Expensive Data Is the Kind You Thought You’d Already Paid For
This is the line we come back to again and again.
When data fails, the cost doesn’t disappear. It just moves. Into extra campaign rounds. Internal explanations. Lost confidence. Missed opportunity.
Cheap data often looks like a saving. Until you factor in everything else it touches.
Final Thought
Price matters. Of course it does.
But in data-led marketing, the cheapest option is rarely the most cost-effective one.
A better question than “how much does it cost?” is this:
What’s actually been removed to make it that cheap?
If the answer is validation, freshness or compliance, the saving probably won’t last very long.