By Adam Cumes, Agency Sales Director, EMEA for Dun & Bradstreet
In the age of automation and efficiency, marketers are under more pressure than ever to deliver results with fewer resources. Budgets are tightening, expectations are rising, and the directive to “do more with less” is echoing across marketing departments worldwide. In this environment, data often feels like a place to save. After all, aren’t all contact records basically the same?
Consider this scenario: A B2B marketing team, facing budget cuts, decides to buy a lower-cost dataset to fuel their next campaign. It looks promising on the surface: thousands of names and emails at a fraction of the cost. But as the campaign rolls out, engagement is anemic. Bounce rates spike. A handful of recipients even lodge privacy complaints. Not only does the campaign underperform, but legal now wants to investigate. What seemed like a savvy savings move ends up eroding ROI, damaging trust, and increasing risk exposure.
So, what do we really pay when we buy cheap data? Often, far more than the price tag suggests.
The Myth of Short-Term Savings
Low-cost data is tempting, especially when every dollar counts. With a flood of vendors offering high volumes at bargain prices, it’s easy to assume you’re getting a good deal. But in many cases, those deals are built on outdated or unverified information, mismatched records, or opaque sourcing methods. What you save in dollars, you lose in effectiveness.
The appeal is understandable. Economic uncertainty has made efficiency the mantra of the day. Marketing teams are leaner, cycles are shorter, and procurement is scrutinizing every contract. In such an environment, low-cost data can seem like an easy way to keep campaigns running. But that illusion quickly fades when the hidden costs start to surface:
Marketing Ineffectiveness. Data is the foundation of modern marketing. If that foundation is shaky, everything built on top of it suffers. Poor data quality means bad match rates, wasted impressions, and missed opportunities. You can’t personalize messaging to someone you can’t accurately identify. And if you can’t deliver relevant, timely content, your audience simply tunes out.
Low-quality data also hinders optimization. When analytics are skewed by inaccurate inputs, performance benchmarks become unreliable. You may find yourself investing more in tactics that appear to work but are built on faulty assumptions, creating a cycle of inefficiency that’s hard to escape.
Compliance and Risk Exposure. Regulatory compliance is another area where cheap data becomes costly. Privacy laws like GDPR and CCPA require companies to maintain clear consent records and transparent sourcing while giving people control over their data. Many low-cost providers simply can’t meet those standards.
When campaigns are built on unverifiable, outdated, or unauthorized information, they risk violating privacy regulations, which can lead to fines, legal action, or mandatory audits. But perhaps even more damaging is the erosion of trust. Privacy violations or even just poor targeting practices can make customers feel surveilled or disrespected. And once trust is broken, it’s hard to repair.
Erosion of Trust and Brand Equity. Customers today are savvy, whether buying for themselves or their businesses. They expect brands to know who they are and to respect their preferences. When a message lands in their inbox that’s clearly off-base—sent to the wrong person, referencing outdated information, or making irrelevant assumptions—it signals that your brand doesn’t care enough to get it right.
But the damage doesn’t stop there. Internally, low-quality data can create tension between marketing and sales, who may see vastly different pictures of the same customer. It can lead to distrust in dashboards, misalignment in strategy, and wasted hours reconciling inconsistent systems. Over time, the result is organizational friction that slows growth and frustrates teams.
How to Prioritize Value Over Price
Reversing these outcomes starts with a mindset shift: from treating data as a commodity to viewing it as infrastructure.
Start by embracing data strategies that prioritize quality, consistency, and applicability across platforms over volume alone. The right strategy isn’t about choosing the cheapest source. It’s about choosing partners who can offer transparency, governance, and interoperability.
Next, insist on auditable, transparent data pipelines. You should know where your data comes from, how it’s validated, and whether it meets compliance requirements. Avoid providers who operate as black boxes. Instead, work with sources that can explain their matching logic, validation methods, and consent practices.
Finally, invest in persistent identity resolution models. In today’s omnichannel world, your audiences aren’t static. They interact across devices, roles, and platforms. Persistent identity resolution ensures you’re not just guessing who someone is based on a single interaction. Instead, it connects behaviors and signals over time, enabling you to deliver more accurate, relevant, and respectful experiences.
Data strategy is not a line item. It’s a core component of your brand’s infrastructure. Choosing cheap data might help you hit a short-term budget target, but it often undermines the very outcomes you’re trying to achieve. The real ROI comes not from how much data you can buy for the lowest cost, but from how effectively that data drives outcomes across your marketing, sales, and service functions.
