Open any third-party data pitch and you'll likely find the same two numbers: 95% US coverage and ten-thousand-plus attributes. Reach and attribute count have become table stakes, claimed by everyone and differentiating no one. The spec sheets have converged. The performance behind them has not.

So when two vendors promise to reach nearly every US adult, how do you actually know which one reaches your best audience? Or whether you're paying twice for the same households across overlapping sources? 

Most organizations can't answer that. The deficit in data isn't supply. It's clarity about the data you already buy. And in a market where companies invest more than $22 billion a year in third-party data, that's an expensive question mark. 

Today's data marketplace is fragmented across dozens of providers, each with its own source methodologies, identity graphs, contractual terms, and privacy and compliance standards. There’s no common yardstick for evaluating true quality, which means there’s no reliable way to judge ROI.

The result is waste hiding in plain sight: redundant sources, low-quality attributes, and poorly matched records that drain spend. A “95% coverage” claim tells you a record exists somewhere, but it tells you nothing about whether that record is accurate, differentiated, or predictive of how a real customer will behave. This is the trap of buying data as a commodity. 

Here's the shift that changes the conversation: third-party data isn't a static product you buy off a shelf. Its value depends entirely on how it's evaluated, combined, and maintained.

Two vendors can license overlapping raw sources and deliver completely different performance, because the value isn’t in the raw feed. It's created in the methodology: how sources are aggregated, cross-validated, deduplicated, and optimized into the best possible view of a consumer or business contact.

This holds whether you're building consumer audiences or B2B contact data. The raw inputs differ, with household demographics and behaviors on one side and firmographics, titles, and verified business contacts on the other, but the principle is identical: a single source is never the whole picture, and the value is in how multiple sources are reconciled into one trustworthy view. A B2B record that's stale on title or wrong on company size wastes budget exactly the way a consumer record with poor coverage does: it sends good spend after the wrong target.

That's the principle behind Merkle's Data Valuation Lab. Rather than treating coverage and attribute count as the finish line, the Lab aggregates and optimizes data from across the marketplace, then stress-tests it across four dimensions of real performance:

1. Coverage — Match rate measures presence; coverage measures truth. It isn't enough for a record to exist. It must reflect reality.
2. Accuracy — Wrong signals don't just miss, they mislead. Attributes are validated through consensus models and correlation analysis before they earn a place in the portfolio.
3. Descriptive power — A thousand variables mean nothing if none of them differentiate. Depth and specificity beat raw counts every time.
4. Predictive power — Targeting is only as strong as the data behind it. Lab-validated, multi-sourced data has been proven to outperform single-source alternatives in live campaign models.

Crucially, the process is transparent and replicable. Every source, methodology, and scoring criterion is open to client review. Teams can understand the value of what they’re already paying for and determine whether they’re getting the best ROI for their data spend.

Most organizations sit somewhere on a short progression toward data they can actually trust. Knowing which stage you're in is the point of the audit.

Stage 1 — Buying on spec

Vendors are chosen based on coverage claims and attribute counts. Overlapping sources accumulate. No one can say which one is actually performing.

Stage 2 — Cleaning the data

Hygiene improves, with duplicates flagged and records standardized, but value remains unmeasured. Clean data and valuable data are not the same thing.

Stage 3 — Evaluating the data

Sources are benchmarked across coverage, accuracy, descriptive power, and predictive power. Underperformers are identified. Spend starts moving toward what works.

Stage 4 — Optimizing continuously

Data is treated as a managed asset, re-validated over time, and consolidated to the smallest set of sources that delivers the strongest outcomes. Efficiency and quality stop being a tradeoff.

The distance between a Stage 1 organization and a Stage 4 one is measurable in acquisition costs, conversion, and the dollars spent on data that never moves the needle.

Consider a large membership organization managing three overlapping data sources. Their setup was driving up costs, creating data integrity issues, and generating customer complaints from conflicting records.

A rigorous evaluation proved a single superior data set could replace all three, outperforming the incumbents on every dimension: 81% coverage versus 69% and 46% for the competing sources, the highest predictive power of any vendor tested, a 14% lift in segmentation granularity, and top accuracy across the majority of expected variable relationships.

The business impact: $1.1 million in cost savings, 20,000 new members, and 7 million incremental prospects. Fewer sources, lower cost, and better performance.

The market is catching on to the language of evaluation. Data owners have begun marketing their own versions of a “data audit,” the clearest sign yet that the ground is shifting from spec sheets to scrutiny. But a label is not a method. As “audit” becomes a category buzzword, the burden falls on buyers to ask what's actually happening under the hood.

A real audit should be able to answer four questions about your data, in your numbers, before you sign anything:

1. Is it benchmarked across all four dimensions—coverage, accuracy, descriptive power, and predictive power—or only scrubbed for hygiene? Most “audits” stop at deduplication and call it analysis. Hygiene tells you the data is clean but doesn't tell you if it’s valuable.
2. Is the methodology transparent and replicable? Can you see every source, scoring criterion, and step, and could a third party reproduce the result? If the evaluation is a black box, it's a sales tool, not an audit.
3. Is it measured against your real outcomes, in a live model, rather than against the vendor's own marketing claims?
4. Does it quantify what to cut? A genuine evaluation is willing to tell you that you're overpaying, even when that means recommending less data.

An evaluation that can't answer all four isn't rigorous; it's just repackaged data hygiene. Reach and attribute counts have already converged across the industry. The next thing data owners will try to converge on is the language of evaluation. The methodology behind it is far harder to copy, and it's the only part that actually changes a client's ROI.

Here's the uncomfortable part: if you're buying data on coverage claims today, you probably can't say whether you're overpaying. The only way to find out is to look.

Our evaluation begins with a match test that costs nothing, moves no data out of your environment, and runs in minutes. It surfaces duplicate records, coverage gaps, and overlap across your existing sources before any commitment, any contract, or any switch. The full audit goes deeper across all four dimensions, but the first step is designed to be the easiest decision you'll make this quarter: a no-risk read on whether your data spend is working as hard as you think it is.

When did you last check? If the answer is “I'm not sure,” a single match test can help.