Data Testing 101

Whether this is your first time buying data or your tenth, we believe one thing should always be true:

You should always test data before buying it.

In many ways, buying data without testing it is like buying a car without ever giving it a test drive. On paper, everything looks great and exciting. But then you get behind the wheel and realize it’s a stick shift you don’t know how to drive, maybe you don’t quite fit in the seats, or maybe it just doesn’t feel good to drive. None of those issues show up in a spec sheet, but they matter a lot once you actually use the car.

The same is true for data.

Testing isn’t a “nice to have” or something you only do if you have extra time. It’s a critical part of the data buying process, especially if this is your first time sourcing third-party data for your business.

In this post, we’ll walk through why testing matters (and what can go wrong when you skip it), and how you can run a meaningful data evaluation, even if this is your first time.

Let’s dive in!

Having seen (hundreds of) thousands of customers go through the data buying process, we’ve seen two big traps teams fall into when it comes to testing:

Both approaches usually lead to the same problems.

If you’ve never bought third-party data before, it’s hard to know what’s normal (and this varies by use case):

Without testing (and comparing multiple providers), it’s difficult to develop an intuition for what’s available in the market for your use case and what tradeoffs exist between quality, coverage, functionality and cost.

Testing helps ground you and your team’s expectations in real-life data.

Surprisingly (or maybe not), data is rarely black and white.

Even when measuring or tracking the same thing, every dataset has its own unique:

Those details matter because they determine where the data performs well and where it doesn’t.

If you don’t understand those nuances, you risk:

Testing helps you learn how the data behaves for your use case before you’ve invested time and money embedding it into your production systems or business processes.

Even great data can fall apart at the last mile.

A small proof-of-concept often reveals things like:

These aren’t reasons not to buy data, but they are definitely things you want to know early on while you have the most flexibility.

With a better understanding of why testing data is important, let’s now take a look at how to actually run an effective data evaluation. A good data test doesn’t have to be massive or complicated, but it does need to be intentional.

No matter how experienced you are in the data buying process, here are our recommendations for running a strong data test:

Everyone starts by pulling a few familiar examples, and that’s a great place to start.

Just don’t stop there.

Whether that small handful of data looks great or raises concern, you need to look at a representative sample in order to start drawing conclusions.

This means testing with:

Aim to test at least a few hundred to a few thousand records, and try to use data that is as close as possible to what you would use in production (for example matching to records from actual user requests, or your CRM). If you can’t or don’t want to use production data, try to find a representative sample that mirrors it as closely as possible.

The goal is to learn how the data behaves at scale, not just in cherry-picked cases.

Context matters.

Testing one vendor in isolation makes it hard to know whether something is “good”, “bad”, or just “normal for this type of data”.

Running the same test across multiple vendors helps you:

Even if you end up choosing the first provider you tested, the comparison itself is incredibly valuable and educational in knowing how to effectively leverage the data.

No matter what level of experience you have with the data buying process, you should always ask questions to better understand the product.

Often asking about the data’s lineage is a good place to start, such as:

These details aren’t just “nice to know” - they directly inform how confidently you can apply the data to your use case.

Beyond understanding the data itself, asking questions also tells you something important about the provider: it helps paint a picture of the working relationship between you and your potential provider. If you need support in the future, do you feel that they would be able to help?

A good data provider’s responsibility is to ensure that you are educated, informed, and enabled - starting from the very first interaction.

So ask questions to understand the data and to understand the provider as well.

Documentation often tells you specifics that the sales pages, one pagers, and case studies don’t.

Good data documentation should cover:

Well-documented data is easier to test, easier to integrate, and easier to trust. It also helps clarify incorrect assumptions early on (or, tying back to the earlier point, good questions to ask your provider).

So yes, read the docs.

A meaningful test needs a hypothesis.

So before you pull data, ask yourself:

You don’t need perfect metrics by any means, but having some criteria upfront keeps the evaluation focused and actionable.

You don’t need a full production rollout.

A lightweight proof-of-concept that mocks how the data would flow into and throughout your system is often enough to:

We’ve often seen this small step make the difference between getting value from the data immediately - or never seeing value at all.

Over our 10+ years in the space, we’ve seen customers run deep, thoughtful evaluations: testing multiple vendors, defining clear success criteria, and measuring real ROI.

We’ve also seen customers buy data without any testing at all, and the difference between the two is almost night and day:

The happiest, most successful customers are almost always the ones who invested time upfront to understand what they were buying. They know what the data can do, what it can’t, and how it creates value for them.

Our goal is to help every prospective customer run a meaningful data evaluation, so that you can be confident in your decision, whether that leads to buying data or not.

That’s why:

You don’t have to figure this out alone. Our team has worked across hundreds (if not thousands) of real-world use cases, and we’re happy to help you build an evaluation that actually measures expected ROI.

* If you’re curious about which testing plan option is best for you, we wrote a handy guide all about this.

Whether you end up buying data from PDL or not, we believe this:

Our aim is to be a reliable partner on your data journey, particularly if this is your first time navigating the data provider landscape.

We’re here to help at every step of the way.

Thanks for reading, and happy testing.