Data Testing 101 - Job Posting Data

In our previous post, we covered a simple but important idea:

But not all datasets should be tested the same way.

Job posting data behaves very differently from traditional B2B company or person data. It changes constantly, comes from many different sources, and often requires more context to evaluate correctly.

That means the testing process needs to be different too.

Whether you’re evaluating hiring activity across thousands of companies or just a small set of strategic accounts, this guide will help you run a more meaningful job posting data evaluation: one that reflects how the data will actually perform in the real world.

Let’s dive in!

At a high level, job posting data sounds simple: a collection of open roles associated with companies.

In practice, it’s much more dynamic and fragmented than many first-time buyers expect.

Job posting data is:

Because of this, two datasets that look similar at first glance can behave very differently once you start using them operationally.

And unlike more static datasets, a quick spot check usually isn’t enough to understand how the data will perform in production.

One of the most important parts of testing job posting data is aligning the evaluation with your actual use case.

We typically see two broad categories of buyers:

You care about:

In these cases, testing should focus on:

You care about:

In these cases, testing should focus more heavily on:

Neither approach is inherently better, but they require different evaluation criteria.

One of the most common mistakes we see is evaluating job posting datasets primarily on total volume, even when the actual use case depends far more on precision within a relatively small set of companies.

Your evaluation is only as useful as the sample you test against.

If your use case depends on broad market coverage:

If your use case depends on a specific account list:

In both cases, the goal is the same:

Test the data in a way that reflects how you will actually use it – not just what’s quick to query.

Coverage is one of the first things buyers evaluate, but it’s also one of the easiest metrics to misinterpret.

Instead of asking:

Ask:

A dataset with millions of postings may still perform poorly if it consistently misses activity from your target accounts, industries, or regions.

For broader use cases, ask questions like:

For targeted use cases:

One of the advantages of job posting data is that much of it can be verified externally.

Job postings are public by nature, which means you can compare the dataset directly against company career pages and live job listings.

A few useful ways to validate the data:

This step is especially important for targeted-account use cases, where confidence in individual records matters more than aggregate trends.

Freshness is often one of the biggest differentiators between job posting datasets.

For many workflows, a dataset that is delayed or inconsistently updated quickly loses value.

Some important questions to ask include:

Some practical ways to test this:

You do not need a perfect methodology here. Even lightweight testing can reveal important patterns about how the data behaves over time.

Job posting data contains a large number of edge cases, and those edge cases often determine how usable the data is in production.

Some areas worth paying close attention to:

These issues do not always show up in high-level metrics, but they can have a significant downstream impact on analytics, enrichment, routing, and sales workflows.

The good news is that most of these issues are relatively easy to identify once you know where to look.

Once you’ve completed your evaluation, there are a few characteristics that consistently separate stronger datasets from weaker ones.

The best datasets are not necessarily the biggest. Rather, they are the datasets that reliably capture the hiring activity most relevant to your business.

That usually means:

Strong datasets tend to reflect hiring activity quickly and consistently.

Look for:

Beyond title, company, and location, useful datasets often include:

The more structured and standardized the data is, the easier it becomes to combine with existing datasets and operationalize downstream.

High-quality job posting data should map cleanly to the correct companies and entities.

That includes:

Strong entity resolution becomes especially important when integrating job posting data into broader GTM, analytics, or enrichment workflows.

Job posting data can be incredibly valuable, but it also requires a more thoughtful evaluation process than many first-time buyers expect.

The most successful teams are usually not the ones who run the largest tests or purchase the biggest datasets, they are the ones who take the time to understand how the data behaves within their specific workflow and use case.

At the end of the day, the core things to evaluate are relatively simple:

A strong testing process helps you build realistic expectations, understand tradeoffs between providers, and ultimately choose a dataset that delivers real operational value.

If you’d like help designing a job posting data evaluation or understanding the broader provider landscape, reach out to the PDL team. We’re always happy to help teams run thoughtful, practical data tests.