If you are shopping for B2B data, you have read the same three claims from every provider: high accuracy, broad coverage, and fresh data. The problem is that almost none of those claims come with a way to check them, so buyers end up choosing on brand familiarity or a sales demo rather than evidence. B2B data should be easier to compare, and the fix is not another accuracy badge: it is providers publishing what they actually hold, how they collect it, how fresh it is, and where they are a good fit. This article lays out what “easy to compare” should mean, shows what PredictLeads publishes about itself, and gives you a framework to verify any provider, including us.

TLDR

Most B2B data vendors make near-identical claims about accuracy, coverage, and freshness, and buyers rarely get a way to verify them.
“Easy to compare” means six things published in the open: what the data is, what each dataset contains, how fresh it is, how far back it goes, how it is collected, and where the provider fits.
Different providers have genuine strengths in different categories, so a fair comparison expects no single vendor to win everything.
You can verify a provider yourself: read its docs, trace a single record to its source, test a live endpoint, and check an independent benchmark.
PredictLeads publishes per-dataset record counts, start years, refresh cadences, named collection sources, and a live API playground, and it tracks 130M+ companies across 195 countries.

What “easy to compare” actually means

Easy to compare means a buyer can answer the questions behind each marketing claim without a sales call. Accuracy, coverage, and freshness are not facts until they are attached to numbers, dates, and sources you can inspect. A provider that is comfortable being compared publishes the raw material for that inspection and lets you draw your own conclusion.

Here is the gap between a claim and something you can check:

The claim	The question behind it	What a transparent provider publishes
Broad coverage	How many companies, and where?	Total company count, countries covered, and a record count per dataset
Fresh data	How current is a single record?	Refresh cadence plus a timestamp on every field
Deep history	How far back does it go?	A start year for each dataset
Accurate detection	Where does the data come from?	The named collection sources and the method
Verifiable	Can I trace one record?	A source link on each record, plus live docs and a test playground
The right fit	Where is this provider actually strong?	An honest scope and results from independent benchmarks

If you want the buyer-side version of this, our guide on how to evaluate a technographic data provider walks through sources, freshness, coverage, and delivery in more detail, and the broader B2B data enrichment guide covers how these fields land in your workflow.

Why B2B data is hard to compare today

B2B data is hard to compare because the industry standardized on adjectives instead of numbers. “Accuracy” rarely comes with a definition, a test set, or a date. “Coverage” is often a single headline figure with no per-dataset breakdown, so a large company count can hide thin data in the categories you actually need. “Freshness” is frequently a promise rather than a timestamp, which means you cannot tell whether a record was confirmed last week or last year.

Two structural problems make this worse. First, many datasets are point-in-time by nature but delivered without timestamps, so you lose the ability to tell current from stale. Second, technographic and signal data are often presented as certainties. A missing technology detection, for example, is frequently read as a company having “dropped” a tool, when a missing detection can also come from a script change, a recrawl gap, or a changed signature. Honest comparison starts with honest framing: a signal is evidence, not proof.

PredictLeads addresses the first problem directly: every record carries first_seen_at and last_seen_at, so you can tell exactly when a datapoint was confirmed and build trendlines from it.

What PredictLeads publishes about itself

PredictLeads is a B2B company intelligence and data provider, not a platform, CRM, or contact database, and it publishes the specifics behind every one of its claims. Below is the same information we would want any provider to put on the table.

Coverage, per dataset

PredictLeads tracks 130.7M+ companies across 195 countries, including 18,265 public companies, with 30+ attributes per company record updated daily. Coverage is published dataset by dataset rather than as a single number:

Technology Detections: ~1.5 billion detections across 95M+ domains, spanning 50,000+ tracked technologies.
Job Openings: 279.2M+ historical records across 2.9M+ websites, with 10.2M active at any time.
News Events: 10M+ structured signals across 37 event categories.
Financing Events: 210,800+ funding events, from pre-angel through late-stage rounds.
Connections (Key Customers): 371.8M+ categorized company relationships.
Similar Companies: available for 18.9M+ companies, with up to 50 lookalikes each and a written reason on the top 20 matches.
Website Evolution: 776M+ subpages tracked over time.
Products: 16.8M+ offerings; GitHub Repositories: 510,900+ repos; Startup Platform Posts: 287,600+ posts.

How far back the data goes

History is published as a start year per dataset, so you know the depth behind each trendline. Technology Detections and Job Openings go back to 2018, News Events and Financing Events to 2016, Connections to 2019, and Website Evolution to 2021. That matters because a velocity calculation, such as hiring acceleration or subpage build-out, is only as good as the history behind it.

How fresh each record is

Freshness is published as a cadence, not a promise. High-traffic websites are crawled multiple times daily, Job Openings are refreshed approximately every 36 hours, and webhooks push new signals as they are detected. Every field carries first_seen_at and last_seen_at, so freshness is verifiable on a record, not just asserted on a marketing page.

How the data is collected

Methodology is named, not hidden. Technology Detections are drawn from five source types: website script tags, DNS records, IP ranges, cookies, and job descriptions that list a technology as a required skill. The multi-source approach, including detection behind the login wall through the behind_firewall field and a source_count on each detection, is what lets you see how many independent signals confirmed a record. Detections are framed as evidence of which technologies a company uses or has recently used, never as a claim that a tool is installed and running. Job Openings are classified with industry-standard O*NET occupation codes, and every dataset is built only from publicly available information. For the full picture on technographic sourcing, see what technographic data is and how job openings data improves technographic accuracy.

How you can verify it

Verification does not require a contract. The full schema is documented at docs.predictleads.com, the OpenAPI schema is published, and you can test live endpoints in the SwaggerUI playground before you commit. Each Technology Detection links back to the exact source, whether that is a subpage URL, a job posting, or a DNS record, so you can trace a single record to its origin. You can browse the datasets on the Companies and Similar Companies pages, then confirm the fields against the docs.

How you get the data, and how it is governed

PredictLeads delivers through four methods: API, flat files, webhooks, and MCP, at a rate limit of 60 requests per second and a 99.9% monthly uptime target. On governance, it is SOC 2 Type II certified, GDPR and CCPA compliant, collects only publicly available information, and holds no personal contact data and no material non-public information. Teams such as Instantly, Clay, Surfe, FactSet, and Dealroom build on this data. These are the specifics we would expect from any provider, and the same ones you should ask a shortlist to publish.

How to compare B2B data providers yourself

You can compare providers on evidence in four steps, without waiting for a sales cycle. This works for us and for anyone else on your shortlist.

Read the docs before the deck. A provider that publishes a full schema and endpoint reference is showing you what it holds. Vague docs are a signal in themselves.
Trace one record to its source. Pick a company you know well and check whether a detection, event, or connection links back to something you can open and read.
Test a live endpoint. If there is a playground or a free tier, run a real query against an account you understand and judge the result yourself.
Check an independent benchmark. Third-party tests using the same method across vendors tell you more than any single provider’s self-reported accuracy.

For a category-by-category view of the market, our comparison of the best B2B data providers in 2026 lays out where different vendors focus, which is the honest starting point for a shortlist.

Independent benchmarking, and why we welcome it

One example of this transparency approach is OpenBenchmarks, an independent project that grades company data APIs on a frozen test set and publishes every raw request and response. PredictLeads is measured there alongside other providers using the same methodology, and the results make our point better than we could: in the similar-companies benchmark, PredictLeads led top-of-list precision at 89.8% Precision@10 while placing around sixth on total volume returned. That is exactly the kind of split we mean by different strengths. We are comfortable being ranked first in one measure and mid-pack in another, because a buyer who cares about top-of-list quality and a buyer who cares about raw volume are looking for different things, and both deserve to see the numbers.

Final thoughts on making B2B data easy to compare

We would rather make PredictLeads easy to evaluate than simply tell people we are the best. Every provider claims accuracy, coverage, and freshness, so those words have stopped carrying information. What carries information is a published record count, a start year, a refresh cadence, a named source, a traceable record, and an independent benchmark. Put those on the table and comparison gets easier for everyone. Perhaps ask yourself which is better for buyers and, over time, better for the providers who have nothing to hide.

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Frequently Asked Questions

Why is B2B data so hard to compare in 2026?

B2B data is hard to compare because most providers describe their data. Usually this comes with adjectives such as “accurate” and “fresh” rather than numbers you can verify. Coverage is often a single headline figure with no per-dataset breakdown, and freshness is a promise rather than a timestamp. The fix is published specifics: record counts, start years, refresh cadences, and named sources. PredictLeads publishes coverage per dataset and puts first_seen_at and last_seen_at on every record.

How can I verify a B2B data provider’s accuracy claims?

Verify accuracy by testing rather than trusting: read the provider’s schema docs, trace a single record back to its source, run a live query on a company you know, and check an independent benchmark. A provider that publishes a full API reference and a test playground is showing you what it holds. PredictLeads documents its full schema at docs.predictleads.com, offers a SwaggerUI playground, and links every Technology Detection back to its source URL.

What should a transparent B2B data provider publish?

A transparent provider publishes six things: what the data is, what each dataset contains, how fresh it is, how far back it goes, how it is collected, and where the provider fits. Those specifics turn a marketing claim into something a buyer can check. PredictLeads publishes per-dataset record counts, start years from 2016 to 2021 depending on the dataset, refresh cadences, and its five technology detection sources.

How current is PredictLeads data?

PredictLeads freshness is published as a cadence and confirmed on each record. High-traffic websites are crawled multiple times daily, Job Openings are refreshed approximately every 36 hours, and webhooks push new signals as they are detected. Because every field carries first_seen_at and last_seen_at, you can confirm when a specific datapoint was last seen rather than relying on a general promise.

Does PredictLeads expect to win every benchmark category?

No, and that is the point of publishing the results. Different providers have genuine strengths in different categories, so a fair comparison expects no single vendor to lead everything. In the OpenBenchmarks similar-companies test, PredictLeads led top-of-list precision at 89.8% Precision@10 while placing around sixth on total volume, which reflects a focus on quality at the top of the list. Buyers with different priorities deserve to see both numbers.