Every company sits on data journalists would use. Almost nobody knows how to find it or offer it up.

I’ve been pitching data-driven stories for three years across legal tech, e-commerce, HR, fintech, and consumer retail. I’ve watched brands with tiny databases land major national coverage, and brands with genuinely remarkable datasets land nothing — because nobody looked for the story, or it got packaged in a way that made a journalist’s eyes glaze over.

Your data doesn’t have to be massive. It doesn’t have to be proprietary in any technical sense. It just has to be real, specific, and reveal something non-obvious about a topic the journalist already cares about.

That’s the entire bar. Here’s how to clear it.

“The data doesn’t have to be massive. It doesn’t have to be proprietary in any technical sense. It just has to be real, specific, and reveal something non-obvious about a topic the journalist cares about.”

— Salva Jovells, Presslei

Why Journalist Data Needs Are Structurally Underserved

Journalists need data. Every option they have for getting it is bad.

They can cite public datasets (ONS, BLS, academic research) — but so can every other journalist, so there’s no exclusivity in it. They can commission a survey — but that costs money and time most editorial budgets don’t have. They can quote another journalist’s data — but that’s a chain of inference nobody wants to be at the end of.

What’s left is you: a primary source with genuine visibility into whatever they’re covering.

A property management company that has processed 40,000 rental applications knows things about tenant behavior, rental pricing, and approval rates that no public dataset captures. A logistics SaaS handling 200,000 shipments a month has delivery-performance and carrier-reliability data that no industry report tracks in real time. A recruitment platform that’s seen 500,000 applications in two years knows more about application patterns and hiring timelines than anyone outside the company.

Journalists will use your data the moment you make it easy: clear access, a clean methodology, and an angle already built in.

Step 1: The Data Audit — Finding What You Already Have

Start with a structured inventory of the data your company generates as a byproduct of just operating. Most companies have never done this for PR purposes — and the exercise almost always surfaces more usable material than anyone expects.

Work through these categories:

Transaction data: What do customers actually do? Purchase patterns, frequency, volume, timing, geographic variation. What does it tell you about your category that isn’t publicly known?

Failure and friction data: Where do users drop off? Where does the process break down? Friction data is often more newsworthy than success data, because it quantifies a problem the industry already acknowledges but nobody’s put a number on.

Behavioral change data: How has behavior shifted over time? Year-on-year comparisons are high value because journalists can frame them as trend stories. A 40% change in any meaningful metric is a story. A consistent multi-year trend is a better one.

Geographic variation: Do users in different cities or countries behave differently? Regional variation is inherently interesting — “London vs. Manchester” or “US vs. Europe” angles reliably get picked up by local and international editions.

Timing patterns: When does your category behave counter-intuitively? Peak times nobody expects, seasonality that contradicts conventional wisdom, day-of-week patterns that reveal something about user psychology.

Outcome data: For B2B platforms — what separates clients who get the best outcomes from those who don’t? This is particularly useful for thought-leadership stories in trade press.

Document every category you have access to. Don’t filter for newsworthiness yet — just get the full inventory down. You’ll score it in the next step.

Step 2: Testing for Newsworthiness

Not all data is PR-worthy. Run every dataset through this filter before you invest in packaging — it saves time and produces stronger pitches.

Five questions decide whether a finding is newsworthy:

Is it surprising? Does it contradict what people assume about your category? Data that confirms what everyone already knows isn’t a story. Data that quantifies something people suspected but couldn’t prove is.

Is it actionable? Can a reader do something with it, or make a better decision because of it? Journalists write for audiences who act on information — a finding that affects real decisions gets covered.

Is it timely? Does it connect to something already in the news cycle? A dataset that speaks to a trend journalists are currently covering gets picked up fast. The same data pitched cold might not land at all.

Is it significant? Does the magnitude matter? A 3% variation is rarely a story; a 40% variation usually is. The bar depends on the category — in healthcare, even 5% can be significant; in consumer retail, you probably need 20%+ to move the needle.

Is it defensible? Can you explain the methodology clearly and honestly? Journalists will ask. A finding built on a too-small sample, an obvious selection bias, or caveats that undercut the headline won’t make it into print. Methodological honesty is a prerequisite, not an optional extra.

Score every finding against these five. Prioritize anything with 4-5 yes answers. Shelve 2-3 for later. Discard anything under 2 — it’s not there yet.

Step 3: Anonymization Done Right

The question I hear most at this stage: “But isn’t our data confidential?”

Yes. That’s exactly why you anonymize it before it goes anywhere near a journalist. Anonymization is standard practice and non-negotiable — but it’s not complicated once you know what it means in a PR context.

What anonymization means here: individual clients, customers, or users are never identifiable. The data is presented as aggregate patterns across the whole set, not as individual cases. Nobody reading the story should be able to reverse-engineer who any specific person or client is.

What it doesn’t mean: you don’t have to hide that the data comes from your platform. Being transparent about the source is what makes it credible. “Analysis of 25,000 rental applications processed through [Your Platform] in 2025 found that…” is the correct framing. The data coming from you isn’t a problem — it’s the credential.

The standard process:

1. Aggregate to at minimum n=50 per data point — don’t report on groups smaller than 50, they risk identification
2. Round percentages to the nearest whole number or tenth
3. Strip any geographic detail precise enough to identify an individual or small cluster
4. Remove all personally identifiable information before analysis
5. Have legal or compliance review the anonymization before anything goes external

For most companies this takes a few hours with your data team. The legal review adds time but isn’t optional — especially for anything touching health, financial, or employment data.

“Every company sits on data that journalists would find interesting. The skill isn’t having data — it’s knowing which question to ask it.”
— Salva Jovells, Presslei

Step 4: Packaging the Story for a Journalist

A raw data finding is not a PR story. How you frame it, what context you add, and how much work you save the journalist is what turns a finding into coverage.

The structure that consistently works:

The headline finding: one number, one clear statement. “Companies using more than three project management tools lose an average of 4.2 hours per employee per week to tool-switching costs.” Not: “Our research reveals interesting productivity insights related to digital tool adoption.”

The trend context: why does this matter now? What’s happening in the news cycle, the industry, or the world that makes it timely?

The supporting data: two or three additional points that corroborate the headline and add dimension. Not your whole dataset — just what makes the story richer.

The human implication: what does this mean for real people or organizations? Translating data into consequences is technically the journalist’s job — but pre-articulating it makes it far more likely they write the story instead of moving to something easier.

The spokesperson: who can speak to this data in a quote a journalist can actually use? The quote should add context, not restate the finding in corporate language.

The methodology summary: sample size, time period, source, caveats. One sentence in the pitch email. One paragraph in the full briefing document.

Package all of it into a two-page press document — not a pitch email. The email is a short 150-200 word summary that links to or attaches the document. The document is what the journalist actually writes from.

Step 5: Matching Data Stories to the Right Journalists

Data pitches need different targeting than expert-commentary pitches. Not every journalist is equipped or motivated to cover a data story.

Journalists who cover data stories well:

— Data journalists at major publications. These reporters specialize in data-driven narratives — The Guardian, FT, New York Times, and most national broadsheets have dedicated data desks actively looking for original datasets.
— Sector correspondents with an analytical bent — the business, tech, and specialist reporters who regularly cite statistics and research. Spot them by looking at who in your target publications already cites data most often.
— Freelance feature writers — magazine and long-form writers have more room to develop a data story than a news reporter, and are actively hunting for original research to anchor features.

Journalists to skip for data pitches:
— Breaking news reporters on a 2-hour deadline
— Opinion columnists — they want a take, not data
— Journalists who cover your topic but write anecdote-first, not evidence-first

Do/Don’t: Packaging Data for Journalists

Real Examples: Data Stories That Earned National Coverage

Here are the shapes of data story that consistently earn Tier 1 coverage, with the structure that made each one work:

The behavioral shift story: an e-commerce brand analyzed 180,000 orders over 24 months and found average basket size up 31% while purchase frequency dropped 22% — people buying more per visit, but visiting less often. That headline finding earned coverage in The Times, the Daily Mail, and three retail trade titles.

The geographic variation story: a property platform analyzed rental inquiry data across 40 UK cities and found demand-to-supply ratios in three mid-sized cities had overtaken London — contradicting the narrative that London’s rental crisis is unique. The Guardian’s property correspondent ran it as a feature.

The failure rate story: an HR tech company analyzed 85,000 hiring processes and found roles with more than two interview stages had a 34% higher candidate withdrawal rate. The counter-intuitive hook — more thorough hiring loses more candidates — ran in the Financial Times, Personnel Today, and HR magazine.

What all three share: a specific number, a counter-intuitive finding, a clear tie to something journalists were already covering, and a sample size large enough to be credible.

Using Google Trends to Validate and Time Your Data Stories

Before pitching, use Google Trends to confirm journalists are actually covering your topic area.

Search the topic your data addresses. Rising search interest over the last 90 days means journalists are likely writing about it already. Time your pitch to a natural news hook — a regulatory announcement, an industry conference, an annual report — and your odds of placement go up significantly.

Google Trends also surfaces the specific angle journalists are chasing. The related queries section shows what people are actually searching in connection with your topic — often the same angle a journalist is pursuing, and a useful steer for how to frame your finding.

For a deeper dive, our Google Trends for PR guide covers the exact search patterns and filters that surface PR-relevant angles.

Turning One Dataset Into Multiple Stories

One of the most underused moves in data PR: a rich dataset doesn’t produce one story. It produces six to twelve — each with a different angle, a different journalist tier, and a different publication type.

From a single dataset of, say, 60,000 SME payroll transactions:

1. National angle: “UK SMEs paid an average of £2,300 more per employee in 2025 than 2024” → national business press
2. Sector angle: “Tech sector SME payroll grew 3x faster than retail” → tech and retail trade press
3. Geographic angle: “Manchester SME wage growth outpaced London for the first time in a decade” → regional press (Manchester Evening News, etc.)
4. Seasonal angle: “Q4 payroll errors spike 40% — the Christmas rush cost UK SMEs £120m in 2025” → trade press + reactive PR timing in November
5. Policy response angle: “SMEs absorbed 90% of minimum wage increases through payroll — only 10% passed costs to consumers” → policy-oriented publications, FT, economics correspondents
6. Counter-intuitive angle: “The smallest SMEs (2-5 employees) have the most accurate payroll, not the largest” → HR and finance trade press

That’s a six-story PR strategy from one dataset. Budget the data production work once; extract multiple campaigns from it over 6-12 months by varying the angle, the audience, and the timing.

Building the Data-to-PR Pipeline

Once one data story lands, systematize it so stories get produced on a schedule instead of one-off.

Monthly data review (2-3 hours): a recurring meeting between marketing/PR and the data or analytics team — what does the latest data show, what’s changed, what story angles does it suggest for next quarter.

Quarterly story calendar: based on those reviews plus your industry’s news calendar, plan which angles you’ll pitch in the next 90 days and when. Match timing to news moments that make the data more relevant.

Data packaging standard: a repeatable format — headline finding, methodology summary, supporting data points, spokesperson quote, charts — every time. A template means you’re not reinventing packaging for every story.

Journalist tracking: keep a record of who covered your previous data stories, which angle they ran with, and what they asked for when they came back for more. These relationships are your most valuable long-term asset.

Run this properly and data-driven PR becomes a predictable channel, not a periodic scramble. You know roughly when you’ll pitch, what you’ll pitch, and who’s likely to cover it — because you’ve done it before and tracked the results.

This is where reactive PR and data PR start working together: your data gives you a proactive story calendar, and reactive capability lets you inject data findings into breaking news cycles when the opportunity shows up.

For the research this approach is built on, our breakdown of a 5,272-placement UK digital PR study shows what share of top-performing placements were data-led versus commentary-led — the gap is significant.

Salva Jovells is the founder of Presslei, a reactive PR agency whose method is built on research analyzing 5,272 real media placements earned by a leading UK digital PR agency. Read how to pitch journalists effectively or see which PR KPIs actually matter for measuring campaign performance.

About the Author

Salvador Jovells

Founder of Presslei. 12+ years in ecommerce SEO across international markets. After a decade of link buying for Hockerty and Sumissura, I reverse-engineered 5,272 earned media placements and founded a reactive PR agency that builds authority through data-driven stories journalists actually want to publish. Based in Zurich.