Every company is sitting on data that journalists would find useful. The problem is almost nobody knows how to look for it.

I’ve been pitching data-driven stories for three years across categories as different as legal tech, e-commerce, HR, fintech, and consumer retail. In that time I’ve watched brands with small databases produce major national coverage and brands with genuinely remarkable datasets produce nothing — because they either never looked for the story or they packaged it in a way that made journalists’ eyes glaze over.

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.

That’s the entire bar. Let me show you 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

Before we get into the how, it’s worth understanding why data from companies is valuable to journalists in the first place — because understanding the demand makes you better at supplying it.

Journalists covering business, technology, and social trends need data to support their stories. But their access to original data is limited. They can cite publicly available datasets (ONS, BLS, academic research) but these are available to every journalist and don’t give them exclusivity.

They can commission surveys, but that costs money and time most editorial budgets don’t have. They can quote other journalists citing other data, but that’s a chain of inference nobody wants to be at the end of.

What they actually want is data that reveals something real about their topic from a primary source that has genuine visibility into the phenomena they’re writing about. That’s you.

A property management company that has processed 40,000 rental applications has visibility into tenant behavior, rental pricing trends, and application-to-approval rates that no publicly available dataset captures.

A logistics SaaS that handles 200,000 shipments per month has data on delivery performance and carrier reliability that no industry report aggregates in real time. A recruitment platform that has seen 500,000 job applications in the last two years has data on application patterns, dropout rates, and hiring timelines that nobody else has.

Journalists know this. They’ll use your data if you make it easy to access, clearly explain the methodology, and give them the angle they need to build a story around it.

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

The first step is a structured inventory of the data your company generates as a natural byproduct of operations. Most companies have never done this for PR purposes, and the exercise consistently surfaces more usable material than anyone expects.

Work through these categories:

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

Failure and friction data: Where do users drop off? Where does the process break down? What percentage of transactions encounter a specific problem? Friction data is often more newsworthy than success data because it reveals problems that the industry acknowledges but nobody has quantified.

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

Geographic variation: Do your users in different cities, regions, or countries behave differently? Geographic data often produces stories because regional variation is inherently interesting (“London vs. Manchester” or “US vs. Europe” angles are reliably picked up by local and international editions).

Timing patterns: When does your category behave in counter-intuitive ways? Peak times that aren’t when people expect, seasonality that contradicts conventional wisdom, day-of-week patterns that reveal something about user psychology.

Outcome data: For B2B platforms, what does success look like in your data? What distinguishes clients who get the best outcomes from those who don’t? Outcome data is particularly useful for thought leadership stories in trade press.

Document every data category you have access to. Don’t filter for newsworthiness yet — just inventory what exists. You’ll evaluate potential stories in the next step.

Step 2: Testing for Newsworthiness

Not all data is PR-worthy. Running every dataset through a newsworthiness filter before you invest in packaging saves significant time and produces stronger pitches.

The five questions that determine whether a data finding is newsworthy:

Is it surprising? Does the finding contradict what people assume about your category? Data that confirms what everyone already knows is not a story. Data that reveals the opposite of the conventional wisdom, or that quantifies something people assumed but couldn’t prove, is a story.

Is it actionable? Can a reader do something with this information, or make a better decision based on it? Journalists write for audiences, and audiences engage with information that helps them act. Data that reveals a problem, opportunity, or pattern that affects their decisions gets covered.

Is it timely? Does the data connect to something already in the news cycle? A dataset that reveals something about a trend journalists are currently covering gets picked up immediately. The same data, pitched without a news hook, might not land at all.

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

Is it defensible? Can you explain the methodology clearly and honestly? Journalists will ask. If the data comes from a too-small sample, has an obvious selection bias, or requires complicated caveats that undermine the headline finding, it won’t make it into print. Methodological honesty is a prerequisite, not an optional extra.

For each finding in your data audit, score it against these five questions. Prioritize the findings with 4-5 yes answers. Set aside the ones with 2-3 for future use. Discard the ones with fewer than 2 — they’re not there yet.

Step 3: Anonymization Done Right

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

Yes. And that’s exactly why you anonymize it before it goes anywhere near a journalist. Anonymization is the standard and the requirement — but it’s also not complicated once you understand what it means in a PR context.

What anonymization means for PR purposes:

Individual clients, customers, or users are never identifiable. The data is presented as aggregate patterns across the whole dataset, not as individual cases. Nobody reading the story should be able to reverse-engineer who any specific client, customer, or user is.

What it doesn’t mean:

You don’t have to hide the fact that the data comes from your platform. In fact, 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 comes from your platform. That’s not a problem — it’s the credential.

The standard anonymization process:

1. Aggregate to at minimum n=50 per data point (don’t report findings from groups of fewer than 50 individuals or transactions — smaller groups risk identification)
2. Round percentages to the nearest whole number or tenth
3. Remove any geographic data precise enough to identify a specific individual or small cluster
4. Strip all personally identifiable information from any dataset before analysis
5. Have your legal or compliance team review the anonymization before any data goes external

For most companies, this process takes a few hours with your data team. The legal review adds time but is not optional — particularly for any data touching health, financial, or employment information.

“Every company sits on data that journalists would find interesting. The skill is not in having data — it is in knowing which question to ask it.”
— Salva Jovells, Presslei

Step 4: Packaging the Story for a Journalist

Raw data findings are not a PR story. The packaging — how you frame the finding, what context you provide, and how you make the journalist’s job easy — is what determines whether a finding becomes coverage.

The structure that consistently works:

The headline finding: One number, one clear statement. “Companies that use 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 finding matter now? What’s happening in the news cycle, in the industry, or in the world that makes this finding timely and relevant?

The supporting data: Two or three additional data points that corroborate and add dimension to the headline finding. Not everything from your dataset — just the data that makes the story richer.

The human implication: What does this mean for real people or organizations? Translating data into human consequences is the journalist’s job, but making it easy for them by pre-articulating the implication increases the chance they’ll write the story rather than move on to something easier.

The spokesperson: Who from your company can speak to this data in a quote that a journalist can use? The quote should add context or perspective, not just restate the finding in corporate language.

The methodology summary: Sample size, time period, source, and any relevant caveats. In the pitch email, this is one sentence. In the detailed briefing document you provide on request, it’s a paragraph.

Package all of this into a two-page press document, not a pitch email. The pitch email is a short (150-200 word) summary that links to or attaches the document. The document is what a journalist uses to actually write the story.

Step 5: Matching Data Stories to the Right Journalists

Data-driven pitches require different targeting than expert commentary pitches. Not all journalists are equally equipped or motivated to cover data stories.

The journalists who cover data stories well tend to be:

– Data journalists at major publications — these reporters specialize in data-driven narratives. Publications like The Guardian, FT, New York Times, and many national broadsheets have dedicated data desks. These journalists actively look for original datasets.
– Sector correspondents with analytical orientations — the business, tech, and specialist reporters who regularly include statistics and research in their articles.

You can identify them by looking at which journalists in your target publications most frequently cite data and studies.
– Freelancers who write features — feature writers for magazines and long-form publications have more space to develop data stories than news reporters and are actively looking for original research to anchor feature pieces.

The journalists to avoid for data pitches:
– Breaking news reporters working on a 2-hour deadline
– Opinion columnists (they want perspectives, not data)
– Journalists who cover your topic but whose style is anecdote-driven rather than evidence-driven

Do/Don’t: Packaging Data for Journalists

Real Examples: Data Stories That Earned National Coverage

To make this concrete, here are the types of data stories from our 5,272-placement database that consistently earn Tier 1 coverage, with the structure that made them work:

The behavioral shift story: An e-commerce client analyzed 180,000 orders over 24 months and found that average basket size had increased 31% while purchase frequency had dropped 22% — consumers buying more per visit but visiting less often. The headline finding earned coverage in The Times, the Daily Mail, and three retail trade publications.

The geographic variation story: A property platform analyzed rental inquiry data across 40 UK cities and found that demand-to-supply ratios in three mid-sized cities had surpassed London — a finding that contradicted the prevailing narrative about London’s rental crisis being 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 that roles that went through more than two interview stages had a 34% higher candidate withdrawal rate. That counter-intuitive finding (more thorough hiring processes lose more candidates) ran in The Financial Times, Personnel Today, and HR magazine.

What all three have in common: a specific number, a counter-intuitive finding, a clear connection 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 a data story, use Google Trends to confirm that journalists are actively covering your topic area.

Search for the topic your data addresses. If search interest is rising over the last 90 days, journalists are likely to be writing about it. If you can time your data story pitch to coincide with a natural news hook (a regulatory announcement, an industry conference, an annual report), the likelihood of placement increases significantly.

Google Trends also helps you identify the specific angle journalists are most interested in. The related queries section shows you what people are searching for in connection with your topic — these are often the angles that journalists are pursuing, and they can help you frame your data finding in the way most likely to resonate.

For a deeper dive on this approach, our Google Trends for PR guide covers the exact search patterns and filtering methods that surface PR-relevant story angles.

Turning One Dataset Into Multiple Stories

One of the most underused techniques in data-driven PR is the multi-story extraction. A rich dataset doesn’t produce one story — it produces six to twelve, each with a different angle, different target journalist tier, and 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

This is a six-story PR strategy from a single 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 you’ve run one successful data story, the goal is to systematize the process so that data stories are produced regularly rather than one-off.

The pipeline looks like this:

Monthly data review (2-3 hours): A recurring meeting between marketing/PR and the data or analytics team to review what the latest data shows, identify any significant changes or emerging patterns, and flag potential story angles for the next quarter.

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

Data packaging standard: A repeatable format for how data stories are prepared — always including the headline finding, methodology summary, supporting data points, spokesperson quote, and charts. Having this as a template means you’re not reinventing the packaging process each time.

Journalist tracking: Keep records of which journalists covered your previous data stories, what angle they used, and what they said when they came back for more data. These relationships are your most valuable asset in the long run.

For brands that run this pipeline properly, data-driven PR becomes a predictable channel rather than a periodic activity. You know roughly when you’ll pitch, what you’ll pitch, and who will likely cover it — because you’ve done it before and tracked the results.

This is the level at which reactive PR and data PR start to work together: your data provides the proactive story calendar, and your reactive capability lets you inject data findings into breaking news cycles when the opportunity presents itself.

For the full picture of what this looks like in results, our analysis of 5,272 placements breaks down what percentage of top-performing placements were data-led versus commentary-led — and the gap is significant.

Salva Jovells is the founder of Presslei, a reactive PR agency built on data from 5,272+ real media placements. Read how to pitch journalists effectively or see which PR KPIs actually matter for measuring campaign performance.

About the Author

Salva 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.