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Teams are producing more content than ever, yet they are getting less attention. That is exactly where Data PR comes in: not as a "fancy report," but as a way to verifiable hypotheses and substance to be part of the discourse.
In this whitepaper, you get a reality check from the world of data journalism: Marie-Louise Timcke (Head of Data Journalism at SZ) explains why most pitches fail – and what newsrooms actually need: data access instead of result slides, transparency instead of buzzwords.
It also includes a practical playbook: 7 steps covering everything from goal setting and hypotheses to data foundations and methodology, as well as angles for each medium, one-pager rules, and distribution. Plus, honest rules of thumb (e.g., minimum sample sizes, budget).
And, of course, a reference case showing how Data PR works as a system: the awork Work Happiness Report (114 publications in 2025 incl. major media outlets + dpa).
Ideal for anyone looking to use PR as a business lever. Read for free now.
Viel Spaß beim lesen.
For decision-makers (CEO/CMO/Head of Comms/PR Lead) and experienced communication teams at growth companies, market leaders, digital SMEs, and businesses in transition who don't need "more content" – but more relevance.
Data PR isn't a contest for the fanciest report. It’s a way to participate in the discourse with verifiable theses, executed so skillfully that editorial teams want to work with them, and your communications benefit in the long run.
The rules of the game in communications have shifted: attention is scarce, editorial teams are more selective, and at the same time, the output of generic (AI-generated) content is growing. In many industries, this leads to a paradoxical effect: teams produce more but are noticed less.
Data PR steps in where classic corporate communications often fall flat. Because data provides something that is always in short supply in newsrooms: substance. Not as proof for your product, but as a basis for providing context. Good data stories reveal patterns, test assumptions, and help decision-makers navigate complex debates.
And that is the real opportunity: with Data PR, you can turn corporate communications into a contribution to the discourse without getting lost in opinions or self-promotion.
Data PR means: you use your own data to tell a journalistically valuable story . This is more than just "we conducted a survey." It is a story with a thesis, context, methodology, and ideally, access to the data.
Marie-Louise Timcke (Head of Data Journalism at the SZ) made the most important distinction very clear in the webinar . When asked about typical mistakes, she essentially said: many pitches are "only results-oriented." She receives findings from data collections, but not the underlying data – and that is not enough for her. She puts it very clearly:
"I don't report on results... I want to be able to work with the data myself."
That is the benchmark. Data PR means: you build your story in such a way that journalists can independently verify, calculate, and set their own focus – instead of simply adopting your headline.
What Data PR is not:
Data PR is not worth it because "data always works with journalists." It is worth it because it makes your communication strategically more stable and can generate several effects at the same time.
When data provides genuine news value, the reason for coverage is built-in. You don't need to artificially inflate why something matters—the data shows it. In an increasingly selective media landscape, that is a distinct advantage.
Data doesn't replace a point of view. But it gives your perspective a solid foundation. Being transparent about what the data shows (and what it doesn't) makes you appear competent and ensures you are taken seriously as a source.
Many companies confuse true visibility with campaign spikes. Data PR can be established as a recurring communication anchor: annually or semi-annually, supplemented by thematic deep dives and commentary on current debates. This builds authority not in a single press day, but consistently over time. And that truly builds trust—which, incidentally, also applies to large language models, as they rely on recurring and up-to-date information.
A dataset is not a "one-and-done" asset. When planned correctly, it functions as a content system across multiple channels: Earned (press) → Owned (landing page/report/blog) → Social (charts/angles) → Sales (decks) → Employer Branding (HR angles). You leverage the same substance instead of starting from scratch every week.
An underestimated point: Good data collection forces you to be clear. Which hypothesis are we testing? Which message counts? What are we not saying? This makes communication more consistent internally and more credible externally.
The webinar with Marie-Louise Timcke is a very honest reality check: Data PR is welcome. But only if it is editorially useful.
According to Timcke, the most common dealbreaker is results without a data foundation. She doesn't just want to know "what the outcome was"—she wants to work with the data herself and set her own focus.
This doesn't mean you always have to make the entire dataset public. However, you do need some form of transparency and verifiability: disclose your methodology, explain your calculations, and be open to follow-up questions. In the webinar, Timcke describes an example where a company couldn't provide all the details, but they explained their calculations very explicitly and were available to answer questions at any time—that builds trust.
When it comes to format, she is pragmatic but consistent: "The main thing is that it's machine-readable." Ideally Excel/CSV, or even JSON/XML/API, as long as it's a "clean" format that doesn't require expensive specialized software.
That sounds trivial, but it's a tough practical filter: if you provide data that editorial teams have to laboriously "clean up" first, you reduce the chance that anyone will use it at all.
When asked about visualizations, Timcke is very clear: "I don't need them, I'll make them myself anyway." If graphics exist, she is happy to use them as a guide, but what she really wants is the data behind the graphic.
This is an important point for PR teams: don't invest in high-end infographics first. Invest in clean data preparation and accessibility. We also say: save yourself the effort of elaborate graphics; many editorial teams will design them in their own corporate identity anyway.
Yes, that is also part of the reality. Timcke says literally in the webinar: "I'm a millennial. Please don't call me. I won't pick up."
This isn't just a "funny side fact," but a reflection of the working reality: data PR often requires explanation, but the initial contact must still be low-friction. A good email with a clean introduction beats any attempt at phone persuasion, at least for her.
Nevertheless, our experience shows: a short (well-prepared) phone call gives you a faster sense of whether the story holds up or what the journalists on the other end of the line still need. However, the rule here is: always follow the preference of the person you are contacting—this is often stated in their LinkedIn profile or author page.
At their core, there are three patterns that ruin data PR pitches:
Data PR rarely fails because "we don't have any data." It fails due to a lack of strategy, checking the methodology too late, and failing to build a clear story from the data. This approach has proven successful in practice:
Don't start with the question "What data do we have?", but with: What should this story achieve? Thought leadership, trust-building, reputation, leads, employer branding. It makes a huge difference. Our principle: Goal setting is key. Otherwise, you get lost in details that don't contribute to the right things.
Good data PR needs a thesis that is relevant enough to make someone outside your bubble pause for a moment. The question isn't "can we measure this," but: "What would it mean if this were true?"
Conscious "thinking through pruning" helps here: better to have five hypotheses and one that hits home – rather than twenty mediocre ones.
You generally have three options:
Your how-to guide also mentions a very practical rule of thumb: for major media outlets, surveys are usually only worth it starting at N=1000 (ideally representative)—and such sample sizes from providers like Appinio typically start at roughly around 4,000 euros (significantly more depending on the target group/incidence).
This is important because data PR needs to be not only creative but also predictable.
If you only realize at the end that your findings aren't robust, it gets expensive—in terms of time, nerves, and credibility. Distinguish between correlation and causation, define limitations, and remain precise. We believe transparency pays off, and journalists appreciate this honesty.
One of the most common PR traps is the "one angle fits all" approach. Our philosophy: individuality. Never use the same angle for every media outlet.
This means developing different entry points—business, societal, HR, tech, regional—and tailoring your pitch for each one.
An insight into our geptress guidelines: A factsheet or one-pager should be so concise that journalists can grasp the relevant points "at a glance"—as a rule of thumb, a maximum of one A4 page or 2,500–3,000 characters.
Important: This doesn't mean cramming everything into bullet points. It means: focus, a clear structure, and logical context. And please, keep the marketing jargon out of it.
Data PR is not a "send it out once and hope for the best" discipline. Our internal approach focuses on two points that teams often underestimate:
And a pragmatic addition from our Head of PR Operations, Tina O’Meara:
Timing can also mean intentionally taking advantage of "news lulls." Well-timed data stories often get more coverage as a result.
If you want to understand how data PR can become a recurring communication stream rather than just a one-off campaign, awork is a perfect reference case.
awork wanted to position the topic of work happiness not as a buzzword for "new work," but as a business-relevant discussion. This requires more than just opinions—it needs reliable data and recurring hooks.
Since 2022, awork has conducted an annual Work Happiness Report (surveying 1,000 knowledge workers, among others). The key move: the report isn't treated as a one-time download, but as a recurring communication anchor that can be tied to ongoing debates.
Instead of "publish the report and move on," the topic is strategically translated into PR work every year: through exclusive first releases, journalistic analysis of the results, and follow-up opportunities that dive deeper into specific findings or comment on current workplace debates. At the same time, the report serves as a lead magnet and a content anchor for owned channels.
In 2025, the Work Happiness Report generated 114 media mentions, including placements in leading publications such as WELT, ZEIT, SZ, t3n, STERN, Tagesspiegel, BILD and DER SPIEGEL as well as additional reach via dpa. On top of that, there is a pull effect: recurring media inquiries and journalistic interest that lasts well beyond the launch.
Because it’s not just data. It’s a setup that takes journalistic needs seriously: clear relevance, recurring topicality, accessible angles, and a story that works beyond just launch day.
Data PR is one of the cleanest ways to translate substance into visibility. You provide editorial teams with material they can actually use, while simultaneously building thought leadership, content, and lead generation.
When you set it up as a system (thesis → data foundation → methodology → journalistic preparation → distribution → repurposing), "let's do a study" turns into a communication lever that lasts.
If you want to know what Data PR potential lies within your data – and which stories can truly become press-worthy: Get in touch with us. And with a little luck, you might get one of our exclusive Data PR potential checks with Elia.
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