In today’s digital economy, data is one of the most valuable assets. But while many companies talk about “sharing data,” true innovation happens when organizations move beyond sharing to collaborating with data.
What is data collaboration?
Data collaboration means that two or more parties work together on insights without ever handing over their raw data. Instead of moving or exposing sensitive information, each party keeps full control. Secure technologies like data clean rooms make it possible to combine datasets, analyze overlaps, build audiences, and measure results – all in a privacy-safe environment.
Unlike simple data sharing, collaboration is about unlocking joint value without losing control. It empowers companies to:
- Enrich their understanding of customers
- Discover new audiences and opportunities
- Measure campaign impact across partners
- Strengthen trust through privacy-first practices
In short: data collaboration creates value together, while keeping data protected. It’s not about giving data away – it’s about making it work smarter, side by side.
How does it work?
The key lies in technologies that enable collaboration without exposing raw data. Traditionally, sharing meant copying or transferring data – which carries risks around privacy, compliance, and control. Data collaboration solves this differently:
- Data clean rooms: Secure, neutral environments where datasets from multiple parties can be matched and analyzed. Raw data never leaves the owner’s system – only aggregated insights are revealed.
- Privacy-enhancing technologies (PETs): Methods like encryption, hashing, and differential privacy ensure that even when datasets are compared, sensitive information cannot be re-identified.
- Controlled outputs: Only the agreed results are visible – for example, the size of a target audience, overlap between customer bases, or campaign performance – never the underlying data.
This makes it possible for companies to combine intelligence, generate insights, and optimize campaigns without ever giving up control of their own data.
Retailer + brand
Imagine a supermarket chain and a beverage company.
- The retailer knows who buys drinks in-store but doesn’t know how those customers interact with the brand online.
- The brand knows who engages with its website, loyalty program, and campaigns but doesn’t know how that translates into in-store sales.
With data collaboration in a cleanroom:
- Both parties upload their data in encrypted form.
- The clean room identifies overlap between the retailer’s shoppers and the brand’s audiences – without exposing individual customer data.
- Together, they see which campaigns actually drive in-store sales and can build new audience segments for the future.
Result: The retailer sells more through targeted promotions, the brand gains clear insights into effectiveness, and neither side ever loses control of its data.
Publisher + advertiser
Now consider a news publisher and a car brand.
- The publisher has rich first-party data from subscribers and readers.
- The advertiser has CRM data, site visitors, and records of past car buyers.
With data collaboration:
- Both parties match their datasets securely in a clean room.
- Overlaps are identified – for example, which readers are also in the advertiser’s target audience.
- The advertiser runs campaigns through the publisher’s inventory, targeting exactly the right readers – without ever accessing the publisher’s raw data.
Result: The publisher monetizes its audience more effectively while protecting privacy. The advertiser reaches relevant buyers with precision. Both sides strengthen their partnership – built on data collaboration, not data sharing.
Smarter, not riskier
That’s why data collaboration is not just safer – it’s smarter. It opens doors to insights, growth, and partnerships that simple data sharing could never achieve. Companies that embrace collaboration gain an edge in customer understanding, campaign performance, and trust – all while keeping control of their data.