Data Collaboration Is Only Useful If It Leads to a Better Decision

By Becky Johnson, Advertising Week Writer & Podcast Host

Marketers have more data than ever, but much of it remains scattered across departments, platforms, publishers, retailers, and business partners. Bringing that information together could help companies understand customers, evaluate performance, and make better decisions. It could also create an enormous privacy, security, and compliance problem if the process requires every participant to surrender control of its data.

I recently spoke with Juan Baron, Head of International and Global Accounts at Decentriq, for an episode of Modern Marketing & Measurement. Our conversation kept returning to one important distinction: successful data collaboration isn’t about moving more information into one place. It’s about allowing companies to learn from their combined data while everyone remains in control of what happens to it.

Collaboration doesn’t have to mean handing over the raw data.

Data collaboration traditionally sounds like a trade. One company provides information, another company provides its own, and someone attempts to combine the two. That becomes much harder when the organizations involved are retailers, publishers, banks, healthcare companies, or different divisions of the same global business. The information may be commercially sensitive, personally identifiable, or subject to strict internal and external compliance rules.

Juan explained that a data clean room allows those organizations to match information and perform analyses without transferring all their raw data to another party. The data owners can see what someone wants to do, review the proposed query or model, and decide whether to approve it. If they don’t approve it, nothing happens to their information. That changes collaboration from a broad transfer of data into a controlled process built around a specific business question.

An insight is more useful than another pile of information.

Companies don’t need to combine data simply for the satisfaction of knowing they have combined it. They may want to understand an audience more clearly, evaluate a partnership, improve campaign planning, or find patterns they cannot see within their own information. The collaboration should have a purpose, and the result should help someone make a decision.

Juan described the goal as getting intelligence from the data and creating a business impact. That means the output isn’t necessarily another database filled with individual customer records. It may be an aggregated insight or analysis that gives each participant useful information without exposing the underlying data. More access doesn’t automatically produce more value, and in many cases, the useful result is the answer to a question, not possession of every piece of information used to reach it.

Audit-friendly shouldn’t be an afterthought.

Marketing teams may begin with the business opportunity, but privacy officers, information security teams, and legal departments need to understand exactly what will happen to the data. Juan talked about the importance of hardware-based encryption and tamper-proof audit logs that create a record of what occurred within the environment. That gives the people responsible for security and compliance something concrete to review.

Instead of asking an organization to trust that its data will be handled correctly, the process can show who had access, what analysis was requested, what was approved, and what was produced. That level of transparency doesn’t have to slow collaboration down. It gives companies a structure they can understand, approve, and continue using.

Start with one useful collaboration.

Data clean rooms can support highly customized analytics involving multiple organizations, but not every company needs to begin there. Juan described a phased approach that allows organizations to start with a no-code experience and a defined collaboration model. A publisher might contribute first-party audience information, while a brand brings its own customer data. The two can begin with a clear use case and expand once they understand the process and see its value.

More advanced teams can eventually move into customized analytics, data science projects, SQL queries, or Python models. The important part is that sophistication follows the business need. Companies can begin with one collaboration that produces a useful result, then build from there.

Data collaboration should be a living process.

Customer data changes, people update their privacy preferences, regulations evolve, and companies develop new questions. Partnerships also take on new goals, which means a collaboration built around one static transfer of information will quickly become outdated.

Juan explained that participating organizations can continue updating their data, rerunning established models, and proposing new analyses. Each data owner still has the opportunity to review and approve what happens next, making data collaboration less like a one-time project and more like an ongoing working relationship.

The value isn’t simply that two companies can safely combine information once. It’s that they can continue learning together, asking better questions, and producing insights that lead to real business decisions without either side giving up control.