Marketers need a fuller view of their data, but bringing information together across departments, publishers, retailers, and other partners can create serious privacy and compliance concerns. Juan Baron, Head of International and Global Accounts at Decentriq, explains how data clean rooms allow organizations to match data, run analyses, and gain useful insights without sharing raw information. He also discusses why an approach that starts simply, keeps every data owner in control, and evolves with the organization can turn data collaboration into measurable business value.
On this episode, we talk about how brands can work across departments, publishers, retailers, and other partners without giving up control of their data, why auditability matters from the beginning, and how a flexible approach to data collaboration can lead to better insights and stronger business decisions.
Five Main Points
1. Data collaboration should produce business value, not simply combine more data.
The goal is to match information, gather intelligence, and produce insights that marketers and other business leaders can use to make better decisions.
2. Organizations can collaborate without transferring their raw data.
Each data owner maintains control over how its information is used. Partners can review proposed queries, models, or analyses, approve them, and share only the aggregated results.
3. Privacy and security need to be built into the infrastructure.
Juan explains how hardware-based encryption, controlled access, and tamper-proof audit logs can give legal, privacy, and information security teams a clear record of what is happening with sensitive data.
4. Companies do not have to begin with the most complex use case.
A no-code approach allows brands, publishers, and other partners to begin with more straightforward collaborations. As their needs and experience grow, they can move into more customized analytics and data science projects.
5. Data collaboration must remain flexible over time.
Customer preferences, privacy choices, regulations, and business needs continue to change. A living process allows organizations to update their data, rerun existing models, propose new analyses, and maintain control throughout the relationship.
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