AI Is Breaking Down Data Silos — Without Breaking Privacy

The world of marketing has quickly moved away from legacy, invasive practices that took liberties with customer data, and into a new, privacy-first era.

Direct collaborations based on first-party data have become the most effective way for brands and media owners to identify shared customers, develop audience insights, and plan, activate, and measure campaigns. While these collaborations bring many opportunities, they also present a number of challenges. AI is helping all parties maximize these partnerships.

Data collaboration can’t compromise customer data

Direct data collaborations between a brand and a publisher enable each partner to discover where their audiences overlap, unlocking more effective targeting and giving brands the chance to meet their target customers in premium inventory.

But one-to-one partnerships are limited, and if marketers really want to get to grips with customer behavior and maximize the full potential of data collaboration, they need to work with multiple partners. And this is the first hurdle that must be cleared: creating multi-party collaboration solutions that are flexible enough to enable each party to connect, participate seamlessly, derive value from data, and achieve their desired outcomes in a secure and private way.

The keyword here is ‘private’. Data protection regulations and consumer awareness of privacy issues require organizations to safeguard their first-party data. This is absolutely non-negotiable.

How every party can maintain the integrity of its data

This means that several technological tools must be implemented. Privacy Enhancing Technologies, or PETs for short, such as decentralized data processing and secure multi-party computation, mean that data is never moved, co-located, or centralized, so it cannot be leaked to a partner either accidentally or deliberately. Every party has complete control over its own data.

When it comes to building custom marketing models that can be adapted to specific purposes, ensuring secure and private connections is key, and can only be achieved by layering the right PETs to meet the needs of each collaboration.

AI’s role in powering effective, fast data collaborations

Another key issue with working with multiple partners is that all of the data matching and analysis is incredibly resource-intensive. While these collaborations promise to tap into a rich source of insights, actually surfacing these insights isn’t easy. In practice, fragmented data access and inconsistent metrics mean that connecting all the data and making it actionable takes significant time and effort.

And this is where Artificial Intelligence comes in. AI has the power to solve this problem and take data collaboration strategies to the next level. Advertisers want to benefit from the granular data unlocked by collaboration to make fast and effective decisions while their campaigns are live, and don’t have the time to wait for specialist data analysts or Business Intelligence teams to sift through dozens of independent data sources and metrics.

AI brings with it the potential to extract real-time insights from billions of data signals from multiple sources, enabling marketers to easily digest these insights and take action when it matters. AI models can be trained to predict audience behavior based on patterns derived from real-time data on how consumers engage with content, brands, platforms, and products. It’s a game-changer when it comes to surfacing insights that will help marketers better plan, execute, optimize, and measure their activities.

Putting theory into practice requires transparency

While the theory is sound, in practice, there’s a very real need for organizations adopting AI in their marketing strategies to be completely transparent about the data they are using to train their AI models.

Limited or incomplete data sets will only have limited use in training AI models. Many large publishers share only aggregated data with advertisers, and they may not even share this data in real-time. There’s no way that even the most advanced AI models will derive any useful insights without comprehensive, granular data, so media owners must commit to being more forthcoming with their measurement data if they really want to move the needle for their advertising partners.

PETs hold the key to this. Creating collaborative environments protected by PETs and enabled by AI, brands, retailers, and publishers can extract maximum value from their first-party data while ensuring it maintains its integrity.

Building and training effective AI models requires a focus on privacy

It’s possible to build custom AI models optimized to drive specific business outcomes, with brands able to derive insights through a single point of access and put them into practice instantly. Marketers can do this for each collaboration, even each campaign.

But while it’s hard not to be excited about the potential of AI in revolutionizing data collaboration, we mustn’t overlook the vital role that PETs will play in helping brands build custom marketing models without ever moving, copying, or sharing data.