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AI Models Need More Than Just First-Party Data to Excel

It’s no secret that data drives AI. The kind of data you use makes all the difference between a model that just works and one that truly excels. While first-party data – what a company collects directly from the source – is often touted as the best, most reliable source for training AI models, first-party data alone isn’t enough to build robust AI systems capable of deep insights.

The reality is that building comprehensive AI models — particularly those that need to understand complex, cross-category relationships — requires a tremendous amount of data. From my experience in AI model development, it’s clear that even with vast amounts of first-party data, there are often major gaps that need filling. As AI models become more sophisticated, so does the need for diverse and expansive datasets that go beyond any one organization’s own data pool.

Limits of first-party data

First-party data has its benefits, of course. It’s typically more accurate and reliable because it’s collected directly by a business or organization. You have control over it, you know where it comes from, and you have direct insight into its accuracy. But while first-party data might be enough for basic, narrow AI applications, it often lacks the scope needed for broader, more nuanced models.

Consider an AI model designed to make recommendations across multiple lifestyle categories — movies, music, dining, travel, and so on. To achieve this, the model needs a vast array of interconnected data points to connect these domains. If the data only comes from a single source, it’s likely too narrow to cover the relationships and overlaps that exist in real life. To build cross-domain models that truly capture human preferences, you need enough data to bridge the gaps between categories, not just within them.

While first-party data is valuable, it’s just one piece of a massive puzzle.

This means the data must be not only vast but diverse. You could have millions or even billions of data points within one category, but if the goal is to connect, for example, a movie preference to a dining choice, then it’s essential to have data that spans multiple domains. For organizations developing these types of models, it quickly becomes apparent that first-party data isn’t sufficient on its own to generate this level of insight.

Many companies turning to third-party data

To create truly effective AI models, many companies turn to third-party data. This type of data, collected by other organizations and made available for purchase or through partnerships, adds breadth and much-needed context to an otherwise limited first-party dataset. I’ve seen firsthand how expanding data sources unlocks connections that would otherwise remain undetected, like when our recent analysis revealed Michelob Ultra as the top beer brand for swing state voters. By integrating third-party data, it’s possible to link various categories in ways that make sense to users but might not be apparent from first-party data alone.

A popular technique in the field is transfer learning. In essence, this means taking insights from one domain and applying them to another. It’s a way to make an AI model more versatile by applying insights it has already learned in a well-covered area to fill gaps in a less-covered area. For example, if a model has a wealth of information about movie preferences, it can leverage that knowledge to infer potential music preferences. This cross-pollination enriches the model and helps fill gaps where first-party data alone would fall short.

Of course, third-party data isn’t a perfect solution. It comes with its own set of challenges, like data quality and managing biases. However, by thoughtfully integrating it with existing first-party data, AI developers can create models that are far more comprehensive and capable of making nuanced connections across different categories.

Challenge of bias in first-party data

Another key issue with relying solely on first-party data is that it can introduce significant biases into the model. Most first-party datasets reflect the specific customers or users of that organization, which can result in a model that’s too narrowly tailored. This can be particularly problematic in recommendation systems, where the goal is to introduce users to new content or products they might not have discovered on their own.

When a model is trained solely on first-party data, it may end up simply reinforcing what the user already knows and likes. For instance, a recommendation engine trained only on a retailer’s existing customer data might end up over-recommending popular items, rather than suggesting something unique and relevant to a specific user’s broader interests. By introducing third-party data, we can counteract some of these biases and create models that offer richer, more diverse sets of recommendations.

A broader view of AI development

Building effective AI models requires a holistic view of data sources. While first-party data is valuable, it’s just one piece of a massive puzzle. As AI technology continues to evolve, the importance of data diversity will only grow. Companies will need to embrace a broader approach to data collection, incorporating third-party sources and exploring partnerships to fill in the gaps.

Leveraging multiple data sources is essential. For those of us building AI systems, we need to ensure that the data reflects a wide range of perspectives and contexts. Only by doing this can we develop AI models that truly resonate with users and deliver insights that feel relevant and personalized.

For organizations looking to stay ahead, the message is clear: Don’t just rely on what you know. Embrace what others know too. By doing so, AI systems will successfully become more attuned to the complexities of human behavior.

Author

  • Photo of Michael Diolosa, CTO of Qloo

    Mike Diolosa is the CTO at Qloo, a leading AI company demystifying the intricacies of global consumer tastes and preferences without the use of personally identifiable information.

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