AI Products: Using Data Effectively

We live in an increasingly mobile-first world. People are using mobile technology on a daily basis in every part of the world. With this, our ability to access data has never been so easy or impactful. For example, there are 3.5 billion mobile devices around the world. As a result, we can pull billions of data points to optimize our AI products. Furthermore, consumers have access to products that use computing services in virtually every function of their lives. While we use smartphones most frequently, AI technology is everywhere – from kitchen appliances to our cars.

While these factors present a tremendous opportunity for AI products to reach a wider audience, product managers are facing new challenges. Most notably, how do you make sense of all this data? Said differently, where do you start in trying to understand how to make your products better?

We can all agree that access to customer data helps us make better decisions. However, when you have billions of data points, your ability to find a starting point is not straightforward. In addition, the increasing product footprint for AI technology presents another challenge. From smart TVs to wearables, you have to select the best platform and associated data to guide your decision-making process.

To start making sense of these factors, I like to think about using data to build AI products in these three areas:

  1. Artificial Intelligence (AI).

    • AI products begin and end with the algorithms on which we build them. Ultimately, AI products are most successful when they utilize data to produce outcomes that resonate with customers. Most importantly, they must be set up to continuously adapt and optimize results based on intelligent learning from customer use cases.
  2. User Experience (UI).

    • Identifying the data points that are most directly associated with your user experience is critical for guiding product design. Ultimately, every decision you make in creating workable solutions for your customers comes from data collected by your product.
  3. Personalization (I).

    • Over time, your customer’s usage patterns and preferences will impact the user data that you access in order to continuously improve your product. In the end, you must be conscious of how changes in user interactions will influence your data. Most importantly, you need to zero in on the data set that provides the greatest opportunity for addressing the needs of your broadest audience.

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About the speaker
Aparna Chennapragada Google, Vice President Member

Aparna Chennapragada is the Vice President of VR/AR products at Google. In a previous position, she led Google Now, driving efforts to assist users in a mobile world. Aparna is a product management veteran in information discovery, having led efforts across Google Search and YouTube.