When AI was introduced to the business community, visions of how we could use it to meet customers’ needs came clearly into focus. And, with those visions, came the promise for AI to revolutionize customer experience. The acquisition and successful management of data were early stumbling blocks on our collective journey toward that future. But now, organizations are getting better at managing data and leveraging it too. Still, the visions of perfecting the customer experience with AI remain barely out of reach.
To achieve this higher-order AI, the progressive companies are asking, “What’s next?” In addition to the typical approach of sucking up more data and managing it better, one needs to consider more innovative approaches. While data maturity varies wildly across companies and industries, I’m starting to see transformational uses of data at the cutting edge. Companies can use these innovative data assets (or create their own) to power more advanced AI. Innovations like these introduce data where none existed before, use insights as data and reach for data beyond the typical customer journey.
Companies are finding ways to create data where none previously existed and making it possible to meet needs no one previously considered. The practice of finding or creating data to solve a known problem is not new. But what if you start with a novel data source and then think of novel ways you can use that data?
For example, a company called Label Insight captures attributes for food and beverage products directly from the labels. This goes beyond logistics data, like product size and weight, and includes ingredients, nutrition and marketing claims. Now Label Insight offers a centralized source that dimensionalizes the entire grocery store for anyone who wants to subscribe. What could a company do with this data? Use it to create a recommendation service that suggests products to consumers the way Netflix suggests shows to watch? What about a service that builds weekly menus and shopping lists based on a user’s nutritional requirements?
In healthcare, Trialtrove is a source for pharmaceutical clinical trial data. It uses several thousand sources to deliver the most comprehensive trial intelligence. The possibilities are endless, and they’re enabled by a simple impulse to make new data and see where it leads.
We think of data, then we think of insights from data. But what happens when we think of insights as the data? When you’ve gathered enough insights on something, it’s possible to use those to drive more impressive conclusions.
