The Power Behind AI Product Data
Understand the significance of prioritizing data readiness, involving phases like research, data quality partnerships, and addressing internal resistance, ensuring a robust foundation for successful AI integration.
Data for product transformation
Before product managers can begin the work needed to start building and developing their product, getting it ready for testing, and releasing it to their customers, they need to get really clear on the strategy of how they will position their product. Now that we’ve gone over the process of how product managers can approach potential AI embellishments, we can add one more layer of scrutiny to this list. This additional layer focuses on the data, which is what will power every single item on these lists.
Data readiness
Once product managers begin the work of understanding what they can do with the data sources they have and which data sources they’ll need to make the items on their list a reality, we’re getting close to actually having a plan.
In the following lessons, we will be addressing the key areas of data readiness. Preparing and researching the data we have available, assessing the quality of the data we have, using the data for benchmarking the current and future adoption of our product, partnering with our data team, and, ultimately, defining success through our data will all be necessary steps to ensuring we’ve covered our bases when it comes to data readiness and availability for AI.