Category: Analytics & Data Science

Starbucks doesn’t simply sell huge numbers of hot and cold drinks around the world — it also gathers huge amounts of data from over 100 million transactions a week. How does it use this data? And what role do A.I. and the internet of things (IoT) play in this?

In 2019, Gartner predicted that 80% of AI projects wouldn’t scale to provide business value — that the data science projects would fail. While numerous other posts (see here, here, and here) have provided differing reasons for data science failure, they mask the underlying cause: the lack of a data science product manager.

While companies have been building data products well before AI became the next big thing, the formalization of data product management positions is relatively recent. This is a direct function of data’s new-found role as a key product and competitive advantage.

If you have read my recent post, you will know that I am interested in product management and data science. These 2 fields combined create a data-driven product development process, which is a powerful approach in a lot of SaaS companies nowadays. Recently, I went to a product meetup in Imgur headquarter in San Francisco, and had a chance to mingle with other product people as well as learn about the trendy product practices.