In Case You Missed It: November 2023 Recap
Bridging Bytes and Feelings: Rana el Kaliouby's Emotional AI Crusade; Unstructured Data Unravelled; 3 Reasons Your Company Shouldn't Invest Into Data; Product Data Teams 101
This is the Three Data Point Thursday, making your business smarter with data & AI.
Data is emotionless; algorithms are inhuman. But that’s kind of the point of it, right? Remove emotions and all the fuzzy stuff to get objective decisions and correct calculations.
So, what’s the natural reaction to data and algorithms by humans? We get scared; we have a natural aversion to both of them. In fact, the United States, home to the powerhouses of AI, is the country with the highest AI fear index following a recent survey.
So, what do great data leaders have to do? They must build empathy into their business, products, and algorithms.
TimeGPT is amazing news! But most people dismiss two important things:
It’s not just about time GPS; time series models are becoming available at large - especially generic ones everyone can use will provide a huge advancement across machine learning.
Timed data is becoming more and more important; just because it wasn’t in the past doesn’t mean it won’t in the future.
Data is the new oil, right? Just like turning oil into money, turning data into money is a freakishly hard task only a few companies have really figured out how to do.
It's a little like becoming a YouTuber; millions of people think success is right around the corner, wake up at 6 a.m. to make videos - the reality is, only 0.0001% of them will make any money.
Introducing the concept of product data teams means installing a product leader into some/every data team. The crucial part is that this product leader can change the team's roadmap in a way he sees is best for the department and the company, in collaboration with all potential customers inside the company, but without many constraints.
The product leader is also responsible for covering the What, not just the How of the work.
This is usually done by providing product management education to a senior/lead data person on the team or by installing a new product manager.