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24 October 2018
Zao Wu
Zao Wu's focus is on trends in APAC, the Chinese market in particular. He believes it’s important to keep an eye on other industries, not just financial services. When he's not paying attention to disruption coming from every angle, you'll find Zao reading or fiddling around with computer games.
WHERE ARE YOU FROM ORIGINALLY / WHERE DID YOU GROW UP?
The short version is that I grew up in the UK. The longer version is that I was born in China, and moved around quite a bit (lived in the Southern Hemisphere) and then around the various parts of the UK. I currently live in Hong Kong .
WHAT IS YOUR FAVORITE CITY/PLACE YOU’VE TRAVELED?
Tough call since there are different things to like about each city. London makes me think of wintry evenings, Singapore of sudden, 3-minute rainstorms, Beijing of the summer noon, Glasgow of autumn winds and Belfast of scorching 23°C summer days. Hong Kong makes me think of dim sum, and Chengdu's hotpot is pretty awesome.
FAVORITE ANIMAL?
I like otters.
Any Pets?
Not at the moment, but I used to have pet chickens, a tortoise and a dog, but at different times.
FAVORITE HOBBY?
Reading, doing analytics and posting the findings to my data science blog and fiddling around with computer games.
FAVORITE BAND / MUSIC / Song?
This is completely dependent on the setting and my mood.
FAVORITE FOOD DISH
Fajitas
What's your story?
So my background is in Maths/Statistics/Finance and later on Computer Science, and was initially planning a career in research. During my postgraduate degree, a graduating student gifted me some of his books. I found myself in possession of a certain book called the 80-20 principle, which opened up the world of consulting to me. So I guess whatever I'm reading at any given stage may well have a huge impact on the future.
What advice would you give to someone entering the industry?
My advice for folks looking to enter the industry is to stay curious: don’t just take others’ words for it, but carefully listen to their reasons and then judge for yourself. To use a machine learning analogy, it’s like we’re classifiers: being receptive ensures that you have the largest amount of training data, and your judgment (or how effective you are as a classifier) is your biggest value add.
