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Yingzhao Ouyang
Data and AI Transformation Specialist
Driving value in cloud, data and generative AI solutions for enterprises
All my posts, latest first
The AI Disruption Part 2: Inside the Sixth Technology Wave
Originally published on Medium on 3 June 2026 Part 1 showed how the AI dividend is already being allocated unevenly, with early-career white-collar workers absorbing much of the adjustment. Part 2 asks what kind of technological wave produces that pattern and why … The AI Disruption Part 2: Inside the Sixth Technology Wave Read More »
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The AI Disruption Part 1: How AI is Reshaping Work Before Our Institutions are Ready
Originally published on Medium on 27 May 2026 AI is not just another productivity tool. It is changing who gets paid for productivity. In this first part of the series, I look past the hype and examine what the data actually says … The AI Disruption Part 1: How AI is Reshaping Work Before Our Institutions are Ready…
Read More » The AI Disruption Part 1: How AI is Reshaping Work Before Our Institutions are Ready
Chips, Cells and Code: How Singapore is Applying its 40-Year Industrial Playbook to AI
Originally published on Medium on 19 February 2026 In the 1980s, the big bet was chips. Singapore set out to become a serious player in semiconductors and electronics, starting from low‑cost assembly and eventually becoming a key node in the global chip … Chips, Cells and Code: How Singapore is Applying its 40-Year Industrial Playbook to AI Read…
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Build Your Own LinkedIn Analytics Part 12: What’s Next? Open Sourcing and Community
In the 12th and final post of the series, I release the open-source repository that implements the LinkedIn analytics pipelines, and discuss future plans.
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Build Your Own LinkedIn Analytics Part 11: Key Takeaways and Lessons Learned
In the 11th and penultimate post of the series, I look back on what has been achieved, what can be done better and what has been learned.
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Build Your Own LinkedIn Analytics Part 10: Observing the Pipeline
In the 10th post of the series, I show how to set up observability on our data pipeline to monitor its condition and act as necessary.
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