"A Data-Driven Roadmap to Enterprise AI Strategy"
AI is transforming every aspect of our daily lives and the data landscape is becoming increasing open and transparent. Between the high level academia and low level algorithms, where should the modern business leader start on their AI journey and harness true value from their data?
On the back of our recent successful data and AI transformation case study at Transport NSW, let us show you a step by step, proven approach towards enterprise-wide AI adoption.
By Yun Zhi Lin - Chief Innovation Officer, Contino
"Data Science - The Human Factor"
The critical factors for Data Science projects are human factors, specifically trust and factors which support trust. Winning over users to trust your model and your organisation is often decisive not just for whether a data science project is successful, but frequently for whether it proceeds at all.
In my role as Lead Data Scientist at RightShip, I have been central to the development of the Qi algorithm which estimates the likelihood of particular vessels experiencing a maritime casualty (an industry wide definition of maritime accident defined by the International Maritime Organisation). This algorithm has been implemented and is used by RightShip customers exposed to shipping risk in a variety of ways - from ports and charterers to banks and insurers. While this project was successful overall, like every project there were aspects that were less successful than others - we will see that the differences were driven by the ability of the model's users and audienes to trust the model in different ways.
By Robert De Graaf, Senior Data Scientist, RightShip
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