Episode Title : Five Types of thinking for high performing Data Scientists

Episode Summary: Artificial Intelligence is all the rage currently. But there was a time when AI has gone through ‘AI Winter’ when there was not much interest in AI. Dr. Anand Rao has gone through those AI Winters. To avoid AI winter, we need to be cognizant of AI risks. Should be balance between AI innovation and risks. Should not reduce customer risk. In thos episode, Anand talks about five types of thinking that data scientists should focus on to be high performing data scientists. As the Global head of AI at PWC, Anand knows a lot about the customer uptake of AI and (un)surprisingly only 20% of the companies are actually deploying AI. Listen to the episode to find out which functions are adopting AI the most.

01:50: Global AI lead, Partner Global Analytics Insights lead, AI Innovation partner; Both on the client-facing side as well as product side.

03:00: Trends – Confusion about data (big Data, IOT Data,); Automation; Analytics; AI; All the technology is fine but where is the value?

05:00 (Headliner): Trends (1)  Convergence between Data, Models, and Software. (2) End to End lifecycle approach deployment of data, models, software (3) Governance, Risk and Controls to reap the benefit and minimize downside

08:40: To avoid AI winter, we need to be cognizant of AI risks. Should be balance between AI innovation and risks. Should not reduce customer risk.

10:45: High Tech firms are primary promoters of AI and adopters; Financial services are next; Healthcare, Retail, Manufacturing/Energy follow the leaders. Agriculture is surprisingly also an adaptor of AI.

12:54 (Headliner): 30 to 40% of companies are in exploration. 50% are in experimentation stage; Only 20% are in deployment stage.

14:47: Leading use cases are in operations. AI use cases for strategy. Example is strategizing business models for a car share of autonomous vehicles. AI being used to present scenarios in strategy.

21:15:  5 types of thinking for high performing data scientists. (1) Models Thinking (2) Systems Thinking (3) Agent-based thinking (4) Behaviorl thinking (5) COmputational thinking

25:00 (Headliner): You don’t always need data to start with. But once you have a mental model, you can always work with Data later to work with. Need to always start with a problem that needs to be solved and the mental model. Then start thinking about data that is needed.

Resources mentioned in this episode:

Podcast website: https://DataTransformersPodcast.Com

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