3 Takeaways, beyond Data is NOT boring:

The data that you throw away is the data that tomorrow you might be able to use. For instance, as part of a machine learning model that could bring true value to an organization.
It's important for organizations to have explicit statements about their AI ethics.
Federated learning has some magical capabilities, especially for financial services customers, when it comes to building predictive models without violating local laws on how data is stored or used because the data never leaves the premises.

Key Quotes:

"In some sense, we all need to be pack rats when it comes to our data.” - Josh
“I think it's critically important for organizations to speak explicitly about data ethics with the people or organizations from which you are collecting data and be very clear with them, as well as with your employees, about what it is that you're willing to do and not do with that information.” - Josh
“Data is where it all happens. That's the fuel that runs all the ML stuff...The issue is how do we get our hands around all that data, and how do we get it to the point where it is appropriate for building predictive models? Just because you have the data doesn't mean that the data has been cleaned, curated and de biased - so that it's actually in shape to be used for building models.” - Josh

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Links

Josh Simons LinkedIn

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About the Hosts

Matthew O'Neill is a husband, dad, geek and Industry Managing Director, Advanced Technology Group in the Office of the CTO at VMware.

You can find Matthew on LinkedIn and Twitter.

Brian Hayes is an audiophile, dad, builder of sheds, maker of mirth, world traveller and EMEA Financial Services Industry Lead at VMware.

You can find Brian on LinkedIn.

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