The Future Data Economy with Roger Chen - Episode 21
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Summary

Data is an increasingly sought after raw material for business in the modern economy. One of the factors driving this trend is the increase in applications for machine learning and AI which require large quantities of information to work from. As the demand for data becomes more widespread the market for providing it will begin transform the ways that information is collected and shared among and between organizations. With his experience as a chair for the O’Reilly AI conference and an investor for data driven businesses Roger Chen is well versed in the challenges and solutions being facing us. In this episode he shares his perspective on the ways that businesses can work together to create shared data resources that will allow them to reduce the redundancy of their foundational data and improve their overall effectiveness in collecting useful training sets for their particular products.

Preamble

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• Your host is Tobias Macey and today I’m interviewing Roger Chen about data liquidity and its impact on our future economies

Interview

• Introduction

• How did you get involved in the area of data management?

• You wrote an essay discussing how the increasing usage of machine learning and artificial intelligence applications will result in a demand for data that necessitates what you refer to as ‘Data Liquidity’. Can you explain what you mean by that term?

• What are some examples of the types of data that you envision as being foundational to multiple organizations and problem domains?

• Can you provide some examples of the structures that could be created to facilitate data sharing across organizational boundaries?

• Many companies view their data as a strategic asset and are therefore loathe to provide access to other individuals or organizations. What encouragement can you provide that would convince them to externalize any of that information?

• What kinds of storage and transmission infrastructure and tooling are necessary to allow for wider distribution of, and collaboration on, data assets?

• What do you view as being the privacy implications from creating and sharing these larger pools of data inventory?

• What do you view as some of the technical challenges associated with identifying and separating shared data from those that are specific to the business model of the organization?

• With broader access to large data sets, how do you anticipate that impacting the types of businesses or products that are possible for smaller organizations?

Contact Info

• @rgrchen) on Twitter

• LinkedIn)

• Angel List)

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

• Electrical Engineering)

• Berkeley)

• Silicon Nanophotonics)

• Data Liquidity In The Age Of Inference)

• Data Silos)

• Example of a Data Commons Cooperative)

• Google Maps Moat): An article describing how Google Maps has refined raw data to create a new product

• Genomics)

• Phenomics)

• ImageNet)

• Open Data)

• Data Brokerage)

• Smart Contracts)

• IPFS)

• Dat Protocol)

• Homomorphic Encryption)

• FileCoin)

• Data Programming)

• Snorkel

• Website)

• Podcast Interview)

The intro and outro music is from The Hug) by The Freak Fandango Orchestra) / CC BY-SA)

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