The Future Data Economy with Roger Chen - Episode 21
March 4th, 2018
42 mins 47 secs
About this Episode
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.
- Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
- When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
- Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
- You can help support the show by checking out the Patreon page which is linked from the site.
- To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
- A few announcements:
- The O’Reilly AI Conference is also coming up. Happening April 29th to the 30th in New York it will give you a solid understanding of the latest breakthroughs and best practices in AI for business. Go to dataengineeringpodcast.com/aicon-new-york to register and save 20%
- If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to dataengineeringpodcast.com/odsc-east-2018 and register.
- Your host is Tobias Macey and today I’m interviewing Roger Chen about data liquidity and its impact on our future economies
- 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?
- @rgrchen on Twitter
- Angel List
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Electrical Engineering
- 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
- Open Data
- Data Brokerage
- Smart Contracts
- Dat Protocol
- Homomorphic Encryption
- Data Programming
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SASupport Data Engineering Podcast