The Data Exchange Podcast: Jian Pei on how organizations can assess the value of data objectively, systematically and quantitatively.
Subscribe: Apple • Android • Spotify • Stitcher • Google • RSS
In this episode of the Data Exchange, I speak with Jian Pei, Professor, School of Computing Science, Simon Fraser University. His research spans data science, big data, data mining, and database systems. But in this podcast we talk about tools for estimating the economic value of data. Jian recently wrote a comprehensive paper (“A Survey on Data Pricing: from Economics to Data Science”) that examines the research literature pertaining to valuing both digital goods and data products. The rise of blockchains and cryptocurrencies have recently led to NFTs (non-fungible tokens), tokens that have proven useful for valuing digital goods like artwork and collectibles. Our conversation centered on tools for valuing data products.
In 2018, I gave a keynote at Strata Data London where I discussed data markets in the context of privacy regulations (GDPR) that were about to come into effect. Along the way I sketched out some key ways one could try to approximate the value of data, including: measuring the impact of data on machine learning and statistical models, learning the valuation of data-centric companies, comparing the cost of using data labeling services, and more.
As Jian Pei notes, companies will increasingly need to have tools and processes for valuing data:
- ❛ First, if your company needs a CFO, you probably need to have your CFO look at your data. Data is your key resource. Second, when you think about data, consider bringing in economists. Build a team of economists to work with you on your data products. Data is far beyond just technology. Third, within every company there are many upstream and downstream applications of data. So the importance of and value of data tends to be more than people initially assume.
Subscribe to our Newsletter:
We also publish a popular newsletter where we share highlights from recent episodes, trends in AI / machine learning / data, and a collection of recommendations.
Related content and resources:
- A video version of this conversation is available on our YouTube channel.
- “Data collection and data markets in the age of privacy and machine learning”
- Omer Dror: “Data exchanges and their applications in healthcare and the life sciences”
- Assaf Araki and Ben Lorica: The Growing Importance of Metadata Management Systems
- Sonal Goyal and Ben Lorica in conversation with Jenn Webb: “Creating Master Data at Scale with AI”
- Jesse Anderson and Ben Lorica in conversation with Jenn Webb: “A Unified Management Model for Successful Data-Focused Teams”
[Photo by Arnaud Mariat on Unsplash]