Thanks for being here!
Announcement(s):
We have a new daily data product that offers end-of-day account balances across various types of accounts (savings, checking, personal, business, etc). Anonymous small regional & community banks. No PII. Very granular.
RBLX gift card data continues to be spot on … if you care about the stock RBLX, this is data to which you should have access.
Theme that emerged in this week’s email is … demand for data is increasing, tools are catching up.
Quotes:
We are DATA, not compute constrained - Bindu Reddy
“Some companies with unique data assets will be able to monetize those assets more effectively.” - Abraham Thomas
News Articles
Podcasts
Cool Charts
Final Thoughts (Jevon's Effect)
#1 – Abraham Thomas wrote Data in the Age of AI. May 2023.
My Take: Great article. The author runs through the decline of storage & compute costs & how this has begun to shift value toward owners of data. Data feeds into everything … this has been exacerbated with the advent of ChatGPT.
New term for me is “golden data”, or data of exceptional quality for a given use case.
The confidence chain (series of proofs required to ensure “good” data):
Signatures
Provenance
Identity
Quality
Curation
There are 2-3 other ideas that resonated with me in this article: trust hierarchies, Jevons effect (see Final Thoughts below), the categorization of “picks & shovels”, flywheel result of generative models, etc.
#2 – Seattle Data Guy published A Decade In Data Engineering - What Has Changed?. May 2023.
My Take: Related video here. I really like SGD’s big picture thought pieces, great for perspective of where we (the data industry) were, where we are, & where we are headed. Taking a step back to think about why we use the tools we use is important. Are we using the best tools or the best marketed tools?
Of most interest to me was the “2023…The Post Modern Data Stack”.
#3 – Hedgeweek published Cutting through the noise. May 2023.
My Take: Reality check warning. Data offers a ton of potential, as highlighted in the the above article(s), but in practice there is so much data & it is so poorly organized, that even the most sophisticated firms (i.e. HF’s) with the most resources & the most to gain/lose, struggle to manage it all. The data industry is moving in the right direction but there will continue to be fits & starts along the way.
BONUS: Alex Izydorczyk’s Product Analytics Approach to Alternative Data. May 2023. “External data users in all industries focus more on solving data fidelity and availability issues than internal data scientists need to.”
BONUS 2: Matthew Bernath’s The New Goldmine: Harnessing Alternative Data for Economic Insights. May 2023. “…advanced analytics and artificial intelligence have made it possible to extract meaningful insights from these massive, unstructured datasets. Machine learning algorithms can detect patterns and trends in the data, predicting economic outcomes with a level of accuracy that was previously unthinkable.”
What else I am reading:
Elementl raises $33M Series B for its data orchestration platform based on Dagstert. Good video at bottom of the article.
NBER’s publication From Transaction Data to Economic Statistics: Constructing Real-time,High-frequency, Geographic Measures of Consumer Spending. This is a long paper, of most interest to me was the methodology section page 7.
Kathleen Velasquez & Geoffrey Keating of Twillio published 3 Steps to Unlock Your Customer Data and Drive Revenue
Goodbrand’s Do you sell data? Don't! Tell Data Stories and the Data will sell itself.
ThinkDataWorks published The No Bullsh*t Guide to Data Strategy
#1 – Invest Like the Best Podcast interviewed Henry Schuck - Building ZoomInfo. May 2023.
My Take: $ZI might be the most successful pure data business (yes, I know, all businesses are data businesses). I am ready to take on the world after listening to Henry. Great energy! The ideal of delivering all pertinent data to the right person at the right time is on the horizon. Henry discusses the iterative process of building a great product. Similar to Jeff Bezos mantra of “it is always day 1”.
Ensure competence, reliability, and motive are aligned (gaps closed) as you move towards being operationally excellent.
Future – take all of those insights (millions of inputs) & deliver to user today so they can be maximally effective.
What makes a great salesperson?
1- Do you understand who our customers are and how they leverage our solutions?
2- Do you understand our products?
3- Do you connect the dots between the pain points of the customer & the value our products offer?
4- Do you follow a structures process that the customer agrees to that drives to a decision?
Plus, an attitude component…competitive, problem-solver, & hustle.
Highlights (94-minute run time):
Minute 02:30 – interview starts.
Minute 04:00 – msg to his team upon IPO of $ZI: focus on building a championship team.
Minute 07:00 – how to effectively apply pressure.
Minute 13:20 – what is the ZoomInfo product philosophy?
Minute 15:30 – Get the data & then automate use of that data…unique data & workflow.
Minute 19:30 – Sophistication of most big company “go to market engines” is 4/10 … room for improvement
Minute 25:00 – characteristics of a great salesperson
Minute 40:40 – History of ZoomInfo; Day 0 through Year 7
Minute 46:00 – getting to price; describe pain first…fear of loss powerful
Minute 50:00 – Power: I control a desirable scarce resource & we are talking about how you can get it.
Minute 53:00 – M&A: why, what is motivation? Some examples
Minute 61:00 – what strategy drives next capital allocation move?
Minute 65:00 – thoughts on AI … what does it mean to be ready for AI?
Minute 72:00 – managing a bigger operation
Minute 76:00 – next chapter … what do we have to prove?
Source: Seattle Data Guy’s A Decade In Data Engineering - What Has Changed?. May 2023.
Link to picture here.
Source: I came across the Jevon’s Effect1 in Abraham Thomas’ article Data in the Age of AI.
I had not heard of it and figured I’d do a little digging.
The Jevons effect occurs when the effect from increased demand predominates, and the improved efficiency results in a faster rate of resource utilization.
Jevons made his initial observation as it related to coal production. The more efficiently coal was produced, the more cheaply coal would be made available, the more coal would be in demand. The more revenue for coal companies to increase production efficiencies … flywheel process. The Jevons effect gets mentioned frequently as a flaw within environmental economics.
Q: How does this relate to data?
A: As we get better at finding & working with data, demand will increase for that data.
An example from my life is Spotify Wrapped … this is summarized data of my Spotify listening habits presented beautifully. I did not know I wanted this data, but find it fascinating & want more like it.
Snowflake is another example (h/t David Ruthven) where it has become so much easier and cheaper to engage with data that I now need more data to feed the beast & build better products, etc.
Not sure if this is a perfect example of Jevon’s effect, but Alex Izydorczyk highlights the value of third party data. In the Apollo example, something has to give, price/value not sustainable.
Bottom line, the cheaper & easier it is to find & work with data, the more data will be in demand. This activity will create more data. Commence flywheel action!
Source: Jevons Paradox and Software Efficiency. Matt Rickard. June 2021.
Other sources I reviewed: Wikipedia, The Forum Network, ResearchGate.
Hi John, really enjoy your insights into the data world. What’s the best way to get in touch with you?