Alternative Data Weekly #287
Theme: Access Is Cheap. Insight Is Expensive; The New Bottleneck Is Interpretation.
Special thanks to our sponsor, Alternata.
Alternata is the data monetization expert. We do the work to turn enterprise data into recurring revenue for data owners, and connect data buyers to novel signals they can’t find elsewhere.
QUOTES
“This shift (towards the gov’t incorporating non-traditional data) is not about replacing official figures, but augmenting them — reshaping how policymakers, investors, and data providers interpret the economy in real time. – Julia Meigh
News
Pods
Charts
Final Thoughts (One Throat to Choke)
#1 – Steven Carroll published Alternative Data: uncovering opportunities amid the bluster and chaos. April 2026.
My Take: Alt Data is no longer new and, frankly, never really lived up to the hype. Steven offers a few reasons why. Too many vendors. Too tough to wrangle. Ultimately, the juice is not always worth the squeeze.
I remain optimistic that data will increase in value, particularly proprietary sources of data that can provide a useful signal. AI can make the data more accessible to more people (and agents!), expanding the TAM to reach the lofty levels everyone expected a few years ago.
#2 – Adrian Krebs published Direct Data Sourcing at Scale. April 2026.
My Take: Web scraping has been a hot topic lately. LLMs getting to the point where they can do the work, and cloud compute is making things affordable … which puts this type of data sourcing within reach for everyone.
How do we differentiate if anyone can collect this type of data? First is following industry standards (this is table stakes for any serious player), and next is to do it consistently. Then, it is to focus on delivering alpha, or unique signal, rather than just access. This is hard to do and even harder to do well.
#3 – Daniel Hall published How Alternative Data is Changing Consumer Lending Decisions. March 2026.
My Take: In theory, using more sources of data will expand access to capital (i.e., I pay my phone bill every month, therefore I am a good credit). See our sponsor from this week, Alternata…they specialize in this stuff. Alt Data is another piece in the puzzle and does not replace existing traditional data sources; it rather enhances and tells a better story about the person or company in question. One interesting use case I’ve heard (not in the article) is that a person who regularly lets their phone battery go to zero is a bad credit risk. Related article: Federal housing agencies to allow alternate credit scores in mortgage applications.
Privacy becomes an issue, but if the person wants to give the bank access to their detailed shopping information, then that is the consumer’s choice.
BONUS: America’s AI Action Plan. July 2025. “High-quality data has become a national strategic asset as governments pursue AI innovation goals and capitalize on the technology’s economic benefits.”
What else I am reading:
Don D’Amico published Your data vendor has a side deal. June 2026.
James Timmons published Quitting my job to research AI software design. April 2026.
Julia Meigh published Inside the Fed’s Data Playbook: Alternative Datasets Driving Policy. April 2026.
Scot Hamilton published HIPS Don’t Lie: [Expletive] Tony Gwynn. April 2026. (The Guardian’s Jose Ramirez is this generation’s version of Tony Gwynn).
The Terminalist published The Distribution Fallacy — Moats, Mechanisms, and Misreads. April 2026 (I will give this a close read before next Friday’s ADW … didn’t have time this week but wanted to include).
My friends at Gemsen (sister company of SymetryML) are presenting at Fintech Sandbox at 11 am ET on April 28. Check it out!
Source: Stan Altshuller interviews Jack Killea of Maiden Century. How AI Is Democratizing Alternative Data for Hedge Funds | Stan Altshuller with Jack Killea. March 2026.
My Take: This is a good conversation between two guys who have been in the space for a while. They discuss, among other things, how AI is changing the way hedge funds discover and use alternative data.
Q: How has the data consumption model changed for HFs? A: Things are changing quickly (matter of weeks!).
For HF’s & institutional investors, alternative data (3rd party sources) is now table stakes. Need to meet customers where they are.
Why now? How have things changed?
People are getting better at marrying various data sets to get a better read on reality.
MC has helped data vendors expedite sales to quantamental investors.
People are getting better at designing the data and the nuances of how data gets pulled together and used for investment purposes.
Too much stuff to look at. Most doesn’t generate signal…interpreting all the information and simply knowing where to look is a big deal.
Ideas for new data providers:
B2B spend has been the holy grail for the past few years
Make it less challenging to work with (Maiden Century pitch)
Alt data has not been commoditized. There are infinite ways to combine the data to generate novel signal.
AI’s impact on data strategy is essentially that the barrier to finding the next important data point has been lowered.
AI gets better the more you work with it.
“We are a context engine as a company.”
One last one … I am a believer that AI will increase employment…. at 31:45 Jack says “headcount will go up as a result of AI”.
HIGHLIGHTS (36-Minute Run Time)
Minute 01:30 – interview starts, Jack’s background.
Minute 02:30 – data consumption models are changing.
Minute 05:45 – different types of customers & some background on the past decade of alternative data.
Minute 12:00 – does AI make working with data more scalable?
Minute 14:00 – separating signal from noise.
Minute 16:00 – new data partners that are interesting.
Minute 22:00 – AI is shrinking the gap between big and small investors.
Minute 25:00 – the flywheel of working with AI.
Minute 29:30 – proactive agents using MC data?
Minute 32:45 – will AI manage money for us?
SOURCE: Peter Baumann’s Strategic Patterns for AI Transformation. April 2026.
My Take: These AI tools are changing how we work (I think more slowly than everyone expects, but yes, things will change) … this is a good thought piece on how that might happen.
BONUS: Barr Moses & Oren Yunger published Chief Data Officer Responsibilities in the AI Era: What CDOs Are Telling Us (and What It Means)
What the winning CDOs are doing:
Uplevel technically.
Own agent-readiness, not just AI-readiness.
Take ownership of AI observability.
Become the enterprise guardrails function.
Drive revenue, not just risk management.
One throat to choke.
I’ve been trying to educate myself about how humans fit into an AI future.
Humans will be in the loop. Increasingly in roles where trust, judgment, and accountability matter most. The customer still wants a person who owns the outcome. One throat to choke.
Even BCG is saying a version of the same thing in their consultant-speak (AI Will Reshape More Jobs Than It Replaces): AI is more likely to reshape jobs than erase them. The work doesn’t disappear; it moves toward what AI can’t fully deliver. Relationships. Context. Confidence. Responsibility.
There will be disruption. There will be opportunity.
If you have a role that resembles the knocker-uppers as alarm clocks came of age, move yourself closer to the customer, closer to the decision, and closer to accountability.
In the age of AI, the best seat will be the human seat in the relationship.
Source 1: BCG
Source 2: Alex Imas via Timothy B. Lee.
Human in the loop and human in the lead. That’s where the puck is going for knowledge workers in an AI world.