This episode explains why data businesses are slow to grow but highly profitable once established. Auren Hoffman categorizes data companies into four types and argues that pure data businesses are like eating broccoli—hard to sell at first, but extremely sticky once customers integrate the data into their systems. He highlights ZoomInfo (the only pure data company to break out in 20 years), FICO (its credit score became the universal standard for loans), and Bloomberg (clients can't quit its terminals). He warns that the most common mistake in data startups is trying to build both data and applications at once.
Auren Hoffman (CEO of Safegraph) delves into the data business on the program, proposing a 2x2 matrix to classify data companies, with core dimensions including "religious" (high stickiness) and "application-based." He defines great businesses as those with high gross margins, network effects, and l
Auren Hoffman (CEO of Safegraph, former founder of LiveRamp) deconstructs data businesses using a 2x2 matrix, with the core dimensions being "Past vs Future" (Truth vs Religion) and "Data vs Application". He argues that pure data businesses are like "eating broccoli"—extremely slow early growth, very difficult to sell, but once a moat is built, they become highly sticky, high-margin long-term businesses. Over the past 20 years, the only pure data company that has truly broken through is ZoomInfo.
Auren Hoffman argues that all data companies can be placed into a 2x2 matrix along two dimensions: the horizontal axis is "Past Facts (Truth) vs. Future Predictions (Religion)," and the vertical axis is "Pure Data vs. Application."
Hoffman emphasizes that small companies (revenue <$50 million) can only focus on one quadrant; large companies can deploy across multiple quadrants.
Hoffman uses the "broccoli vs. sugar" analogy to characterize the growth profile of data businesses: data companies grow extremely slowly in the early stages but exhibit exceptionally strong long-term stickiness.
Hoffman points out that early-stage data businesses may appear to have poor gross margins — because purchasing external data is accounted for as "cost of goods sold" (COGS), even if it is a fixed cost. "At $5 million in annual revenue, a $2 million data procurement looks like a terrible business; but at $100 million, it’s just a rounding error."
Hoffman argues that the moat of data companies comes from three unique dimensions: high market share, brand effect, and the "frequency of change" of the data itself.
Hoffman also emphasizes that "joinability" is key: the more easily data can be linked with other datasets, the higher its value. "Dollars, Unix timestamps, and stock tickers are all excellent join keys. If your industry lacks a good join key, creating and open-sourcing one could be highly valuable."
Hoffman points out that the most common failure in data entrepreneurship is "trying to occupy multiple quadrants at once"; successful founders need the humility to "play a supporting role."
Three companies Hoffman recommends studying: Experian/FICO (understanding the credit data ecosystem), Bloomberg (the moat of high fixed costs plus bundling model), and ZoomInfo (the only pure data company to break through in the past 20 years).
| Position | Analyst View | Key Data |
|---|---|---|
| FICO | Bullish ("phenomenal business") | Converts predictions into "currency," becoming the universal standard for loan securitization |
| ZoomInfo | Bullish ("the only pure data company to break through in the past 20 years") | Strong brand effect—"I don't know if their data is actually better than competitors, I just use ZoomInfo" |
| Bloomberg | Bullish ("increasingly incredible") | High fixed costs + bundling model; "could fire the top 20 employees and the business would still be fine" |
| Verisk | Bullish ("worth studying for everyone") | Started as an insurance data cooperative, transitioned from non-profit to for-profit |
| Experian / Equifax / TransUnion | Neutral (industry structure analysis) | The U.S. has 3 credit bureaus (rare), most countries have only 1-2 |
| Twilio | Neutral (brand case) | Brand becomes the default choice—developers don't know competitors' names |
| Stripe | Neutral (analogy) | High market share, customer acquisition costs keep declining |
| Neutral (data cooperative case) | User-maintained database, but cannot directly sell data (trust issues) | |
| CoStar | Neutral (application layer case) | Real estate industry data maturity is low, must build applications |
| G2 | Bullish (Hoffman investment) | "Yelp for software," application layer + proprietary data |
| Second Measure | Neutral (acquired by Bloomberg) | Built UI on top of Yodlee's raw data |
| Salesforce | Risk warning (data monetization limited) | Contract terms prohibit data sharing, cannot transition into a data business |
1. Hoffman defines a "great business": one that would still perform well even if the top 100 employees were fired. The best example is Visa—"Even if my deceased grandmother were running it, it would probably still function well." Core characteristics: customer acquisition costs decline year over year, and high market share.
2. Data companies are "archivists" rather than "constitutional drafters"—they must accept a supporting role. "Madison and Hamilton stand on the pedestal; we are the archivists maintaining the constitution. No one knows our names, but we are still important."
3. The frequency of data change is a core metric for value. "Facts about the War of 1812 no longer change, so their value is low. Data is like AWS—customer spending increases, but the amount of computing they receive also rises significantly, while the cost per data element continues to decline."
4. The "joinability" of data determines its value ceiling. "The more connectable data is, the higher its value. The dollar, Unix time, and stock tickers are all excellent join keys. If your industry lacks one, create and open-source it."
5. The most common failure in data entrepreneurship: attempting to occupy multiple quadrants simultaneously. "Wait until revenue reaches $300 million before adding other business lines. Until then, stay extremely focused."
6. Early-stage gross margins for data companies may look poor—because data procurement is recorded as COGS, even if it is a fixed cost. "At $5 million in annual revenue, $2 million in data procurement looks like a terrible business; by $100 million, it is just a rounding error."
7. Most industries are still in the very early stages of data utilization. "Even among hedge funds, no more than 100 are truly adept at using alternative data. In the real estate industry, fewer than 5 can use data effectively."
8. Hoffman's "Four Nouns of Data" framework: all data companies revolve around one of four nouns—people, places, organizations, or products. "Almost all data companies center on at least one of these nouns, sometimes intersecting, and usually crossing with time or price."