While companies are curious about competitors, this data rarely leads to an immediate, concrete business decision that directly impacts revenue. This lack of actionability makes it a 'nice-to-have' with low willingness to pay, resulting in a challenging market with high churn.
Contrary to the popular advice to 'hire great people and get out of their way,' a CEO's job is to identify the three most critical company initiatives. They must then dive deep into the weeds to guarantee their success, as only the CEO has the unique context and authority to unblock them.
A common mistake is building a visually impressive data product (like Google Earth) that is interesting but doesn't solve a core, recurring business problem. The most valuable products (like Google Maps) are less about novelty and more about solving a frequent, practical need.
When growth flattens, data companies must expand their value proposition. This involves three key strategies: finding new end markets, solving the next step in the customer's workflow (e.g., location selection), and acquiring tangential datasets to create a more complete solution.
Data businesses have high fixed costs to create an asset, not variable per-customer costs. This model shows poor initial gross margins but scales exceptionally well as revenue grows against fixed COGS. Investors often misunderstand this, penalizing data companies for a fundamentally powerful economic model.
YipitData had data on millions of companies but could only afford to process it for a few hundred public tickers due to high manual cleaning costs. AI and LLMs have now made it economically viable to tag and structure this messy, long-tail data at scale, creating massive new product opportunities.
Conventional wisdom to 'stay focused' is flawed. Breakthrough growth often comes from making many small, exploratory bets. YipitData's success wasn't from perfecting one thing, but from the one small, tangential bet each year that drove 90% of the growth while others failed.
Hedge funds have a constant, daily need to make informed buy, sell, or hold decisions, creating a clear business problem that data solves. Corporations often lack this frequent, high-stakes decision-making cycle, making the value proposition of external data less immediate and harder to justify.