The classic scientific model involved devising a theory and then collecting data to test it. The modern paradigm, driven by big data, often reverses this. Progress now frequently comes from analyzing massive datasets first to discover patterns, and only then forming hypotheses to explain them.

Many accepted financial rules are not timeless. Stocks only began consistently outperforming bonds after WWII, and inflation-adjusted US home prices were flat for a century before 1997. This reveals that much financial advice is based on recent history, not immutable laws, making it a poor guide for the future.

David Booth's close relationship with researcher Eugene Fama allowed him to get a draft of the seminal three-factor model paper in 1991. He immediately acted on it, flying a client to Chicago to hear the research directly from Fama and launching value strategies before the academic world had seen the published work.

The historical information asymmetry between professional and retail investors is gone. Tools like ChatGPT and Perplexity allow any individual to access and synthesize financial data, reports, and analysis at a level previously reserved for institutions, effectively leveling the playing field for stock picking.

In the 80s, credit was binary: a high score got a card, a low score got nothing. Capital One pioneered an "information-based strategy," using data to test and price risk for consumers just below the traditional cutoff, effectively creating the modern data-driven lending model.

Investors often judge investments over three to five years, a statistically meaningless timeframe. Academic research suggests it requires approximately 64 years of performance data to know with confidence whether an active manager's outperformance is due to genuine skill (alpha) or simply luck, highlighting the folly of short-term evaluation.

The Bloomberg terminal's breakthrough was not simply displaying data, but integrating the tools needed to analyze and act on it. It was built around the user's entire workflow—calculating, graphing, and messaging—which existing data screens completely ignored.

The emergence of venture capital as a major asset class was unlocked by the new ability to mathematically measure and price risk. Similarly, the current impact investing movement is being driven by our newfound technological capacity (via big data and computing) to quantify a company's social and environmental effects.

MDT deliberately avoids competing on acquiring novel, expensive datasets (informational edge). Instead, they focus on their analytical edge: applying sophisticated machine learning tools to long-history, high-quality standard datasets like financials and prices to find differentiated insights.