At the last meeting of The Presidents Forum, we explored a provocative idea: why good data matters more than the best tools. The conversation sparked questions about how the concept of “good data” has evolved over time. To answer that, let’s look back—because the story of Big Data begins not in Silicon Valley, but in 19th-century economics.

When Alfred Marshall introduced his Law of Demand more than a century ago, he couldn’t have imagined how relevant it would become to modern business technology. His insight was simple: when the price of something drops and consumers value it highly, demand doesn’t just rise — it explodes.

Fast-forward to the digital economy. IBM recognized that storage was no different from any other product. As the cost of storing data fell dramatically, companies behaved exactly as Marshall would have predicted: they started storing much more. What began with financial records and customer files quickly expanded into every form of information: emails, social media, transaction logs, sensor readings, and unstructured data that might prove valuable someday.

But more data created a new problem: complexity. Businesses now had mountains of information scattered across systems, formats, and silos. The breakthrough came with IBM’s development of ETL (Extract, Transform, Load)—a method to consolidate data, standardize it, and make it usable for analysis. ETL was the critical bridge that turned falling storage costs into meaningful business insights.

This was the true beginning of Big Data. As storage costs continued to drop and ETL matured, organizations shifted from saving only what was necessary to capturing everything they could. That shift laid the foundation for today’s data lakes, cloud platforms, and AI-driven analytics, where businesses don’t just collect information — they use it to predict customer behavior, optimize operations, and shape strategy.

In the end, Big Data is not just a technology story — it’s an economics story. Marshall’s 19th-century principle explains why businesses today treat data not as a cost to contain, but as an asset to maximize. The companies that thrive are those that recognize this truth: tools come and go, but good data endures. That’s why the future of competitive advantage lies not in the latest technology, but in building the discipline to turn raw data into trusted, actionable insight.