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By Nick Hart, President & CEO, Data Foundation
Organizations rushing to implement AI are making a critical mistake: hoarding data like pack rats hoard possessions. They're collecting everything accessible, storing massive datasets "just in case," and inheriting legacy systems without documentation—all while assuming more data automatically means better AI. The reality? This digital clutter creates expensive storage costs, multiplies security risks, and obscures valuable insights. As the White House AI Action Plan emphasizes building "world-class scientific datasets," the real question isn't how much data to collect, but which data serves strategic purposes. This piece explores four principles for data efficiency: collaborating on knowledge needs before collecting, establishing dedicated data leadership through CDOs, emphasizing data minimization over maximization, and systematically archiving what no longer serves its purpose. America's AI leadership depends not just on having the most data, but on having the right data, used responsibly, with clear purpose and strong governance.
This article was originally published in Forbes on November 24, 2025, as part of the Forbes Technology Council. The original can be found at https://www.forbes.com/councils/forbestechcouncil/2025/11/24/from-data-hoarding-to-data-strategy-building-ai-that-actually-works/
