In one of the most intriguing articles I’ve read in a long time, The Economist’s Capitalism’s unlikely heroes suggests a different perspective on the rise of activist hedge-fund investors. These brash and vocal billionaires take small positions in public companies and act to fix mismanagement by trying to convince other shareholders to support cost-cutting, spin-offs, and returning cash to shareholders.

Unlike buy-out private equity, the activist hedge funds buy only a small amount of shares, and so they neither burden the target with loads of debt nor strip companies of their assets (that’s so 1980s). Unlike Wall Street investors, activists get actively involved in management decisions. Naturally, companies’ chiefs abhor them. Critics call them vultures. Boards try to poison-pill them.

More interesting than the acrimony between companies’ top executives and tormentors like Bill Ackerman and Dan Loeb is the phenomenal rise in the level of activity of these activists’ funds. According to The Economist, they’ve got $100 billion in their war chests (about 20% of all hedge-fund capital inflows in 2014). Last year they launched 344 campaigns against public companies including P&G, Apple, Microsoft, Pepsi, and even Netflix. As shocking as it may sound, one out of two companies on the S&P 500 index has shown an activist shareholder on its stock registry in the past five years.

Why is there such an increase in activists’ funds? Have companies gotten worse and caused an immune response?

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My colleague Ben Gilad posted a terrific essay on the shortcomings of “data-driven” decision-making.

I love data. I spent 15 years playing with the PIMS database alongside luminaries such as Michael Porter, Sidney Schoeffler, Robert Buzzell, and more. Many years later, I can run a few hundred million simulations before breakfast and tell you what they mean before the coffee’s temperature drops to 80 degrees F. I told you, I love data.

But data isn’t learning, and learning isn’t just about the amount of data. Is one data point enough for learning? How about a trillion?

When smart people fail, their failures are often (not always) because what they think they know is wrong. There’s often a deeper failure, a failure of knowledge, learning, and framing, rather than a failure of execution or data.

To see why, let’s look at rats.1

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