Famous Christensen Quotes

Christensen died a few months after I arrived at Harvard, but his legacy looms large at HBS, in no small part because of his famous book, How Will You Measure Your Life?[51] Christensen analyzes a good life well lived in the same way he would assess a company, and the book is well worth reading in its entirety.

Correlation is enough,” 2 then-Wired editor in chief Chris Anderson famously declared in 2008. We can, he implied, solve innovation problems by the sheer brute force of the data deluge. Ever since Michael Lewis chronicled the Oakland A’s unlikely success in Moneyball (who knew on-base percentage was a better indicator of offensive success than batting averages?), organizations have been trying to find the Moneyball equivalent of customer data that will lead to innovation success. Yet few have. Innovation processes in many companies are structured and disciplined, and the talent applying them is highly skilled. There are careful stage-gates, rapid iterations, and checks and balances built into most organizations’ innovation processes. Risks are carefully calculated and mitigated. Principles like six-sigma have pervaded innovation process design so we now have precise measurements and strict requirements for new products to meet at each stage of their development. From the outside, it looks like companies have mastered an awfully precise, scientific process. But for most of them, innovation is still painfully hit or miss. And worst of all, all this activity gives the illusion of progress, without actually causing it. Companies are spending exponentially more to achieve only modest incremental innovations while completely missing the mark on the breakthrough innovations critical to long-term, sustainable growth. As Yogi Berra famously observed: “We’re lost, but we’re making good time!” What’s gone so wrong? Here is the fundamental problem: the masses and masses of data that companies accumulate are not organized in a way that enables them to reliably predict which ideas will succeed. Instead the data is along the lines of “this customer looks like that one,” “this product has similar performance attributes as that one,” and “these people behaved the same way in the past,” or “68 percent of customers say they prefer version A over version B.” None of that data, however, actually tells you why customers make the choices that they do.