Two great paragraphs:
The fundamental problem is, most of the masses of customer data companies create is structured to show correlations: This customer looks like that one, or 68% of customers say they prefer version A to version B. While it’s exciting to find patterns in the numbers, they don’t mean that one thing actually caused another. And though it’s no surprise that correlation isn’t causality, we suspect that most managers have grown comfortable basing decisions on correlations.
After decades of watching great companies fail, we’ve come to the conclusion that the focus on correlation—and on knowing more and more about customers—is taking firms in the wrong direction. What they really need to home in on is the progress that the customer is trying to make in a given circumstance—what the customer hopes to accomplish. This is what we’ve come to call the job to be done.
In conclusion, here's his argument as to why big data only is the second tool to reach for. The first thing is to understand the desires and intentions of your customers.
Many organizations have unwittingly designed innovation processes that produce inconsistent and disappointing outcomes. They spend time and money compiling data-rich models that make them masters of description but failures at prediction. But firms don’t have to continue down that path. Innovation can be far more predictable—and far more profitable—if you start by identifying jobs that customers are struggling to get done. Without that lens, you’re doomed to hit-or-miss innovation. With it, you can leave relying on luck to your competitors.Bravo - I'd rather rely on understanding and insight than data.