If you’ve gotten this far, you’ve read in Chapter 6 how to generate great ideas through the actionable hypothesis framework. You’ve used the techniques in Chapters 7–9 to convince yourself and your peers that they are great ideas. Now, it’s time to put them into action. But taking action on a great idea is much harder than generating the idea in the first place.
Insight does not beget action. Actions are costly, and they involve trade-offs. Data and analytics—including machine learning and artificial intelligence (AI)—can make the implementation of our ideas more effective, more efficient, and more fulfilling to our team members who use them. And if we can make the implementation of our ideas better, we can provide more value to our organization and our customers without sacrificing our bottom line.
Operational enablement is the practice of making actionable ideas work, by providing the technological and people-skill capabilities that are necessary to achieve our desired outcomes. The practice of operational enablement makes it more efficient to execute an idea, or a portfolio of ideas, by providing shared resources that reduce implementation costs.
Data are integral to operational enablement. When there are ongoing business processes that must run if the business is to operate, there are often data that are defined inputs to those ...
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