The most common misunderstanding of disruptive innovations is to overestimate their impact in the short term and underestimate it in the long term. Another common misunderstanding is to associate disruptive with good.

Try to learn as fast as you can from the wizards and then steal what you can appropriate from them and use it to modernize your existing business model (without disrupting it).

The only way an established enterprise can dramatically increase its stock price is by adding a net new high-growth earnings engine to its existing portfolio.

Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.

Sustaining innovations are the key to consistent performance, whereas disruptive innovations are the key to dramatic changes in power.

The biggest problem is typically overly ambitious expectations combined with undercapitalization.

The increasing presence of cloud computing and mobile smart phones is driving the digitization of everything across both consumer and enterprise domains. It is hard to imagine any area of human activity which is not being reengineered under this influence, either at present or in the very near future.

Don't think you have to be the disrupter to win. A fast-following disruptee will do very well if you can bring your existing customers and ecosystem along with you.

The danger is to cling to comfort and custom at a time when events demand breaking away from both. But it is also foolish to jump at every startling moment. Darwin selects primarily for prudent fast-following.

Systems are corporate funded mechanisms for increasing efficiency programs are user funded mechanisms for increasing effectiveness. Programs should generally be charged back to users, systems should never be. Allocating corporate overhead to the operating units is simply a mistake.

Without big data, you are blind and deaf and in the middle of a freeway.

The single greatest business opportunity that is now emerging in the global marketplace is the ability to analyze digital log data to trace digital actions and from those traces to develop algorithms that can predict future outcomes with greater accuracy.