In his 2003 book, Open Innovation, Henry Chesbrough defined this important concept. In short, open innovation is a product or technology development model that extends beyond the boundaries of a firm to involve others in a collaborative way. Today, much of this activity uses various social networking tools and technologies to empower people to generate ideas, fine-tune concepts, share knowledge or solve critical problems.
When you look at the evolution of digital measurement in the enterprise and study organizations that have achieved a significant degree of maturity, you’ll notice that they come in two distinct flavors: the analytic and the informational. Analytic organizations have strong teams studying the data and driving testing, personalization and customer lifecycle strategies. Informational organizations have widespread, engaged usage of data across the organization with key stakeholders absorbing and using data intelligently to make decisions. It’s not impossible for an enterprise to be both analytic and informational, but the two aren’t necessarily related either. You might expect that organizations that have gotten to be good in measurement would be mature in both areas, but that’s not really the common case. Instead, it seems that most enterprises have either a culture or a problem set that drives them to excel in one direction or the other.
“Garbage in, garbage out” is the cliché of data-haters everywhere. “It is not true that companies need good data to use predictive analytics,” Taylor said. “The techniques can be robust in the face of terrible data, because they were invented by people who had terrible data,” he noted.
Revolution R Open (RRO) is the enhanced distribution of R from Revolution Analytics. RRO is based on version 3.1.1 of the statistical software R and includes additional capabilities for improved performance, reproducibility and platform support.