1.2. Moving beyond Traditional Six Sigma

It is our belief that the tools, techniques, and workflows in common use with Six Sigma efforts are typically not aligned with the key idea of discovery. In the early days of Six Sigma, relevant data rarely existed, and a team was often challenged to collect data on its own. As part of the Measure phase, a team usually conducted a brainstorming session to identify which features of a process should be measured. In some sense, this brainstorming session was the team's only involvement in hypothesis generation. The data collected were precious, and hypothesis testing methods were critical in separating signals from noise.

Project teams struggling with a lack of useful data generally rely on an abundance of subjective input, and often require hypothesis testing to minimize the risk of bad decisions. This emphasis on hypothesis testing is reasonable in an environment where data are sparse. In contrast, today's Six Sigma teams often find warehouses of data that are relevant to their efforts. Their challenge is to wade through the data to discover prominent features, to separate the remarkable from the unremarkable.

These data-rich environments call for a shift in emphasis from confirmatory methods, such as hypothesis testing, to exploratory methods with a major emphasis on the display of data to reveal prominent features that are hidden in the data. Since the human interpretation of the data context is a vital part of the discovery process, these exploratory techniques cannot be fully automated. Also, with large quantities of data, hypothesis testing itself becomes less useful—statistical significance comes easily, and may have little to do with practical importance.

Of course, the simple abundance of data in a warehouse does not guarantee its relevance for improvement or problem solving. In fact, it is our experience that teams working in what they believe to be data-rich environments sometimes find that the available data are of poor quality, or are largely irrelevant to their efforts. Visualization methods can be instrumental in helping teams quickly reach this conclusion. In these cases, teams need to revert to techniques such as brainstorming, cause-and-effect diagrams, and process maps, which drive efforts to collect the proper data. But, as we shall see, even in situations where only few relevant data are available, visualization techniques, supported as appropriate by confirmatory methods, prove invaluable in identifying telling features of the data.