You have dearly bought data at hand, possibly even a lot, but you have no time or the software to worthily deal with it. Or all you see is a flat glistening snowfield without any hills or vales. Maybe you have even got stuck in such a snowfield. Possibly in a foggy area in a pale moonlight or it is even dark night. This might is the situation where you want to rely on a snowy owl’s skills and advice (observational studies, data mining, and analytics). Moreover, since the snowy owl is wise, it knows that data is not information, information is not knowledge, and knowledge is not wisdom. For that reason, it has learned to wisely ask nature in order to make the best from a cold winter (i.e., by statistically designed experiments to make the best out of our always limited budgets).

You are aware of the fact that quality means much more than simply compliance or non-compliance of a product or service. Rather, you also want to see trends, magnitudes, and significances; so that you can direct your processes and services into the right channels (quality by design, QbD). At the same time, you know that your data is a treasure and that statistics is the science and art of dealing with data, which solely wants to protect you from deriving wrong decisions.

Fields with expertise not limited to statistics include development of pharmaceutical processes and products, manufacturing of pharmaceutical products (i.e., quality control, QC; statistical process control, SPC). Because of statistics’ generality, also any data from, e.g., medicine, politics, retail business, sports, or tourism is welcome too. Knowledge in financial business and econometrics is rather limited at sOs, but nevertheless also welcome, of course.

Instead of following the still quite traditional trial-and-error approach, you know that with a systematic approach you can get answers to more questions with a given budget. Basically, you define the questions you have and, next, you balance the number of questions to meet your budget by favoring more important questions over less important ones (Design of Experiments, DOE). This is definitely a topic where you can get statistical support from sOs to form together with your own expertise a beneficially interdisciplinary solution.

You know about the nice side effect of statistics in supporting a structured mode of proceeding. This holds true especially for statistically designed experiments. As a consequence, you also know about its telling effect onto documentation and communication, with which you can score well with your stake holders.