As we all try to figure out just exactly what Industry 4.0 and Quality 4.0 mean it is not an exaggeration to say “Data is your most valuable asset. Yet we all struggle to actually get a benefit from this data and data integrity is an area of intense regulatory concern.
To truly have value our data needs to be properly defined, relevant to the tasks at hand, structured such that it is easy to find and understand, and of high-enough quality that it can be trusted. Without that we just have noise.
Understand why data matters, how to pick the right metrics, and how to ask the right questions from data. Understand correlation vs. causation to be able to make decisions about when to act on analysis and when not to is critical.
In the 2013 article Keep Up with Your Quants, Thomas Davenport lists six questions that should be asked to evaluate conclusions obtained from data:
1. What was the source of your data?
2. How well do the sample data represent the population?
3. Does your data distribution include outliers? How did they affect the results?
4. What assumptions are behind your analysis? Might certain conditions render your assumptions and your model invalid?
5. Why did you decide on that particular analytical approach? What alternatives did you consider?
6. How likely is it that the independent variables are actually causing the changes in the dependent variable? Might other analyses establish causality more clearly?
Framing data, being able to ask the right questions, is critical to being able to use that data and make decisions. In the past it was adequate enough for a quality professional to have a familiarity with a few basic tools. Today it is critical to understand basic statistics. As Nate Silver advises in an interview with HBR. “The best training is almost always going to be hands on training,” he says. “Getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.”