As a person working in quality manufacturing, it’s probably in your DNA to look at a quality challenge and  choose Six Sigma or something similar as the framework for getting to the answer. It’s also likely that you’re spending a lot of time gathering and analyzing data, applying hypothesis testing, and looking for that root cause to an issue or improvement. Your role is part detective, part data analyst.

However, depending on your industry, you’ve also probably noticed that products, processes, and issues are becoming more complex, and the amount of data to analyze is either too large or too small, scattered in disparate systems, and never available in a clean, ready format. So the challenge becomes not just defining the problem, but also defining the data set and then cleaning and manipulating it so that it can be analyzed.

You might also have concerns about this process: What if you think you’ve introduced a bias and found what you were subconsciously looking for? How did you go about cleaning the data? What assumptions did you make, and was there another way to construct the data set and hypotheses?

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