Generally speaking, I have a problem with authority. I don’t like being told what to do or how to do it. I’m not proud of that.
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I recall debating with my high school trigonometry teacher regarding the value of the homework “process”—specifically in those situations where the student in question did not require practice to get an A. And, if said student was getting a 98 percent on the exams, why spend effort trying for those last two points? In the world of cost/benefit analysis, what’s the point? That homework effort may have cut into said student’s social time, and said student had to make choices.
To this day, I believe that I had a valid point. Although, based on an unfortunate turn of events that resulted in an 89 percent and a difficult explanation to my parents, I’d now recommend there’s probably a time and place to disagree with an existing process. Touché, Mr. Petrunyak. Touché.
Fast-forward many decades, and now my job is to deliver products in the most effective and efficient way possible, and to continuously improve on our methods and approaches. This often involves “process,” including analysis, diagrams, and checklists to get better and better at what we do. Although I have no personal vendetta against process diagrams, checklists, and the general process of getting better, I believe, as with homework, they are tools that have their time and place.
Case in point: Minitab, like many companies, has a variety of products at different stages of the product life cycle. We have products that have been around for a while, products that are newly introduced or on the cusp of introduction, and products that are a mere twinkle in a statistician’s eye. So, although we actively pursue process improvement, its application varies dramatically.
‘Established’ stage
Products that have been in the field for years tend to have an assigned team and a developed architecture. We also have plenty of data available on usage, issues, etc. In these cases, we’re often interested in improved cadence or efficiency, so we’ll employ many traditional continuous process improvement (CPI) methods. We regularly assess the data and make improvements in the development methodologies to improve our ability to get features to our customers in a more efficient manner. We can glean from the data how best to spend our testing dollars based on configurations in the field and risk areas of the applications. We can use the data to identify high-risk areas requiring mitigation strategies. It’s the perfect place for some good old-fashioned data analysis.
The example below is from a one-year, post-release assessment performed by our product development team. At Minitab, that team owns the release, both pre- and post-release. The team owns the results and adjusts based on its findings. A Pareto chart depicts the types of issues found. From that, the team identifies and implements areas of improvement.
‘Show time’ stage
For products that are newly introduced or on the cusp of introduction, it’s a different ball game. We don’t jump out of the gate thinking, “Great! Let’s pull out the Pareto charts!” Instead, we step back, observe, and learn. We don’t have an excessive amount of data on usage yet, so we aren’t churning out a variety of charts. We evaluate the information as it comes and we adjust, but it’s not process-heavy. We are evolving the process as we are learning.
‘Twinkle in the eye’ stage
This is someplace we don’t go peddling our CPI wares. Innovation drives this, not process. Sure, there’s market data and analysis. And sure, there’s learning, but we aren’t evaluating the development process like we do with products in the field. For example, we aren’t assessing how “buggy” the code was the first time we prototyped something; it doesn’t matter at this point, and it’s not how to win statistician friends. If anyone would ever want to win a statistician friend, that is.
So, we learn and we get better. But we recognize the limits of standardizing our improvement processes. What fits for one part of the business doesn’t necessarily fit for another. But through careful consideration and effective data analysis, the way we’ve tailored our approach works for us.
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