Goal Setting Is Often An Act of Desperation: Part 3 In Their Own Words

In part 3 of this series, John Dues and host Andrew Stotz talk about the final 5 lessons for data analysis in education. Dive into this discussion to learn more about why data analysis is essential and how to do it right.
TRANSCRIPT
0:00:02.4 Andrew Stotz: My name is Andrew Stotz and I'll be your host as we continue our journey into the teachings of Dr. W. Edwards Deming. Today I'm continuing my discussion with John Dues who is part of the new generation of educators striving to apply Dr. Deming's principles to unleash student joy in learning. This is episode 23 and we're talking about goal setting through a Deming lens. John, take it away. 
0:00:30.8 John Dues: It's good to be back, Andrew. Yeah, in this first episode of this four-part series, we talked about why goal setting is often an act of desperation. And if you remember early on, I sort of proposed those four conditions that organizations should understand about their systems prior to ever setting a goal. Those four were capability, variation, stability, and then by what method are you going to improve your system? And then in the last episode, I introduced the first five lessons of the 10 key lessons for data analysis. And remember, these lessons were set up to avoid what I call these arbitrary and capricious education goals, which are basically unreasonable goals without consideration of those four things, the system capability, variation, and stability, and then not having a method. So, it might be helpful just to recap those first five lessons. I'll just list them out and folks that want to hear the details can listen to the last episode.
 
0:01:31.8 JD: But lesson one was data have no meaning apart from their context. So, we've got to contextualize the data. Lesson two was we don't manage or control the data. The data is the voice of the process. So, it's sort of, you know, the data over time shows us what's happening and we don't really have control over that data. We do have control under that underlying process. Lesson three was plot the dots for any data that occurs in time order. So, take it out of a two-point comparison or take it out of a spreadsheet and put it on a line chart that shows the data over time. Lesson four was two or three data points are not a trend. So again, get beyond the typical two-point limited comparison this month and last month, this year and last year, this same month, last year, those types of things, this week, last week.
 
0:02:25.6 JD: And then lesson five was, show enough data in your baseline to illustrate the previous level of variation. So, we want to get a sense of how the data is changing over time and we need a baseline amount of data, whether that's 12 points, 15 points, 20 points, there's sort of different takes on that. But somewhere in that 12-to-20-point range is really the amount of data we want to have in our baseline. So, we understand how it's moving up and down over time sort of naturally. Sort of at the outset of those two episodes, we also talked about centering the process behavior charts, like the ones we viewed in many of our episodes. And we put those in the center because it's a great tool for looking at data over time, just like we've been talking about.
 
0:03:11.4 JD: And I think when we use this methodology, and when you start to fully grasp the methodology, you start to be able to understand messages that are actually contained in the data. You can differentiate between those actual special events, those special causes, and just those everyday up and downs, what we've called common causes. And in so doing, we can understand the difference between reacting to noise and understanding actual signals of significance in that data. And so, I think that's a sort of a good primer to then get into lessons six through 10.
 
0:03:51.2 AS: Can't wait.
 
0:03:53.3 JD: Cool. We'll jump in then.
 
0:03:56.1 AS: Yeah. I'm just thinking about my goal setting and how much this helps me think ab