Goal Setting Is Often An Act of Desperation: Part 3

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 about how to improve my goal setting. And I think one of the biggest ones that's missing that we talked about before is by what method. And many people think that they're setting strategy, when in fact, they're just setting stretch targets with nothing under it. And they achieve it by luck or are baffled why they don't achieve it. And then they lash out at their employees.

0:04:31.4 JD: Yeah, there was really... I mean, that goes back to one of those four conditions of setting goal capability. You have to understand how capable your system is before you can set, it's fine to set a stretch goal, but it has to be within the bounds of the system. Otherwise, it's just maybe not an uncertainty, but a mathematical improbability. That's not good. Like you're saying, it's not a good way to operate if you're a worker in that system. So, lesson six then, to continue the lessons.

0:05:06.8 JD: So, lesson six is "the goal of data analysis in schools is not just to look at past results, but also, and perhaps more importantly, to look forward and predict what is likely to occur in the future," right? So that's why centering the process behavior charts is so important, because they allow you to interpret data that takes variation into account, allows you to classify the data into the routine or common cause variation or the exceptional, that's the special cause variation, and allows us to turn our focus to that underlying or the behavior of the underlying system that produced the results. And it's this focus on the system and its processes that's then the basis for working towards continual improvement.

0:06:00.6 AS: And I was just thinking about number six, the goal is to predict what is likely to occur in the future. And I was just thinking, and what's likely to occur in the future is exactly what's happening now, or the trend that's happening, unless we change something in the system, I guess.

0:06:16.4 JD: Yeah. And that's why just setting the stretch goal is often disconnected from any type of reality, because we have this idea that somehow something magical is going to happen in the future that didn't happen in the past. And nothing magical is going to happen unless we are intentional about doing something differently to bring about that change.

0:06:39.5 AS: And that's a great lesson for the listeners and the viewers. It's like, have you been just setting stretch targets and pushing people to achieve these stretch targets? And not really understanding that your role is to understand that you're going to get the same result unless you start to look at how do we improve the method, the system, that type of thing.

0:07:05.0 JD: Yeah. And usually when you have those stretch goals, you've looked at what happened last year, and then you base the stretch goal on last year. But perhaps, you're seeing, for the last three or four years, the data has been steadily decreasing, right? And you can't realize that if you haven't charted that over the last three or four years, hopefully beyond that. So, you have no idea or it could have been trending positively, and you may under shoot your stretch goal because you missed a trend that was already in motion because of something that happened in the past.

0:07:44.8 AS: You made a chart for me, a run chart on my intake for my Valuation Masterclass Bootcamp. And we've been working on our marketing, and I presented it to the team and we talked about that's the capability of our system based upon for me to say, I want 500 students when we've been only getting 50 is just ridiculous. And that helped us all to see that if we are going to go to the next level of where we want to be, we've got to change what we're doing, the method that we're getting there, the system that we're running and what we're operating to get there or else we're going to continue to get this output. And so if the goal is to predict what is likely to occur in the future, if we don't make any changes, it's probably going to continue to be like it is in that control chart.

0:08:42.8 JD: Yeah. And that example is, in a nutshell, the System of Profound Knowledge in action in an organization where you're understanding variation in something that's important to you, enrollment in your course. You're doing that analysis with the team. So, there's the psychological component and you're saying, well, what's our theory of knowledge? So, what's our theory for how we're going to bring about some type of improvement? And so, now you're going to run probably something like a PDSA. And so now you have all those lenses of the System of Profound Knowledge that you're bringing together to work on that problem. And that's all it is really in a nutshell.

0:09:22.2 AS: Yeah. And the solution's not necessarily right there. Sometimes it is, but sometimes it's not. And we've got to iterate. Okay. Should we be doing marketing in-house or should we be doing it out using an outsourced service? What if we improve and increase the volume of our marketing? What effect would that have? What if we decrease the... What if we change to this method or that method? Those are all things that we are in the process of testing. I think the hardest thing in business, in my opinion, with this is to test one thing at a time.

0:09:58.5 JD: Yeah.

0:09:58.7 AS: I just, we I want to test everything.

0:10:00.4 JD: Yeah. Yeah. I read in the Toyota Kata that I think we've talked about before here, which talks about Toyota's improvement process. I read this in the book, I don't know if this is totally always true, but basically they focus on single factor experiments for that reason, even in a place as complex and as full of engineers as Toyota, they largely focus on single factor experiments. They can actually tell what it is that brought about the change. I mean, I'm sure they do other more complicated things. They would have to write a design of exp