Principles for Transformation

Over the last two months, I described six common management myths W. Edwards Deming worked to dispel. In January, I tackled the myth of best practices, the myth of the hero educator, and the myth of performance appraisal. In February, I turned to another set of three myths including the myth of merit pay, the myth of accountability, and the myth of extrinsic motivators. The point of these two posts was to help education systems leaders see what not to do. I’m now going to turn to a set of 14 Principles that can be used by educational leaders to guide their transformation work. I’ll kick things off this month with an introduction to the Principles for Transformation. After this introduction, I’ll write twice monthly posts describing each principle.

Knowledge about Variation

There is variation in everything we observe and measure in schools. Knowledge about Variation provides a tool kit by which to understand this variation. Educators are inundated with data, but what’s much more difficult is knowing how to interpret and make sound decisions with it. Do this year’s state test scores indicate that our district is improving? Was last month’s drop in per pupil revenue a sign of things to come? Did attendance rates improve this week because of the intervention we put in place or was it due to something else? The ability to answer questions like these is fundamental to our ability to make improvements.

Goal Setting is Often an Act of Desperation, Part III

For the past three months, I’ve been writing about organizational goal-setting. In Part I of the series, I proposed four conditions that organizations should understand prior to setting a goal. In Part II, I introduced the idea of “arbitrary and capricious” education goals and key data analysis lessons 1-5 . In this installment, I’ll outline key lessons 6-10 and then tie up the series in Part IV with an applied example from United Schools Network.

Goal Setting is Often an Act of Desperation, Part I

At a recent district leadership team meeting, I put the following quote up on a slide: “Goal setting is often an act of desperation.”1 We are in the midst of updating our strategic plan at United Schools Network, so the purpose of the quote was to start a discussion on healthy goal-setting and to provide a framework for any goal-setting the team would do as a part of this process. I think the typical reaction to the quote is something like the following: “But I thought goal-setting was something highly effective people and organizations do?” I would argue however, that this is rarely the case, be it in organizations or accountability systems, and only can be true if a number of conditions are met during the process.

Writing Fiction

Last month, I discussed a powerful tool, the process behavior chart, that can be used to filter the noise out of our data. The whole point of this series has been to think through how to properly interpret and react to data, which includes the filtering process. Unfortunately, much of what happens on the data analysis front in the education sector is akin to writing fiction. Writing fiction will be the main topic of this post.

Filtering Out the Noise

Last month, I discussed the difference between information and knowledge by analogizing the two concepts to data ponds (information) and data streams (knowledge). A key idea in the transformation of information to knowledge is adding the element of time and visualizing the data in a tool called a process behavior chart. Part of the power of the process behavior chart (PBC) is its ability to filter out the noise in our data; the idea of filtering out data “noise” is the focus of this post.

Data Ponds & Streams

Last month, I outlined why data has no meaning apart from their context. The discussion centered on some key ideas for presenting data in context as well as a logical definition of improvement. I also introduced an example of how data is often misinterpreted in the education sector. In this post, I’ll begin to lay the foundation for understanding variation in quality improvement work; this will be a precursor to comprehending why so much of the data analysis that is done in organizations is akin to writing fiction.

Data Has No Meaning Apart from Their Context

In the K-12 education sector, one of the primary uses of data is in state accountability systems. Many states now issue district and school report cards typically based on various performance metrics such as proficiency rates on standardized tests, absenteeism rates, and college and career readiness indicators. Unfortunately though, as James Leonard stated so eloquently in The New Philosophy for K-12 Education:

Absent an understanding of the type of variation present, any discussion of accountability is a burlesque!

Numerical Naiveté

Dr. Russell Ackoff, the eminent systems thinker said, “Managers do not solve problems, they manage messes.” My take would be more optimistic than Ackoff’s assuming managers knew something about variation. This is in fact why I’ve spent so much time over the last year studying this idea as it fits into W. Edwards Deming’s theoretical framework, the System of Profound Knowledge.

Leadership & the Essential Elements of Transformation

The case I’ve been making for organizational transformation is based on the premise that our education system is not broken. Rather, it is a system that is operating exactly as it was designed to operate, and it’s producing exactly what it was designed to produce. I do not believe that our schools need reform or restructuring but rather a change in state. This transformative change in state does not occur overnight, but instead is a process that unfolds over 5-10 years (as Deming would put it, “There is no instant pudding!”).