February 10, 2024
Mastering Work Intake: From Chaos to Predictable Delivery Chapter 8 shatters myths! Once upon a time in a land far, far away, I believed a few of these myths. Ok, it was just about five years ago that I was dissuaded from the last of my misbeliefs. I believe that many of my misconceptions are founded in the collision of words between classical statistics and words Shewhart used for Process Behavior Charts (PBC).
(more…)January 20, 2024
The question of how much data is required to determine what is happening in a system is a perennial bugaboo. Those predisposed to acting tend to think less is more, while those with more reticence sometimes wait forever to make a decision. The question of how much data is needed is more than just a footnote in flow data.
(more…)August 19, 2021
I took a lot of statistics, quantitative analysis, and math courses in university. I think it was a function of going to three schools during every available semester and building up a boatload of credits before LSU gave me a diploma and made me leave the state. I still remember the day I learned partial differential equations (I finally could understand the footnotes in my economics texts). With all of that, I was not exposed to the idea of a base rate fallacy (known also as base rate bias) until several years later when I was working in the garment industry. Twice this week I have run into scenarios that are examples of base rate fallacies which suggest that many people either don’t understand the concept or are blinded by raw numbers (a shiny object phenomenon).
(more…)August 23, 2018
Don’t assume no meat mean no taste!
Monte Carlo analysis provides a way to handle answering questions with significant uncertainty in the inputs that influence the outcome of the work so you can have the difficult “when, what, and how much” type conversations with sponsors, stakeholders, and marketing people. That definition is an explicit admission that almost ALL of the hard questions asked about projects cannot be answered using simple a + b = c formulas or arguments. This leads us to use tools like Monte Carlo analysis. There are four common assumptions often overlooked or misunderstood when using Monte Carlo methods. (more…)
August 21, 2018
What is the possibility
One of the most often used saying in agile is that yesterday’s weather is a good predictor of tomorrow’s performance. I have lived in Louisiana where if you blink the weather will change. I currently live near Cleveland (and I like it), and in 2014 the temperature went from 39 F to -11 F in less than 24 hours. I went running on both days; they were very different. Even if I grant that yesterday can be an important indicator of performance tomorrow, a sample size of one does not capture the degree of variability that might be present in the environment. Why does anyone care about variability in performance? As suggested in Agile Metrics: An Interlude, leaders have not stopped asking what they can get, when they can get it and how much it will cost type questions. Even if they don’t ask all of those questions there will be questions about budgets. These are not evil, unagile people; they are business people that need to plan things like cash flows and making payroll. Answering when they can deliver product to the market or when a change to the HR portal will be made are important questions. Just relying on yesterday’s weather is not always sufficient and there is no Oracle of Delphi that provides a single, unambiguous answer to any of those questions. The answers are always a range. In most circumstances, each possible outcome is more or less probable than the next. Uncertainty makes it difficult to have a conversation about when, what and how much. Monte Carlo analysis provides a way to handle answering questions with significant uncertainty in the inputs that influence the outcome of the work so you can have the difficult when, what and how much type conversations. (more…)
January 13, 2018
A New Copy!
Chapter 10A of Daniel S. Vacanti’s Actionable Agile Metrics for Predictability: An Introduction (buy a copy today) is a very short chapter (I should have planned to include this in chapter 10B, but hindsight is 20/20) covering a discussion of histograms but given the time and space we can spend additional time with an example. The chapter is titled Cycle Time Histograms. (more…)
November 29, 2016
Just because you can measure it, should you?
Over the last two weeks, we published three articles on vanity metrics:
Vanity Metrics in Software Organizations
Vanity Metrics Have a Downside!
One of those articles elicited an interesting discussion on Twitter. The discussion was predominately between @EtharUK (Ethar Alali) and @GregerWikstrand. The discussion started by using the blog entry from 4 Big Myths of Profile Pictures from OKCupid to illustrate vanity metrics, and then shifted to a discussion of whether vanity metrics can be recognized based on statistical validity. The nice thing about Twitter is that responses are forced to be succinct. See the whole conversation here. (more…)
February 27, 2016
How to Measure Anything, Finding the Value of “Intangibles in Business” Third Edition
Chapter 10 of How to Measure Anything, Finding the Value of “Intangibles in Business” Third Edition is titled, Bayes: Adding to What You Know Now. Here is a summary of the chapter in a few bullet points:
February 13, 2016
How to Measure Anything, Finding the Value of “Intangibles in Business” Third Edition
Chapter 9 of How to Measure Anything, Finding the Value of “Intangibles in Business” Third Edition titled, Sampling Reality: How Observing Some Things Tells Us about All Things. Here is a summary of the chapter in four bullet points:
January 23, 2016
How to Measure Anything, Finding the Value of “Intangibles in Business” Third Edition
Chapter 6 of How to Measure Anything, Finding the Value of “Intangibles in Business” Third Edition, is titled: Quantifying Risk Through Modeling. Chapter 6 builds on the basics described in Chapter 4 (define the decision and data that will be needed) and Chapter 5 (determine what is known). Hubbard addresses the process of quantifying risk in two overarching themes. The first theme is the quantification of risk and the second is using the Monte Carlo analysis to model outcomes. (more…)
Software Process and Measurement 