Here's the quote that tells it all
I've blocked the name to avoid all the whining that will result.
Lao Tzu was a 6th century BC philosopher who's pithy quotes are used an misused through civilization. The core problem with Tzu's quote here is he never took a probability and statistics class. Nor did he met the statisticians George E. P. Box and Gwilym Jenkins and their Box Jenkins methodology.
It may be that the original poster didn't take that probability and statistics class either, or may have never heard of the Box/Jenkins method. There are others in the #NE group that assume that probability and statistics applied to spending other peoples money is cryptic and unnecessary.
Well are here some facts.
- When money is spent that doesn't belong to you, you are obligated to tell the people who's money it is what you are going to do with it. This seems to be a common sense approach to providing value for that money.
- When spending other peoples money, it is a good idea to know how much money to ask for. Asking can be incremental or it can be all in. But in the end, the person with the money likely needs to know how much money is going to be needed to get the things - capabilities - needed in exchange for that money.
Box Jenkins
So back to the quote. Since Tzu would have been unaware of Drs. Box and Jenkins, the quote sounds logical in 6th century B.C. Of course it's not logical, since the realm of probability and statistics entered the vernacular in the mid-1600's, with the introduction of laws of evidence. Early there were games of chance in Greece, but no notation for writing down the rules. Things really happening in the 18th century Jacob Bernoulli's Ars Conjectandi (posthumous, 1713) and Abraham de Moivre's The Doctrine of Chances (1718) put probability on a sound mathematical footing, showing how to calculate a wide range of complex probabilities. Good statistical thinking started in the mid-1700's with the collection of demographic and economic data and its analysis.
What Box and Jenkins did was develop an algorithm for forecasting the future based on the past. Yes, Virginia there is a Santa Claus. We can forecast the future given the past. We do it every single day, every single hour of the day. So when I hear you can't forecast the future, that person must not have attended a class where probability and statistics was taught. I pray that the current Computer Science courses teach probability and statistics. And those graduates move on to writing code for money with the understanding of the basis of the underlying methods for making statistically sound forecasts using Box/Jenkins.
But back to the problem at hand. Given past performance - Vasco's very clear and concise description of measuring Stories produced (instead of Story Points) is the basis for measuring the past. This is many times called Reference Class Forecasting (RCF). RCF is used in a wide variety of domains, from Oil & Gas exploration (where I first encountered it), to economics, to heavy construction, and the estimates for cost and schedule for software intensive projects - where I work now.
Here's the essence of Box Jenkins. For those seeking further understanding, here's a reading list for statistical forecasting.
Why Is This Not Understood?
Why the blanket statement you can't forecast the future or forecasting - same as estimating - is a waste of time? I really can't say. But I sense it comes from bad experiences being on teams where estimating was badly used, by badly performing managers, for all the wrong reasons.
But that is absolutely no excuse for not understanding the High School level probability and statistics behind using a time series of the past - which Vasco has described in detail - to forecast the range of possible outcomes for the future. Notice the notion of range. No 100% - that's simple BS - and anyone suggesting so is ignorant of all mathematical modeling processes. But confidence ranges, with error bands on the confidence are how daily estimates and forecasts are made. From how many apples to stock at Whole Foods, to the flow control of the air traffic arriving in the Denver TCA, to the weather itself, to the Estimate At Complete for our software projects.
Here's one of many posts about probabilistic foreccasting. And a simple algorithm.
- Follow Vasco's advice. Break down the work into small chunks. Small enough to have them have approximate representation of the actual work. This is called binning in the statistical sampling world.
- Perform the work.
- Calibrate the work effort with the time it tool for the work. The result is a cardinal number showing the capacity for work.
- Look at the backlog of work, calibrated for the same cardinal bin size
- Capture the actual performance over the past and put that in a time series/
- Download R and use that time series to forecast the future work, using parameters of your choice. But using the Box/Jenkins ARIMA function built into R.
It's that simple. For hot shot, walk on water software developers R and the process should be a walk in the park. But you have to want to do it. You have to want to have a credible answer when those with the money ask how much and how long. And you have to come to see that statement like making estimates inhibits innovation as not just simply nonsense, but provocateur nonsense.
That's How You Forecast the Future and act like a responsible provider of value to those with the money.
