Now that #noestimates has become fairly mainstream, you could wonder how #noestimates can help you predict the future? When I saw that InfoQ included #noestimates as part of their “State of Agile” article for 2014 it was clear to me that #noestimates will continue to be part of our common future.
Predictions are necessary
We have always needed and will always need predictions of the future. In agile software development, the normal approach is to ask the team that is going to do the job to estimate how much work it will require. That is then used as one input to a prediction (or forecast) of what will be delivered when.
â€” Greger Wikstrand PhD (@GregerWikstrand) 19 december 2014
We should recognize that there are limits to how well we can predict the future. But still, we keep doing it. How many times have you heard that the “cure for cancer” is only “ten years away”? Too many to count probably. It is hard to predict what will happen in software development as well. It is especially true for solving bugs. Solving the problem is often trivial, finding it is unpredictable.
But still, it is necessary to predict the future. Let’s take a simple example. I have sheep. In spring, summer and autumn they live by grazing the land. In the winter, it is up to me to provide them with sustenance. That sustenance will be a combination of hay, straw, minerals, salt etc. But how much will I need of each? This is something I need to decide in early spring because the hay needs to be sown, grown and harvested early to provide the premium quality feed that sheep require.
There are many factors to consider in this decision. On one hand, there are the sheep. What are their nutrional needs next winter? That will depend on how many they are, the weather, how many lambs they carry, the number of non-vegetative days during the winter and so on. On the other hand, it will depend on the quality of the feed. How nutritious is the feed? How much water, fibers, protein etc does it contain?
I cannot avoid to do a prediction just because it is hard. If the sheep do not get enough feed, they will starve. If I buy too much feed, I will not cover my costs. (I am not aming for profitability, the operation is too small for that, but it is nice when it covers its own costs.) Somehow, I need to come up with an estimate for how many bales of hay will be required to meet the ewes’ needs.
Estimates and predictions
I cannot ask the sheep how much they will consume from the backlog (sorry, I mean hayloft) per sprint. I’ll have to use another technique such estimation by analogy, parametric estimation or rely on historical data. In fact, I need to use a combination of all of these methods to estimate what the weather will be like and what the nutrional needs of the sheep will be. Based on that information, I will predict how many bales of hay will be consumed, add a risk margin and then place the order to my supplier.
It won’t be enough to just predict how much hay I need. I also need to follow up continuously to ensure that hay is being consumed at a sustainable pace (literally). Too much hay and the sheep will get fat and it won’t last. Too little hay and they will starve. At the end of each sprint (sorry, I mean week) I assess their condition to ensure that they stay in the sweet spot.
Predicting the future with #noestimates
So, can you predict the future with #noestimates? Will you be able to tell the customer what will be delivered, when and how much it will cost? Is that even the right question to ask? The discussion on #noestimates is usually limited to software development but I believe that this post, by analogy, gives some small illustration of the enormous difficulties to be overcome before it will be possible to use #noestimates as a basis to predict the future.
Oh, and yes, if you are wondering. I am planning to write a longer piece on what my sheep taught me about agile. But first, I need to finish my answer to Mike Lehr on this:
@GregerWikstrand Are there personality differences btwn those who like to work in agile and those that don't? I suspect "yes." What r they?
— Mike Lehr (@MikeLehrOZA) 18 december 2014