Law of Instinct
I love data, but not much credit is given to hunch; gut, instinct. Colin Powell, in his Laws of Leadership, shares what he calls his Law of Instinct. He claims the following:
Part I:
Use the formula P@40-to-70, in which P stands for the probability of success and the numbers indicate the percentage of information obtained.
Part II:
Once the information is in the 40 to 70 range, go with your gut. Don’t take action if you have only enough information to give you less than a 40 percent chance of being right, but don’t wait until you have enough facts to be 100 percent sure, because by then it is almost always too late.
Today, excessive delays in the name of information-gathering breeds “analysis paralysis.” Procrastination in the name of reducing risk actually increases risk.
Visually, what Colin Powell is describing is the Law of Diminishing Returns where, over time, the value of information diminishes. Perhaps that relationship might look like this:
(click image for a larger picture)
The circle represents an inflection point at which for each marginal unit of information gathered, the value starts to go down or the cost-effort-benefit tradeoff for the next marginal unit of information is less than the cost to obtain it.
Instinct in Product Development
In product development — any type of product, software, material, or otherwise — there is often a discovery process at the outset. The trap that most companies fall into is excessive market research, thinking that the more we know, the less risk we’ll face. As General Colin Powell points out, that type of thinking is flawed. The truth is that the more delays there are, the risk just increases: knowing more doesn’t translate to less risk or higher probability of success.
Big-Design-Up-Front Design is the poster child for Analysis Paralysis in product development. In product development, that typically takes the form of “Requirements Gathering Ad-Infinitum” — which is a term I use to indicate incessant requirements gathering with the aim of exhaustively gathering customer requirements, but the activity itself takes a form of its own and — often — it leads to documenting a lot of stuff, but nothing tangible has been produced that brings value to the customer.
Little’s Law is your Friend
A queueing system is a model with the following structure: customers arrive and join a queue to wait for service given by n servers. After receiving service, the customer exits the system. A fundamental result of queueing theory is little’s law.
Theorem: for a queueing system in steady state, the average length of the queue is equivalent to the average arrival rate multiplied by the average waiting time. in other words,
L = λW
Little’s Law is a fundamental principle in business, mathematics, and has applications to many real-world problems. One of those real-world problems is in product development.
First, a definition:
WIP/TIP: Work-in-process of Things-in-process. For the purposes of this article, they are synonymous. Being “in-process” means the work or things have entered a state-of-affairs but have not yet exited. The “work” can be anything: materials, components, sales orders, software code, software testing, projects, customer inquiries, checks, phone calls to return reports suppliers to qualify, repair orders, or emails waiting to be answered, etc.
For product development, we can use a transformation of Little’s Law, like the following:
[(Throughput) = (Things-in-Process) / (Average Completion Rate)]
What this equation tells us and what experience has shown time-after-time, is that the number one driver of Product Development Cycle Time are the “things-in-process”. There is no quicker way to reduce the cycle time by which your company can get a product from concept-to-delivery than through first prioritizing all the projects or products and focusing on the ones that make strategic and tactical sense, and killing the lower priority projects.
You might be thinking: “True, but couldn’t we also increase the average completion rate”? You’re right, but the impact of doing that is much lower than reducing the TIP — that is, influencing the average completion rate is rather difficult and is often a function of available resources, scope creep, market demands and changes, etc. Here’s the bottom line: the number one driver for shipping products quicker is by focusing on the important ones and killing the unimportant ones.
How Batchy Are You?
From a Lean Thinking perspective, Powell is really advocating for a less batchy approach and one that obeys the one-piece flow principle. Gathering a lot market research and a lot of data is really a batchy approach, whereas the one-piece flow approach from Lean is one for which Colin Powell is arguing.
Specifically, Powell — whether he knew it or not — is really arguing for “What is the right batch size?” In the case of information, Powell is arguing for ~40% to 70% relative to success as the batch size. Since one-piece flow is not possible in some cases, then asking the right batch size is the better approach. An approach that looked at the right batch size and also followed an iterative model — that would be an approach that is more customer-facing and will have a higher likelihood of success (or lower likelihood of failure).
Little’s Law, The Law of Diminishing Marginal Returns & Powell’s Law of Instinct
There are several principles at play: Little’s Law is true — as Work-in-Process grows and the Cycle Time to complete each unit increases, Throughput decreases. The end result is that products aren’t shipped on-time or at all and the customer loses.
The Law of Diminishing Marginal Returns teaches us that there is a point at which obtaininig the next marginal unit — of any type of unit — might not be worth the cost to obtain it.
Colin Powell’s Law of Instinct teaches us how to reconcile the Little’s Law and the Law of Diminishing Marginal Returns. He advocates that we rely on hunch, gut, and instinct. Once we have enough information to be within the probability of success of 40% to 70%, then go with your gut.
WP Cumulus Flash tag cloud by Roy Tanck and Luke Morton requires Flash Player 9 or better.
If you enjoyed this post, please consider to leave a comment or subscribe to the feed and get future articles delivered to your feed reader.
Comments
@Mike,
I apologize for the confusion — the chart simply shows what Diminishing Marginal Returns would look like — not a specific chart for Powell’s Law of Instinct.
Also, since the principle holds, you can always replace the variables (x,y) with anything you’d like. The point is that the relationship of x against y.
Thanks for reading shmula.com — I appreciate it.
As a data consultant, this has been a constant challenge of what is the right amount of data. While my skill-set could go for more, the ability to scale deep analysis across an enterprise is not feasible and could lead to nothing getting done. Thanks!
RT: Loved this! @shmula analysis paralysis, under the guise of reducing risks, actually increases risks: http://is.gd/gsgp
Retweeting @shmula: analysis paralysis, under the guise of reducing risks, actually increases risks: http://is.gd/gsgp
Leave a comment
Additional comments powered by BackType
i dont get your graph, which line is P the probabilty of succes and which line is ? the numbers indicating the percentage of the total information obtained?