The Pull of Well-Crafted Product Visions and Release Goals

There was even a trace of mild exhilaration in their attitude. At least, they had a clear-cut task ahead of them. The nine months of indecision, of speculation about what might happen, of aimless drifting with the pack were over. Now they simply had to get themselves out, however appallingly difficult that might be. [1]

In the early 20th Century, Sir Ernest Shackleton led an expedition attempting to cross the South Pole on foot. He was unsuccessful in that attempt. What he succeeded at, however, was something far more impressive. After nearly two years of battling conditions south of the Antarctic Circle, Shackleton saw to it that all 27 men of his crew made it safely home. As Alfred Lansing notes, “Though they had failed dismally even to come close to the expedition’s original objective, they knew now that somehow they had done much, much more than ever they set out to do.”

There is much I could write about the lessons from Shackleton, his crew, and the Endurance that apply to our own individual endeavors – personal and professional. For the moment, I wish to reflect on the sheer clarity of the goal 28 men had in 1915-1916: To survive, by any means and nothing short of complete dedicated effort.

To be sure, their goal was self-serving – no one can judge them for that – and no product team is ever likely to be placed in a situation of delivering in the face of such high stakes. Indeed, the lessons from Endurance are striking in their contrast to just how feeble the drama is that is often brought into product delivery schedules. We call them “death marches,” but we know not of what we speak.

One of the things we can learn from Endurance is the power of a clearly defined objective. Do or die. That’s pretty damn clear. Time and time again, Shackleton’s crew were face with completing seemingly impossible tasks under the harshest of conditions with the barest of resources and vanishingly small chances for success.

What kept them going? Certainly, the will and desire to live. There were many other factors, too. What interests me in this post is reflected in the opening quote. The emergence of a well-defined task that cleared away the fog of speculation, indecision, and uncertainty. Episodes like this are described multiple times in Lansing’s book.

Why this is important to something like a product vision is that it clearly illustrates a phenomenon I learned about recently called “The Goal Gradient Hypothesis,” which basically says our efforts increase as we get closer to our goals. But here’s the rub. We have to know and understand what the goal is. “Do or die” is clear and leaves little room for misunderstanding. “Let’s go build a killer app,” not so much.

From the research:

We found that members of a café RP accelerated their coffee purchases as they progressed toward earning a free coffee. The goal-gradient effect also generalized to a very different incentive system, in which shorter goal distance led members to visit a song-rating Web site more frequently, rate more songs during each visit, and persist longer in the rating effort. Importantly, in both incentive systems, we observed the phenomenon of postreward resetting, whereby customers who accelerated toward their first reward exhibited a slowdown in their efforts when they began work (and subsequently accelerated) toward their second reward. [2]

Far away goals, like a product vision, are much less motivating than near-term goals, such as sprint goals. And yet it is the product vision that can, if well-crafted and well-communicated, pull a team forward during a postreward resetting period.

But perhaps the most important lesson from the research – as far as product development is concerned – is that incentives matter.  How an organization structures these is important. Since most people fail The Marshmallow Test, rewarding success on smaller goals that lead to a larger goal is likely to help teams stay focused and dedicated in the long run. Rather than one large post-product release celebration, smaller rewards after each successful sprint are more likely to keep teams engaged and productive.

References

[1] Lansing, A. (1957) Endurance: Shackleton’s Incredible Voyage, pg. 80

[2] Kivetz, R., Urminsky, O., Zheng, Y (2006) The Goal-Gradient Hypothesis Resurrected: Purchase Acceleration, Illusionary Goal Progress, and Customer Retention, Journal of Marketing Research, 39 Vol. XLIII (February 2006), 39–58

Improving the Signal to Noise Ratio – Coda

In a Scientific American column delightfully named “The Artful Amoeba” there is an article on a little critter called the “fire chaser” beetle: How a Half-Inch Beetle Finds Fires 80 Miles Away – Fire chaser beetles’ ability to sense heat borders on the spooky

Why a creature would choose to enter a situation from which all other forest creatures are enthusiastically attempting to exit is a compelling question of natural history. But it turns out the beetle has a very good reason. Freshly burnt trees are fire chaser beetle baby food. Their only baby food.

Fire chaser beetles are thus so hell bent on that objective that they have been known to bite firefighters, mistaking them, perhaps, for unusually squishy and unpleasant-smelling trees.

This part is interesting:

A flying fire chaser beetle appears to be trying to give itself up to the authorities. Its second set of legs reach for the sky at what appears to be an awkward and uncomfortable angle.

But the beetle has a good reason. It’s getting its legs out of the way of its heat eyes, pits filled with infrared sensors tucked just behind its legs.

A strategy suggested by the fire beetle life cycle is if you want to maximize a signal to noise ratio, iterate through three simple things:

  1. Work to develop a super well defined signal/goal/objective.
  2. Remove every possible barrier to receiving information about that signal – mental, emotional, even physical – that you can think of or that you discover over time.
  3. Repeat

Also, the “Way of the Amoeba” is now the “Way of the Artful Amoeba.” Update your phrase books accordingly.

Improving the Signal to Noise Ratio – Revisited

Additional thoughts about signals and noise that have been rattling around in my brain since first posting on this topic.

At the risk of becoming too ethereal about all this, before there is signal and before there is noise, there is data. Cold, harsh, cruelly indifferent data. It is after raw data encounters some sort of filter or boundary, something that triggers a calculation to evaluate what that data means or whether it is relevant to whomever is on the other side of the filter, that it begins to be characterized as “signal” or “noise.”

Since we’re talking about humans in this series of posts, that filter is an amazingly complex system built from both physiological and psychological elements. The small amount of physical data that hits our senses and actually makes it to our brains is then filtered by beliefs, values, biases, attitudes, emotions, and those pesky unicorns that can’t seem to stop talking while I’m trying to think! It’s after all this processing that data has now been sorted according to “signal” (what’s relevant) and “noise” (what’s irrelevant) for any particular individual. Our individual systems of filters impart value judgments on the data such that each of us, essentially, creates “signal” and “noise” from the raw data.

That’s a long winded way to say:

data -> [filter] -> signal, noise

Now apply this to everyone on the planet.

data -> [filter 1] -> signal 1, noise 1

data -> [filter 2] -> signal 2, noise 2

data -> [filter n] -> signal n, noise n

As an example, Google, itself a filter, is a useful one. Let’s assume for a moment that Google is some naturally occurring phenomenon and not a filter created by humans with their own set of filters driving what it means to create a let’s be evil good search engine. To retrieve 1,000,000 pieces of information, my friend, Bob, entered search criteria of interest to him, i.e. “filter 1.” Maybe he searched for “healthy keto diet recipes”. Scanning those search results, I determine (using my “filter 2”) 100% of the search results are useless because my filter is “how do i force the noisy unicorns in my head to shut the hell up”. The Venn diagram of those two search results is likely to show a vanishingly small set of relationships between the two. (Disclaimer: I have no knowledge of the carbohydrate content of unicorns nor how tasty they may be when served with capers and a lemon dill sauce.)

Google may return 1,000,000 search results. But only a small subset is viewable at a time. What of the rest of the result set that I know nothing about? Is it signal? Is it noise? Is it just data that has yet to be subjected to anyone’s system of filters? Because Google found stuff, does that make it signal? Accepting all 1,000,000 search results as signal seems to require a willingness to believe that Google knows best when it comes to determining what’s important to me. This would apply to any filter not our own.

All systems for distinguishing signal from noise are imperfect and some of us on the Intertubes are seeking ways to better tune our particular systems. The system I use lets non-relevant data fall through the sieve so that the gold nuggets are easier to find. Perhaps at some future date I’ll unwittingly re-pan the same chunk of data through an experienced-refined sieve and a newly relevant gem will emerge from the dirt. But until that time, I’ll trust my filters, let the dirt go as noise, and lurch forward.

Improving the Signal to Noise Ratio – In Defense of Noise

[This post follows from Improving the Signal to Noise Ratio.]

All signal all the time may not be a good thing. So I’d like to offer a defense for noise: It’s needed.

Signal is signal because there is noise. Without the presence of noise we risk living in the proverbial echo chamber. When we know what’s bad, we are better equipped to recognize what’s good. I deliberately tune into the noise on occasion for no other reason than to subject my ideas to a bit of rough and tumble. Its why I blog. Its why I participate in several select forums. “Here’s what I think, world. What say you?”

Of course, noise is noise because there is signal. Once we’ve had an experience of “better” we are then more skilled at recognizing what’s bad. I remember the food I grew up on as being good, but today I view some of it as poison (Wonder Bread anyone?) And there are subjects for which I no longer check out the noise. The exposure is too harmful.

There are subjects for which I seem to be swimming in noise and casting around for any sort of signal that suggests “better.” I’m recalling a joke about the two young fish who swim past an older fish. The older fish says to the younger fish, “The water sure is nice today.” A little further on, one of the young fish asks the other, “What’s water?” I’m hoping to catch that older fish in my net. He knows something I don’t.

To understand what I mean by noise being necessary it is important to understand the metaphor I’m using, where it applies and where it doesn’t.

Taking the metaphor literally, in the domain of electrical engineering, for example, the signal to noise ratio is indeed an established measure with clear unit definitions as to what is reflected by the ratio – decibels, for example. In this domain the goal is to push always for maximum signal and minimum noise.

In the world of biological systems, however, noise is most definitely needed. One of many examples I can think of is related to an underlying driver to evolution: mutations. In an evolving organism, anything that would potentially upset the genetic status quo is a threat to survival. Indeed, most mutations are at best benign or at worst lethal such that the organism or it’s progeny never survive and the mutation is selected against as evolutionary “noise.”

However, some mutations are a net benefit to survival and add to the evolutionary “signal.” We, as 21st Century homo sapiens, are who we are because of an uncountable number of noisy mutations that we’ll never know about because they didn’t survive. Even so, surviving mutations are not automatically “pure” signal. There are “noisy” mutations, such as that related to sickle cell anemia. Biological systems can’t recognize a mutation as “noise” or “signal” before the mutation occurs, only after, when they’ve been tested by the rough and tumble of life. This is why I speak in terms of “net benefit.”

For humans trying to find our way in the messy, sloppy world of human interactions and thought, pure signal can be just as undesirable as pure noise. I’ll defer to John Cook, who I think expresses more succinctly the idea I was clumsily trying to convey:

If you have a crackly recording, you want to remove the crackling and leave the music. If you do it well, you can remove most of the crackling effect and reveal the music, but the music signal will be slightly diminished. If you filter too aggressively, you’ll get rid of more noise, but create a dull version of the music. In the extreme, you get a single hum that’s the average of the entire recording.

This is a metaphor for life. If you only value your own opinion, you’re an idiot in the oldest sense of the word, someone in his or her own world. Your work may have a strong signal, but it also has a lot of noise. Getting even one outside opinion greatly cuts down on the noise. But it also cuts down on the signal to some extent. If you get too many opinions, the noise may be gone and the signal with it. Trying to please too many people leads to work that is offensively bland.

The goal in human systems is NOT to push always for maximum signal and minimum noise. For example, this is reflected in Justice Brandeis’s comment: “If there be time to expose through discussion the falsehood and fallacies, to avert the evil by the process of education, the remedy to be applied is more speech, not enforced silence.” So my amended thesis is: In the domain of human interactions and thought, noise is needed by anyone seeking to both evaluate and improve the quality of the signal they are following.

A final thought…

If we were to press for eliminating as much “noise” as possible from human systems much like the goal for electrical noise, I’m left with the question “Who decides what qualifies as noise?”

Improving the Signal to Noise Ratio

A question was posed, “Why not be an information sponge?”

I’d have to characterize myself as more of an information amoeba – (IIRC, the amoeba is, by weight, the most vicious life form on earth) – on the hunt for information and after internalizing it, going into rest mode while I decompose and reassemble it into something of use to my understanding of the world. Yum.

More generally, to be an effective and successful consumer of information these days, the Way of the Sponge (WotS, passive, information washes through them and they absorb everything) is no longer tenable and the Way of the Amoeba (WotA, active, information washes over them and they hunt down what they need) is likely to be the more successful strategy. The WotA requires considerable energy but the rewards are commensurate with the effort. WotS…well, there’s your obsessive processed food eating TV binge-watcher right there. Mr. Square Bob Sponge Pants.

What’s implied by the WotA vs the WotS is that the former has a more active role in optimizing the informational signal to noise ratio than the latter. So a few thoughts to begin with on signals and noise.

Depending on the moment and the context, one person’s signal is another person’s noise. Across the moments that make up a lifetime, one person’s noise may become the same person’s signal and vice versa. When I was in high school, I found Frank Sinatra’s voice annoying and not something to be mingled with my collection of Mozart, Bach, and Vivaldi. Today…well, to disparage the Chairman of the Board is fightin’ words in my house. Over time, at least, noise can become signal and signal become noise.

But I’m speaking here of the signal quality and not it’s quantity (i.e. volume)

Some years ago I came across Stuart Kauffman’s idea of the adjacent possible:

It may be that biospheres, as a secular trend, maximize the rate of exploration of the adjacent possible. If they did it too fast, they would destroy their own internal organization, so there may be internal gating mechanisms. This is why I call this an average secular trend, since they explore the adjacent possible as fast as they can get away with it.

This has been interpreted in a variety of ways. I carry this around in my head as a distillation from several of the more faithful versions: Expand the edge of what I know by studying the things that are close by. Over time, there is an accumulation of loosely coupled ideas and facts that begin to coalesce into a deeper meaning, a signal, if you will, relevant to my life.

With this insight, I’ve been able to be more deliberate and directed about what I want or need to know. I’ve learned to be a good custodian of the edge and what I allow to occupy space on that edge. These are my “internal gating mechanisms.” It isn’t an easy task, but there are some easy wins. For starters, learning to unplug completely. Especially from social media and what tragically passes for “news reporting” or “journalism”these days.

The task is largely one of filtering. I very rarely directly visit information sources. Rather, I leverage RSS feeds and employ filtering rules. I pull information of interest rather than have it pushed at me by “news” web sites, cable or TV channels, or newspapers. While this means I will occasionally miss some cool stuff, it’s more than compensated by the boost in signal quality achieved by excluding all the sludge from the edge. I suspect I still get the cool stuff, just in a slightly different form or revealed by a different source that makes it through the filter. In this way, it’s a matter of modulating the quantity such that the signal is easier to find.

There is a caution to consider while optimizing a signal-to-noise ratio, something reflected in Kauffman’s comments around the rate of exploration for new ideas: “If they did it too fast, they would destroy their own internal organization…”

Before the Internet, before PCs were a commodity, before television was popular it was much, much easier to find time to think. In fact, it was never something that had to be looked for or sought out. I think that’s what is different today. It takes WORK to find a quiet space and time to think. While my humble little RSS filters do a great job of keeping a high signal-to-noise ratio with all things Internet, accomplishing the same thing in the physical world is becoming more and more difficult.

The “attention economy,” or whatever it’s being called today, is reaching a truly disturbing level of invasion. It seems I’ve used more electrician’s tape to cover up camera lenses and microphones in the past year than I’ve used on actual electrical wires. The number of appliances and gadgets in the home with glowing screens crying out for bluetooth or wifi access like leaches seeking blood are their own source of noise. This is my current battleground for finding the signal within the noise.

Enough about filtering. What about boundaries. Fences make for good neighbors, said someone wise and experienced. And there’s a good chance that applies to information organization, too. Keeping the spiritual information in my head separate from my shopping list probably helps me stop short of forming some sort of cult around Costco. ( “All praise ‘Bulk,’ the God of Stuff!)

An amoeba has a much more develop boundary between self and other than a sponge and that’s probably a net gain even with the drawback of extra energy required to fuel that arrangement. Intellectually, we have our beliefs and values that mark where those edges between self and other are defined.

So I’ll stop for now with the question, “What are the strategies and mental models that promote permeability for desired or needed information while keeping, as much as possible, the garbage ‘out there?’”

 

Busting Assumptions

The video in this post is one I show when talking about the need to question assumptions while working to integrate Agile principles and practices into an organization. It was taken with the dash camera in my car. The drama seems to make it easier for people to see the different points of view and associated assumptions in play. (The embedded video is a lower resolution, adapted for the web, but it still shows most of what I wish to point out.)

First off, no one was injured in this event beyond a few sets of rattled nerves, including mine. This happened fast, however, there were signals that immediately preceded the event which suggested something strange was about to happen. The key moment is replayed at the end of the video at 1/4 speed for a second chance to notice what happened.

  1. The truck ahead of me was slowing down. Unusual behavior when the expectation is that traffic would be flowing.
  2. The driver in the truck was signaling that they intended to move to the left, either to switch lanes or turn left.
  3. This activity was happening as we approached an intersection.

Something didn’t seem right to me so I had started to slow down. That’s why it looks like the driver of the Jeep appears to be speeding up.

So what were the assumptions that can be guessed?

An important piece of information is that the road in the video is a two lane one way street. The driver of the Jeep clearly understood this and assumed everyone else on the road would be following the rules of the road. The driver of the truck appears to be assuming he is driving on a two lane two way street and so prepared to turn left onto a side street. His signaling and subsequent behavior suggest this. So the driver of the truck was assuming everyone else on the road was operating under this incorrect understanding. So when he began his left hand turn he wasn’t expecting the need to check the left hand lane for cars coming up from behind him. One second difference, literally, in the timing and this could have ended badly for several people.

Assumptions are unconscious and everyone has them. By design they never represent the full picture. Yet we almost always act as if they do and, more importantly, that they are shared by everyone around us. Events like those in the video clearly demonstrate that is not the case. If it was, there would be far fewer road accidents.

Organizations that are seeking to implement Agile principles and practices are guaranteed to be operating under a mountain of assumptions for how work can or “should” be done. They’re easy to spot based on how strongly people react when someone fails to follow the rules. It’s important to examine these assumptions so they can be either validated, updated, or retired.