Archive for the ‘Powerful Ideas’ Category

by Richard Martin

An engineer employed by Google—James Damore—was recently fired for writing and circulating an internal memo criticizing aspects of Google’s diversity policies, specifically corporate goals regarding the ratio of women and men in software work. I won’t go into the details of the arguments for and against, except to illustrate how the selection of the aim has an impact on the scope and validity of the ways and means of achieving it.

My purpose is to look at the structure and logic of the problem to show that the objective conditions and delineates the analysis of the problem, how it is resolved, and what are considered acceptable and unacceptable questions and factors for consideration, planning, and decision-making. This newsletter is longer than usual because I think a failure to understand the logic of arguments and reasoning underlie most social and organizational conflict. This can in turn have a major impact on performance and readiness.

Google’s objective appears to be (approximately) equal numbers of men and women in software development, programming and engineering. It follows that only options which have a realistic chance of achieving that aim should be developed and considered for implementation. Anything that questions that objective should not be considered any further as it may undermine its achievement and take resources from better uses.

Damore’s memo didn’t question gender equality in itself, rather the wisdom of Google’s goal in service of that aim. His questions and skepticism weren’t addressed at the ways and means of achieving the aim, but rather at the goal itself. In other words, Damore’s memo was at a different logical level than the stated Google policy. If you set the goal as a 50/50 split of men and women in engineering and other technical jobs, and that’s non-negotiable, then it follows that you must, of necessity, consider only alternatives that can achieve that. On the other hand, if the goal is gender/sexual equality in general, then aiming for 50/50 split may or may not be a realistic or desirable way of achieving that.

This is where values and underlying beliefs come into play. In terms of beliefs, there are three big assumptions leading to the Google objective of 50/50 split. IF men and women have equal capabilities (in any meaningful and statistically significant sense), and IF there is no coercion (explicit and/or implicit), and IF there is no stereotyping (subtle or not so subtle) in hiring and managerial practices by Google or any other employers, then it follows that aiming for an equal split (or close thereto) between men and women is a desirable and achievable goal.

All three of these IFS are empirical questions that can be answered through rigorous research and analysis. For the record, my personal belief is that any statistically significant capability and performance differences that are demonstrated scientifically between men and women are merely of academic interest IF AND ONLY IF there is no coercion and no stereotyping. With that said, capability, coercion and stereotyping can be slippery concepts. Ideology can influence all three, especially coercion, as it is related to power and hierarchical relationships.

So much for the underlying assumptions and beliefs. What about values? In a culture that values well-defined sex roles, it follows that sexual/gender differences, coercion, and stereotyping won’t even be questioned. They will simply be assumed and justified, usually based on what is viewed as common sense and custom. We on the other hand, live in a society that values sexual and gender equality. Why? Because we have an even higher level value which we call freedom of choice. We believe that anyone should be allowed (and even encouraged) to choose whatever education, job, and career that they want. And what someone wants should be defined by whatever mix of challenge, interest, satisfaction, pleasure, ease, investment, and compensation they find most appealing at any specific time, so long as there is demand for that work, and it doesn’t undermine someone else’s goals through coercion or stereotyping. All this follows necessarily from our western values of individualism and self-actualization.

If we value freedom of choice, then it follows that people should be allowed and encouraged to choose whichever career they deem most acceptable and satisfactory to them. However, this may or may not result in a 50/50 split, either within any specific organization, or society in general. It could be 10/90, 60/40, 49.999/50.001, or another other ratio. And that’s only in one specific work area, in this case software-related jobs.

I have no doubt that Google’s senior managers believe firmly in sexual and gender equality. I would also bet that most, if not all, its leaders and employees hold deeply to the values of non-coercion, non-stereotyping, and freedom, at least as regard career and occupational choice. Google has apparently chosen to pursue a 50/50 split between men and women as the means of achieving the goal of gender equality and diversity. From that perspective, the Damore memo can undermine its implementation and achievement.

On the other hand, Damore raises some interesting questions. Can Google’s stated policies and goals generate coercion and stereotyping of men? Can the 50/50 goal lead to a kind of affirmative action where capable men are being sidelined by less-than-capable women? Could this undermine the company’s long-term viability, sustainability, and culture of performance? By adopting a quantitative goal, is Google trying to solve a social problem that it didn’t create and for which it may not be well adapted?

I don’t have the answers to such questions, and I suspect no one else does either, at least not in the short term. But aren’t they worth asking and examining? By firing Damore, Google has sent a clear message that the decision to pursue literal sexual/gender equality is taken and will not be undermined. Management has taken a stand will not brook internal opposition or questioning. The train has left the station. On the other hand, Damore raises valuable questions from the standpoint of corporate governance and societal change. It’s not Google’s job to solve all of society’s problems, but nor can the issues be ignored by such a big and influential economic player.

My purpose here has been to analyze the logical structure of the problem and the goals these lead to. I chose the Google-Damore case because it allowed me to highlight what I consider to be the most salient aspects of decision-making and management. I’ve shown how goals are conditioned by values, assumptions, and beliefs, and that goals then limit or expand the problem space. We must choose our goals judiciously and calibrate them to our underlying beliefs and values, as this directly influences the scope and validity of our plans and readiness to implement them.

© 2017 Alcera Consulting Inc. This article may be used for non-commercial use with proper attribution.

It’s that time again, when all the predictions and forecasts of gloom and doom—or better times ahead—come at us. In the interest of jumping on that gravy train, I make only one prediction for 2017:

There will be predictions, and most of them will be wrong.

A few will be somewhat right, usually something self-evident and not particularly informative: “The stock market will fluctuate.” Well, thanks for that…

Some will be a bit off the mark, but most will be completely wrong; a few will be wildly off the mark. This will lead many media commentators to lament in June that such and such never could have been predicted.

There are fields where there are reasonably accurate predictions, but they tend to be in the sciences. This is because predictions in physics, chemistry, meteorology, etc. are based on empirically-based, quantitative models, and they are put through a process of peer review to ascertain their effectiveness.

The question, then, is what predictions outside of pure sciences can be trusted. Not many, but there are still some precious metals in the pile of slag. You must make your own assessments as these forecasts come out and judge how much credence to lend them. Here are my six simple rules for evaluating the credibility of predictions and the prognosticators making them.

Rule 1—Consider the source (Expertise Rule). Do they have genuine expertise in the subject matter? Are they disinterested parties or participants in the predicted process? In other words, do they have a stake in the outcome? Psychology and common sense dictate that interested parties are seldom as objective as they claim.

Rule 2—Identify the theory or model underlying the prediction (Model Rule). Do they generate predictions based on an explicit explanatory model? Or, do they just seem to wing it, based on intuition and past results?

Rule 3—Determine how the model was developed and tested (Cherry-Picking Rule). Was the explanatory model created through purely statistical legerdemain? In other words, did they analyze a bucket load of data and then look for patterns, or did they instead develop a theoretical model and then see how the data conformed to their predictions. The first approach is called cherry picking; it’s like shooting at the wall and then drawing a target around the closest bunch of bullet-holes. The second approach is the only truly valid one.

Rule 4—Look at the data (Secret Knowledge Rule). Do they provide their inputs and data, or otherwise reveal what and how empirical information was used in formulating their predictions? If they don’t, then how can their models be validated and tested?

Rule 5—Consider the timeframe (Horizon Rule). Some predictions turn out reasonably accurate as to amplitude or outcome. They just never specify WHEN they will come to fruition. I predict that the Dow will hit 25,000… eventually. Makes a big difference.

Rule 6—Compare past predictions to actual results (Performance Rule). This one is self-evident, but the usual case is that past predictions are quickly forgotten in the rush to generate and consume new ones.

You’ll notice that none of these rules gives you an exact answer. That’s because there rarely IS an exact answer, except in tightly constrained situations. Business, finance, economics, politics, sports, and military strategy are all highly complex and somewhat chaotic. Beware the prognosticators who claim inerrant accuracy and foresight.

We may not know precisely what will happen in the future, but we can be better prepared.

That’s why we all need the readiness principles and prudential approaches that I write, educate, and consult on.

Here are some of my other thoughts on these matters:

What Goes Up: The S-Curve and its Many Applications

Trend or Bandwagon?

Beware the Prognosticators

Let’s Have Some Perspective

Stop Predicting, Start Experimenting

Surprised by the Normal

Remember Richard’s Business Readiness Process in 2017!
  1. Ensure vigilance through situational awareness.
  2. Do preliminary assessment of tasks and time.
  3. Activate organization or team.
  4. Conduct reconnaissance.
  5. Do detailed situational estimate.
  6. Conduct wargame and decide on optimal course(s) of action.
  7. Perform risk management and contingency planning.
  8. Communicate plan and issue direction.
  9. Build organizational robustness.
  10. Ensure operational continuity.
  11. Lead and control execution.
  12. Assess performance.

Call me if you would like a 90-minute Business Readiness Briefing in early 2017!

My name is Richard Martin and I’m an expert on applying readiness principles to position companies and leaders to grow and thrive by shaping and exploiting change and opportunity, instead of just passively succumbing to uncertainty and risk.

© 2017 Alcera Consulting Inc. This article may be used for non-commercial use with proper attribution.

Last week I introduced the concept of self-similarity and showed its relevance for power law distributions. In this post I discuss the applicability of self-similarity in S-curves.

To recap briefly, self-similarity implies that a structure looks essentially the same at all levels of “magnification” or scale. You can zoom in on any part of a “power curve,” and it will look like… a power curve, with basically the same appearance as at the higher scale.

The same phenomenon can be seen in s-curves, with the difference that the scale invariance is less apparent, at least initially. The following diagram shows how each phase on an s-curve can be broken out into smaller, constituent s-curves at the next lower level. By extension, each of these subordinate s-curves can be parsed in the same, self-similar way. The structure is recursive and nested. If you want to grow, develop, or improve in any way, you must see it as a succession of s-curves at all levels of scale.

self-similarity-s-curve

This is why I’ve titled this post “Growth is a stairway, not a high jump!”. You make progress in increments, climbing from one step to the next in a succession of achievable bounds. This breaks progress and improvement into (to paraphrase Neil Armstrong) a series of “one small step” moves so you can make “one giant leap” for your bigger purpose… or goals.

This is more manageable from a psychological standpoint as well as logistically. It also makes risk more manageable. As I illustrate in the following diagram, there are risks at each transition to a succeeding s-curve. Risk can arise from making a jump–even a small one–to a higher level of performance and engagement. It can also arise from a drop in performance at this critical juncture. We can seldom know and do everything that is needed at the new level. We need to learn–which is why progress is depicted as an s-curve in the first place. We start out with low performance at the new level and a high potential for mistakes. If we’re focused on learning from our mistakes and on improvement, we get progressively better until we hit the rapid growth stage, and continue up the “learning” curve from there on in. When we hit the inevitable plateau, we must jump–or drop–to the next curve.

risk-at-thresholds

The final point is that performance or growth can bog down or slip at any point, for any number of reasons. We can stop or slip back down the curve we’re already on. I call this regression. Even more consequential is when we drop back to a previous curve. I call this retrogression, and I’ve illustrated it in detail in the following diagram. It shows how you can fall from any performance level to any other, usually through neglect, over-confidence, smugness, or simply through inattention to changing conditions in the environment. For instance, new technology, new competitors, changing demographics, all these can make our current success or standing shaky or even irrelevant.

retrogression-s-curve

I don’t say this to be overly pessimistic, but rather realistic. Stasis is death. Movement is crucial. Business, life, performance, everything, they are what is called a “red queen” race. You have to work just to stay in place and work even harder to make progress, grow, develop, get better.

We’ll address these issues and many more in my coming posts under the topic of “Ideas,” so stay tuned to this space.

My name is Richard Martin and, as indicated by the title of this blog, I’m an expert on applying readiness principles to position companies and leaders to grow and thrive by shaping and exploiting change and opportunity, instead of just passively succumbing to uncertainty and risk.

© 2016 Alcera Consulting Inc. This article may be used for non-commercial use with proper attribution.

In my last two posts under “Ideas” I introduced the concepts of the S-curve and Power Law (a.k.a. Pareto’s Law, Zipf’s Law, or the 80/20 rule).

In this post I discuss the concept of self-similarity. I view it as an adjunct to the S-curve and Power Law that multiplies their effectiveness for anticipating change and other dynamic interactions in society, businesses, and other forms of organization.

According to Wikipedia: “In mathematics, a self-similar object is exactly or approximately similar to a part of itself (i.e. the whole has the same shape as one or more of the parts). Many objects in the real world, such as coastlines, are statistically self-similar: parts of them show the same statistical properties at many scales.” The term fractal is also frequently used to characterize self-similar structures.

Furthermore, self-similarity is characterized by scale invariance. Again according to Wikipedia: “In physics, mathematics, statistics, and economics, scale invariance is a feature of objects or laws that do not change if scales of length, energy, or other variables, are multiplied by a common factor.

(My emphasis in both quotes.)

In practice, this means that it is difficult or even impossible for an observer to detect the system level depicted just by looking at a picture. As mentioned above, coastlines are the paradigmatic illustration of this phenomenon. You can look at satellite image of a coastline at various scales and, barring the presence of a scale indicator (e.g. a boat or a human on the picture), you can’t determine the scale with any certainty. Moreover, this is also a statistical effect, as the underlying math is the same or very similar at all levels of magnification.

There are many phenomena in nature and society with this characteristic structure. However, for business and strategy, the most crucial realization comes from the self-similar (or fractal) nature of power curves and s-curves. Take any distribution governed by a power law. If you hone in on any particular segment of the distribution, you find that it is also governed by essentially the same power law. In other words, the distribution looks basically the same at all levels of magnification, or scale. The follow diagram shows this effect.

self-similarity-in-power-laws

No matter what you’re measuring or tracking–it could be total sales, the performance of your salespeople, the relative impact of your clients–you are likely to notice a power law working at all scales. This was illustrated in my Power Law article last week by the example of real estate agents in the Greater Toronto area. I’ve reproduced those two graphs here, as they show how a power law is in evidence at two different scales.

treb-6-and-under treb-gif

Although not as stark at the level of agents with only 0 to 6 deals, we can see that the two scales are broadly similar. What about practical applications? Well, for one, we can see that the effect is likely to be similar at all levels and in all categories of agents. For instance, if you break out each category (7-12 deals all the way to 201 plus deals), you will probably find the same pattern. A small number of top performers skewing the results of the group upward.

This type of distribution plays havoc with our basic assumptions of normally distributed performance or effects. If we were to assume a normal distribution (Gaussian distribution in technical statistical terms) for real estate agents, we could easily be fooled into thinking that there is an “average” performance, a “typical” real estate agent. But this could not be further from the truth. In a normal distribution, the mode, median, and mean are all very close to having the same value. This means that the arithmetic mean could give a false understanding of the performance distribution for a sales group. In actuality, the mode (most frequent value), median (the middle value), and the actual mean could be different, with the latter possibly heavily skewed in the direction of the highest performing class of sales people. This is what we see with the distributions of real estate agents above.

Would the arithmetic mean of this distribution truly represent the average or typical performance of a real estate agent in the Greater Toronto area? Obviously not. If we look at the numbers of deals, 0-6 is the modal value, and represents about 50% of the total number of agents! This means that the largest number of real estate agents are actually sluggish performers, and even don’t participate in any deals at all! If you’re looking, say, to providing products and services to real estate agents–at least in the Greater Toronto area in 2011–then you’d be better to look at the actual performance distribution at varying scales so you can segment the market properly.

These relationships tend to hold across time and space for any particular phenomenon. We can safely assume that the distribution of real estate agent performance is broadly similar no matter when and where you build your sample. While it’s ultimately a question for empirical investigation, in my experience, self-similar power laws are ubiquitous in market dynamics and human performance. You can apply this insight to all market and performance numbers and you will get similar results. This enables much better analysis, planning, and strategy to gain and sustain a competitive edge.

I’ll explore scale-invariance and self-similarity in s-curves in my next “Ideas” post. In subsequent ones I’ll also look at the broader implications of self-similarity, particular as they relate to hierarchy in organizations, specifically what I call “nested hierarchical planning” and “nested hierarchical vigilance.”

© 2016 Alcera Consulting Inc. This article may be forwarded, reproduced, or otherwise referenced for non-commercial use with proper attribution. All other rights are reserved and explicit permission is required for commercial use.

The “Power Law” is one of the most useful concepts for making predictions and decisions in business and management.

The power law shows how two variables–one dependent, the other independent–covary. Mathematically, one varies as a function of the other by being raised to a certain power (exponent).

The following diagram shows this type of relationship. Often these are depicted on log or log-log graphs, but I show the “power curve” as an asymptote on both axes of the graph to highlight the non-linearity of the relationship between the two variables.

power-law-basic

A concrete example will help. The great majority of earthquakes are of very low magnitude. High magnitude earthquakes are much rarer than low magnitude earthquakes. In fact, their magnitude varies in inverse exponential proportion to the total number of earthquakes. In practice, this means that there are literally thousands of earthquakes every day around the world, but magnitude 6, 7, and 8 ones are much rarer. The most powerful earthquakes of all, over 9 on the Richter, scale are very rare. They can happen only a few times a century, or even less. This doesn’t mean that the magnitude of any particular earthquake can be predicted. It does however imply that given a sufficiently large sample, we will eventually see a frequency-magnitude distribution that resembles the graph above.

This type of relationship is ubiquitous in nature, and that includes our human and social natures. There was a whole book written on this topic–The Long Tail, by Chris Anderson–with emphasis on the right side of the graph. In his book, Anderson described how the internet has made many businesses or ideas viable which would not previously even have been known. He called this the long tail because there are musicians, artists, artisans, crafts workers, professionals, etc. who can provide their productions and services to people around the world, even though they can’t compete with the more traditional providers who dominate markets by occupying the left side of the power curve. This makes for much more diversity and many more opportunities to get known and appreciated, and to develop a following because it lowers traditional barriers to entry and long-term viability.

This type of relationship is also depicted in the following diagram. I show the relationship between number of clients and the number purchases, interactions, or value of each category of client that characterizes the market and product distributions of most, if not all, companies (including my own clients).

power-law-of-clients-and-value

For instance, I’ve been working with a banking client. This graph shows the relationship between number of clients and the number of products/services that each client has with the bank. The total market size for this bank is about 80,000 potential users of its services. Of these potential users of its services, the great majority, about 85 %, have no business relationship with the bank. Of the 13,000 or so that do use the bank’s services, the majority only use less than 3 of over 20 products and services. As we move to the right, there are less clients, but their interactions with the bank are more intensive. In other words, there are are many fewer clients in categories to the right, but they use many more of the bank’s services, which in turn generate much greater value. On the other hand, there are no actual clients who do all of their banking and meet all of their financial needs and objectives, much less use all of the bank’s services. This is why we can depict the lower right part of the curve as an asymptote. You never actually reach complete saturation or use.

We’ve all noticed these types of power-law relationships in our professional and personal lives, our management and business experiences, and even in some natural phenomena. This relationship is often referred to as the 80/20 law, Pareto’s Law, or Zipf’s Law. It shows up in such truisms as: 80 % of my problems are caused by 20 % (rates can vary) of my people; most of my sales and profits come from a small number of sales reps; only a few of my clients provide most of my revenue and profits; this product category accounts for 45 % of my sales, but 70 % of my profits; etc., etc.

The following diagram is a further illustration of the principle. It comes from an online article by Mark McLean of the Toronto Real Estate Board (TREB) and shows an almost perfect example of a power-law distribution in the number of deals done by different categories of real estate agents who are members of TREB.  We can see that only a very small number of agents in TREB can be considered highly successful, prolific even.

treb-gif

Of those agents having completed 6 or less deals in a year, a similar relationship holds, although it’s less stark:

treb-6-and-under

Whatever we wish to call them, power-law distributions and relationships underlie much of the correlations and dynamics that surround us. We can use them in making general predictions and, along with the S-curve phenomenon I described in a previous post, we have two very powerful tools and concepts for understanding the world around us. Moreover, power laws and S-curves are not only ubiquitous, they tend to show what’s called “self-similarity,” or a fractal pattern. I’ll discuss that third powerful concept next week.

© 2016 Alcera Consulting Inc.

This article may be forwarded, reproduced, or otherwise referenced for non-commercial use with proper attribution. All other rights are reserved and explicit permission is required for commercial use.

By Richard Martin, Expert in Business Readiness and Exploiting Change

One of the most useful ideas for conceptualizing any kind of change process is the S-curve. Perhaps you’ve seen one of these before. It looks like this:

typical-s-curve

The S-curve is the way natural and human phenomena grow and develop over time. For instance, the plot of a growth of bacteria or yeast in a laboratory follows the exact S-curve. Technically, it’s known as a logistic function, and when we plot it as a rate of growth, rather than cumulative growth, it forms a bell curve, although it doesn’t follow a normal, Gaussian, distribution. In other words, when something starts growing or spreading, it first starts very slowly, then it speeds up until it hits it hits a maximum, after which the growth/spread rate slows down until it basically tends to zero.

The S-curve approximates the cumulative growth or spread of just about any natural or man-made phenomenon, such as:

  • Penetration of a new market segment
  • Growth of new product/service category
  • Learning stages
  • Interest in topics
  • Abilities (which tend to plateau after a time)
  • Etc.

One of the more relevant business applications is in strategy formulation and execution. Take a look at the following S-Curve application. It shows how we can map the different phases of a product or market life cycle onto the S-curve. This gives an intuitive understanding that all good things must come to an end or, as I imply in the title of this piece, “What goes up, must (eventually) come down (or at least level off).”

product-market-life-cycle-phases

New products or markets start as ideas, often as an external start up. I pluralize this because there should be a relatively high number of “experiments” and trials underway at any one time within a diversified company. Another strategy is to watch out for promising startups outside the business (or in an internal “skunkworks”) and then invest in them or simply acquire them once they start entering their rapid growth phase. Companies should have businesses (various combinations of product-market mix) in all stages of the life cycle in order to ensure a constant stream of growth generating ideas and strategic business units.

Another important phenomenon to note is the presence of a decline phase. Unless there is continuing investment in a business line or concept, it will eventually go into decline. We don’t necessarily know when, but we DO know it will happen at some point. This is another reason to be constantly replenishing the pipeline at the earlier life cycle stages of startup and rapid growth. The capital needed to invest in future ideas and growth will often come from the “milk cows” that are businesses in the maturity or plateau stage, although the latter can also provide a good source of financial capital through divestment.

© 2016 Richard Martin. Reproduction, forwarding, and quotes are permitted with proper attribution.

 

Diversity is important, but not necessarily for the reasons that are commonly put forward. Yes, it’s important to have a variety of inputs and perspectives so we can maximize our performance and creativity. It’s also critical that organizations be representative of their respective clienteles or constituencies.

More fundamental, however, is the fact that there are a multitude of personalities, preferences, talents, interests, and attitudes. There is simply no “one size fits all” solution to any needs or wants. This has organizational implications but it also has societal ones as well.

If you try to impose a single or limited number of ways of doing things or of fulfilling needs, you will quickly run into the fact that most people don’t think in the same way or necessarily want the same things. The simple example of musical tastes illustrates this observation. Some people like classical music, others, jazz, rock, blues, folk, country, or any other number of styles and idioms. There’s no accounting for taste. One person’s melody is another’s cacophony. We can’t say “this is real music,” while what young people listen to is “just noise.”

We can go even further when we look at other more impactful activities and preferences. I find mixed martial arts in a cage to be quite barbaric. The image of a brute pounding someone underneath him (or her) comes readily to mind. But then, no one has forced any of the competitors into the ring, at least as far as I know. The same goes for someone who willingly gets into boxing, wrestling or other fighting sports. And what about someone who takes up mountain climbing or sky-diving, or who wants to practice a dangerous occupation or who enjoys work that is normally considered unpleasant. I couldn’t imagine myself being a health care professional, for instance.

This is where freedom comes into play. We need freedom—which I define as a “live and let live” attitude—because there are simply too many diverse preferences, talents, and proclivities. What happens between consenting adults is their business, so long as it doesn’t hurt anyone else, no matter what bystanders and other non-interested parties say or think.

If we’re all going to get along and continue to build and develop some kind of community and society, then we simply have to have outlets and possibilities for ALL people. This is why personal freedom and preference should trump everything else. And also why diversity isn’t just about performance and representiveness.

© 2016 Richard Martin. Reproduction, forwarding and quotes permitted with proper attribution.