Quantification Archive

Working the Right Problem

Finding and working the right problem is often a problem.

Solving problems is like painting. Prepping is ninety percent. That means ensuring we’re solving the right problem. It’s a common problem.

As example, a call center supported software for sales people. The sales people were giving them poor reviews. Supervisors listened to calls. Support representatives answered questions well. Supervisors were puzzled. However, representatives were answering the right questions to the wrong problems. They weren’t qualifying sales people’s questions to ensure they were asking the right questions.

Writing down the problem helps to find the right one. In “Are You Solving the Right Problem?” (Harvard Business Review, September 2012 edition) Dwayne Spradlin outlines steps suitable for large teams and complex problems. Step 4 is “Write the Problem Statement.” He also gives three examples of “The Power of Defining the Problem.”

Trimming Spradlin’s approach to fit small teams and individuals, it’s similar to planning’s benefits. It’s the process not the outcome that helps us. In finding the right problem, it’s important to:

Surrounding these are our biases. They encourage us to view problems in ways comfortable to us. We make them fit our experience, expertise, budgets, schedules, understanding and many others.

There are eight generalized biases influencing our perspectives too. They encourage us to view problems in ways we can easily understand, accept and resolve. As a result, we’ll end up tackling the easy but wrong problem.

Problems are also like crime scenes. They need enough scope to contain the information and give the perspective we need. Our need for security often as certainty, clarity and simplicity will emotionally trigger us to overemphasize statistics. Expanding our scope means searching and analyzing different types of information, not just the quantifiable.

Details help here. A glass filled with sand looks full. Diving deeper, gaps appear among the sand particles, more spaces to fill. Analogously, they are the gaps hard data leaves to intangible factors. It also means challenging definitions, demanding more specificity and applicability.

Yes, this is work and difficulty, naturally deterring us. This can’t deter us from the right problem though. If it does, paint will peel, and we’ll soon be painting again.

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This entry is part 2 of 3 in the series Apocalyptic Decision Making

Four Horsemen of Apocalyptic Decision Making

Four Horsemen of Apocalyptic Decision Making

Nathan Bennett and G. James Lemoine (“What VUCA Really Means for You” [Harvard Business Review, January 2014 edition]) superficially introduced the four horsemen of apocalyptic decision making (Volatility, Uncertainty, Complexity and Ambiguity [VUCA]). This post dives deeper into volatility, and future ones will address the others. While two or more are often at play, for my analyses only the one under discussion will be. The objective is to increase our comfort level in dealing with these four.

Frequent and extreme changes characterize volatility. If the other three are not in the picture that means our event is not overly complex, ambiguous or uncertain. Using Bennett’s and Lemoine’s example of prices fluctuating after a natural disaster, that means prices might fluctuate wildly over the short run, but we reasonably should be able to predict their long term. That means ensuring we’re in position to “ride out the storm.” Once that’s assured, short-term opportunities might avail themselves.

Other examples include:

  • Suppliers dumping product onto the market, issuing recalls or severely reducing production
  • Employers laying off or massively hiring talent we typically need
  • New competitors making a splash without sustainability
  • Legislative changes drastically altering the “rules of play”

Again, assuming the other three are not present, the long term should be reasonably simple, clear and certain. Our allies will be history and experience in finding related patterns, and statistical and data analysis in determining long-term trends. Once done, we’ll need to re-verify that current policies, processes and procedures will get us there.

Managerially, we’ll need to avert the natural reaction of responding immediately to every change or new information. Once we’ve determined the best long-term destination for riding out the storm, and once we’ve assured ourselves our infrastructure can carry us there, it’s a matter of securing the resources to do that.

 

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This entry is part 4 of 4 in the series Inspirational

Book of Job illustrates the relationship between quality and quantity.

Book of Job illustrates relationship between quality and quantity.

The Book of Job is my favorite in the Bible because despite Job’s many sufferings God’s love is better. Translating into nonsectarian and business perspectives; even though quantity outnumbers quality, quality is better than quantity.

In our lives and business, quantity is easy to measure, easy to see. Quality isn’t because it’s highly subjective and variable depending upon circumstances. Returning to Job, even though God’s love is outnumbered by all the sufferings, it is still better.

As illustration, imagine if only good happened. We would come to take it for granted. As proof, consider all the miracles we take for granted. In fact, we do so to such a degree that many of us don’t believe in miracles.

At some point, even waking and getting out of bed is a miracle. If we keep asking, “How?” and “Why?” eventually we arrive at, “We don’t know.” Moreover, we’re not even talking about complex things such as love or empathy. Twenty years ago, we thought we had a handle on empathy, but the more we study it, the more it becomes a multi-headed monster of questions. Even our brains are miracles. Everyday a miracles stares at us from the mirror.  Yes, we often take both for granted.

Many bad things happen so we appreciate the good. Movies play this out endlessly as bad guys always outnumber good ones, but the good guys are always better. In business, the average always outnumber the best but the best are always better, whether it’s customers, employees, competitors, products or services.

Job reminds us that quality is better than quantity no matter how much quantity outnumbers quality. More importantly though, Job reminds us that quality is there even if we can’t see and touch it. The key is not letting numbers distract us.

 

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This entry is part 3 of 4 in the series Inspirational

Imagine a world where all personality assessments use you as the benchmark, the center of the universe for the rest of us. For example, you would neither be extroverted or introverted, everyone else though would be more extroverted or introverted than you would be. This can be done. We have the technology. Demystifying these assessments will demonstrate.

Simply, all personality assessments use benchmarks arbitrarily established usually by dividing a sample population in half. People create a measuring protocol, take measurements (Figure #1), divide the results in half (Figure #2), tweak their benchmark as results accumulate and then declare one side to have this attribute and the other that attribute. By adding other dimensions, they can classify us further (Figure #3). Therefore, we aren’t really introverts and extroverts, we are either more extroverted or introverted than their benchmark. There is no absolute standard; it’s all relative.

Personality Assessments Demystified

So, if we happen to discover a parallel Earth totally inhabited by extreme extroverts, many of whom scoring off the charts, they would disorient the benchmark. Accounting for them, the creators would adjust it thus reclassifying many extroverts as introverts (Figure #4, shaded area) because there is no gold standard for personalities. Consequently, they use an artificial one.

Returning to you, you could be that gold standard, that center of the universe by which we could measure all personality attributes.  This would be much better for all of us. At least you exist. Statistical averages very often don’t. For example, the average person is half male, half female. How many of those exist? Moreover, a mother birthed you. Benchmarks are just sophisticated averages existing motionless on paper, birthed by statistics.

No, you would definitely make a better benchmark. How does it feel being the center of the universe?

 

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Sand and Water in GlassIf you don’t like the rules, change them. The article, “Boundary Problems” [The Economist, August 3, 2013 edition], reports that the United States “has changed the way it measures GDP.” More importantly, the article summarizes the history and depth of this statistic’s arbitrary nature. The questions it poses serve as a problem-solving primer on attacking any business statistics to uncover hidden problems and solutions.

When we write down a problem to solve, attacking definitions is a great idea. Attacking the origins of statistics is essentially the same thing when we attack the assumptions defining them such as what, how, when and where to count. Their arbitrary birth comes from the fact that someone has to make these assumptions. Once we shake the foundations of these assumptions, we take on a different perspective, thus creating problem-solving opportunities.

This happens because just as no set of sporting statistics can capture the reality of a sporting event, no set of business statistics can do the same for a business. Statistics perform their quantifying job by ignoring parts of reality. Within these statistical voids defining our problem are solutions. If you fill a 12-ounce glass with fine, playground sand, even though it looks full, you can add about 4-5 ounces of water. This serves as a metaphor for the illusionary fullness statistics often present us and for the hidden solutions in their gaps.

To uncover these gaps, we ask others and ourselves questions such as, why are we counting:

  • This?
  • In this way?
  • At that time?
  • At that place?

Then, we ask, What tangibles and intangibles are missing?

Since numbers naturally attract us, we tend to focus here. So, let’s ask, What’s the most vital immeasurable of our businesses? We just found a statistical gap filled with solutions.

 

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Early on, I learned about GIGO, Garbage In Garbage Out. Translated, if you don’t input good data into the computer don’t expect good data in return. Even though a common assumption, as computers become more sophisticated so has their GIGO. They not only receive data from us but also from sensors programmed by us. Consequently, algorithms that are more sophisticated manipulate this data, making it increasingly hard to see inaccuracies and to believe it could be inaccurate.

The article, “Burning Question” (The Atlantic, September 2012 edition), by Michael Behar exemplifies this by exploring the problem long-standing computer models had in predicting the wildfires of 2012. Much of it had to do with the computer models operating on historical experience, thus assuming fires would behave pretty much as they did in the past. As a result, the article delivers this point, which really applies to all of us when we enlist the help of computers:

. . . some experts worry that younger fire analysts lean a bit too heavily on their data-crunching skills, and have little field experience. Dawson is thankful to have spent his early career fighting fires with an ax and a shovel.

This challenges us further because even if we suspect the data, they can still influence us. They anchor in our minds and we unconsciously judge other data against them, even our own observations. Thus, not only does the data taint our observations, but the computer’s sophistication could lead us to doubt what we saw. It’s very similar to people giving false confessions because they believe the police know what they are doing, except here we believe the computer “knows what it’s doing.”

So remember, if you want your computers to work better, remember to apply your experience. After all, they don’t have any.

 

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This entry is part 13 of 15 in the series Creative Innovation

One of the points Giovanni Gavetti makes in “The New Psychology of Strategic Leadership” (Harvard Business Review, July-August 2011 edition) about associative thinking but holds true for all aspects of creative innovation and decision making are our own biases. As a result of “the human mind’s confirmatory nature,” “Strategists often look selectively for evidence that supports the analogy” they’ve formed in associative thinking.

In other words, when doing our research we are more inclined to focus on evidence, or types of evidence, supporting our points rather than contradicting them. For instance, we might value statistical evidence over anecdotal or empirical evidence. We might value evidence produced by the scientific method rather than an alternative process such as trial and err. Yet, in both cases, accepting different types of evidence or evidence produced by different processes, stimulates creativity. Moreover, by holding the team to these things, such as requiring quantification, not only do we restrict creativity but we reinforce the status quo, inertia.

However, it’s difficult for people to come out from under their own biases. This means it becomes incumbent for the managers of these teams to be prepared and have the talent to lead the change that innovation brings. One thing that truly distinguishes leadership from management is the degree to which each must promote change. That includes change in evidence and processes the team will consider in evaluating options.

Thus, while diversity in our creative innovation teams is important, diversity in our approaches and processes to tackle problems and to make decisions are too. We can look at an organization’s policies and processes as a form of “group bias” that can impose itself on our teams and drastically negate their inherent advantages.

Beware of not only individual biases but institutional ones too.

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1978 Reprint
Penguin Books 1968

A reader requested more clarification and examples of Clausewitz’s friction in business. Project Management is excellent for this because of its detailed planning, more so than Strategic Planning. In Project Management, friction shows most measurably in two ways:

  1. Friction = Actual cost – Budget
  2. Friction = Actual completion date – deadline

Despite detailed planning though, cost overruns and delays frequently appear, especially in extremely large projects as the article, “Overdue and Over Budget, Over and Over Again” (The Economist, June 11, 2005 edition), highlights. The article “Six Myths of Product Development” (Harvard Business Review, May 2012 edition) by Stefan Thomke and Donald Reinertsen goes even further to enumerate six myths of Project Management, giving us a flavor for the challenges and “friction” in them:

  1. High utilization will improve performance
  2. Processing work in large batches saves money
  3. Assuming the plan is great and problems are with execution
  4. The sooner we start the sooner we finish
  5. More features begets increased client satisfaction
  6. Getting it right the first time increases success

In relating all this to Clausewitz’s four sources of friction we find:

  1. Difficulty and danger: the cost overruns and exceeded deadlines could threaten the viable of the project not to mention the jobs and careers of those involved
  2. Physical effort: the amount and type of work is often difficult to coordinate efficiently thus necessitating Project Managers
  3. Ambiguous information: incomplete, unknown and changing information can necessitate reassessing the initial plan, budgets and deadlines
  4. Multitude of participants: many people with different skill sets and levels can work on these projects, including a hierarchy of Project Managers

The application of Clausewitz’s friction to Project Management is similar to saying that our joints are more susceptible to injury than our bones. In our plans, certain points are more likely to cause cost overruns and delays than others are.

 

Related post: Clausewitz’s Friction: Difference between Plans and Reality

 

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This entry is part 11 of 15 in the series Creative Innovation

Business prizes quantification; yet, ironically, it restricts creativity and innovation in two ways:

  • Encouraging electrical activity in our brains which restricts idea generation
  • Compelling people to confine their ideas to the quantifiable ones

Evangelia G. Chrysikou’s article “Put Your Creative Brain to Work” (Scientific American Mind, July/August 2012 edition, pgs: 26-27) summarizes the body of research to date by saying “idea generation is associated with a state of lower cognitive control” and

. . . generating novel applications for objects also seems to benefit from less filtering of knowledge and experiences, which enables people to consider a greater variety of possible answers.

Essentially, high cognitive control of our brain emits different electrical waves (beta) than low cognitive control (alpha) does. In Heidi K. Gardner’s article, “Coming Through When It Matters Most” (Harvard Business Review, April 2012 edition), we find quantification correlates to people’s need for certainty and conservatism by writing:

In high pressure situations . . . [people] support their responses with hard, usually quantitative, evidence instead of anecdotes and comparisons . . . Enthusiasm for innovation and improvisation gives way to concern for strict professionalism and covering all the basis.

My previous post, “Knowledge States”, helps us see the restrictive nature of quantification by the amount of knowledge we filter from the problem when we do so. This is partially why altering our normal problem-solving process (i.e. don’t worry about quantifying the problem or aspects of it) can be such an effective problem-solving technique.

Thus, the focus on numbers not only alters us physiologically in terms of the electrical waves our brains emit but also mentally in terms of compelling us to filter out unquantifiable knowledge that might contain the solution. Let’s face reality: problems are like squirrels. Neither goes away simply because we cannot quantify them.

 

Related posts:

 

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Someone once said to me that you can’t find your way if you don’t know where you are. I countered that that would mean a compass would be useless to you. That’s not true.

Unfortunately, when people talk about intuition in problem solving, then tend to think it should be as specific as cognition is. If it were, it wouldn’t be intuition. Intuition plays more of an introductory role in our thinking and behavioral processes. In this sense, our intuition acts as a compass. When we’re lost we have any number of directions to explore. A compass helps to narrow our selection. Intuition does the same in problem solving.

Many, many forces influence us without our conscious knowledge. On the knowledge map, we might feel these influences as awareness or knowing without having any proof or quantification to support them. These forces also influence our thought processes and encourage us to find rationales to support them.

Typically, we will experience these as feelings or sensations to:

  • Talk to a certain person or people
  • Analyze certain information
  • Visit a certain department, office or facility
  • Attend an certain event
  • Perform a certain analysis or experiment
  • Collect certain information

Now, I’m not referring to the normal, routine feelings that come about as a result of a planned problem-solving approach or one that conforms to a certain methodology. These feelings will encourage you to deviate from that process or plan. Since processes reduce flexibility, it’s important that we don’t become so focused that we ignore the opportunities posed by our intuition. Spontaneity and flexibility are important problem-solving attributes even if it simply means a “chance” encounter that aides us.

Next time you get that feeling to go off-process or off-plan, do it. Experiment!

 

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