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10 examples that show the value of cognitive diversity

In a previous post, the benefits of crowdsourcing were described as follows:

When trying to solve a challenge, what is the probability that any one person will have the best solution for it? It’s a simple mathematical reality: the odds of any single person providing the top answer are low.

How do we get around this? Partly by more participants; increased shots on goal. But even more important is diversity of thinking. People contributing based on their diverse cognitive toolkits

Cognitive diversity is a vital contributor to innovation. Bringing together people who have different expertise, heuristics and perspectives to solve problems has shown value time and again.  Professor Scott Page’s The Difference is a terrific book outlining the frameworks and value of cognitive diversity.

I thought it would be useful to collect some cases that highlight the value of cognitive diversity. The theories are powerful, but we respond strongly to specific examples. Collected below are ten cases of where cognitive diversity has shown its value. Feel free to use them for your own work as needed.


 

1. The mystery of our kidney tubules

Problem: Human kidneys have tubules. For years, they were assumed to be leftover, useless artifacts of our natural evolution. Hence physiologists assumed they had no purpose. Meaning their care and study could be safely ignored.

How diversity helped: One day, an engineer looked at the loops. He saw something different. He realized they were actually part of something called a countercurrent multiplier. These mechanisms concentrate liquids in a system. Suddenly, tubules were no longer evolution’s leftover junk. They were seen for what they were: vital parts of our kidneys’ operations.

An engineer with no special biological expertise saw things in a total different way.

Reference: Edward de Bono’s Lateral Thinking: An Introduction


 

2. Why this particular pathology in a drug discovery trial?

Problem: A pharmaceutical company’s R&D group was conducting a discovery study for a new drug. They couldn’t understand the toxicological significance of a particular observed pathology. They consulted with multiple experts in toxicology, but none could answer the question.

How diversity helped: The pharma firm then ran a crowdsourcing campaign, and within a few the mystery was solved. The solver? A woman with a PhD in protein crystallography using methods common in her field. An area unrelated to toxicology.

The woman with the PhD brought a completely different perspective to solving the problem.

Reference: Harvard Business School, et al study, The Value of Openness in Scientific Problem Solving (page 11 pdf)


 

3. Who has a higher probability to solve a problem?

Problem: People with expertise in a particular domain are challenged to provide workable solutions to tough problems. But these are the ones we regularly turn to for help.

How diversity helped: Researchers analyzed the outcomes of InnoCentive’s crowdsourcing challenges. What they found was surprising. For each challenge, they identified the domain of the problem. They then looked at the winning solvers. What were their domains of expertise? They found that people whose domain of expertise was six degrees away from the domain of the problem were three times likelier to solve the problem.

Getting people who are outside the domain of the problem provides higher odds of finding the best solution to a problem.

Reference: Harvard Business School, et al study, The Value of Openness in Scientific Problem Solving (page 24 pdf)


 

4. Stop new diamond fractures from happening

Problem: In the process of cutting diamonds, new fractures are introduced into the diamond. These fractures don’t show up until the diamond is in use. Manufacturers wanted to split diamonds along their natural fractures without creating new ones. But didn’t know how.

How diversity helped: This is a case of investigators consciously looking at different realms to find a solution, applying the TRIZ method. The solution came in an area quite different than diamonds: green peppers. Food companies need to split the peppers and remove their seeds. Peppers are placed in a chamber to which air pressure is increased significantly. The peppers shrink and fracture at the stem. Then the pressure is rapidly dropped causing them to burst at the weakest point and the seed pod to be ejected. A similar technique applied to diamond cutting resulted in the crystals splitting along their natural fracture lines with no additional damage.

This is an example of consciously seeking solutions outside the domain of the problem. Which is the crux of cognitive diversity.

Reference: QFD Institute, TRIZ workshop


 

5. Reducing surgery infections

Problem: Surgery exposes patients to infections, even with all the efforts to maintain a clean surgical environment. Reducing these infections would result in better outcomes for patients.

How diversity helped: 3M brought together people from three different areas: an expert in wound healing; an animal surgeon; and a specialist in theatrical makeup with expertise in adhering materials to skin. They developed a breakthrough product to prevent surgical infections.

While the wound specialist is consistent with what we’d expect, the makeup artist inclusion was quite different. She brought a particular expertise that turned out to be relevant to solving the problem.

Reference: Eric von Hippel, et al, Performance Assessment of the Lead User Idea Generation Process (pdf)


 

6. How to really help the homeless?

Problem: Homelessness has proven to be an intractable, chronic issue for cities. Municipalities spend money on treatment, overnight shelter, food. But the issue has vexed city officials everywhere.

How diversity helped: Sam Tsemberis is a psychologist. His training was to treat the mental health of people. He took a job to treat homeless people in the early 1990s. Not to solve homelessness, with its complex set of causes. Just to treat individuals, which fit his expertise. However, he saw things differently with the homeless. They operated in a set of complex circumstances. He felt the dominant thinking of experts in the homeless field was wrong, that homeless people are quite resourceful.

Lacking any prior experience in solving the homeless issue, Tsemberis assembled a team of people who also lacked any experience in addressing homelessness at scale. One was a recovering heroin addict. Another was a formerly homeless person. Another was a psychologist. And the last, Hilary Melton, was a poet and a survivor of incest.

Their solution? Giving permanent housing to the homeless. And it has proven remarkably successful thus far. Utah employed the method, and eliminated homelessness. Phoenix applied it and eliminated chronic homelessness among veterans.

Fresh eyes came up with a solution that challenged the dominant thinking in the field.

Reference: Washington Post, Meet the outsider who accidentally solved chronic homelessness


 

7. Predicting solar particle storms

Problem: When in space, astronauts are at risk from solar particle storms. Knowing when these storms are going to happen is important for their safety. However, NASA had spent 30 years unsuccessfully trying to figure out how to predict these storms.

How diversity helped: NASA cast a challenge on InnoCentive, exposing the problem to much broader types of expertise. And they received a solution much better than anything they had ever developed. A retired telecommunications engineer in New Hampshire saw the issue as one involved magnetic coupling between the sun and the Earth.

The solver was not in the space field. He had no connection to NASA. He was located far from NASA operations. His fresh perspective brought new insight to the problem.

Reference: Forbes India, The importance of diversity of thought for solving wicked problems


 

8. Who starts billion dollar companies?

Problem: What is the ‘look’ of success in start-ups? Certainly understanding these characteristics could go a long way toward identifying promising ventures.

How diversity helped: Shasta Ventures ran an analysis of 32 companies that are high flyers, including Uber, Twitter, Dropbox, Twitch, etc. They looked at the companies way back when they were raising their Series A. They found a few different traits. One that stands out:

Three-out-of-four of the companies in our survey were built and run by people who were doing it for the first time. They did not have a win under their belt or deep experience in their field, but were passionate about their product and had a unique perspective on how to serve their target customer. Having a fresh perspective is important in tackling a category as people with industry experience are often constrained by what is ‘not possible’ and why it ‘won’t work’.

Shasta notes here what others have found. People get stuck in knowing what they know. Innovation benefits from fresh perspectives.

Reference: Tod Francis, Shasta Ventures, What did Billion Dollar Companies Look Like at the Series A?


 

9. Stopping a key enzyme that powers the AIDS virus

Problem: For a decade, scientists have tried to understand the structure of an enzyme that is critical to reproduction of the AIDS virus. If they could finally figure out the structure, that would allow them to develop drugs to fight AIDS.

How diversity helped: Researchers added the AIDS reproduction enzyme structure to the online game FoldIt. In FoldIt, players try their hand at folding various proteins. Proteins are core building blocks, and they fold in very specific ways. Scientists have a hard time replicating the folding sequence; researchers started FoldIt to see how amateurs could do at replicating the folding.

In this case, insights about the enzyme’s folding were provided by FoldIt gamers (not scientific experts) within three weeks. Their strategies were instrumental in helping scientists to understand the enzyme, and initiate work to neutralize it.

Reference: Scientific American, Foldit Gamers Solve Riddle of HIV Enzyme within 3 Weeks


 

10. Auto-Tune to…ahem…enhance recordings

Problem: Music producers spent significant time putting together music from multiple takes. The process was laborious, but needed to ensure high quality recordings for release. Humans inevitably had inconsistencies, either in voice or instruments.

How diversity helped: Exxon engineer Andy Hildebrand had spent 18 years working in seismic data exploration. He developed a technique using a mathematical model called autocorrelation. His approach involved sending sound waves into the ground and then recording their reflections.

It turns out autocorrelation is also good for detecting pitch in music as well. Hildebrand, who had taken some music classes, recognized an opportunity to improve the quality of music. He introduced Auto-Tune to the industry. And the rest is history.

In this case, knowledge from one industry – oil exploration – was applied to an entirely different field – music. The cognitive diversity was a conscious application of expertise from one realm to another.

Reference: New Yorker Magazine, The Gerbil’s Revenge


 

While the most natural human tendency is to depend on those we know with expertise in a given, the preceding examples show the value of getting fresh perspectives. When everyone “knows what we know”, it’s time to expand your options.

I’m @bhc3 on Twitter.

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How self-driving vehicles can fix the San Francisco housing crunch

In the San Francisco Bay Area, home prices have seen significant appreciation the last few years:

Source: Paragon Real Estate Group

Source: Paragon Real Estate Group

In the Bay Area, skyrocketing home prices and rents have driven people out of the area. They look for homes in further-out suburbs and exurbs, extending their commutes to work. And the Bay Area leads the nation in the percentage of people who are mega-commuters (pdf). If you’re raising a family, you accept that long commutes (e.g. hour or more) are the price you pay to have a home for your children.

It’s frankly worrying the way prices continue to rise and people are being pushed out further and further from the employment centers of the Bay Area. Similar types of scenarios are playing out in New York City, Washington D.C., Los Angeles, Chicago, etc. It causes a rising level of stress for working parents, trying to excel both at work and at raising their children.

The general response has been a call for the creation of more affordable housing. Which is a very desirable objective, and needs to be pursued. There is no other near-term relief.

But project yourself forward a few decades. A time when the roads will be dominated by self-driving vehicles. Many benefits to individuals and society will open up. One valuable outcome we will see is this:

————————————————————-

Commute time becomes regular work time

————————————————————-

Once you’re freed from having to drive, having to pay attention to the road, you’re afforded new options. Participate in conference calls. Respond to emails. Write documents. Prepare presentations. Run the numbers. Access files in the corporate drive. Engage in conversations. Read reports. Update project plans. Etcetera, etcetera, etc…

As Bay Area companies compete for workers, I expect that they will recognize the challenge of the housing market. Rather than continue to pay escalating salaries so people can afford to live in San Francisco, Santa Clara, Palo Alto, etc., companies will try a different approach. Hire people who live much further out.

See the map below, outlining Northern California median home prices:

Northern California regional home prices

Let’s take Roseville, CA as an example. Roseville is 106 miles away from San Francisco, with at least a two-hour commute. And look at the home price difference. You can buy a typical home in Roseville for nearly $700,000 less than what it costs in San Francisco. Per Chase Bank’s mortgage calculator, one can afford a house in Roseville with $100,000 in household income. In San Francisco, you need a household income of $266,400.

Look at that from a Bay Area employer’s perspective. You can spend a lot less on workers who live further out. Of course, that comes at a cost in terms of worker productivity. Or does it?

Let’s assume companies get wise to the benefits of hiring people who live far away from the office. You can imagine a worker’s daily schedule looking something like this.

Extreme commuters daily schedule

Such a schedule would provide for:

  • Productive work time during expected work hours
  • In-person face time at the office
  • Parenting time
  • More money in the employee’s bank account
  • Less money spent on compensation by employers

Self-driving cars are really the enabling technology for this schedule. We can look at the much discussed Google Buses for some insight. On Quora, Google employee Mary Xu says she uses the bus commute to Mountain View as productive work time. Which validates the possibilities here. However, in a discussion forum about Google Buses, user gnomatic notes that the shared resource of the bus does restrict worker productivity. Wifi can be overtaxed, and phone calls are considered bad etiquette. Which means that individual vehicles are better for realizing the worker productivity.

Self-driving vehicles will radically change the game for us in the decades ahead, a point made also by Reid Hoffman in a terrific post. I fully expect the nature of where we live and work to be altered by autonomous vehicles.

I’m @bhc3 on Twitter.

Avoiding innovation errors through jobs-to-be-done analysis

The lean startup movement was developed to address an issue that bedeviled many entrepreneurs: how to introduce something new without blowing all your capital and time on the wrong offering. The premise is that someone has a vision for a new thing, and needs to iteratively test that vision (“fail fast”) to find product-market fit. It’s been a success as an innovation theory, and has penetrated the corporate world as well.

In a recent post, Mike Boysen takes issue with the fail fast approach. He argues that better understanding of customers’ jobs-to-be-done (i.e. what someone is trying to get done, regardless of solutions are used) at the front-end is superior to guessing continuously about whether something will be adopted by the market. To quote:

How many hypotheses does it take until you get it right? Is there any guarantee that you started in the right Universe? Can you quantify the value of your idea?

How many times does it take to win the lottery?

Mike advocates for organizations to invest more time at the front end understanding their customers’ jobs-to-be-done rather than iteratively guessing. I agree with him in principle. However, in my work with enterprises, I know that such an approach is a long way off as a standard course of action. There’s the Ideal vs. the Reality:

JTBD analysis - innovation ideal vs reality

The top process – Ideal – shows the right point to understand your target market’s jobs-to-be-done. It’s similar to what Strategyn’s Tony Ulwick outlines for outcome-driven innovation. In the Ideal flow, proper analysis has uncovered opportunities for underserved jobs-to-be-done. You then ideate ways to address the underserved outcomes. Finally, a develop-test-learn approach is valuable for identifying an optimal way to deliver the product or service.

However, here’s the Reality: most companies aren’t doing that. They don’t invest time in ongoing research to understand the jobs-to-be-done. Instead, ideas are generated in multiple ways. The bottom flow marked Reality highlights a process with more structure than most organizations actually have. Whether an organization follows all processes or not, the key is this: ideas are being generated continuously from a number of courses divorced from deep knowledge of jobs-to-be-done.

Inside-out analysis

In my experience working with large organizations, I’ve noticed that ideas tend to go through what I call “inside-out” analysis. Ideas are evaluated first on criteria that reflect the company’s own internal concerns. What’s important to us inside these four walls? Examples of such criteria:

  • Fits current plans?
  • Feasible with current assets?
  • Addresses key company goal?
  • Financials pencil out?
  • Leverages core competencies?

For operational, low level ideas inside-out analysis can work. Most of the decision parameters are knowable and the impact of a poor decision can be reversed. But as the scope of the idea increases, it’s insufficient to rely on inside-out analysis.

False positives, false negatives

Starting with the organization’s own needs first leads to two types of errors:

  • False positive: the idea matches the internal needs of the organization, with flying colors. That creates a too-quick mindset of ‘yes’ without understanding the customer perspective. This opens the door for bad ideas to be greenlighted.
  • False negative: the idea falls short on the internal criteria, or even more likely, on someone’s personal agenda. It never gets a fair hearing in terms of whether the market would value it. The idea is rejected prematurely.

In both cases, the lack of perspective about the idea’s intended beneficiaries leads to innovation errors. False positives are part of a generally rosy view about innovation. It’s good to try things out, it’s how we find our way forward. But it isn’t necessarily an objective of companies to spend money in such a pursuit. Mitigating the risk of investing limited resources in the wrong ideas is important.

In the realm of corporate innovation, false negatives are the bigger sin. They are the missed opportunities. The cases where someone actually had a bead on the future, but was snuffed out by entrenched executives, sclerotic processes or heavy-handed evaluations. Kodak, a legendary company sunk by the digital revolution, actually invented the digital camera in the 1970s. As the inventor, Steven Sasson, related to the New York Times:

“My prototype was big as a toaster, but the technical people loved it,” Mr. Sasson said. “But it was filmless photography, so management’s reaction was, ‘that’s cute — but don’t tell anyone about it.’ ”

It’s debatable whether the world was ready for digital photography at the time, as there was not yet much in the way of supporting infrastructure. But Kodak’s inside-out analysis focused on its effect on their core film business. And thus a promising idea was killed.

Start with outside-in analysis

Thus organizations find themselves with a gap in the innovation process. In the ideal world, rigor is brought to understanding the jobs-to-be-done opportunities at the front-end. In reality, much of innovation is generated without analysis of customers’ jobs beforehand. People will always continue to propose and to try out ideas on their own. Unfortunately, the easiest, most available basis of understanding the idea’s potential starts with an inside-out analysis. The gap falls between those few companies that invest in understanding customers’ jobs-to-be-done, and the majority who go right to inside-out analysis.

What’s needed is a way to bring the customers’ perspective into the process much earlier. Get that outside-in look quickly.

Three jobs-to-be-done tests

In my work with large organizations, I have been advising a switch in the process of evaluating ideas. The initial assessment of an idea should be outside-in focused. Specifically, there are three tests that any idea beyond the internal incremental level should pass:

jobs-to-be-done three tests

Each of the tests examines a critical part of the decision chain for customers.

Targets real job of enough people

The first test is actually two tests:

  1. Do people actually have the job-to-be-done that the idea intends to address?
  2. Are there enough of these people?

This is the simplest, most basic test. Most ideas should pass this, but not all. As written here previously, the Color app was developed to allow anyone – strangers, friends – within a short range to share pictures taken at a location. While a novel application of the Social Local Mobile (SoLoMo) trends, Color actually didn’t address a job-to-be-done of enough people.

A lot better than current solution

Assuming a real job-to-be-done, consideration must next be given to the incumbent solution used by the target customers. On what points does the proposed idea better satisfy the job-to-be-done than what is being done today? This should be a clear analysis. The improvement doesn’t have to be purely functional. It may better satisfy emotional needs. The key is that there is a clear understanding of how the proposed idea is better.

And not just a little better. It needs to be materially better to overcome people’s natural conservatism. Nobel Laureate Daniel Kahneman discusses two factors that drive this conservatism in his book, Thinking, Fast and Slow:

  • Endowment effect: We overvalue something we have currently over something we could get. Think of that old saying, “a bird in the hand is worth two in the bush”.
  • Uncertainty effect: Our bias shifts toward loss aversion when we consider how certain the touted benefits of something new are. The chance that something doesn’t live up to its potential looms larger in our psyche, and our aversion to loss causes to overweight the probability that something won’t live up to its potential.

In innovation, the rule-of-thumb that something needs to be ten times better than what it would replace reflects our inherent conservatism. I’ve argued that the problem with bitcoin is that it fails to substantially improve our current solutions to payments: government-issued currency.

Value exceeds cost to beneficiary

The final test is the most challenging. It requires you to walk in the shoes of your intended beneficiaries (e.g. customers). It’s an analysis of marginal benefits and costs:

Value of improvement over current solution > Incremental costs of adopting your new idea

Not the costs of the company to provide the idea, but those that are borne by the customer. These costs include monetary, learning processes, connections to other solutions, loss of existing data, etc. It’s a holistic look at tangible and intangible costs. Which admittedly, is the hardest analysis to do.

An example where the incremental costs didn’t cover the improvements is of a tire that Michelin introduced in the 1990s. The tire had a sensor and could run for 125 miles after being punctured. A sensor in the car would let the driver know about the issue. But for drivers, a daunting issue emerged: how do you get those tires fixed/replaced? They required special equipment that garages didn’t have and weren’t going to purchase. The costs of these superior tires did not outweigh the costs of not being able to get them fixed/replaced.

Recognize your points of uncertainty

While I present the three jobs-to-be-done tests as a sequential flow of yes/no decisions, in reality they are better utilized as measures of uncertainty. Think of them as gauges:

JTBD tests - certainty meters

Treat innovation as a learning activity. Develop an understanding for what’s needed to get to ‘yes’ for each of the tests. This approach is consistent with the lean startup philosophy. It provides guidance to the development of a promising idea.

Mike Boysen makes the fundamental point about understanding customers’ jobs-to-be-done to drive innovation. Use these three tests for those times when you cannot invest the time/resources at the front end to understand the opportunities.

I’m @bhc3 on Twitter.