Author Archives: Greg Satell

About Greg Satell

Greg Satell is a popular speaker and consultant. His latest book, Cascades: How to Create a Movement That Drives Transformational Change, is available now. Follow his blog at Digital Tonto or on Twitter @Digital Tonto.

Transformation is Human Not Digital

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Transformation is Human Not Digital

GUEST POST from Greg Satell

A decade ago, many still questioned the relevance of digital technology. While Internet penetration was already significant, e-commerce made up less than 6% of retail sales. Mobile and cloud computing were just getting started and artificial intelligence was still more science fiction than reality.

Yet today, all of those things are not only viable technologies, but increasingly key to effectively competing in the marketplace. Unfortunately, implementing these new technologies can be a thorny process. In fact, research by McKinsey found that fewer than one third of digital transformation efforts succeed.

For the most part, these failures have less to do with technology and more to do with managing the cultural and organizational challenges that a technological shift creates. It’s relatively easy to find a vendor that can implement a system for you, but much harder to prepare your organization to adapt to new technology. Here’s what you need to keep in mind:

Start With Business Objectives

Probably the most common trap that organizations fall into is focusing on technology rather than on specific business objectives. All too often, firms seek to “move to the cloud” or “develop AI capabilities.” That’s a sure sign you’re headed down the wrong path.

“The first question you have to ask is what business outcome you are trying to drive,” Roman Stanek, CEO at GoodData, told me. “Projects start by trying to implement a particular technical approach and not surprisingly, front-line managers and employees don’t find it useful. There’s no real adoption and no ROI.”

So start by asking yourself business related questions, such as “How could we better serve our customers through faster, more flexible technology?” or “How could artificial intelligence transform our business?” Once you understand your business goals, you can work your way back to the technology decisions.

Automate The Most Tedious Tasks First

Technological change often inspires fear. One of the most basic mistakes many firms make is to try to use new technology to try and replace humans and save costs rather than to augment and empower them to improve performance and deliver added value. This not only kills employee morale and slows adoption, it usually delivers worse results.

A much better approach is to use technology to improve the effectiveness of human employees. For example, one study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

The best way to do this is to start with the most boring and tedious tasks first. Those are what humans are worst at. Machines don’t get bored or tired. Humans, on the other hand, thrive on interaction and like to solve problems. So instead of looking to replace workers, look instead to make them more productive.

Perhaps most importantly, this approach can actually improve morale. Factory workers actively collaborate with robots they program themselves to do low-level tasks. In some cases, soldiers build such strong ties with robots that do dangerous jobs that they hold funerals for them when they “die.”

Shift Your Organization And Your Business Model

Another common mistake is to think that you can make a major technological shift and keep the rest of your business intact. For example, shifting to the cloud can save on infrastructure costs, but the benefits won’t last long if you don’t figure out how to redeploy those resources in some productive way.

For example, when I talked to Barry Libenson, Global CIO of the data giant, Experian, about his company’s shift to the cloud, he told me that “The organizational changes were pretty enormous. We had to physically reconfigure how people were organized. We also needed different skill sets in different places so that required more changes and so on.”

The shift to the cloud made Experian more agile, but more importantly it opened up new business opportunities. Its shift to the cloud allowed the company to create Ascend, a “data on demand” platform that allows its customers to make credit decisions based on near real time data, which is now its fastest growing business.

“All of the shifts we made were focused on opening up new markets and serving our customers better,” Libenson says, and that’s what helped make the technological shift so successful. Because it was focused on business results, it was that much easier to get everybody behind it, gain momentum and create a true transformation.

Humans Collaborating With Machines

Consider how different work was 20 years ago, when Windows 95 was still relatively new and only a minority of executives regularly used programs like Word, Excel and PowerPoint. We largely communicated by phone and memos typed up by secretaries. Data analysis was something you did with a pencil, paper and a desk calculator.

Clearly, the nature of work has changed. We spend far less time quietly working away at our desks and far more interacting with others. Much of the value has shifted from cognitive skills to social skills as collaboration increasingly becomes a competitive advantage. In the future, we can only expect these trends to strengthen and accelerate.

To understand what we can expect, look at what’s happened in the banking industry. When automatic teller machines first appeared in the early 1970s, most people thought it would lead to less branches and tellers, but actually just the opposite happened. Today, there are more than twice the number of bank tellers employed as in the 1970s, because they do things that machines can’t do, like solve unusual problems, show empathy and up-sell.

That’s why we need to treat any technological transformation as a human transformation. The high value work of the future will involve humans collaborating with other humans to design work for machines. Get the human part right and the technology will take care of itself.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Dall-E via Bing

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Why More Women Are Needed in Innovation

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Why More Women Are Needed in Innovation

GUEST POST from Greg Satell

Every once in a while I get a comment from an audience member after a keynote speech or from someone who read my book, Mapping Innovation, about why so few women are included. Embarrassed, I try to explain that, as in many male dominated fields, women are woefully underrepresented in science and technology.

This has nothing to do with innate ability. In fact, you don’t have to look far to find women at the very apex of innovation, such as Jennifer Doudna, who pioneered CRISPR or Jocelyn Bell Burnell, who received the Breakthrough Prize for her discovery of pulsars a few years ago. In earlier days, women like Grace Hopper and Marie Curie made outsized impacts.

The preponderance of evidence shows that women can vastly improve innovation efforts, but are often shunted aside. In fact, throughout history, men have taken credit for discoveries that were actually achieved by women. So, while giving women a larger role in innovation would be just and fair, even more importantly it would improve performance.

The Power Of Diversity

Over the past few decades there have been many efforts to increase diversity in organizations. Unfortunately, all too often these are seen more as a matter of political correctness than serious management initiatives. After all, so the thinking goes, why not just pick the best man for the job?

The truth is that there is abundant scientific evidence that diversity improves performance. For example, researchers at the University of Michigan found that diverse groups can solve problems better than a more homogenous team of greater objective ability. Another study that simulated markets showed that ethnic diversity deflated asset bubbles.

While the studies noted above merely simulate diversity in a controlled setting there is also evidence from the real world that diversity produces better outcomes. A McKinsey report that covered 366 public companies in a variety of countries and industries found that those which were more ethnically and gender diverse performed significantly better than others.

The problem is that when you narrow the backgrounds, experiences and outlooks of the people on your team, you are limiting the number of solution spaces that can be explored. At best, you will come up with fewer ideas and at worst, you run the risk of creating an echo chamber where inherent biases are normalized and groupthink sets in.

How Women Improve Performance

While increasing diversity in general increases performance, there is also evidence that women specifically have a major impact. In fact, in one wide ranging study, in which researchers at MIT and Carnegie Mellon sought to identify a general intelligence score for teams, they not only found that teams that included women got better results, but that the higher the proportion of women was, the better the teams did.

At first, the finding seems peculiar, but when you dig deeper it begins to make more sense. The study also found that in the high performing teams members rated well on a test of social sensitivity and took turns when speaking. Perhaps not surprisingly, women do better on these parameters than men do.

Social sensitivity tests ask respondents to infer someone’s emotional state by looking at a picture and women tend score higher than men. As for taking turns in conversation, there’s a reason why we call it “mansplaining” and not “womansplaining.” Women usually are better listeners.

The findings of the study are consistent with something I’ve noticed in my innovation research. The best innovators are nothing like the mercurial, aggressive stereotype, but tend to be quiet geniuses. Often they aren’t the kinds of people that are immediately impressive, but those who listen to others and generously share insights.

Changing The Social Dynamic

One of the reasons that women often get overlooked, besides good old fashioned sexism, is that that there are vast misconceptions about what makes someone a good innovator. All too often, we imagine the best innovators to be like Steve Jobs—brash, aggressive and domineering—when actually just the opposite is true.

Make no mistake, great innovators are great collaborators. That’s why the research finds that successful teams score high in social sensitivity, take turns talking and listening to each other rather, rather than competing to dominate the conversation. It is never any one idea that solves a difficult problem, but how ideas are combined to arrive at an optimal solution.

So while it is true that these skills are more common in women, men have the capacity to develop them as well. In fact, probably the best way for men to learn them is to have more exposure to women in the workplace. Being exposed to a more collaborative working style can only help.

So besides the moral and just aspects of getting more women into innovation related fields and giving them better access to good, high paying jobs, there is also a practical element as well. Women make teams more productive.

Building The Next Generation

Social researchers have found evidence that that the main reason that women are less likely to go into STEM fields has more to do with cultural biases than it does with any innate ability. For example, boys are more encouraged to play with building toys during childhood and develop spatial skills early on, while girls can build the same skills with the same training.

Cultural bias also plays a role in the amount of encouragement young students get. STEM subjects can be challenging, and studies have found that boys often receive more support than girls because of educators’ belief in their innate talent. That’s probably why even girls who have high aptitude for math and science are less likely to choose a STEM major than boys of even lesser ability.

Yet cultural biases can evolve over time and there are a number of programs designed to change attitudes about women and innovation. For example Girls Who Code provides training and encouragement for young women and UNESCO’s TeachHer initiative is designed to provide better educational opportunities.

Perhaps most of all, initiatives like these can create role models and peer support. When young women see people like the Jennifer Doudna, Jocelyn Bell Burnell and the star physicist Lisa Randall achieve great things in STEM fields, they’ll be more likely to choose a similar path. With more women innovating, we’ll all be better off.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Dall-E via Bing

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4 Ways to Create Something Truly Original

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4 Ways to Create Something Truly Original

GUEST POST from Greg Satell

I study innovators for a living. Every year, I interview dozens of men and women who’ve achieved remarkable things. For my own part, I publish about a hundred articles a year and my second book, Cascades, has sold well since coming out five years ago. While my achievements pale in comparison to many of those I interview, many believe my work to be original.

The most destructive myth about creativity is that there are innate traits that allow some people to be creative, while others, who lack these, cannot. The truth is that in decades of research on creativity, nobody has been able to identify any such traits. In my experience, great innovators come in all shapes and sizes.

Still, despite the diversity of original innovators themselves, there are some common principles in how they approach their work and these are things that anyone can apply. That doesn’t mean everyone can be world famous, but the evidence clearly shows that anyone can be creative and, even if it’s not a major breakthrough, make some contribution to the world.

1. Explore

In 2006, Jennifer Doudna got a call from a colleague at the University of California at Berkeley, Jillian Banfield, who she knew only by reputation. Banfield’s area of research interest, obscure bacteria living in extreme conditions, was only tangentially related to Doudna’s work, studying the biochemistry of RNA and other cell structures.

The purpose of the call was to interest Doudna in studying an emerging phenomenon that was recently discovered in microbiology, a strange sequence of DNA found in bacteria. The function of the sequences were not yet clear, but some early evidence suggested that they might be involved in some kind of immune function, helping bacteria to defend themselves against viruses.

Intrigued, Doudna began to research the sequences, called CRISPR, in her own lab and, in 2012, discovered that they could be used as a powerful new tool for editing genes. Today, CRISPR is creating a revolution in genomics, completely redefining what was considered to be possible in just a few short years.

Many have observed the role of serendipity in innovation, such as in Alexander Fleming’s chance discovery of penicillin. Yet in every case, once you look a little deeper, you find that even the most unexpected discoveries were the product of intense exploration. Like Fleming and penicillin, Doudna wasn’t looking for a gene editing technology, but she was investigating a wide number of phenomena that were previously unexplained.

The first step for innovation is exploration. All who wander are not lost.

2. Combine

I’m a relentless fact checker. Over the years, I’ve found that even if you’ve done significant research, reading papers and interviewing experts, it’s amazingly easy to get things wildly wrong. I’ve also found that fact checking can lead you to new information you didn’t know existed. So before I publish anything of significance, I always make sure to reach out to someone who can correct my foolishness before it becomes public.

That’s why when I was finishing up Cascades, I reached out to Duncan Watts to look over two chapters on the science of networks, a field which he helped pioneer. As usual, Duncan was gracious and helpful, and pointed me towards a paper of his that I might want to include. He did so somewhat apologetically, not wanting to push his work on me, but observed that since I had largely based both chapters on his work already, it was probably okay.

This was entirely true. Much of the first half of my book is based on Duncan’s ideas. What’s more, much of the second half of the book is based on insights from my friend Srdja Popović , who trains activists around the world to create revolutionary movements. There are a number of others as well, all of who shared their wisdom with me.

None of this, of course, was at all original, but the combination is. In fact, the key insight of the book is that Duncan’s mathematical models and the on-the-ground tactics of Srdja and others are intensely related. They can inform each other in ways that both men, who are mostly unfamiliar with each other’s work, had not addressed and, I believe, are important.

3. Refine

I first got interested in Duncan’s work in 2006. I was running a large digital business at the time and, with social networks becoming a powerful force online, I thought that learning some basic concepts of network science would be useful. Much to my surprise, I found that the ideas had a powerful resonance in an unexpected area.

Two years earlier, I had found myself in the middle of the Orange Revolution in Ukraine. What struck me at the time was how nobody seemed to have the first idea what was happening or why — not the journalists I worked with everyday, or the political and business leaders I would meet with regularly, nobody.

So I was excited to find, in Duncan’s work, a mathematical explanation for many of the seemingly inexplicable things that I had seen and experienced first-hand. Yet still, I had only a faint sense of what I was on to. Sure, there were obvious connections and possibilities, but I had no real framework to make the insights actionable.

That was 12 years ago (and 15 since the Orange Revolution began) and I’ve been working to refine those initial ideas ever since. Over that period, there has been no shortage of blind allies and wrong turns. Nevertheless, I kept at it and continued to learn. It took over a decade before I was able to pull everything together into something worth publishing.

4. Validate

The connection between Duncan and Srdja’s work wasn’t completely out of the blue. In fact, Duncan had made a short reference to Otpor, the movement which Srdja had helped lead, and its overthrow of Serbian dictator Slobodan Milošević in his book, Six Degrees. Yet there was no guarantee that the significance went any further than that.

So I began to widen my search. I looked at social movements throughout history to see if similar patterns held or whether the Orange Revolution in Ukraine and similar events in Serbia were anomalies. I struck up a working friendship with Srdja, read his book, Blueprint for Revolution and pored through the training materials on his organization’s website.

Yet to be truly useful, I needed to see if the same concepts could be applied more broadly. So I also researched and spoke to a number of leaders in other fields, such as corporate executives and people who led movements to transform heathcare, education and other things. Anywhere I could find anyone that created transformational change, I sought them out to find how they were able to succeed where so many others failed.

What I found was that while there were vast difference among changemakers, they had all eventually arrived at similar principles that made them successful, which I could validate. It took me nearly 15 years, but the journey that began with that initial connection between two vastly different sets of ideas eventually became something that I could consider to be coherent and useful.

In that way, my experience reflects many of the innovators of vastly greater accomplishment that I research and study. Truly original work doesn’t emerge fully formed from a brainstorm or sudden epiphany. It’s long years that follow, combining, refining and validating that makes the difference between an errant idea and something useful.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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Why Materials Science is the Most Important Technology of This Decade

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Why Materials Science is the Most Important Technology of This Decade

GUEST POST from Greg Satell

Think of just about any major challenge we will face over the next decade and materials are at the center of it. To build a new clean energy future, we need more efficient solar panels, wind turbines and batteries. Manufacturers need new materials to create more advanced products. We also need to replace materials subject to supply disruptions, like rare earth elements.

Traditionally, developing new materials has been a slow, painstaking process. To find the properties they’re looking for, researchers would often have to test hundreds — or even thousands — of materials one by one. That made materials research prohibitively expensive for most industries.

Yet today, we’re in the midst of a materials revolution. Scientists are using powerful simulation techniques, as well as machine learning algorithms, to propel innovation forward at blazing speed and even point them toward possibilities they had never considered. Over the next decade, the rapid advancement in materials science will have a massive impact.

The Seeds Of The Materials Revolution

In 2005, Gerd Ceder was a Professor of Materials Science at MIT working on computational methods to predict new materials. Traditionally, materials scientists worked mostly through trial and error, working to identify materials that had properties which would be commercially valuable. Gerd was working to automate that process using sophisticated computer models that simulate the physics of materials.

Things took a turn when an executive at Duracell, then a division of Procter & Gamble, asked if Ceder could use the methods he was developing to explore possibilities on a large scale to discover and design new materials for alkaline batteries. So he put together a team of a half dozen “young guns” and formed a company to execute the vision.

The first project went well and the team was able to patent a number of new materials that hadn’t existed before. Then another company came calling, which led to another project and more after that. Yet despite the initial success, Ceder began to realize that there was a problem. Although the team’s projects were successful, the overall impact was limited.

“We began to realize we’re generating all this valuable data and it’s being locked away in corporate vaults. We wanted to do something in a more public way,” Ceder told me. As luck would have it, it was just then that one of the team members was leaving MIT for family reasons and that chance event would propel the project to new heights.

The Birth Of The Materials Project

In 2008, Kristin Persson’s husband took a job in California, so she left Ceder’s group at MIT and joined Lawrence Berkeley National Laboratory (LBL) as a research scientist. Yet rather than mourn the loss of a key colleague, the team saw the move as an opportunity to shift their work into high gear.

“At MIT, we pretty much hacked everything together,” Ceder explains. “It all worked, but it was a bit buggy and would have never scaled beyond our small team. At a National Lab, however, they had the resources to build it out properly and create a platform that could really drive things forward.” So Persson hit the ground running, got a small grant and stitched together a team to combine the materials work with the high performance supercomputing done at the lab.

“At LBL there were world class computing people,” Persson told me. “So we began an active collaboration with people that were on the cutting edge of computer science, but didn’t know anything about materials and our little band of ‘materials hackers’. It was that interdisciplinary collaboration that was really the secret sauce and helped us gain ground quickly.”

Traditional, materials science could take a class of alloys for use in, say, the auto industry and calculate things like weight vs. tensile strength. There might be a few hundred of those materials in the literature. But with the system they built at LBL, they could calculate thousands. That meant engineers could identify candidate materials exponentially faster, test them in the real world and create better products.

Yet again, they felt that the impact of their work was limited. After all, not many engineers from private industry spend time at National Laboratories. “Our earlier work convinced us that we were on the cusp of something much bigger,” Persson remembers. That’s what led them to create The Materials Project, a massive online database that anyone in the world can access.

A Massive Materials Initiative

The Materials Project went online early in 2011 and drew a few thousand people. From there it grew like a virus and today has more than 50,000 users, a number that grows by about 50-100 per day. Yet its impact has become even greater than that. The success of the project caught the attention of Tom Kalil, then Deputy Director at the White House Office of Science and Technology Policy, who saw the potential to create a much wider initiative.

In the summer of 2011, the Obama administration announced the Materials Genome Initiative (MGI) to coordinate work across agencies such as the Department of Energy, NASA, the Department of Energy and others to expand and complement the work being done at LBL. These efforts, taken together, are creating a revolution in materials science and the impacts are just beginning to be felt by private industry.

The MGI is based on three basic pillars. The first is computational approaches that can accurately predict materials properties, like the ones Gerd Ceder’s team pioneered. The second is high throughput experimentation to expand materials libraries and the third are programs that mine existing materials in the scientific literature and promote the sharing of materials data.

For example, one project applied machine learning algorithms to experimental materials data to identify forms of a super strong alloy called metallic glass. While scientists have long recognized its value as an alternative to steel and as a protective coating, it is so rare that relatively few forms of it were known. Using the new methods, however, researchers were able to perform the work 200 times faster and identify 20,000 in a single year!

Creating A True Materials Revolution

Thomas Edison famously remarked that if he tried 10,000 experiments that failed, he didn’t actually consider it a failure, but found 10,000 things that didn’t work. That’s true, but it’s also incredibly tedious, time consuming and expensive. The new methods, however, have the potential to automate those 10,000 failures, which is creating a revolution in materials science.

For example, at the Joint Center for Energy Storage Research (JCESR), a US government initiative to create the next generation of advanced batteries, the major challenge now is not so much to identify potential battery chemistries, but that the materials to make those chemistries work don’t exist yet. Historically, that would have been an insurmountable problem, but not anymore.

“Using high performance computing simulations, materials genomes and other techniques that have been developed over the last decade or so, we can often eliminate as much as 99% of the possibilities that won’t work,” George Crabtree, Director at JCESR told me. “That means we can focus our efforts on the remaining 1% that may have serious potential, and we can advance much farther, much faster for far less money.”

The work is also quickly making an impact on Industry. Greg Mulholland, President of Citrine Informatics, a firm that applies machine learning to materials development, told me, “We’ve seen a huge broadening of companies and industries that are contacting us and a new sense of urgency. For companies that historically invested in materials research, they want everything yesterday. For others that haven’t, they are racing to get up to speed.”

Jim Warren, a Director at the Materials Genome Initiative, thinks that is just the start. “When you can discover new materials for hundreds of thousands or millions dollars rather than tens or hundreds of millions you are going to see a vast expansion of use cases and industries that benefit,” he told me.

As we have learned from the digital revolution, any time you get a 10x improvement in efficiency, you end up with a transformative commercial impact. Just about everybody I’ve talked to working in materials thinks that pace of advancement is easily achievable over the next decade. Welcome to the materials revolution.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Dall-E on Bing

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Change the World One Shared Purpose at a Time

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Change the World One Shared Purpose at a Time

GUEST POST from Greg Satell

In 1847, a young doctor named Ignaz Semmelweis had a major breakthrough. Working in a maternity ward, he discovered that a regime of hand washing could dramatically lower the incidence of childbed fever. Unfortunately, the medical establishment rejected his ideas and the germ theory of disease didn’t take hold until decades later.

The phenomenon is now known as the Semmelweis effect, the tendency for people to reject new knowledge that contradicts established beliefs. Whether you are a CEO trying to launch a new initiative, a political leader pushing for an important reform or a social activist advocating for a cause, you need more than a big idea to change the world.

The problem is that a new idea has to replace an old one and the status quo has inertia on its side. Even those who are easily convinced have to convince those around them and those, in turn, need to convince others still until the long chain of influence results in a change of the zeitgeist. That’s why to truly make an impact, you need small groups, loosely connected, but united by a shared purpose.

1. Small Groups And Local Majorities

To understand how new ideas take hold it’s helpful to look at a series of conformity experiments conducted by Solomon Asch in the 1950s. The design of the study was simple, but ingenious. Asch merely showed a group of people pairs of cards like these:

Each person in the group was asked to match the line on the left with the line of the same length on the right. However, there was a catch: almost everyone in the room was a confederate who gave the wrong answer. When it came to the real subjects’ turn to answer, most conformed to the majority opinion even when it was obviously incorrect.

The idea that people have a tendency toward conformity is nothing new, but that they would give obviously wrong answers to simple and unambiguous questions was indeed shocking. Now think about how hard it is for a more complex idea to take hold across a broad spectrum of people, each with their own biases and opinions.

The truth is that majorities don’t just rule, they also influence, even local majorities. So if you want an idea to gain traction, the best strategy is not to try to immediately spread it far and wide, but to start with groups small enough to convince a majority. Once you do that, you can begin to work to achieve wider acceptance.

2. Loose Connections

One important aspect of Asch’s conformity studies was that the results were far from uniform. A quarter of the subjects never conformed, some always did, and others were somewhere in the middle. We all have different thresholds for conformity that vary widely, depending on a variety of factors, such as our confidence in our knowledge of a subject.

The sociologist Mark Granovetter addressed this aspect with his threshold model of collective behavior. As a thought experiment, he asks us to imagine a diverse group of people milling around in a square. Some are natural deviants, always ready to start trouble, most are susceptible to provocation in varying degrees and the remainder is made up of unusually solid citizens, almost never engaging in antisocial behavior.

You can see a graphic representation of how the model plays out above. In the example on the left, a miscreant throws a rock and breaks a window. That’s all it takes for his friend next to him to start and then others with slightly higher thresholds join in as well. Before you know it, a full scale riot ensues.

The example on the right is slightly different. After the first few troublemakers start, there is no one around with a low enough threshold to join in. Rather than the contagion spreading, it fizzles out, the three miscreants are isolated and little note is made of the incident. Although the groups are outwardly similar, a slight change in conformity thresholds makes a big difference.

It’s a relatively simplistic example, but through another concept Granovetter developed called the strength of weak ties, we can see how it can lead to large scale change in the final graphic below as an idea moves from group to group.

The top cluster is identical to the one in the first example and a local majority forms. However, no cluster is an island because people tend to belong to multiple groups. For example, we form relationships with people in our neighborhood, from work, religious communities and so on. So an idea that saturates one group soon spreads to others.

Notice how the exposure to multiple groups can help overcome higher thresholds of resistance, because of the influence emanating from additional groups through weak links. Physicists have a name for this type of phenomenon — percolation — and configurations like the ones in the diagram are called a percolating cluster.

As I explain in my book, Cascades, there is significant evidence that this is how ideas actually do spread in the real world. So if you want an idea to gain traction, the best strategy is not to try to convince everybody all at once, but to start with small groups with low resistance thresholds. They, in turn, can help you convince others and build momentum.

3. Forging A Shared Purpose

As many have observed in recent years, you don’t really need leaders to spread ideas. Some, like LOLcats, go viral all on their own. Yet if it’s an idea that you consider to be important, you don’t want to leave things to chance. In many cases, such as the Occupy Movement, even an initially popular idea can spin out of control and lose credibility.

That’s where the importance of leadership comes in. The role of a leader is not so much to guide and direct action, but to inspire and empower belief and a sense of shared purpose. You can’t expect people to do what you want, they first have to want what you want, which is why you can’t change fundamental behaviors without changing fundamental beliefs.

Now we can see where Ignaz Semmelweis went wrong. Rather than working to gain allies among likeminded people, he castigated the medical establishment—those who had high resistance thresholds to a challenge of established beliefs. Instead of being hailed as an innovator, he died in an insane asylum, ironically from an infection he contracted there.

So we need to redefine how we think about leadership. In his book, Leaders: Myth And Reality, General Stanley McChrystal defines leadership as “a complex system of relationships between leaders and followers, in a particular context, that provides meaning to its members.” Control, as attractive as it may seem, is always an illusion.

You Can’t Overpower, You Must Attract

All too often, we think creating change is about charismatic leaders and catchy slogans. People see Martin Luther King Jr. and “I have a dream” or Obama and “Yes, we can,” and think that you need a heroic leader to make change happen. In a similar way, they see CEOs like Steve Jobs or Elon Musk thrill audiences on stage and think that’s what entrepreneurship is all about.

This is a trap. Movements like Occupy didn’t fail because they lacked a Mandela or Gandhi, any more than countless startups fail because they lack a Steve Jobs or Elon Musk. Successful movements like Otpor in Serbia and Pora in Ukraine prevailed against incredible odds, in much more difficult environments, without visible leaders. Bill Gates isn’t really such a charmer and neither are Sergey Brin and Larry Page, the founders of Google.

Most often, change efforts fail because they seek to overpower rather than attract. Semmelweis sent angry letters to his critics, rather than address their concerns. Many of the Occupy activists were shrill and vulgar. Silicon Valley entrepreneurs are often known for their arrogance as much as for their technical prowess.

The problem is that fantasies about overpowering your foes are much more romantic than doing the hard work of building traction in small groups and then painstakingly linking them together through forging a sense of shared purpose. Yet if you want to truly change the world, or even just your little corner of it, that’s what you need to do.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: DigitalTonto.com and Dall-E on Bing

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Powering the Google Innovation Machine with the World’s Top Minds

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Powering the Google Innovation Machine with the World's Top Minds

GUEST POST from Greg Satell

It’s no secret that Google is one of the most innovative companies on the planet. Besides pioneering and then dominating the search industry, it has also become a leader in developing futuristic technologies such as artificial intelligence, driverless cars and quantum computing. It has even launched a life science company.

What makes Google so successful is not one particular process, but how it integrates multiple strategies into a seamless whole. For example, Google Brain started out as a 20% time project, then migrated out to its “X” Division to accelerate development and finally came back to the mothership, where it now collaborates closely with engineering teams to build new products.

Yet perhaps its most important strategy, in fact the one that makes much of the rest possible, is how it partners with top scientists in the academic world. This is no “quick hit,” but a well thought out, long-term game plan designed to establish deep relationships based on cutting edge science and embed that knowledge deeply into just about everything Google does.

Building Deep Relationships to the Academic Community

“We design a variety programs that widen and deepen our relationships with academic scientists,” Maggie Johnson, who heads up University Relations at Google, told me. In fact, there are three distinct ways that Google engages directly with scientists beyond the typical research partnerships with universities.

The first is its Faculty Research Awards program, which are small one-year grants, usually to graduate students or postdocs whose work may be of interest to Google. These are unrestricted gifts, although recipients are highly encouraged to publish their work publicly, that allow the company to develop relationships with young talent at the beginning of their careers.

While anybody can apply for a Faculty Research Award, Focused Research Awards are only available by invitation. Typically, these are awarded to more senior researchers that Google has already had some contact with and last two to three years. However, they are also unrestricted grants that researchers can use as they see fit.

The third way that Google engages with scientists to to proactively engage leaders in a particular field of interest. Geoffrey Hinton, for example, is a pioneer in neural networks and widely considered one of the top AI experts in the world. He splits his time between his faculty position at the University of Toronto and working on Google Brain.

“Spinning In” World Class Scientists

The academic research programs provide many benefits to Google as a company. They give access to the most promising students for recruiting, allow it to help shape university curriculums and keep it connected to breakthrough research in important fields. However, the most direct benefits probably come inviting researchers to spend a sabbatical year at Google, which it calls its Visiting Faculty Program.

For example, Andrew Ng, a top AI researcher, decided to spend a year working at Google and quickly formed a close working relationship with two of the company’s brightest minds, Greg Corrado and Jeff Dean, who were interested in what was then a new brand of artificial intelligence called deep learning. Their collaboration became the Google Brain project.

The Visiting Faculty Program touches on everything Google does. Recently they’ve had people visiting the company like John Canny at UC Berkeley, who helped with the development of TPU’s, chips specialized to run Google’s AI algorithms and Michael Rabin, a Turing Award winning mathematician who was working on auction algorithms. For every Google priority, at least one of the world’s top minds is working with the company on it.

What makes the sabbatical program unusual is how deeply it is integrated into everyday work at the company. “In most cases, these scientists have already been working with our teams through one of our other programs, so the groundwork for a productive relationship has already been laid,” Maggie Johnson told me.

Developing “Win-Win” Relationships

One of the things that makes Google’s outreach to researchers work so well is that it is truly a win-win arrangement. Yes, the company gets top experts in important fields to work on its problems, but the researchers themselves get to work with unparalleled tools and data sets. They also get a much better sense of what problems are considered important in a commercial environment.

Katya Scheinberg, a Professor at Lehigh University who focuses on optimization problems, found working at Google to be a logical extension of her earlier collaboration with the company. “I had been working on large-scale machine learning problems and had some connections with Google scientists. So spending part of my sabbatical year at the company seemed fairly natural. I learned a lot about the practical problems that private sector researchers are working on,” she told me.

Since leaving Google, she’s found that her time at the company has shifted the focus of her research. “Working at Google got me interested in some different problems and alerted me to the possibility of applying some approaches I had worked on before to different fields of application.”

Sometimes scholars stay for longer and can have a transformative impact on the company. As noted above, Andrew Ng spent several years at the company. Andrew Moore, a renowned computer scientist and a former Dean of Carnegie Mellon’s computer program, took a leave of absence from his university to set up Google’s Research Center in Pittsburgh. Lasting relationships like these are rare in industry, but incredibly valuable.

Connecting to Discovery Is Something Anyone Can Do, But You Have to Make the Effort

Clearly, Google is an unusual company. There’s not many places that can attract the type of talent that it can. However, just about any business can, for example, support the work of a young graduate student or postdoc at a local university. In much the same way, inviting even a senior researcher to come for a short time is not prohibitively expensive.

Innovation is never a single event, but a process of discovery, engineering and transformation. It is by connecting to discovery that businesses can truly see into the future and develop the next generation of breakthrough products. Unfortunately, few businesses realize the importance of connecting with the academic world.

Make no mistake, if you don’t discover, you won’t invent and if you don’t invent you will be disrupted eventually. It’s just a matter of time. However, you can’t just show up one day and decide you want to work with the world’s greatest minds. Even Google, with all its resources and acumen, has had to work really hard at it.

It’s made these investments in time, focus and resources because it understands that the search business, as great as it is, won’t deliver outsized profits forever. Today, we no longer have the luxury to manage for stability, but must prepare for disruption.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credit: Dall-E on Bing

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Business Pundits Love to Say These 4 Untrue Things

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Business Pundits Love to Say These 4 Untrue Things

GUEST POST from Greg Satell

Go to just about any business conference and you will see a pundit on stage. He or she will show some company that failed and explain the silly mistakes that they made, then follow-up with a few basic rules to help you avoid those pitfalls and become super successful. You leave feeling confident, because it all seems so simple and easy.

Yet look a little closer and the illusion falls away. Very few of these pundits have ever run a successful business. At the same time, many of the executives that are shown to be so silly today, were hailed as visionaries of their time, often by the same pundits that ridicule them now. Some went on to great success later on.

The truth is that managing a successful enterprise is a very hard and complex thing to do well. It can’t be boiled down to a few simple rules. For every great enterprise that does things one way, you will find one that’s equally successful that goes about things very differently. So to succeed in the long term, we often need to ignore the myths pundits love to repeat.

1. You Need To Move Fast And Break Things

When the iPhone came out in 2007, Microsoft CEO Steve Ballmer dismissed it, saying, “There’s no chance that the iPhone is going to get any significant market share. No chance.” The tech giant recognized the switch too slowly and largely missed out on the mobile market. Microsoft, it seemed, was a dinosaur, soon to become extinct.

Yet actually the opposite happened. Over the next 10 years, the company grew revenues at the impressive annual rate of better than 10% and maintained margins of nearly 30%. Those are very strong numbers. How can a company miss such an enormous opportunity and still survive, much less thrive?

They key to understanding Microsoft’s business isn’t what it missed, but what it was patiently building. While the world was obsessed with mobile, it was developing its servers and tools division, which eventually became the core of its cloud business that is now growing at stellar rates. That’s why Microsoft is once again vying to be the world’s most valuable company.

While agility can be an important asset for developing applications based on technology that is well understood, it is not a great strategy for developing technology that is truly new and different. To do that, you need to explore, discover and invent from scratch. That takes time and patience.

2. Innovation Is About Ideas

There is nothing that pundits and self-styled gurus like to talk about more than the power of ideas. They put up a picture of someone famous, like Albert Einstein, Mahatma Gandhi, Martin Luther King Jr. or, most enthusiastically, Steve Jobs, and revel the audience with a fascinating story about how their ideas changed the world.

The implication is that you can change the world too if only you could find the right idea. So they suggest all manner of exercises, from brainstorming techniques to meditation and mindfulness, designed to get your creative energy flowing so that you can generate more ideas and rise to greatness, just like those fabulous and famous people.

Yet that’s not how innovation happens. Consider Einstein. He didn’t start with an idea, but with a problem. More specifically, he wanted to know what would happen if you shined a lantern while traveling at light speed. It took him ten years to solve that problem with his theory of special relativity. It took him another ten to solve his next problem and arrive at general relativity.

The truth is that if you want to make a real impact, you don’t start with an idea, but by identifying a meaningful problem to be solved. Revolutions don’t begin with a slogan, they begin with a cause.

3. Lowering Costs Will Make You More Competitive

Not all pundits are pie-in-the-sky dreamers. Some are hard-nosed realists and they will tell you that the key to success is focusing on the bottom line. That means a relentless drive toward efficiency and driving down costs so that you can increase margins and achieve a sustainable competitive advantage.

Yet as MIT Professor Zeynep Ton, explains in The Good Jobs Strategy, that’s often not the case, even in the notoriously stingy retail industry, she points to companies like Costco, Trader Joe’s and Spain’s Mercadona as examples of how you can get better results by investing in training and retaining employees to better serve your customers.

The problem with a relentless drive to cut costs and drive efficiency is you often end up impeding the interoperability and exploration it takes to create value. That’s the efficiency paradox. The more we try to optimize operations, the less we are able to identify improvements, react to changes and discover new possibilities.

This is becoming even more important in the age of automation, where it is all too easy to replace employees with robots and algorithms. The truth is that racing to the bottom of the cost curve will almost guarantee that you will become a commodity business. Value never disappears, it just moves to a new place. To compete for the long term, you need to identify value at a higher level, develop new business models and redesign work.

4. Companies That Fail Weren’t Paying Attention

The one thing that you can almost guarantee at any conference is that at least one of the fancy pants gurus will tell a story about a great big company, usually Blockbuster, Kodak or Xerox, that was run by eminently silly people. Because these dull executives were asleep at the wheel, they failed to notice the change swirling around them and drove their enterprises into the ground.

The problem is that these stories are almost never true. Make no mistake, it takes talent, intelligence and ambition to run a significant enterprise. So whenever anybody tells you that there was a simple fix to a complex problem, you should raise your B.S. antenna. You’re probably being sold a fairy tale.

Reality is never simple or clear cut. Executives need to make tough decisions with incomplete information, often in a complex time frame. So rather than looking for easy answers, you would do yourself a much greater service by trying to uncover why smart, diligent leaders with good intentions so often get it wrong and learning from them.

Most of all, you need to internalize the fact that success or failure never boil down to a single decision or event. Even the best of us have bad moments and sometimes the least deserving get lucky. The best you can do is to keep moving forward, continue to learn and, most of the time, ignoring the pundits.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credit: Dall-E on Bing

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Science Fiction Becomes Innovation Reality This Way

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Science Fiction Becomes Innovation Reality This Way

GUEST POST from Greg Satell

When H.G. Wells was born in 1866, there was no electricity or cars or even indoor plumbing. Still, his active imagination conjured up a world of time machines, space travel and genetic engineering. This was all completely fantasy, but his books foresaw many modern inventions, such as email, lasers and nuclear energy.

It’s no accident that people who invent the future are often fans of science fiction. In fact, in Leading Transformation, the former head of Lowe’s innovation lab explains how he hired science fiction writers to help inspire the company to leverage virtual reality and build a new future for the company.

To create anything truly new and different, you often need to discard the constraints of the present. Yet that comes with a problem. How do you transform fantasy into something real and useful? What makes great innovators truly different is how they combine imagination with practical problem solving in order to bring even the wildest pipe dreams into reality.

How A “Memex” Machine Became The Internet

“Consider a future device for individual use,” Vannevar Bush wrote in The Atlantic in 1945, “which is a sort of mechanized private file and library. It needs a name, and, to coin one at random, “memex” will do. A memex is a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.”

It was an unlikely vision for the time. The first computers were just being developed then and were themselves somewhat impractical devices, which is why Bush imagined that the memex would be based on microfilm. Yet Bush was no inveterate dreamer, but in many ways the architect of America’s path to scientific dominance and people took him seriously.

One of those was a young radar technician stationed in the Philippines named Douglas Engelbart, who would return to school to study electrical engineering. It was that expertise, along with Bush’s vision that led him to think of computers, still primitive at the time, as machines that could do more than merely calculate, but “augment the human intellect.”

Engelbart would showcase these ideas in what is now known as the Mother of All Demos in 1968 and Xerox began an enormous research project to bring his vision to market. By 1973, a functional prototype, called the Alto, was built and, a decade later, a young entrepreneur named Steve Jobs would use it to create the Macintosh. The world was never the same.

When Feynman Found “Plenty Of Room At The Bottom”

Richard Feynman was an usual scientist, known almost as much for his pranks as he was for his discoveries. So few were probably surprised at the unconventional title for his address the American Physical Society a few days after Christmas in 1959, There’s Plenty of Room at the Bottom. It was something they had come to expect from the young genius.

Feynman’s fantasy was not unlike Bush’s, except that it was more ambitious. While Bush imagined putting all the world’s knowledge on microfilm, Feynman imagined writing the entire 24 volumes of the Encyclopedia Britannica on the head of a pin. It was just the sort of impractical idea that usually gets people thrown out on physics conferences, not given the stage.

Yet Feynman immediately got down to brass tacks. He proposed using an electron microscope as a writing tool, much like a cathode ray oscilloscope projected images on television screens at the time. He then asked if we can write things on a microscopic scale, why not build things too? Again, he identified problems and proposed potential solutions.

That day, almost single handedly, Feynman invented the field of nanotechnology, although that term wasn’t coined until 1974. These days we use it to etch transistors in silicon wafers to make computer chips and create advanced materials for things like solar cells. Yet the truth is that even now, 60 years after that initial talk, we are just beginning to scratch the surface of Feynman’s vision.

Spacemen, Cavemen And Small World Networks

In the late 1990s, a young graduate student named Duncan Watts was struggling to understand an obscure phenomenon that had baffled scientists for centuries. Known as coupled oscillation, it was a mysterious force that allowed a disparate group of entities, like pacemaker cells in the human heart or certain species of crickets in a forest, to synchronize their behavior.

Nobody could figure out how it worked. Was there some kind of leadership structure with “conductor” leading the synchronized orchestra? Or maybe some complicated web of influence? As much as he pored through the research, tracked the chirping of crickets and tested out formulas to describe behavior, he was still baffled.

What helped break the logjam was two books by the science fiction writer Isaac Asimov. In the first, Caves of Steel, people lived in underground caves and, while everybody was connected to everybody else in their cave, they knew no one outside of it. In the second, The Naked Sun, space colonists led a hermit-like existence, linked to others only through long-range connections.

Neither of these, of course, told Watts anything about pacemaker cells or crickets, but it allowed him to reframe the problem as one of relationships. By imagining the two extremes of the cave people and the space colonists, he was able to come up with a model of relationships that led to the mass synchronization of coupled oscillators.

The paper he would write based on his fantasy-inspired formulas would prove to be a landmark. It would establish an exciting new field of small world networks and lead to a new era in the science of how things are connected, helping to transform fields as diverse as neuroscience, epidemiology and computer science.

Venturing Into The Visceral Abstract

Today we are beset with an dizzying array of problems in the world, climate change, income inequality and the rise of authoritarianism being just a few. Successful businesses face extinction by a seemingly endless wave of disruptions ranging from new technologies to new social phenomenons. It can all seem overwhelming.

It’s important to view these problems from a practical perspective. Each needs to be solved within constraints of economics, politics and competitive pressures. Yet it is also important to realize that the solutions to tough problems will rarely be found in the realm of our experiences. If a solution to any of these problems already existed, they wouldn’t be such tough problems.

The only path through that troubling tautology is fantasy. Once we have exhausted the realm of the possible, we often must venture into the realm of the impossible to see a new direction. A memex machine, a world with “plenty of room at the bottom,” cave societies and space colonies were all ideas of little practical import at the time they were conceived, but benefit us today in important ways through the discoveries they inspired.

We need to accept that we live in a world of the visceral abstract, where the technologies we use to solve the practical problems in our lives are all based on what were once considered utterly impractical ideas. That means to make an impact on reality, we often need to indulge ourselves with fantasy, identify a different path to travel and then begin the work anew.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credit: Pexels

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The Event That Made Einstein an Icon

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The Event That Made Einstein an Icon

GUEST POST from Greg Satell

On April 3rd, 1921, a handful of journalists went to interview a relatively unknown scientist named Albert Einstein. When they arrived to meet his ship they found a crowd of thousands waiting for him, screaming with adulation. Surprised at his popularity, and charmed by his genial personality, the story of Einstein’s arrival made the front page in major newspapers.

It was all a bit of a mistake. The people in the crowd weren’t there to see Einstein, but Chaim Weizmann, the popular Zionist leader that Einstein was traveling with. Nevertheless, that’s how Einstein gained his iconic status. In a way, Einstein didn’t get famous because of relativity, relativity got famous because of Einstein.

This, of course, in no way lessens Einstein’s accomplishments, which were considerable. Yet as Albert-László Barabási, another highly accomplished scientist, explains in The Formula, there is a big difference between success and accomplishment. The truth is that success isn’t what you think it is but, with talent, persistence and some luck, anyone can achieve it.

There Is Virtually No Limit To Success, But There Is To Accomplishment

Einstein was, without a doubt, one of the great scientific minds in history. Yet the first half of the 20th century was a golden age for physics, with many great minds. Niels Bohr, Einstein’s sparring partner at the famous Bohr–Einstein debates (which Bohr is widely considered to have won) was at least as prominent. Yet Einstein towers over all of them.

It’s not just physicists, either. Why is it that Einstein has become a household name and not, say, Watson and Crick, who discovered the structure of DNA, an accomplishment at least as important as relativity? Even less known is Paul Erdős, the most prolific mathematician since Euler in the 18th century, who had an outrageous personality to boot?

For that matter, consider Richard Feynman, who is probably the second most famous physicist of the 20th century. He was, by all accounts, a man of great accomplishment and charisma. However, his fame is probably more due to his performance on TV following the Space Shuttle Challenger disaster than for his theory of quantum electrodynamics.

There are many great golfers, but only one Tiger Woods, just as there are many great basketball players, but only one Lebron James. The truth is that individual human accomplishment is bounded, but success isn’t. Tiger Woods can’t possibly hit every shot perfectly any more than Lebron James can score every point. But chances are, both will outshine all others in the public consciousness, which will drive their fame and fortune.

What’s probably most interesting about Einstein’s fame is that it grew substantially even as he ceased to be a productive scientist, long after he had become, as Robert Oppenheimer put it, “a landmark, not a beacon.”

Success Relies On Networks

Let’s try and deconstruct what happened after Einstein’s arrival in the United States. The day after thousands came to greet Weizmann and the reporters mistakenly assumed that they were there for Einstein, he appeared on the front pages of major newspapers like The New York Times and the Washington Post. For many readers, it may have been the first time they had heard of any physicist.

As I noted above, this period was something of a heyday for physics, with the basic principles of quantum mechanics first becoming established, so it was a topic that was increasingly discussed. Few could understand the details, but many remembered the genius with the crazy white hair they saw in the newspaper. When the subject of physics came up, people would discuss Einstein, which spread his name further.

Barabási himself established this principle of preferential attachment in networks, also known as the “rich get richer” phenomenon or the Matthew effect. When a particular node gains more connections than its rivals, it tends to gain future connections at a faster rate. Even a slight change in early performance leads to a major advantage going forward.

In his book, Barabási details how this principle applies to things as diverse as petitions on Change.org, projects on Kickstarter and books on Amazon. It also applies to websites on the Internet, computers in a network and proteins in our bodies. Look at any connected system and you’ll see preferential attachment at work.

Small Groups, Loosely Connected

The civil rights movement will always be associated with Martin Luther King Jr., but he was far from a solitary figure. In fact, he was just one of the Big Six of civil rights. Yet few today speak of the others. The only one besides King still relatively famous today is John Lewis and that’s largely because of his present role as a US congressman.

Each of these men were not solitary figures either, but leaders of their own organizations, such as the NAACP, The National Urban League and CORE and these, in turn, had hundreds of local chapters. It was King’s connection to all of these that made him the historic icon we know today, because it was all of those small groups, loosely connected, that made up the movement.

In my book, Cascades, I explain how many movements fail to bring change about by trying to emulate events like the March on Washington without first building small groups, loosely connected, but united by a shared purpose. It is those, far more than any charismatic personality or inspirational speech, that makes a movement powerful.

It also helps explain something about Einstein’s iconic status. He was on the ship with Weizman not as a physicist, but as a Zionist activist and that dual status connected him to two separate networks of loosely connected small groups, which enhanced his prestige. So it is quite possible, if not probable, that we equate Einstein with genius today and not, say, Bohr, because of his political activity as much as for his scientific talent.

Randomness Rewards Persistence

None of this should be taken to mean that Einstein could have become a legendary icon if he hadn’t made truly landmark discoveries. It was the combination of his prominence in the scientific community with the happy accident of Weizmann’s adoring crowds being mistaken for his own, that made him a historic figure.

Still, we can imagine an alternate universe in which Einstein becomes just as famous. He was, for example, enormously quotable and very politically active. (He was, at one time, offered the presidency in Israel). So it is completely possible that some other event, combined with his very real accomplishments, would have catapulted him to fame. There is always an element of luck and randomness in every success.

Yet Einstein’s story tells us some very important things about what makes a great success. It is not, as many tell us, simply a matter of working hard to achieve something because human performance is, as noted above, bounded. You can be better than others, but not that much better. At the same time, it takes more than just luck. It is a combination of both and we can do much to increase our chances of benefiting from them.

Einstein was incredibly persistent, working for ten years on special relativity and another ten for general relativity. He was also a great connector, always working to collaborate with other scientists as well as political figures like Weizmann and even little girls needing help with their math homework. That’s what allowed him to benefit from loosely connected small groups.

Perhaps most importantly, these principles of persistence and connection are ones that any of us can apply. We might not all be Einsteins, but with a little luck, we just might make it someday.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credit: misterinnovation.com

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Transformation is a Journey Not a Destination

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Transformation is a Journey Not a Destination

GUEST POST from Greg Satell

When Mohandas Gandhi was a young lawyer he was so shy that he couldn’t even bring himself to speak in an open courtroom. He was also impulsive and had a nasty temper. Nelson Mandela started out as an angry nationalist, who argued vigorously about joining forces with other racial groups in a coalition to fight against Apartheid.

Yet as I explain in my book Cascades, both men learned to conquer themselves and evolved into inspirational leaders that achieved transformational change. Movements, as the name implies, must be kinetic to be successful. They need to start in one place and end up somewhere else, evolving and changing along the way.

The same is true for an organization. To create a real impact on the world, you first must drive change internally. That’s not easy and it doesn’t happen all at once, which is why most transformations fail. However, successful leaders understand that to bring true change about it is not enough to simply plan and direct action, you have to inspire and empower belief.

Building A Genome of Values

When Lou Gerstner took over as CEO of IBM in 1993, the company was near bankruptcy. Many thought it was a dinosaur and should be broken up. Yet Gerstner saw that its customers needed it to help them run their mission-critical systems and the death of IBM was the last thing they wanted. He knew that to save the company, he would have transform it and he started with its values.

“At IBM we had lost sight of our values,” Irving Wladawsky-Berger, one of Gerstner’s chief lieutenants, told me. “IBM had always valued competitiveness, but we had started to compete with each other internally rather than working together to beat the competition. Lou put a stop to that and even let go some senior executives who were known for infighting.”

Pushing top executives out the door is never easy. Most are hard working, ambitious and smart, which is how they got to be top executives in the first place. Yet sometimes you have to fire nasty people, even if they outwardly seem like good performers. That’s how you change the culture and build a collaborative workplace.

In doing so, Gerstner led one of the greatest turnarounds in corporate history. By the late 1990s, his company was thriving again and continues to be profitable to this day. That would have never been true if he saw the problem as one of merely strategy and tactics. IBM had to change from the inside first.

Forging Shared Purpose And Shared Consciousness

When General Stanley McChrystal first took over Special Forces in Iraq, he knew he had a magnificently engineered military machine. No force in the world could match their efficiency, expertise and effectiveness. Yet, although they were winning every battle, they were losing the war.

The problem, as he explained in his book, Team of Teams, wasn’t one of capability, but interoperability. His forces would kill or capture Al Qaeda operatives and collect valuable intelligence. Yet it often took weeks for the prisoners to be questioned and the data to be analyzed. By that time, the information was often no longer relevant or actionable.

What McChrystal realized was that if his forces were going to defeat a network, they had to become a network and he set out to build connections within his organization to improve trust and interoperability. He upgraded liaison officer positions to only include the best operators and embedded commandos into intelligence teams and vice versa.

While formal structure and traditional lines of authority stayed very much in place, operating principles changed markedly. The transformation wasn’t immediate, but soon personal relationships and shared purpose replaced archaic customs, procedures and internal rivalries. Even those resistant to change found themselves outnumbered and began to alter their views and behavior.

That allowed McChrystal to also change the way he led. While in traditional organizations information is passed up through the chain of command and decisions are made at the top, McChrystal saw that model could be flipped. Now, he helped information get to the right place and decisions could be made lower down. As a result, operating efficiency increased by a factor of seventeen and soon the terrorists were on the run.

Forging Cultural Awareness

As one of the largest credit bureaus in the world, Experian’s customers depend on it to help determine which customers are good risks and which aren’t. If its standards are too lax, lending organizations lose money from making bad loans. However, the opposite is also true. There are also consequences if it fails to identify good credit risks.

“One of the things that made the US so successful throughout its history is the principle that everybody can participate in the American dream,” Alexander Lintner, Group President at Experian told me. “Yet today, if you don’t have access to credit, it is very hard to live that dream. You can’t buy a house or a new car or do many other things most people want to do.”

“If we rely solely on traditional credit scores about 26 million working age adults are left out of the credit system,” he continued. “That means our clients are missing out on as many as 26 million potential customers. So at Experian, we’ve been working on extended scores based on alternative data, such as rent and utility bills, to help establish a credit history.”

As a fairly recent immigrant to the country, Lintner knows the problems that having a lack of a formal credit history can cause. He credits his company’s efforts to promote cultural awareness programs internally through Employee Resource Groups for driving a passion to solve problems for customers and the public at large, especially related to financial inclusion.

Transformation Starts At Home

Clearly, Experian didn’t start its Employee Resource Groups as a product development strategy, but to improve the lives of its employees. “We strive to make a very diverse group of people feel that Experian is their home,” Lintner says. Nevertheless, Its internal commitment helped create empathy for those who are excluded from the financial system and helped lead to a solution.

Chances are, that won’t end with using alternative data to improve credit scores, but will affect many other facets of its business. To drive a true desire to solve problems, it must be genuine. Much like Gandhi and Mandela, you have to first drive change internally if you hope to create a real impact on the world.

Wladawsky-Berger talks about IBM’s earlier transformation in similar terms. “Because the transformation was about values first and technology second, we were able to continue to embrace those values as the technology and marketplace continued to evolve,” he told me and credits that transformation in values with the company’s continued profitability. While IBM has had its challenges over the years, nobody talks about breaking it up anymore.

What most organizations fail to understand and internalize is that transformation is always a journey, never a destination. There is no immediate return on investment from cultural change. Investors won’t cheer you on for firing top employees who are disruptive or creating Employee Resource Groups. Yet great companies understand that transformation always starts at home.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credit: Pexels

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