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.

Hire for Diversity and Empathy to Drive Innovation

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Hire for Diversity and Empathy to Drive Innovation

GUEST POST from Greg Satell

One of the questions I get asked quite often, both at conferences and when coaching executives, is what type of personality is best suited for innovation so that they can optimize their hiring. Are technical people better than non-technical people? Introverts better than extroverts? Is it better to hire foxes or hedgehogs?

The first thing I tell them is that there has been no definitive research that has found that any specific personality type contributes to innovation. In fact, in my research I have found that there is not even a particular kind of company. If you look at IBM, Google and Amazon, for example, you’ll find that they innovate very differently.

The second thing I point out is that every business needs something different. For example, Steve Jobs once noted that since Apple had always built integrated products, it never learned how to partner as effectively as Microsoft and he wished it would have. So the best approach to hiring for innovation is to seek out those who can best add to the culture you already have.

Foxes vs. Hedgehogs

In Good to Great, author Jim Collins invokes Isaiah Berlin’s famous essay about foxes and hedgehogs to make a point about management. “The fox,” Berlin wrote, “knows many things, but the hedgehog knows one big thing.” Collins then devotes an entire chapter to explaining why hedgehogs perform better than foxes.

Yet as Phil Rosenzweig points out in The Halo Effect, this is a highly questionable conclusion. Even if it were true that the most successful companies focus on one core skill or one core business, that doesn’t mean that focusing on “one big thing” will make you more successful. What it probably means is that by betting on just one thing you increase your chances of both success and failure.

Think about what would have happened it Apple had said, “we’re going to focus just on computers” or if Amazon had focused on just books. There is also evidence, most notably from Philip Tetlock, that foxes outperform hedgehogs on certain tasks, like making judgments about future events.

So the best strategy would probably be to hire a fox if you’re a hedgehog and to hire a hedgehog if you’re a fox. In other words, If you like to drill down and focus on just one thing, make sure you have people around that can help you integrate with other skills and perspectives. If you like to dabble around, make sure you have people who can drill down.

Introverts vs Extroverts

We tend to see leaders as brash and outgoing, but my colleague at Inc, Jessica Stillman points out that introverts can also make great leaders. They tend to be better listeners, are often more focused and are better prepared than social butterflies are. Those are great qualities to look for when adding someone to add to your team.

Still, you wouldn’t want to have an entire company made up of introverts and, in Social Physics, MIT’s Sandy Pentland explains why. Perhaps more than anything else, innovation needs combination. So it’s important to have people who can help you connect to other teams, both internally and externally, bring in new ideas and help take you in new directions.

Consider Amazon, a company that is not only incredibly successful but also highly technically sophisticated. You might expect that it hires a lot of introverted engineers and I’m sure that’s true. Yet the skill it is most focused on is writing, because it understands that to create a successful product, you need to get a lot of diverse people to work together effectively.

So much like with foxes and hedgehogs, if you’re an introvert you should make sure that you have extroverts that can help you connect and if you are an extrovert, make sure you have people who can focus and listen.

Technical vs. Non-Technical People

By all accounts, Steve Jobs was never more than a mediocre engineer, but was clearly a legendary marketer. Nevertheless, he felt strongly that technical people should be in charge. As he once told his biographer, Walter Isaacson, in an interview:

“I have my own theory about why the decline happens at companies like IBM or Microsoft. The company does a great job, innovates and becomes a monopoly or close to it in some field, and then the quality of the product becomes less important. The product starts valuing the great salesmen, because they’re the ones who can move the needle on revenues, not the product engineers and designers. So the salespeople end up running the company.”

Yet the story is not nearly as clear cut as Jobs makes it out to be. When IBM hit hard times it was Lou Gerstner, who spent his formative professional years as a management consultant, that turned it around. Steve Ballmer clearly made missteps as CEO of Microsoft, particularly in mobile, but also made the early investments in cloud technology led to Microsoft’s comeback.

So much like with foxes vs. hedgehogs and introverts vs. extroverts, the choice between technical and non-technical people is a false one. Far more important is how you build a culture in which people of varied skills and perspective can work closely together with a shared sense of purpose.

Today, as we enter a new era of innovation, organizations will need a far more diverse set of skills than ever before and building a collaborative culture will be key to success.

Collaboration Is The New Competitive Advantage

Over the past few decades, the digital revolution has shaped much of our thinking about how we advance a business. Digital technology required a relatively narrow set of skills, so hiring people adept at those skills was a high priority. Yet now, the digital era is ending and we need to rethink old assumptions.

Over the next decade, new computing architectures like quantum and neuromorphic computing will rise to the fore. Other fields, such as genomics and materials science are entering transformative phases. Rather than living in a virtual world, we’ll be using bits to drive atoms in the physical world.

That will change how we need to innovate. As Angel Diaz of IBM told me a few years back, “…we need more than just clever code. We need computer scientists working with cancer scientists, with climate scientists and with experts in many other fields to tackle grand challenges and make large impacts on the world.”

That’s why today collaboration is becoming a real competitive advantage and we need to focus far less on specific skills and “types” and far more on getting people with diverse skills, backgrounds and perspectives to work together effectively.

— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Unsplash

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We Change the World with Ecosystems Not Inventions

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We Change the World with Ecosystems Not Inventions

GUEST POST from Greg Satell

Imagine yourself as the CEO of a Dow component company in 1919. You are fully aware of the technological forces that would shape much of the 20th century, electricity and internal combustion. You may have even be an early adopter of these technologies. Still, everything seems like business as usual.

What you don’t see, however, is that these inventions are merely the start. Secondary technologies, such as home appliances, radio, highways and shopping malls, would reshape the economy in ways that no one could have predicted. Your company has a roughly 50% chance of remaining on the Dow a decade later.

We are at a similar point today. New inventions, such as quantum computing, neuromorphic chips, synthetic biology and advancements in materials science already exist. It is not those inventions, however, but the ecosystems they spawn that will shape the decades to come. We’re all going to have to learn how to compete in a new era of innovation.

A 50-Year Boom In Productivity

By 1919, electricity was already a 40-year old technology. In 1882, just three years after he had almost literally shocked the world with his revolutionary electric light bulb, Thomas Edison opened his Pearl Street Station, the first commercial electrical distribution plant in the United States. By 1884 it was already servicing over 500 homes.

Yet although electricity and electric lighting were already widespread in 1919, they didn’t have a measurable effect on productivity and a paper by the economist Paul David helps explain why. It took time for manufacturers to adapt their factories to electricity and learn to design workflow to leverage the flexibility that the new technology offered. It was the improved workflow, more than the technology itself, that drove productivity forward.

Automobiles saw a similar evolution. It took time for infrastructure, such as roads and gas stations, to be built. Improved logistics reshaped supply chains and factories moved from cities in the north — close to customers — to small towns in the south, where labor and land were cheaper. That improved the economics of manufacturing further.

Yet all of that was just prelude to the massive changes that would come. Electricity spawned secondary innovations, such as household appliances and radios. Improved logistics reshaped the retail industry, shifting it from corner stores to supermarkets and shopping malls. As Robert Gordon explains in The Rise and Fall of American Growth, these changes resulted in a 50-year boom in productivity between 1920 and 1970.

The Digital Revolution

In 1984, Steve Jobs and Apple launched the Macintosh, which heralded a new era of computing. Based on technology developed for the Xerox Alto in the early 1970s, with a bitmapped screen, a graphical user interface and a mouse, it made computing far more accessible to regular consumers.

Before long, personal computers were everywhere. Kids would use them to write term papers and play video games. Lotus 1-2-3 spreadsheet software became a staple for small businesses and entrepreneurs. Desktop publishing helped democratize the flow of information. The computer age had begun in earnest.

Yet much like electricity and internal combustion earlier in the century, the effect on productivity was negligible, causing the Nobel Prize winning economist Robert Solow to quip, “You can see the computer age everywhere but in the productivity statistics.” In fact, it wouldn’t be till the late 90s that we saw a measurable impact from computers.

Once again, it wasn’t any particular invention that made the difference, but an ecosystem that built up over years. The Internet paved the way for open-source software. Hordes of application developers created industry specific tools to automate almost every imaginable business process. Computers converged with phones to create the mobile era.

The 30 Years Rule

Look back at the two major eras of technology in the 20th century and a consistent theme begins to emerge. An initial discovery of a new phenomenon, such as electricity and internal combustion, is eventually used to create a new invention, like the light bulb or the automobile. This creates some excitement, and builds the fortunes of a few entrepreneurs, but has little impact on society as a whole.

Yet slowly, an ecosystem begins to emerge. Roads and gas stations are built. Household appliances and personal computers are invented. Secondary inventions, such as shopping malls, home appliances, the Internet and application software help create new business models. Those business models create new value and drive productivity.

The truth is that innovation is never a single event, but a process of discovery, engineering and transformation. As a general rule of thumb, it takes about 30 years for all of this to take place, because thousands, if not millions of people need to change their behavior, coordinate their activity and start new businesses.

That’s why the future will always surprise us. It is not any one great event that tips the scales, but some hardly noticeable connection that completes the network. Network scientists call this type of thing an ‘instantaneous phase transition’ and there’s really no way to predict exactly when it will happen, but if you learn to look for telltale signs, you can see one coming.

A New Era Of Innovation

Today, we appear to be in a very similar situation to what those executives faced in 1919. We have decoded the human genome. Artificial intelligence has become a reality that everyone, for the most part, accepts. New computing architectures, such as quantum computers and neuromorphic chips, are in late stages of development by a variety of companies.

Yet once again, the impact has been negligible and it’s not hard to see why. While these inventions, in some cases at least, are relatively mature, they have yet to create the ecosystems that can drive a true transformation. Today, however, we can clearly see those ecosystems being created.

In fact, in artificial intelligence we can already see a fairly well developed ecosystem emerging already. In synthetic biology and genomics we can begin to see one as well, although it is still nascent. IBM has created a Q Network of major companies, research labs and startups to support quantum computing.

Here’s what’s important to know: We can’t predict exactly when the system will tip, but it’s a good bet it will happen in the next decade. It is also likely that the impact will be equal to or greater than the 50 year boom that began in the 1920s. Finally, it won’t be driven by any particular invention, but by ecosystems. You need to start figuring out how you will connect.

— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Pixabay

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Four Change Empowerment Myths

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Four Change Empowerment Myths

GUEST POST from Greg Satell

We live in a transformational age. Powerful technologies like the cloud and artificial intelligence are quickly shifting what it means to compete. Social movements like #MeToo and #BlackLivesMatter are exposing decades of misdeeds and rewriting norms. The stresses of modern life are creating new expectations about the relationship between work and home.

Every senior manager and entrepreneur I talk to understands the need to transform their enterprise, yet most are unsure of how to go about it. They ordinarily don’t teach transformation in business school and most management books minimize the challenge by reducing it to silly platitudes like “adapt or die.”

The truth is that change is hard because the status quo always has inertia on its side. Before we can drive a true transformation, we need to unlearn much of what we thought we knew. Change will not happen just because we want it to, nor can it be willed into existence. To make change happen, we first need to overcome the myths that tend to undermine it.

Myth #1: You Have To Start With A Bang

Traditionally, managers launching a new initiative have aimed to start big. They work to gain approval for a sizable budget as a sign of institutional commitment. They recruit high-profile executives, arrange a big “kick-off” meeting and look to move fast, gain scale and generate some quick wins. All of this is designed to create a sense of urgency and inevitability.

That works well for a conventional initiative, but for something that’s truly transformational, it’s a sure path to failure. Starting with a big bang will often provoke fear and resistance among those who don’t see the need for change. As I explain in my book, Cascades, real change always starts with small groups, loosely connected, united by a shared purpose.

That’s why it’s best to start off with a keystone change that represents a concrete and tangible goal, involves multiple stakeholders and paves the way for future change. That’s how you build credibility and momentum. While the impact of that early keystone change might be limited, a small, but successful, initiative can show what’s possible.

For example, when the global data giant Experian sought to transform itself into a cloud-based enterprise, it started with internal API’s that had limited effect on its business. Yet those early achievements spurred on a full digital transformation. In much the same way, when Wyeth Pharmaceuticals began its shift to lean manufacturing, it started with a single process at a single plant. That helped give birth to a 25% reduction of costs across the board.

Myth #2: You Need A Charismatic Leader And A Catchy Slogan

When people think about truly transformational change, a charismatic leader usually comes to mind. In the political sphere, we think of people like Mahatma Gandhi, Martin Luther King Jr. and Nelson Mandela. On the corporate side, legendary CEOs like Lou Gerstner at IBM and Steve Jobs at Apple pulled off dramatic turnarounds and propelled their companies back to prosperity.

Yet many successful transformations don’t have a charismatic leader. Political movements like Pora in Ukraine and Otpor and Serbia didn’t have clear leadership out front. The notably dry Paul O’Neill pulled of a turnaround at Alcoa that was every bit as impressive as the ones at IBM and Apple. And let’s face it, it wasn’t Bill Gates’s Hollywood smile that made Microsoft the most powerful company of its time.

The truth, as General Stanley McChrystal makes clear in his new book, Leaders: Myth and Reality, is that leadership is not so much about great speeches or snappy slogans or even how gracefully someone takes the stage, but how effectively a leader manages a complex ecosystem of relationships and builds a connection with followers.

And even when we look at charismatic leaders a little more closely, we see that it is what they did off stage that made the difference. Gandhi forged alliances between Hindus and Muslims, upper castes and untouchables as well as other facets of Indian society. Mandela did something similar in South Africa. Martin Luther King Jr. was not a solitary figure, but just one of the Big Six of civil rights.

That’s why McChrystal, whom former Defense Secretary Bob Gates called, “perhaps the finest warrior and leader of men in combat I had ever met,” advises that leaders need to be “empathetic crafters of culture.” A leader’s role is not merely to plan and direct action, but to inspire and empower belief.

Myth #3: You Need To Piece Together A Coalition

While managing stakeholders is critical, all too often it devolves into a game theory exercise in which a strategically minded leader horse trades among competing interests until he or she achieves a 51% consensus. That may be enough to push a particular program through, but any success is bound to be short-lived.

The truth is that you can’t transform fundamental behaviors without transforming fundamental beliefs and to do that you need to forge shared values and a shared consciousness. It’s very hard to get people to do what you want if they don’t already want what you want. On the other hand, if everybody shares basic values and overall objectives, it’s much easier to get everybody moving in the same direction.

For example, the LGBT movement foundered for decades by trying to get society to accept their differences. However, when it changed tack and started focusing on common values, such as the right to live in committed, loving relationships and to raise happy, stable families, public opinion changed in record time. The differences just didn’t seem that important any more.

In a similar vein, when Paul O’Neill took over Alcoa in 1987, the company was struggling. So analysts were puzzled that when asked about his strategy he said that “I intend to make Alcoa the safest company in America.” Yet what O’Neill understood was that safety goes part and parcel with operational excellence. By focusing on safety, it was much easier to get the rank and file on board and, when results improved, other stakeholders got on board too.

Myth #4: You Will End With The Vision You Started With

When Nelson Mandela first joined the struggle to end Apartheid, he was a staunch African nationalist. “I was angry at the white man, not at racism,” he would later write. “While I was not prepared to hurl the white man into the sea, I would have been perfectly happy if he climbed aboard his steamships and left the continent of his own volition.”

Yet Mandela would change those views over time and today is remembered and revered as a global citizen. In fact, it was the constraints imposed by the broad-based coalition he forged that helped him to develop empathy, even for his oppressors, and led him to govern wisely once he was in power.

In much the same way, Lou Gerstner could not have predicted that his tenure as CEO at IBM would be remembered for its embrace of the Internet and open software. Yet it was his commitment to his customers that led him there and brought his company back from the brink of bankruptcy to a new era of of prosperity.

And that is probably the most important thing we need to understand change. In order to make a true impact on the world, we first need to change ourselves. Every successful journey begins not with answers, but with questions. You have to learn how to walk the earth and learn things along the way. You know you’ve failed only when you end up where you started.

— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Pixabay

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We Need To Stop Glorifying Failure

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Here’s What To Do Instead

We Need To Stop Glorifying Failure

GUEST POST from Greg Satell

Over 50% of startups fail (and that number goes up to 75% for venture backed startups). The same is true of about three quarters of corporate transformations, which is probably why the average lifespan on the S&P 500 continues to shrink. These statistics tell a humbling story: few significant endeavors ever actually succeed.

So it’s probably not surprising that we’ve come to glorify failure. We are urged to “fail fast” and are cheered on when we do. Failure, after all, is hard evidence that you’ve tried something difficult and paid the price. Yet failure, as anyone who actually experienced it knows well, is a horrible, painful thing.

As I explain in Cascades, great transformations are achieved not by glorifying failure, but when we learn from mistakes and begin to do things differently. That’s how great enterprises are transformed, industries are disrupted and then remade a new and seemingly all powerful tyrants are overthrown. Failure is something we should never accept, but rather overcome.

Ask The Hard Questions

Go to just about any innovation conference and you will find some pundit on the stage telling the story of some corporate giant, usually Blockbuster, Kodak or Xerox, that stumbled and failed. It is then explained that these firms were run by silly, foolish people who simply didn’t want to see the signs of disruption around them.

These stories are almost never true and, in fact, should be seen as ridiculous on their face. It takes no small amount of intelligence, drive and ambition to run a significant enterprise so to suggest that executives managing highly successful businesses were utter dopes beggars belief. The truth is that smart, hard working people fail all the time.

Once you realize that it forces you to ask some hard questions. Why did these smart, successful people fail? Why weren’t the dangers lurking more obvious? What hidden forces were working against them? Why did they think that they actions they undertook, after no small amount of deliberation, were the best of the available options?

Consider the case of Mahatma Gandhi and his Himalayan miscalculation. In 1919, he organized a series of demonstrations to protest against unjust laws passed by the British Raj. These were successful at first, but soon got out of hand and eventually led to the massacre at Amritsar, in which British soldiers left hundreds dead and more than a thousand wounded.

Most people would have simply concluded that the British were far too cruel and brutal to be dealt with peacefully. Gandhi, however, looked for the error in his own actions and learned from his mistakes. A decade later, rather than embark on a wholesale revolt, he identified a keystone change that would break the logjam. Today, both the salt march that resulted, and Gandhi himself, have become icons.

Test Your Hypotheses (Cheaply)

If you want to get a project going in a typical organization, the first thing you do is try to procure a big budget. So you write up an impressive business plan, examine the political tea leaves and work your contacts. If you’re successful, you can build out a great staff, line up tier-one partners and really hit the ground running.

You also can’t make any mistakes. Unless your plan was truly bulletproof from conception (and it never is) or you just get really lucky, you’re going to make some big, well-funded, well-staffed blunder that you’ll have to scramble to recover from. Unless you catch it early or have the political clout within your organization to get more money, you are likely to fail.

Now consider how Nick Swinmurn started his business. As Eric Ries explained in The Lean Startup, instead of spending money on some expensive marketing study to see if people would buy shoes online, he simply built a cheap site. When he got an order, he would go to the store, buy the pair at retail, and ship it out. He lost money on every sale.

That’s a terrible way to run a business, but a great way to test a business hypothesis. Once he knew that people were willing to buy shoes online, he started Zappos, which quickly grew to dominate the market for selling shoes online. It was sold to Amazon in 2009, ten years after Swinmurn started, for $940 million.

Build A Network

We tend to think that success is the result of hard work and talent. Yet look at any category and one brand tends to dominate. There are many search engines, but only one Google, just like there are many smartphone manufacturers, but only one Apple. Both are great products, but they end up taking the vast majority of profits in their industry. Are they really that much better than their competitors?

The truth is, as Albert-László Barabási explains in The Formula, is that performance is bounded, but success isn’t. You can be better than your competitors, but not that much better. On the other hand, there are no limits to success because networks tend to be dominated by a central node.

To understand why, consider the case of Albert Einstein. Until April 3rd, 1921, he was a prominent scientist, but by no means an icon. In fact, much of his press coverage was negative. But on that date, he arrived in America with the Zionist leader Chaim Weizmann. Reporters covering the event mistook the enormous crowds there to meet Weizmann as fans of Einstein and the story made the first page of all major newspapers.

That, along with his brilliance and endearing personality, is what catapulted Einstein to iconic status. In a similar vein, Google launched its product on the techie-dense Stanford computer network and Apple introduced the iPhone to its already expansive fan base. It’s networks, not nodes, that drive success.

Stop Disrupting And Start Solving Problems

Walk down any grocery store aisle and it becomes clear that there is no shortage of ideas. At any given time there are countless opportunities for line extensions, expansions into new categories, partnerships and other things. Executives spend countless hours discussing the merits and demerits of ideas like these.

Yet innovation isn’t about ideas, it’s about solving problems. That’s why most ideas fail, because they don’t address a meaningful problem that people really need solved. Nobody really needs a different flavor of cereal, but Zappos, Google and Apple all met needs that people cared about and that made all the difference.

That’s why companies that last not only look to solve problems for today’s customers, but also take on grand challenges. These are not “bet the company” type of propositions, but long, sustained efforts that seek to fundamentally change the realm of the possible, like Google’s more than decade long quest to create a self-driving car or IBM’s generational pursuit of quantum computing.

The truth is that you never really have to fail because, if you make your efforts sustainable, you can always learn from mistakes and try again. Failure rarely stems from a lack of effort, but is guaranteed by a myopic vision.

— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Unsplash

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Competing Successfully in an Ecosystem World

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Competing Successfully in an Ecosystem World

GUEST POST from Greg Satell

In 1980, a young Harvard Business School professor named Michael Porter published Competitive Strategy, which drove thinking on the subject for the next 30 years. In essence, he argued that you build sustainable competitive advantage by maximizing bargaining power throughout a value chain.

Yet more recently, that kind of single firm level analysis has been called into question and leaders have learned to look more broadly at ecosystems. In fact, a recent report by Accenture Strategy found that because business models are being constantly disrupted, ecosystems have become a “cornerstone” of future growth.”

While value chains are strictly defined by “primary activities” such as “inbound logistics” and “support activities” like technology, ecosystems have mostly been a nebulous term. Clearly that’s not good enough. If we are going to compete in an ecosystem-driven world, we need to understand how they function and how we can leverage them to drive a business forward.

What Is An Ecosystem?

Ecosystem is a term that gets thrown around quite a bit, but people never seem to define what one is. All too often, it alludes to some indescribable ether that surrounds an enterprise. When you can’t define how an action would impact a customer or partner, you simply invoke “the ecosystem” and that’s supposed to make it all make sense.

Yet it’s important that we define terms that have meaning, because if we don’t they just become a catchall for things that we can’t describe. That’s a problem. As Wittgenstein pointed out long ago, if we can’t define something we don’t really understand it and if we don’t understand something we can’t hope to manage it very well.

Ecosystems are best understood as networks of networks and that tells us a lot. In fact, there is a whole science of networks to guide us. What’s most important about networks is that they are driven by links not nodes, so the most important network activity is connection. Networks are dynamic, always evolving, not static.

That’s where focusing on value chains runs into problems. Maximizing bargaining power within a value chain almost compels us to see things as a static, “winner take all” type of challenge in which you play one partner off against another. When you see things as an ecosystem, however, there is clear value in investing in connections and building up the nodes around you to improve your position.

It Is Ecosystems, Not Inventions That Drive The Future

We tend to think of history as a series of “great men” driving events. So electricity conjures up visions of Edison and his light bulb and automobiles remind us of Henry Ford creating the Model T. Yet the truth is that the impact from those inventions didn’t come till decades after those men brought those inventions to life.

In both cases, it was secondary inventions that drove the impact. Electricity allowed businesses to redesign factories to optimize workflow and drive productivity. Home appliances replaced backbreaking work and freed up energy for other tasks. Roads and gas stations revolutionized product distribution and led to the modern retail industry.

Computers followed a similar path. Digital technology had been around for decades when IBM launched the PC in 1981, yet it wouldn’t be till the late 90s that we first started to see an impact on productivity. The truth is that computers don’t do much by themselves. Applications need to be designed and people need to figure out how to put them to good use.

Notice that it’s impossible to point to any one thing that tipped the scale, because what drove impact was an ecosystem of connections between partners, suppliers and customers who needed to learn how to collaborate effectively. That has far less to do with technology than it does with forging meaningful human relationships and it takes time.

Power Today Lies In The Center Of Ecosystems, Rather Than At The Top Of Hierarchies

Traditionally we’ve seen the world as driven by hierarchies. Kings and queens ruled the world through aristocracies that carried out their orders. Corporate CEO’s outlined strategies that underlings would have to execute. Discipline was enforced through a system of punishments and rewards.

In a hierarchy driven world, you progress by climbing your way to the top. So you do your best to drive the performance of those under you to impress those above you. Success is determined by how high you rise. You learn to put great emphasis on signals that you have made it, such as the title on your business card and the size and location of your office.

In an ecosystem driven world, however, power does not lie at the top of hierarchies, but emanates from the center of networks. So an office on the executive floor may, in fact, diminish your ability to shape events if it leads to disconnection. At the same time, being seen as approachable, rather than high status, may enhance your power.

Here’s where Porter’s ideas about value chains can get you into trouble. If you are constantly trying to maximize your bargaining power, you are likely to weaken connections and find yourself at the periphery, rather than at the center, of networks. In an ecosystem driven world, displaying your power can often serve to undermine it.

You Move To The Center By Connecting Out

As I explain in my book Cascades, the best way move to the center of networks is by connecting out. At first, that may seem counterintuitive because it seems simpler to identify a central hub and connect in. Yet those nodes, by definition, already have a lot of links and your connection is less likely to be meaningful.

Once you understand that networks are dynamic and evolving, it becomes clear that a better strategy is to identify emergent nodes and connect to them early on. As the network grows, the center shifts and you are more likely to improve your position. In an ecosystem world, the best strategy is to widen and deepen connections throughout the network.

AnnaLee Saxenian gives an apt description of how this works in Regional Advantage, where she tells the story of how Boston’s “Technology Highway” lost relevance and Silicon Valley moved to the center of the technology universe. The Boston-based companies saw things in terms of value chains and focused on vertical integration to maximize their bargaining power. The Silicon Valley upstarts, on the other hand, saw an ecosystem and thrived on connection.

Today, of course, technology has exponentially increased our ability to make connections. However, what is crucial to understand is that relationships are essentially a very human activity. You don’t build them through gadgets or algorithms, but my investing your most valuable resource — yourself.

— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Pixabay

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Four Things All Leaders Must Know About Digital Transformation

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(But Most Don’t)

Four Things All Leaders Must Know About Digital Transformation

GUEST POST from Greg Satell

Today, technology has become central to how every business competes. Futuristic advancements like artificial intelligence, big data and cloud computing are no longer pie-in-the-sky propositions, but mission critical initiatives that leaders are racing to implement within their organizations.

Unfortunately, most of these initiatives fail. In fact, McKinsey found that fewer than a third of organizational transformations succeed. That’s incredibly sobering. Imagine any other initiative with that type of expected return not only getting consistently funded, but enthusiastically viewed as a smart bet on the future.

Yet digital transformation doesn’t have to be a sucker’s bet. The truth is that digital transformation is human transformation and that’s where you need to start. Initiatives fail because organizations habitually get blinded by the “gee-whiz” aspects of technology, don’t focus on clear business objectives, scale too fast and then declare victory way too early.

1. Focus on People First, Technology Second

At first, digital transformation seems fairly straightforward. There are many capable vendors that can competently implement cloud technology, automation, artificial intelligence applications or whatever else you’re looking for. So, theoretically at least, a standard procurement process should be effective in sourcing and executing a project.

Yet consider how the the nature of work has changed has changed over the last few decades, due to technological shifts. 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 and collaboration has increasingly become a competitive advantage. New technologies such as the cloud and AI will only strengthen and accelerate these trends.

The truth is that value never disappears it just shifts to another place. Consider the case of bank tellers. There are more than twice the number of bank tellers today than there were before ATM machines, but the work they do is vastly different. They are no longer there to execute transactions, but to advise, solve problems and up-sell. That takes very different skills.

So the first step towards a successful digital transformation is not the technology itself, but thinking about how you can empower your people through it. Where do you expect value to shift to? What new skills will your people need to learn in order to succeed? How can technology help them get where they need to be to serve your customers well?

2. Establish Clear Business Outcomes

Another common mistake executives make when implementing new technology is to focus on the capabilities of the technology itself, rather than the business outcome you hope to achieve. Are you trying to drive transactions, improve service and customer experience or something else entirely? You need to determine that before you can even think about a technical approach.

That’s why every transformational effort should involve operational managers, partners and front-line workers from the start. You need also to talk to customers and see what they actually value, rather than what would simply help operations to run smoother. From there, you can begin to develop a vision for how your business can function differently.

For example, when Barry Libenson first arrived at the data giant Experian as Global CIO in 2015, he spent his first few months talking to customers and the business units that served them. Everywhere he went, he found the same thing: what customers valued most was access to real-time data, which his company’s existing infrastructure could not provide.

From there, the path forward was fairly simple, but not easy. He needed to shift his company from a traditional on-site server architecture to the cloud. That took him three years to accomplish, but it transformed Experian’s business, empowered new business models and led to new revenue streams.

3. Identify A Keystone Change

Once the vision is in place, the tendency, all too often, is to embark on what becomes a “five-year death march” to achieve it. In the end, everybody ends up frustrated, angry and, inevitably, it turns out that by the time the vision is achieved, the technology is out of date.

So instead of trying to swallow the entire vision whole, it’s best to start out with a keystone change. Think about a clear and tangible goal you can achieve in the near term that would require the involvement of multiple stakeholders and pave the way for future, more complex initiatives in the future.

One way to do this is to choose a solution that will help people with tedious, mundane tasks rather than create a new capability. It’s much easier to get people excited by reducing the time and effort they have to expend on something they hate then it is to push them to adopt something new. You always want to attract and empower, rather than bribe or coerce.

For example, in Experian’s case, Libenson started out by creating internal API’s rather than building customer facing features. These didn’t create an enormous impact, but they showed what was possible and built momentum for the larger vision.

4. Treat Transformation As A Journey, Not A Destination

Perhaps the most dangerous part of any transformation is when the initial objectives have been achieved. That’s when motivation begins to weaken and complacency sets in. In my book, Cascades, I call this problem surviving victory and it is a crucial element of every transformational effort.

The key to surviving victory is to plan for it from the start. In Experian’s case, the journey was never about the cloud. That was merely a destination. The vision was always to serve customers better and to develop new business models. That’s why Libenson focused not only on implementing technology, but indoctrinating new values and beliefs.

“Having gone through this transformational process over the past three years and seeing concrete business results, we are much better positioned to adopt those technologies,” he told me. “We’ve made the changes in culture, our organizational structure and skills to be able to adopt new technologies quickly, completely and with better collaboration with our customers.”

That emphasis on values is key, because to change fundamental behaviors you first have to change fundamental beliefs and digital transformation is always about empowering action. Keep your eye on that and you will be likely to succeed where most others fail.

— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Pixabay

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We Are Killing Innovation in America

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We Are Killing Innovation in America

GUEST POST from Greg Satell

Throughout America’s history, technological innovation has been key to security and prosperity. Whether it was through entrepreneurs like Thomas Edison, Henry Ford and Thomas Watson, or government programs like the Manhattan Project, the Apollo Program and the Human Genome Project, The United States has been on the cutting edge.

Today, as we enter a new era of innovation, America remains at the forefront of scientific discoveries in advanced areas such as artificial intelligence, synthetic biology, new computing architectures and materials science. Continued investment in science, both public and private, provides the “seed corn” for continued dominance in the 21st century.

Still, scientific advancement is not enough. We need entrepreneurs to start companies and mid-level technicians and engineers to implement technologies. The truth is that America’s human capital is being hollowed out and that’s becoming a serious problem that we need to address. Once we lose our competitive edge, we might never get it back.

1. Food Insecurity

Awhile back I was speaking to a group of community college administrators and I asked them what their biggest challenge was. I was shocked when every single one of them told me that it was food insecurity. Apparently, it is the number one reason that kids drop out. Only about 20% of students at community colleges earn a degree.

I was even more surprised that there are similar trends at four-year institutions. In fact, a study found that about half of all college students struggle with food insecurity. This number becomes even harder to stomach when you consider that there is also an unprecedented construction boom on college campuses.

So colleges are spending billions to build fancy dorms and rec centers while half of their students don’t have enough to eat. Is it any wonder that they are dropping out? In Weapons of Math Destruction, Cathy O’Neil points out that much of university spending is driven by college rankings like those published by US News & World Report. Maybe a “food insecurity index” should be included?

Any way you look at it, we are undermining a significant portion of our most ambitious young people because we can’t provide them with enough to eat. How can we expect to win the future when kids are dropping out of school to get a meal?

2. Tuition And Student Loans

One of the most important factors that led to American technological and economic dominance has been our commitment to higher education. The Morrill Acts in the 19th century created land grant universities that trained students in agriculture and engineering in every state. Later, the G.I. Bill helped an entire generation go to college and became the basis for a new era of prosperity.

This commitment to education made America the most educated country in the world. More recently, however, we’ve fallen to fifth among OECD countries for post-secondary education. This hasn’t been because less Americans are going to college, in fact, more people go to college today than in 2000. It’s just that the rest of the world is moving faster than we are.

A big factor in our decline has been tuition, which has risen from an average of $15,160 in 1988 to 34,740 in 2018. Not surprisingly, student debt is exploding. It has nearly tripled in the last decade. In fact student debt has become so onerous that it now takes about 20 years to pay off four years for college and even more to pursue a graduate degree.

So the bright young people who we don’t starve we are condemning to decades of what is essentially indentured servitude. That’s no way to run an entrepreneurial economy. In fact, a study done by the Federal Reserve Bank of Philadelphia found that student debt has a measurable negative impact on new business creation.

3. A Broken Healthcare System

There has long been a political debate about whether health care is a right or not and there are certainly moral issues that deserve attention. When I travel internationally, it is not uncommon for people to comment on how barbaric they find our healthcare system, where the uninsured die from treatable diseases and many go bankrupt due to medical costs.

Leaving the moral concerns aside though, our healthcare system represents a huge economic burden. Consider that in the US healthcare expenditures account for roughly 18% of GDP. Most countries in the OECD spend roughly half that. To add insult to injury, healthcare outcomes in the US are generally worse than the OECD average. In fact, the CDC reports that life expectancy is actually declining in America.

Think about trying to run a business that not only produces an inferior product, but also gives up 9 points of margin due to higher costs. Clearly that’s untenable. A study in the Journal of Health Economics also found that, much like student debt, concerns about health insurance inhibits entrepreneurship.

It’s important to note that each of these are uniquely American problems. No other developed country has the same issues with healthcare or student debt. While food insecurity is an issue in some developed countries, it is far more severe in the US. All of this represents a significant competitive disadvantage.

There’s Plenty Of People At The Bottom

Far too often, we see innovation as strictly a matter of startup companies and R&D labs. So we invest in science and entrepreneurship programs to fuel technology. Yet while those things are surely important, they don’t drive advancement by themselves. We need normal, everyday people to make the most out of their potential.

As I explained in Mapping Innovation, developing breakthrough technologies is a process of discovery, engineering and transformation. The transformational part is often overlooked, because it relies not on a single entrepreneur or company, but on an ecosystem to support it. That takes networks of firms working together, each forming a piece of the overall puzzle.

Most of these companies are not household names. They supply components, implement solutions, create complementary goods and so on. Many are small businesses. We need not only geniuses to create the future, but also technicians, consultants and service providers.

In 1959 the physicist Richard Feynman gave a famous talk titled There’s Plenty of Room at the Bottom to alert the scientific community to the possibilities of nanotechnology. I think the same can be said of innovation in America today. Our most valuable resource is our human capital. If we can’t feed, educate and nurture that talent, our future will not be bright.

There’s plenty of people at the bottom with almost limitless potential to increase our national capacity for prosperity, security and well being. Yet instead of empowering them, we undermining them and, in doing so, assuring our own decline.

— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Pixabay

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A Quantum Computing Primer

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A Quantum Computing Primer

GUEST POST from Greg Satell

Every once in a while, a technology comes along with so much potential that people can’t seem to stop talking about it. That’s fun and exciting, but it can also be confusing. Not all of the people who opine really know what they’re talking about and, as the cacophony of voices increases to a loud roar, it’s hard to know what to believe.

We’re beginning to hit that point with quantum computing. Listen to some and you imagine that you’ll be strolling down to your local Apple store to pick one up any day now. Others will tell you that these diabolical machines will kill encryption and bring global commerce to a screeching halt. None of this is true.

What is true though is that quantum computing is not only almost unimaginably powerful, it is also completely different than anything we’ve ever seen before. You won’t use a quantum computer to write emails or to play videos, but the technology will significantly impact our lives over the next decade or two. Here’s a basic guide to what you really need to know.

Computing In 3 Dimensions

Quantum computing, as any expert will tell you, uses quantum effects such as superposition and entanglement to compute, unlike digital computers that use strings of ones and zeros. Yet quantum effects are so confusing that the great physicist Richard Feynman once remarked that nobody, even world class experts like him, really understands them.

So instead of quantum effects, think of quantum computing as a machine that works in three dimensions rather than two-dimensions like digital computers. The benefits of this should be obvious, because you can fit a lot more stuff into three dimensions than you can into two, so a quantum computer can handle vastly more complexity than the ones we’re used to.

Another added benefit is that we live in three dimensions, so quantum computers can simulate the systems we deal with every day, like those in materials and biological organisms. Digital computers can do this to some extent, but some information always gets lost translating the data from a three dimensional world to a two dimensional one, which leads to problems.

I want to stress that this isn’t exactly an accurate description of how quantum computers really work, but it’s close enough for you to get the gist of why they are so different and, potentially, so useful.

Coherence And Error Correction

Everybody makes mistakes and the same goes for machines. When you think of all the billions of calculations a computer makes, you can see how even an infinitesimally small error rate can cause a lot of problems. That’s why computers have error correction mechanisms built into their code to catch mistakes and correct them.

With quantum computers the problem is much tougher because they work with subatomic particles and these systems are incredibly difficult to keep stable. That’s why quantum chips need to be kept within a fraction of a degree of absolute zero. At even a sliver above that, the system “decoheres” and we won’t be able to make sense out of anything.

It also leads to another problem. Because quantum computers are so prone to error, we need a whole lot of quantum bits (or qubits) for each qubit that performs a logical function. In fact, with today’s technology, we need more than a thousand physical qubits (the kind that are in a machine) for each qubit that can reliably perform a logical function.

This is why most of the fears of quantum computing killing encryption and destroying the financial system are mostly unfounded. The most advanced quantum computers today only have about 50 qubits, not nearly enough to crack anything. We will probably have machines that strong in a decade or so, but by that time quantum safe encryption should be fairly common.

Building Practical Applications

Because quantum computers are so different, it’s hard to make them efficient for the tasks that we use traditional computers for because they effectively have to translate two-dimensional digital problems into their three-dimensional quantum world. The error correction issues only compound the problem.

There are some problems, however, that they’re ideally suited to. One is to simulate quantum systems, like molecules and biological systems, which can be tremendously valuable for people like chemists, materials scientists and medical researchers. Another promising area is large optimization problems for use in the financial industry and helping manage complex logistics.

Yet the people who understand those problems know little about quantum computing. In most cases, they’ve never seen a quantum computer before and have trouble making sense out of the data they generate. So they will have to spend some years working with quantum scientists to figure it out and then some more years explaining what they’ve learned to engineers who can build products and services.

We tend to think of innovation as if it is a single event. The reality is that it’s a long process of discovery, engineering and transformation. We are already well into the engineering phase of quantum computing—we have reasonably powerful machines that work—but the transformation phase has just begun.

The End Of The Digital Revolution And A New Era Of Innovation

One of the reasons that quantum computing has been generating so much excitement is that Moore’s Law is ending. The digital revolution was driven by our ability to cram more transistors onto a silicon wafer, so once we are not able to do that anymore, a key avenue of advancement will no longer be viable.

So many assume that quantum computing will simply take over where digital computing left off. It will not. As noted above, quantum computers are fundamentally different than the ones we are used to. They use different logic, require different computing languages and algorithmic approaches and are suited to different tasks.

That means the major impacts from quantum computers won’t hit for a decade or more. That’s not at all unusual. For example, although Apple came out with the Macintosh in 1984, it wasn’t until the late 90s that there was a measurable bump in productivity. It takes time for an ecosystem to evolve around a technology and drive a significant impact.

What’s most important to understand, however, is that the quantum era will open up new worlds of possibility, enabling us to manage almost unthinkable complexity and reshape the physical world. We are, in many ways, just getting started.

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

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Sometimes You Should Collaborate Instead of Compete

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Sometimes You Should Collaborate Instead of Compete

GUEST POST from Greg Satell

Boeing and Airbus are arch-rivals, competing vigorously over decades for supremacy in the global aviation market, much like DowDupont and BASF do in chemicals. Yet all of these companies, along with many others, collaborate at places like the Composites Institute (IACMI). They do this not out of any altruism, of course, but self-interest.

It is at places like the Composites Institute that profit-driven companies can explore the future with top notch scientists from places like Oak Ridge National Laboratory, Michigan State University and Purdue as well as dozens of smaller companies active in the space. To not participate would be to risk being cut out of important developments.

This type of activity is not entirely new. In the 80s, semiconductor firms, along with the Department of Defense, created SEMATECH to regain competitiveness against foreign competition, while still fighting it out in the marketplace. The truth is that sometimes you need to collaborate and sometimes you have to compete. Here’s how to know the difference.

The Value Chain and Competitive Advantage

In Michael Porter’s landmark book, Competitive Advantage, the Harvard professor argued that the key to long-term success was to dominate the value chain by maximizing bargaining power among suppliers, customers, new market entrants and substitute goods. The goal was to create a sustainable competitive advantage your rivals couldn’t hope to match.

Porter’s ideas dominated thinking in corporate strategy for decades, yet they had a fatal flaw that wasn’t always obvious. Thinking in terms of value chains is viable when technology is relatively static, but when the marketplace is rapidly evolving it can get you locked out of important ecosystems and greatly diminish your ability to compete.

To understand why, consider open-source software. When Linux first rose to prominence, Microsoft CEO Steve Ballmer called it a cancer. Yet more recently, its current CEO announced that the company loves Linux. That didn’t happen out of any sort of newfound benevolence, but because it recognized that it couldn’t continue to shut itself out and compete.

To thrive in an ecosystem driven world, you must constantly widen and deepen connections. Instead of always looking to maximize bargaining power, you need to look for opportunities to co-create with customers and suppliers, to integrate your products and services with potential substitutes and to form partnerships with new market entrants.

A New Era Of Innovation

The philosopher Martin Heidegger argued that technological advancement is a process of revealing and building. Scientists reveal new phenomena through exploration and experiment and then later engineers figure out how to channel these phenomena to some specific use. For example, the advancements in theoretical physics revealed in the 1920s and 30s were channeled into transistors and microchips later on.

Eventually, the new technology and its implications are understood well enough to support broad adoption and a transformational period ensues. The need for revealing lessens greatly and value shifts towards building rapidly for use. We have seen much of this in the last 30 years as the digital revolution has shifted its emphasis toward skills like rapid prototyping and iteration.

Yet every technology eventually hits theoretical limits and that’s where we are now with respect to digital technology. The fact is that atoms are only so small and the speed of light is only so fast. So that limits how many transistors can fit on a silicon wafer and how fast we can compute by zipping electrons through them. Make no mistake, the future will not be digital.

So we need to embark on a new cycle of revealing and building in areas like quantum computing, synthetic biology and materials science. These things cannot be rapidly prototyped because we simply don’t understand them well enough yet. We need to explore them to reveal and, eventually, to begin building in earnest once again.

Emerging Platforms For Collaboration

Now we can understand why Boeing and Airbus are happy to join organizations like the Composite Institute. Both need to explore and neither can go it alone. They need partners, like research universities, government labs and other firms to help them uncover new things. As open source enthusiasts are fond of saying, “with enough eyeballs, all bugs are shallow.”

Yet the Composites Institute is just one node in the network of Manufacturing Institutes set up under the Obama Administration to support this type of collaboration. In areas ranging from advanced fabrics and biofabrication to additive manufacturing and wide-gap semiconductors, firms large and small are working with scientists to uncover new principles.

To understand how different this is from earlier eras, consider the case of IBM. When it developed the PC, it did so largely in secret with a skunk works the company set up in Florida. With quantum computing, however, it has built up an expansive network of collaborators, including labs, customers and startups.

They don’t do this out of any newfound altruism, but because it significantly speeds up the exploration process. As George Crabtree, Director of JCESR, a consortium of national labs, research universities and private firms developing advanced battery technology, put it to me. “Usually discovery propagates at the speed of publication, but here, we can operate within the time frame of the next coffee break.”

Innovation Is Never A Single Event

All too often, we view innovation as the work of a single genius who, in a moment of sudden epiphany, conjures up an idea that changes the world. In reality, things never work like that. Innovation is never a single event, but a process of discovery, engineering and transformation, which usually takes about 30 years to create a significant impact.

It’s important to note, however, that in no way means it takes 30 years to develop an innovative product. Far from it, in fact. What it means is that the next big thing is usually already about 29 years old! The truth is that the next big thing always starts out looking like nothing at all. That’s why it is crucial to invest in exploration to reveal it.

As we have seen, exploration is best done in numbers. Businesses today, such as in the semiconductor industry, that rely on the principles of quantum mechanics revealed in the 1920s and 30s were in no way harmed by the fact that those discoveries were published openly and taught in universities. In fact, they greatly benefitted from it.

Yet the products built on those principles are highly proprietary and the secrets behind the design of those products are closely guarded. That’s the key to navigating collaboration and competition. You collaborate to reveal, but compete to develop and build. To build a great enterprise, you need to learn to do both zealously.

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

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How A Networked Culture Drives Experian’s Innovation

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How A Networked Culture Drives Experian's Innovation

GUEST POST from Greg Satell

In Who Says Elephants Can’t Dance, the bestselling memoir of the his historic turnaround at IBM, Lou Gerstner wrote, “I came to see, in my time at IBM, culture isn’t just one aspect of the game—It is the game. In the end, an organization is nothing more than the collective capacity of its people to create value.”

There has been endless discussion about whether change should be driven from the top-down or the bottom-up, but that is, for the most part, a red herring. True transformation tends to move side-to-side, driven through horizontal connections among peers. The best way to create change in an organization is to empower it.

That’s why the data giant Experian invested years networking its organization and found that it paid off when it mattered most. While traditional hierarchies waste valuable time and effort pushing orders down the chain of command, networked organizations can adapt to changing market conditions with far more agility. Transformation begins with a networked culture.

An Innovation Culture Is A Collaborative Culture

One of the most common questions I get asked by senior managers is “How can we find more innovative people?” I know the type they have in mind. Someone energetic and dynamic, full of ideas and able to present them powerfully. It seems like everybody these days is looking for an early version of Steve Jobs. Yet the truth is that an innovative culture is a collaborative culture.

When Justin Hastings arrived at Experian North America as Chief Human Resources Officer, he saw it as his job to support and empower the culture. “Essentially, we run a talent business,” he told me. “My job is to not only supply, maintain and retain that talent, but to make sure those people are are motivated and see real meaning and value in their work.”

“Culture is front and center, just incredibly important to us,” he continued. “It’s the enabler of business performance. Our company is all about driving the innovation that drives value for customers. A big part of what makes that possible is that we work hard to make everybody here feel included, that the company’s success is their success.”

However, Hastings warns that building a culture takes a lot more than just some pleasant platitudes in an employee handbook, nice speeches by the CEO and company events. “You can’t just build a culture from the top down. To be authentic, you have to build your culture organically, through informal networks,” he explains.

The Strength of Weak Ties and Boundary Spanners

In the early 1970s, sociologist Mark Granovetter began researching how professional, technical and managerial workers found jobs in the Boston area. He was somewhat surprised to find that they often found work someone they knew, but not a close contact, like a friend or family member, but someone more removed, like a friend of a friend or a distant cousin. He called this principle the ‘Strength of Weak Ties’.

Further analysis shows why it works. Those who are closest to us know pretty much the same things we do, because they frequent similar places and do similar things. So if we want to gain access to new information, we need to broaden our scope and connect with people further out on the social spectrum.

Hastings noticed this principle at work in Experian’s volunteer efforts. For example, many employees participate in its “Le Tour de Experian” bike rides to benefit charity. They do it to do some good and have some fun, but Hastings saw that the bike riders were also building strong bonds across organizational boundaries and these bonds were resulting in professional collaborations that created value for Experian and its customers.

Network scientists call people like the collaborating bike riders boundary spanners, because although they form strong bonds with each other, they essentially play the role of “weak ties” in Granovetter’s research. They perform a crucial function by linking disparate parts of the organization and helping knowledge and information to circulate.

Hastings figured that he could accelerate the formation of boundary spanners throughout Experian by giving employees the opportunity to organize around things they care about. Experian clubs, like the biking group, are focused on interests, while Employee Resource Groups focus on identity, like Latino heritage, gay pride or military service.

Using Networks To Empower Transformation

When Barry Libenson first arrived at Experian as Global CIO in 2015, he devoted the first few months to getting a sense what its customers wanted. It quickly became clear that what they coveted most was real-time access to data. If he could provide that by shifting Experian’s technology infrastructure to the cloud, it could be an enormous opportunity.

Yet it could also be an enormous problem. “There was a lot of concern that we were going to disrupt our own business and that we would lose control of our data,” Libenson told me. “For years, Experian’s business model had been based on a traditional architecture. There were also security concerns.” To make matters worse, research by McKinsey indicates that roughly 75% of transformational initiatives fail.

So instead of trying to force change through, Libenson sought to empower it. Much like Hastings did with Clubs and Employees Resource Groups, he identified people within the organization that were already enthusiastic about the shift to cloud technology and made sure they were trained to implement it. Those early apostles could then help convert others.

Libenson also saw how Experian’s networked culture helped smooth the way. “Digital transformation is somewhat of a misnomer. You’re not really transforming the technology, but more importantly, the people who use it. Having a networked culture means that you can spread enthusiasm about transformation—as well as the expertise to implement it—much faster and with far less resistance than you could otherwise,” he says.

The Journey Continues

As I explain in Cascades, all too often an initial success gives way to inertia, backsliding and eventually, failure. The truth is, it’s not enough to just drive change, you also need to learn how to survive victory. You do that by focusing on culture and values, rather than on any one particular objective, so that you are constantly preparing for the next challenge.

These days, Experian is highly focused on leveraging artificial intelligence which, much like the shift to the cloud, is both a great opportunity and a potential problem. AI has the potential to vastly improve things like credit scores, but the algorithms can’t be a “black box.” To be effective, they must be auditable and explainable.

Experian’s Datalabs unit is hard at work creating more transparent AI algorithms, and making progress, but the technology will only be valuable if Datalabs scientists can work effectively with professionals from other divisions of the company. So Eric Haller, who leads Datalabs, set up a series of seminars to connect with the rest of the company.

“To implement this technology requires a certain amount of sophistication that is relatively rare,” he told me. “So not only were we putting information out there, through the connections we made we were also able to identify expertise throughout our company we were not aware of. Those new relationships have already opened up new possibilities for collaboration.”

What’s interesting and salient about how the network culture was built at Experian is how it all seems so mundane. Many firms have clubs, employee groups and volunteer efforts. Seminars aren’t particularly unusual, either. Yet it’s not any one program or platform, but how those initiatives are optimized to widen and deepen informal bonds across the organization, that makes the difference.

The truth is that, today, competitiveness is no longer determined by the sum of all efficiencies within a business, but the sum of all connections.

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

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