The 5 types of mentors you need in your life 09-24

Here’s how to assemble your personal dream team, with tips from business expert Anthony Tjan.

Everyone can use a mentor. Scratch that — as it turns out, we could all use five mentors. “The best mentors can help us define and express our inner calling,” says Anthony Tjan, CEO of Boston venture capital firm Cue Ball Group and author of Good People. “But rarely can one person give you everything you need to grow.”

In this short list, Tjan has identified the five kinds of people you should have in your corner. You probably already know them — and it’s possible for one person to cover two or more categories — so use this list as both a guide and a nudge to deepen your bond with them.

One reminder from Tjan: Mentorship is a two-way street — a relationship between humans — and not a transaction. So don’t just march up to people and ask them to advise you. Take the time to develop genuine connections with those you admire, and assist them whenever you can.

Mentor #1: The master of craft

“If you know you want to be the best in your field — whether it’s the greatest editor, football quarterback, entrepreneur — ask, Who are the most iconic figures in that area?” says Tjan. This person can function as your personal Jedi master, someone who’s accumulated their wisdom through years of experience and who can provide insight into your industry and fine-tuning your skills. Turn to this person when you need advice about launching a new initiative or brainstorming where you should work next. “They should help you identify, realize and hone your strengths towards the closest state of perfection as possible,” he says.

Mentor #2: The champion of your cause

This mentor is someone who will talk you up to others, and it’s important to have one of these in your current workplace, says Tjan: “These are people who are advocates and who have your back.” But they’re more than just boosters — often, they can be connectors too, introducing you to useful people in your industry.

Mentor #3: The copilot

Another name for this type: Your best work bud. The copilot is the colleague who can talk you through projects, advise you in navigating the personalities at your company, and listen to you vent over coffee. This kind of mentoring relationship is best when it’s close to equally reciprocal. As Tjan puts it, “you are peers committed to supporting each other, collaborating with each other, and holding each other accountable. And when you have a copilot, both the quality of your work and your engagement level improve.”

Mentor #4: The anchor

This person doesn’t have to work in your industry — in fact, it could be a friend or family member. While your champion supports you to achieve specific career goals, your anchor is a confidante and a sounding board. “We’re all going to hit speed bumps and go through uncertainty in life,” says Tjan. “So we need someone who can give us a psychological lift and help us see light through the cracks during challenging times.” Because the anchor is keeping your overall best interests in mind, they can be particularly insightful when it comes to setting priorities, achieving work-life balance, and not losing sight of your values.

Mentor #5: The reverse mentor

“When we say the word ‘mentor,’ we often conjure up the image of an older person or teacher,” says Tjan. “But I think the counterpoint is as important.” Pay attention to learning from the people you’re mentoring, even though they may have fewer years in the workplace than you. Speaking from his own experience, Tjan says, “Talking to my mentees gives me the opportunity to collect feedback on my leadership style, engage with the younger generation, and keep my perspectives fresh and relevant.”

Will Human Innovation Save Us From Future Extinction? 10-15

Does the human ability to innovate suggest an immunity to total extinction?

Yes and no. Currently, innovation reduces our chance of extinction in some ways, and increases it in others. But if we innovate cleverly, we could become just about immune to extinction.

The species that survive mass extinctions tend to share three characteristics.They're widespread. This means local disasters don't wipe out the entire species, and some small areas, called refugia, tend to be unaffected by global disasters. If you're widespread, it's more likely that you have a population that happens to live in a refugium. 

They're ecological generalists. They can cope with widely varying physical conditions, and they're not fussy about food.

They're r-selected. This means that they breed fast and have short generation times, which allows them to rapidly grow their populations and adapt genetically to new conditions.

Innovation gives humans the ability to be widespread ecological generalists. With technology, we can live in more diverse conditions and places than any other species. And while we can't (currently) grow our populations rapidly like an r-selected species, innovation does allow us to adapt quickly at the cultural level.

Technology also increases our connections to one another and connectivity is a two-edged sword. Many species consist of a network of small, local populations, each of which is somewhat isolated from the others. We call this a metapopulation. The local populations often go extinct, but they are later re-seeded by others, so the metapopulation as a whole survives. 

Humans used to be a metapopulation, but thanks to innovation, we're now globally connected. Archaeologists believe that many past civilizations, such as the Easter Islanders, fell because of unsustainable ecological and cultural innovations. The impact of these disasters was limited because these civilizations were small and disconnected from other such civilizations.

These days, a useful innovation can spread around the world in weeks. So can a lethal one. With many of the technologies and chemicals we're currently inventing, we can't be certain about their long-term effects; human biology is complex enough that we often can't be absolutely certain something won't kill us in a decade until we've waited a decade to see. We try to be careful and test things before they're released, and the probability that any particular invention could kill us all is tiny, but since we're constantly innovating, it's a real possibility.

Pandemics pose the same problem for a well-connected species. There are certain possibilities where species extinction is really hard to avoid; fortunately, they're also very unlikely, but we are definitely not immune from this.

The most likely cause of our extinction, in my opinion, is innovation in machine learning/AI. This could destroy the planet, but even if it doesn't, humans will be ultimately redundant to the dominant systems. They might keep us alive in a zoo somewhere, but I doubt it. A happier scenario (to me at least) is transhumanism, where humans become extinct in a sense because we've managed to liberate ourselves from biology.

So how could innovation prevent our extinction? We seed the galaxy with independently evolving human populations to create a new metapopulation. These local populations would hopefully be sufficiently isolated that some would survive an innovation or disaster that wipes out the rest. They would, of course, evolve in response to local conditions, perhaps creating several new species. So you could say this is still extinction, but it's as close as we'll come to persistence in our ever-changing universe. 

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Business leaders may be overconfident in their ability to respond to disruption 06-30

Disruptive change is accelerating, driven by new technologies and rising competition from both traditional and nontraditional players. As a result, Innosight forecasts that half of companies in the S&P 500 will be replaced over the coming decade, due to loss of market value or acquisitions.

What are leaders doing about these challenges? And how do they feel about their ability to prepare for and manage them? In May 2017, we surveyed more than 300 executives at major corporations with revenues of $2 billion and higher about their current attitudes, experiences, and responses to marketplace disruption. The results tell a mixed story. Key findings include:

• Business leaders may be overconfident in their ability to respond to disruption. 80% of respondents say they recognize they need to transform, and 82% expressed degrees of confidence that their company is prepared to change in response to disruptive trends. Yet there are multiple warning signs in the data—including perceptions of competition and disruption—that suggest they may be in a “confidence bubble.”

• Broad understanding about what they need to do to respond to disruption. Executives see the need for a two-pronged approach to the future. They say they are likely to both transform their core business while also investing in new growth businesses. They also see the need to expand within existing markets and enter into new markets.

• Talent and leadership are concerns. Executives say finding and retaining the right talent is considered the biggest obstacle to transformation. Moreover, 81% say that top management is not open to new ideas.

• Digital and AI may be blind spots. Despite artificial intelligence and digital technologies rapidly transforming markets around the world, executives are downplaying the threat these forces will play to their own businesses. 

“One could say that the worrying thing here is that executives aren’t more worried,” says Scott Anthony, managing partner at Innosight. “The pace of change continues, and digitalization is accelerating, so leaders should be investing more, expecting to reconfigure their organizations and more, and should be paying closer attention to new product ideas and new growth ventures”

Main Finding: The Confidence Bubble

Perhaps the  most striking finding  is the  disconnect between confidence levels  and  specific per- ceptions of threats and  competition. 82% of respondents expressed degrees of confidence that their company is prepared to change in response to disruptive trends. Yet their actual activities fall short of what is required to justify  their confidence. There are multiple warning signs in the data that suggest they have strategy and  organizational blind spots that may undermine their ability to adapt.

• Underestimating new  sources of competition. When asked about the  sources of future competition, fully two-thirds of respondents (67%) think  it will be from “mostly existing” competition while fewer than one-in-four (23%) think  their companies will be facing “mostly new” sources of competition. In a related question, 55% expect competition to come mostly from  within their existing industries, with just 10% saying competition will come from new industries.                                                      
• New  thinking gets short shrift. 81% say new growth products and  ideas sometimes or often do not  get enough attention from  top management.                                                                                                              
• Keeping up, but not  ahead. When it comes to keeping up with the  pace of change, only
7% report their companies are  moving much faster than the  overall market, with only
24% saying somewhat faster.

Modest investment in digital. Digital business models and platforms are  disrupting industry after industry, but  53% of respondents said that they plan either no increase in digital investment or a less than 25% increase.                                                                                                                                              
• Underestimating new  technology. Despite rapid advances in the  emerging technology of artificial intelligence, fully 65% of executives said AI is not  too threatening or not  at all a threat to their business.

“One could say that the  worrying thing here is that executives aren’t more worried,” said Innosight managing partner Scott D. Anthony. “The pace of change continues, and  digitalization is accelerating, so leaders should be investing more, expecting to reconfigure their organizations more, and  should be paying closer attention to new product ideas and  new growth ventures.”


Reproduced from Innosight Research.

The Democratization of Machine Learning: What It Means for Tech Innovation 04-15

The world of high-tech innovation can change the destiny of industries seemingly overnight. Now we are on the cusp of a new grand leap thanks to the democratization of machine learning, a form of artificial intelligence that enables computers to learn without being explicitly programmed. This process of democratization is already underway.

                                     Image credit : Shyam's Imagination Library

Last month, at the CloudNext conference in San Francisco, Google announced its acquisition of Kaggle, an online community for data scientists and machine-learning competitions. Although the move may seem far removed from Google’s core businesses, it speaks to the skyrocketing industry interest in machine learning (ML). Kaggle not only gives Google access to a talented community of data scientists, but also one of the largest repositories of datasets that will help train the next generation of machine-learning algorithms.

As ML algorithms solve bigger and more complex problems, such as language translation and image understanding, training them can require massive amounts of pre-labeled data. To increase access to such data, Google had previously released a labeled dataset created from more than 7 million YouTube videos as part of their YouTube-8M challenge on Kaggle. The acquisition of Kaggle is an interesting next step.

1. Highly scalable computing platforms
2. Even if specialized processors were available, not every company has the capital and skills needed to manage a large-scale computing platform needed to run advanced machine learning on a routine basis. This is where public cloud services such as Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure and others come in. These services offer developers a scalable infrastructure optimized for ML on rent and at a fraction of the cost of setting up on their own.
3. Open-source, deep-learning software frameworks

A major issue in the wide-scale adoption of machine learning is that there are many different software frameworks out there. Big companies are open sourcing their core ML frameworks and trying to push for some standardization. Just as the cost of developing mobile apps fell dramatically as iOS and Android emerged as the two dominant ecosystems, so too will machine learning become more accessible as tools and platforms standardize around a few frameworks. Some of the notable open source frameworks include Google’s TensorFlow, Amazon’s MXNet and Facebook’s Torch.

4. Developer-friendly tools

The final step to democratization of machine learning will be the development of simple drag-and-drop frameworks accessible to those without doctorate degrees or deep data science training. Microsoft Azure ML Studio offers access to many sophisticated ML models through a simple graphical UI. Amazon and Google have rolled out similar software on their cloud platforms as well.

5. Marketplaces for ML algorithms and datasets

Not only do we have an on-demand infrastructure needed to build and run ML algorithms, we even have marketplaces for the algorithms themselves. Need an algorithm for face recognition in images or to add color to black and white photographs? Marketplaces like Algorithmia let you download the algorithm of choice. Further, websites like Kaggle provide the massive datasets one needs to further train these algorithms.

“The final step to democratization of machine learning will be the development of simple drag-and-drop frameworks accessible.”

All of these changes mean that the world of machine learning is no longer restricted to university labs and corporate research centers that have access to massive training data and computing infrastructure.

What are the implications?

Back in the mid- and late-1990s, web development was done by specialists and was accessible only to firms with ample resources. Now, with simple tools like WordPress, Medium and Shopify, any lay person can have a presence on the web. The democratization of machine learning will have a similar impact of lowering entry barriers for individuals and startups.

Further, the emerging ecosystem, consisting of marketplaces for data, algorithms and computing infrastructure, will also make it easier for developers to pick up ML skills. The net result will be lower costs to train and hire talent. We think that the above two factors will be particularly powerful in vertical (industry-specific) use cases such as weather forecasting, healthcare/disease diagnostics, drug discovery and financial risk assessment that have been traditionally cost prohibitive.

Just like cloud computing ushered in the current explosion in startups, the ongoing build-out of machine learning platforms will likely power the next generation of consumer and business tools. The PC platform gave us access to productivity applications like Word and Excel and eventually to web applications like search and social networking. The mobile platform gave us messaging applications and location-based services. The ongoing democratization of ML will likely give us an amazing array of intelligent software and devices powering our world.

Highly scalable computing platforms

Even if specialized processors were available, not every company has the capital and skills needed to manage a large-scale computing platform needed to run advanced machine learning on a routine basis. This is where public cloud services such as Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure and others come in. These services offer developers a scalable infrastructure optimized for ML on rent and at a fraction of the cost of setting up on their own.
Open-source, deep-learning software frameworks

A major issue in the wide-scale adoption of machine learning is that there are many different software frameworks out there. Big companies are open sourcing their core ML frameworks and trying to push for some standardization. Just as the cost of developing mobile apps fell dramatically as iOS and Android emerged as the two dominant ecosystems, so too will machine learning become more accessible as tools and platforms standardize around a few frameworks. Some of the notable open source frameworks include Google’s TensorFlow, Amazon’s MXNet and Facebook’s Torch.
Developer-friendly tools.

The final step to democratization of machine learning will be the development of simple drag-and-drop frameworks accessible to those without doctorate degrees or deep data science training. Microsoft Azure ML Studio offers access to many sophisticated ML models through a simple graphical UI. Amazon and Google have rolled out similar software on their cloud platforms as well.
Marketplaces for ML algorithms and datasets.

Not only do we have an on-demand infrastructure needed to build and run ML algorithms, we even have marketplaces for the algorithms themselves. Need an algorithm for face recognition in images or to add color to black and white photographs? Marketplaces like Algorithmia let you download the algorithm of choice. Further, websites like Kaggle provide the massive datasets one needs to further train these algorithms.

“The final step to democratization of machine learning will be the development of simple drag-and-drop frameworks accessible.”

All of these changes mean that the world of machine learning is no longer restricted to university labs and corporate research centers that have access to massive training data and computing infrastructure.
What are the implications?

Back in the mid- and late-1990s, web development was done by specialists and was accessible only to firms with ample resources. Now, with simple tools like WordPress, Medium and Shopify, any lay person can have a presence on the web. The democratization of machine learning will have a similar impact of lowering entry barriers for individuals and startups.

Further, the emerging ecosystem, consisting of marketplaces for data, algorithms and computing infrastructure, will also make it easier for developers to pick up ML skills. The net result will be lower costs to train and hire talent. We think that the above two factors will be particularly powerful in vertical (industry-specific) use cases such as weather forecasting, healthcare/disease diagnostics, drug discovery and financial risk assessment that have been traditionally cost prohibitive.

Just like cloud computing ushered in the current explosion in startups, the ongoing build-out of machine learning platforms will likely power the next generation of consumer and business tools. The PC platform gave us access to productivity applications like Word and Excel and eventually to web applications like search and social networking. The mobile platform gave us messaging applications and location-based services. The ongoing democratization of ML will likely give us an amazing array of intelligent software and devices powering our world.

Market-based access to data and algorithms will lower entry barriers and lead to an explosion in new applications of AI. As recently as 2015, only large companies like Google, Amazon and Apple had access to the massive data and computing resources needed to train and launch sophisticated AI algorithms. Small startups and individuals simply didn’t have access and were effectively blocked out of the market. That changes now. The democratization of ML gives individuals and startups a chance to get their ideas off the ground and prove their concepts before raising the funds needed to scale.
But access to data is only one way in which ML is being democratized. There is an effort underway to standardize and improve access across all layers of the machine learning stack, including specialized chipsets, scalable computing platforms, software frameworks, tools and ML algorithms.

“Just like cloud computing ushered in the current explosion in startups … machine learning platforms will likely power the next generation of consumer and business tools.”

1. Specialized chipsets

Complex machine-learning algorithms require an incredible amount of computing power, both to train models and implement them in real time. Rather than using general-purpose processors that can handle all kinds of tasks, the focus has shifted towards building specialized hardware that is custom built for ML tasks. With Google’s Tensor Processing Unit (TPU) and NVIDIA’s DGX-1, we now have powerful hardware built specifically for machine learning.

Reproduced from Knowledge@Wharton

Harnessing the Secret Structure of Innovation 03-26

Sustained innovation success is not the result of artful intuition or heroic vision but of a deliberate search using key information signals.

In an era of low growth, companies need innovation more than ever. Leaders can draw on a large body of theory and precedent in pursuit of innovation, ranging from advice on choosing the right spaces to optimizing the product development process to establishing a culture of creativity.1 In practice, though, innovation remains more of an art than a science.

But it doesn’t need to be.

In our research with the London Institute, we made an exciting discovery.2 Innovation, much like marketing and human resources, can be made less reliant on artful intuition by using information in new ways. But this requires a change in perspective: We need to view innovation not as the product of luck or extraordinary vision but as the result of a deliberate search process. This process exploits the underlying structure of successful innovation to identify key information signals, which in turn can be harnessed to construct an advantaged innovation strategy.

Innovation in Legoland

Let’s illustrate the idea using Lego bricks. Think back to your childhood days. You’re in a room with two of your friends, playing with a big box of Legos (say, the beloved “fire station” set). All three of you have the same goal in mind: building as many new toys as possible. As you play, each of you searches through the box and chooses the bricks you believe will help you reach this goal.

Let’s now suppose each of you approaches this differently. Your friend Joey uses what we call an impatient strategy, carefully picking Lego men and their firefighting hats to immediately produce viable toys. You follow your intuition, picking random bricks that look intriguing. Meanwhile, your friend Jill chooses pieces such as axles, wheels, and small base plates that she noticed are common in more complex toys, even though she is not able to use them immediately to produce simpler toys. We call Jill’s approach a patient strategy.

At the end of the afternoon, who will have innovated the most?3 That is, who will have built the most new toys? Our simulations show that this depends on several factors. In the beginning, Joey will lead the way, surging ahead with his impatient strategy. But as the game progresses, fate will appear to shift. Jill’s early moves will begin to seem serendipitous when she’s able to assemble complex fire trucks from her choice of initially useless axles and wheels. It will appear that she was lucky, but we will soon see that she effectively harnessed serendipity.

What about you? Picking components without using any information, you will have built the fewest toys. Your friends had an information-enabled strategy, while you relied only on intuition and chance. 

What can we learn from this? If innovation is a search process, then your component choices today matter greatly in terms of the options they will open up to you tomorrow. Do you pick components that quickly form simple products and give you a return now, or do you choose the components that give you a higher future option value?

We analyzed the mathematics of innovation as a search process for viable product designs (toys) across a universe of components (bricks). We then tested our insights using historical data on innovations in four real environments and made a surprising discovery. You can have an advantaged innovation strategy by using information about the unfolding process of innovation. But there isn’t one superior strategy. The optimal strategy is both time-dependent (as in the Lego game) and space/sector-dependent — Lego is just one of many innovation spaces, each of which has its own characteristics. In innovation, as in business strategy, winning strategies depend on context.

The exhibit below, "Information-Enabled Innovation Strategies Outperform," demonstrates three crucial insights. First, information-enabled strategies outperform strategies that do not use the information generated by the search process. Second, in an earlier phase of the game, an impatient strategy outperforms; in later stages, a patient strategy does. Critically, third, it is possible to have an adaptive strategy, one that changes as the game unfolds and that outperforms in all phases of the game. Developing an adaptive strategy requires you to know when to switch from Joey’s approach to Jill’s. The switching point is knowable and occurs when the complexity of products (the number of unique Lego bricks in each toy) starts to level off. 

Applying the Insight

How can companies harness these insights in practice? To answer this question, we ran simulations based on detailed historical data for a range of datasets, from culinary arts and music to language and software technologies such as those used by Uber, Instagram, and Dropbox. From our findings, we distilled a five-step process for constructing an information-advantaged innovation strategy.

Step 1. Choose your space: Where to play?

The features of your innovation space matter, so it’s important to make a deliberate choice about where you want to compete. Interestingly, it’s not enough to analyze markets or anticipate customers’ needs. To innovate successfully, you also need to understand the structure of your innovation space.
Start by taking a snapshot of key competing products and their components. How complex are the products, and do you have access to the components? As a rule of thumb, choose spaces where product complexity is still low and where you have access to the most prevalent components.


By focusing on immature spaces, you can get ahead of competitors by first employing a rapid-yield, impatient strategy and then later switching to a more patient strategy with delayed rewards. Uber International CV provides a good example. The company entered the embryonic peer-to-peer ride-sharing space three years after it was founded in 2009 as a limousine commissioning company. Uber chose its space wisely: The ride-sharing industry was immature, product complexity was low, and the necessary components were easily accessible. The impatient strategy was to get to market quickly with a ride-sharing app. As we are learning, there is also now what appears to be a patient strategy at work at Uber — self-driving technology with a much higher level of complexity and a much longer period of gestation.

Reproduced from MITSLOAN Management Review

How to Regulate Innovation — Without Killing It 02-11

Image credit : Shyam's Imagination Library

View enlarged image

Digital innovation is giving rise to new business models. Uber and Airbnb are household names today, when not so long ago we were all learning about the sharing economy. The regulations don’t always evolve as quickly as technological change — at least that’s the perception. So what should policy makers and regulators do? Wharton legal studies and business ethics professor Kevin Werbach, who wrote a policy brief about the topic for the Penn Wharton Public Policy Initiative, recently shared his insights into that question with Knowledge@Wharton.

An edited transcript of the conversation appears below.  

Knowledge@Wharton: In your article, you mentioned something called the Internet of the World. Can you tell us what that is?

Kevin Werbach: There’s something big going on, and it’s a bigger trend than most people realize. There are three trends, and each in and of themselves is significant. One is what we often call the sharing economy — it’s really more the on-demand economy. It’s not just about sharing resources, but services like you mentioned, Uber and Airbnb, which give on-demand access to resources. The second piece is the Internet of Things — all kinds of devices, billions of devices getting networked. And the third is big data and analytics — the ability to understand and manipulate trends coming out of all those devices.

What those three things together mean is that all of the world, potentially, is networked. It’s not just that you go somewhere to a computer or you go to your phone to get access to information. It’s that potentially everything is a generator of data, and all that data can be integrated and analyzed and processed and manipulated. What that means is the kinds of trends and the kinds of developments that we saw online are now happening offline. They’re happening to things and physical objects in the world, as well.

Knowledge@Wharton: You point out that the scale of on-demand services is potentially much greater than the legacy industries they challenge. How so?

Werbach: There’s this kind of cheap talk about new technologies disrupting old technologies. And actually, the theory of disruptive innovation — which goes back to Clayton Christensen and Harvard Business School — is a serious academic theory, but far too often people in business and entrepreneurship and in the media use the word “disruption” as just kind of a synonym for new technology. And the reality is, it’s not that you have one market, and suddenly a bunch of new companies come in a replace that market.

“I’m arguing for an openness and a recognition that ‘regulation’ isn’t a dirty word.”

Often what happens — and this is what we’re seeing with things like the on-demand economy — is that the new markets are different. So it’s not that Uber takes the taxi market and every taxi gets replaced by an Uber driver. In fact, Uber has put out some numbers for the past several years that show that the scale of the market they’re tapping into is actually much bigger.

What that means is, [the existence of on-demand services firms] is not just a competitive threat — and certainly it is a competitive threat to the incumbent industries — but it’s creating something new. It’s unlocking latent demand that the previous approaches didn’t reach.

Knowledge@Wharton: You also pointed out that throughout the different technological waves since the 1990s — we went through ecommerce, social media, now mobile– regulations have always been seen as an enemy of innovation. But you say that this digital dichotomy is actually misunderstood. Can you explain that?

Werbach: There’s two pieces to it. One is the term that you referenced that I use in the paper — the digital dichotomy. That is a misunderstanding that the online world is inherently different from the offline world. The reason that’s not true is what I said at the beginning. Increasingly, there is no difference, even if you’re using a physical thing.

So take the Uber example — and it’s such a perfect example. [It’s] a physical person driving a physical car, but from your standpoint running the app and pushing a button and saying, “Make a car appear” — it’s as though that’s something that’s in cyberspace. It’s as though it’s something digital. It’s an extension of the software infrastructure of Uber, even though it’s a physical thing, a physical person driving a physical car.

We tend to assume that there is one set of rules for the real world, and there’s one set of rules for the digital world, and that’s a mistake because, increasingly, there is just the world. Software technology, networks, all these trends, and what I call the Internet of the World are affecting everything. So that’s the first piece: the assumption that we can just ignore the rules of the physical world because we need totally new rules for the digital world.

The larger issue, though, is this question of innovation and regulation. And again, there’s this common assumption that innovation needs to thrive with no regulation, and any time government gets involved, that’s a check and a drain and a block on innovation — and that’s not really the case.

What I talk about in the article you referenced and the larger law review article it’s based on, is that if you go and look at the history of how the internet developed, how electronic commerce developed in the 1990s, a surprising amount of the time it was government action actually facilitating innovation, and the emerging startups actually pushing for that government intervention to help create a more innovative marketplace.

Knowledge@Wharton: That’s an interesting point, and in your article you also pointed to one challenge for regulators, and that is a lot of these new startups don’t really fit neatly into industry categories. The example you use is Uber versus Skype. Can you go through that example?

Werbach: I should be clear. It’s not that regulators always get it right. They make mistakes, and they have lots of flaws and lots of reasons why they act in a certain way, and we should definitely criticize bad regulations. But we just shouldn’t assume necessarily that they are bad, and necessarily what startups do is good.

The Skype and Uber comparison is basically that both of them were companies that when they started were illegal in most jurisdictions. Skype — the very popular internet communications service, originally voice calling, now also video and messaging and so forth, [and] now owned by Microsoft — was illegal in most of the world when it launched, because there were rules saying you could not do a communications service, a telephone service, outside of the existing regulatory infrastructure.

In the U.S., because of what we did — I was at the Federal Communications Commission in the 1990s, when we had to think about voice over IP (Internet Protocol) — we very deliberately left open the door. Even though things like Skype were outside of the regulatory structure, we made a conscious decision to allow them to develop. And that’s an example of regulators consciously deciding not to impose a whole set of rules early on — when these were nascent technologies — allowing them to grow.

Uber is similar. Uber is illegal in most of the cities where it operates. And the story of Skype, I think, is a hopeful story. What happened with Skype is that first of all, you had regulators like the FCC in the U.S. that understood these new internet calling technologies were … a way to lower prices and create better service, and [provide] new services and innovation, and so that we shouldn’t rush to impose all the traditional rules on them. And as these companies grew, they were able to work with regulators to address the rules that were necessary.

Knowledge@Wharton: You believe that government can actually be a positive force in innovative markets. Can you give us more examples of that?

Werbach: We saw a lot of examples with the growth of the internet and electronic commerce, starting 20 years ago. One of them was the antitrust case against Microsoft. Microsoft was the dominant company in the personal computer [market] and in the operating system market, and lots of start-up companies — like Netscape — realized they wanted to innovate, they wanted to build the internet economy as we know it today. You couldn’t have Microsoft standing there, using its power at the time.

It’s hard to realize today, with what’s happened — the growth of Apple and the growth of smartphones and so forth — just how much power Microsoft had as a bottleneck. Microsoft controlled access to the PC, and the PC was the only game in town. Had it not been for that action by the government — filing that antitrust case — Microsoft may have been able to warp or slow down the growth of the open internet economy. And it turned out most of the startups were on the side of the government in the case.

“What stops the algorithm from colluding with someone else’s algorithm behind the scenes to fix prices?”

[More recent cases include] the fight over network neutrality rules, where lots of startup companies went to the Federal Communications Commission and said, “We don’t want broadband providers — the access providers, the internet service providers or ISPs as they’re called — to stop us from getting into the market, or to basically tax us, and say, ‘you can only get to customers if you pay us this special fee.’” They were actually urging government to act in order to create a more open market.

Knowledge@Wharton: You also say that on-demand services would bring what you call algorithmic competition policy questions to the fore. Why is this important?

Werbach: Competition policy — I gave the example of Microsoft — is tremendously important to the digital economy. The Microsoft case was an example where there was a new kind of business model. Microsoft was one of the first to build this platform, network-based business model where Windows benefitted from all the applications on top of Windows, but Windows would always want to ensure that none of those applications would then compete with it. And there were tremendous benefits of that model. You know, Microsoft did great things for innovation, but the Microsoft case put a spotlight on some of the dangers and the downsides.

What we’re seeing now with these next-generation platforms, these on-demand platforms, is a new twist on that model. Companies like Uber and Airbnb are built on algorithms. They’re built on software that understands supply and demand and matches people on both sides of the network. And again, that’s a tremendous boon for competition and innovation. I’m not saying it’s bad, by any means, but it does put the platform owner in a position of unimaginable control.

How do you know that what you are paying for that Uber ride is the efficient price? Uber says, “Well, by definition, it’s what the algorithm gives you.” Well, but who controls the algorithm? And what stops the algorithm from colluding with someone else’s algorithm behind the scenes to fix prices?

Again, we have antitrust doctrines about things like price-fixing, but those are based on people in a smoke-filled room saying, “Okay, you’re going to charge this, and I’m going to charge that.” … Now it’s all happening silently, through software. And so I think this one of the great competition policy challenges of our age is how to prevent those kinds of mechanisms from raising costs and raising prices and hurting consumers, while still allowing flexibility for companies to innovate and do things that, most of the time, actually wind up helping consumers.

Knowledge@Wharton: That brings us to the point you made in your article about algorithmic cartels. How could those come about?

Werbach: The algorithms could talk to other algorithms — and we see this already. You look at pricing on Amazon.com. Amazon has this platform that allows anyone else to [look in] Amazon.com and set their prices. A lot of companies that are sophisticated set their prices algorithmically. They might say, “Amazon is charging this price. Automatically charge 2% less than Amazon’s price.” So when [the product] comes up, they’re the cheapest price.

“You get this increasingly complex war between the algorithms, because they’re all basing their prices on each other.”

What happens is you get this increasingly complex war between the algorithms, because they’re all basing their prices on each other, and so forth. What can potentially happen is companies decide, “Well, no, let’s both agree. We’ll set a price higher, as opposed to competing in a race to the bottom, and we’ll both be better off.” But who’s worse off is consumers. So that’s a concern that we’re starting to see on platforms like Amazon, and it’s more of a concern on these digital on-demand platforms, where again, everything is in software. And we have lots of different actors coming together, and we don’t even know what the mechanism is to get access to the data to see if that’s what’s happening.

Knowledge@Wharton: How do you regulate that?

Werbach: First, you start to have a conversation where the regulators say, “Here’s what we’re trying to achieve.” And the companies say, “Here’s what we’re doing.” And you figure out what’s possible. Ultimately, as I said, there needs to be access to the data. And this is a great opportunity, because these new platforms generate tremendous amounts of data. They use the data internally to be more efficient and to provide better service, but if they could provide more transparency of that data, that would give regulators the opportunity to identify what the market performance is. This can be done in a secure way, in a way that doesn’t harm them with competitors and so forth.

It’s actually making the regulation itself more algorithmic, making the regulation itself more data-driven, which is a healthy and a good thing. And so I think this is potentially the new model we’re going to come to, but it takes the company’s willingness to work together and not to make [sweeping] statements like, “Oh, we don’t need any regulation.”

Knowledge@Wharton: You mention alternatives to direct regulation, which are self-regulation and what you call co-regulation and delegated regulation. Can you explain the differences among all those?

Werbach: These are models that actually are used much more widely elsewhere in the world, especially in Europe for things like internet content. There’s a whole variety of different models, but basically they start with the notion that companies individually and industry collectives and industry groups potentially know the most about their market, and if they’re well-meaning, they can come up with mechanisms that achieve the goals of regulators, without government having to be intrusive, or without government having to be inefficient, because regulatory agencies don’t have the data, and they’re not set up to operate in that way.

The problem is, you need some accountability. Just saying, “Let companies regulate themselves” is meaningless, because there are always incentives for companies to cheat or to game the system or basically help themselves at the expense of the public.

But there are a variety of mechanisms where, for example, government sets goals and then gives industry opportunities to meet them, report on how they’re doing, and provide transparency of the data. There are mechanisms that basically say, “All right, in the first instance, you have this opportunity to act, but if you don’t act in a way that we find appropriate, then we’re going to intervene.”

“Nascent, small innovators should have lots of running room, because even a good rule will kill them off.”

Again, there’s different variations on these mechanisms, but [overall] it’s an approach that says instead of everything starting with the regulator — the regulator says yes or no before anything happens in the marketplace — companies can come into the marketplace, especially new companies.

Nascent, small innovators should have lots of running room, because even a good rule will kill them off when they’re too small.… I’m arguing for an openness and a recognition that ‘regulation’ isn’t a dirty word.

Knowledge@Wharton: Any final thoughts for policy makers and regulators?

Werbach: Regulators have to take action here, too. It’s not that they need to just stay where they are and expect the companies to come to them. Often, there’s lots of legacy in regulation, and some of it is regulators’ fault, and some of it is the fault of, for example, the legislatures that set up the rules. A lot of what we are seeing in these markets is the need for legislative change, for governments to change the structure of the rules, because the rules use terms that no longer make sense, or they have categories that no longer make sense.

There needs to be a lot of dialogue between industry and regulators and legislators, to say, “All right. Where are these glitches? Let’s fix them.” Regulators need to be part of that and not to just assume that the status quo is the right approach. Regulators also need to be open. They need to go to these companies and say to them, “We have shared goals here. We’re not here to put you out of business, but we care about consumers, and we trust that you do, too. So let’s come up with a solution.”

It really has to go both ways, and ultimately, this is about trust. There needs to be a mutual process of generating trust between these industries and the regulators, and in a lot of cases, that’s lacking. But I’m hopeful, and I think the examples that we saw with the growth of the internet really are a story about good work on both sides that facilitated this extraordinary explosion of innovation and wealth creation that we saw.

Reproduced from Knowledge@Wharton

    

Competing Through Joint Innovation 01-22

The Chinese telecommunications company Huawei recently has made significant inroads into European markets using a strategy of innovation partnerships with customers and governments.

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Image credit : Shyam's Imagination Library

Emerging markets such as China and India have become the growth drivers of corporate R&D initiatives from all around the world. Although there is growing evidence that Chinese companies are shifting their innovation focus from cost savings to knowledge-based research, the view by many in the West remains that companies based in emerging markets are not ready to take over the role of leading innovators from their Western competitors. As a result, Chinese multinationals have been at a competitive disadvantage, particularly in strategic technology industries.

What can Chinese multinationals do to overcome Western barriers to entry in strategically important technology industries in which “Made in China” or “Designed in China” are viewed as negatives? What dynamic innovation capabilities — or, put another way, what culturally specific processes — should companies focus on to gain acceptance in the competitive global marketplace?

To answer these questions, I studied Huawei Technologies Co. Ltd., the Chinese telecommunications company that has recently made significant inroads in Europe’s mature and strategically important telecommunications industry. (See “About the Research.”) Huawei, which is based in Shenzhen, is one of the first Chinese multinationals to be competitive in the West in a strategic technology industry, making it a potential role model for companies in China and other parts of Asia that hope to transition from being a follower to being a market leader.

To achieve its position, Huawei has aggressively pursued a strategy of joint innovation with leading European customers and governments. In this article, I will discuss how Huawei worked closely with European customers to develop joint innovation capabilities. In the process, the company was able to emerge as a leader in telecommunications in Europe.

The Origin Of 'The World's Dumbest Idea': Milton Friedman 01-09

No popular idea ever has a single origin. But the idea that the sole purpose of a firm is to make money for its shareholders got going in a major way with an article by Milton Friedman in the New York Times on September 13, 1970.

As the leader of the Chicago school of economics, and the winner of Nobel Prize in Economics in 1976, Friedman has been described by The Economist as "the most influential economist of the second half of the 20th century...possibly of all of it". The impact of the NYT article contributed to George Will calling him “the most consequential public intellectual of the 20th century.”

Friedman’s article was ferocious. Any business executives who pursued a goal other than making money were, he said, “unwitting pup­pets of the intellectual forces that have been undermining the basis of a free society these past decades.” They were guilty of “analytical looseness and lack of rigor.” They had even turned themselves into “unelected government officials” who were illegally taxing employers and customers.

How did the Nobel-prize winner arrive at these conclusions? It’s curious that a paper which accuses others of “analytical looseness and lack of rigor” assumes its conclusion before it begins. “In a free-enterprise, private-property sys­tem,” the article states flatly at the outset as an obvious truth requiring no justification or proof, “a corporate executive is an employee of the owners of the business,” namely the shareholders.

Come again?

If anyone familiar with even the rudiments of the law were to be asked whether a corporate executive is an employee of the shareholders, the answer would be: clearly not. The executive is an employee of the corporation.

An organization is a mere legal fiction

But in the magical world conjured up in this article, an organization is a mere “legal fiction”, which the article simply ignores in order to prove the pre-determined conclusion. The executive “has direct re­sponsibility to his employers.” i.e. the shareholders. “That responsi­bility is to conduct the business in accordance with their desires, which generally will be to make as much money as possible while con­forming to the basic rules of the society, both those embodied in law and those embodied in ethical custom.“ 

What’s interesting is that while the article jettisons one legal reality—the corporation—as a mere legal fiction, it rests its entire argument on another legal reality—the law of agency—as the foundation for the conclusions. The article thus picks and chooses which parts of legal reality are mere “legal fictions” to be ignored and which parts are “rock-solid foundations” for public policy. The choice depends on the predetermined conclusion that is sought to be proved.

A corporate exec­utive who devotes any money for any general social interest would, the article argues, “be spending someone else's money… Insofar as his actions in accord with his ‘social responsi­bility’ reduce returns to stockholders, he is spending their money.”

How did the corporation’s money somehow become the shareholder’s money? Simple. That is the article’s starting assumption. By assuming away the existence of the corporation as a mere “legal fiction”, hey presto! the corporation’s money magically becomes the stockholders' money.

But the conceptual sleight of hand doesn’t stop there. The article goes on: “Insofar as his actions raise the price to customers, he is spending the customers' money.” One moment ago, the organization’s money was the stockholder’s money. But suddenly in this phantasmagorical world, the organization’s money has become the customer’s money. With another wave of Professor Friedman’s conceptual wand, the customers have acquired a notional “right” to a product at a certain price and any money over and above that price has magically become “theirs”.

But even then the intellectual fantasy isn’t finished. The article continued: “Insofar as [the executives’] actions lower the wages of some employees, he is spending their money.” Now suddenly, the organization’s money has become, not the stockholder’s money or the customers’ money, but the employees' money.

Is the money the stockholders’, the customers' or the employees’? Apparently, it can be any of those possibilities, depending on which argument the article is trying to make. In Professor Friedman’s wondrous world, the money is anyone’s except that of the real legal owner of the money: the organization.

One might think that intellectual nonsense of this sort would have been quickly spotted and denounced as absurd. And perhaps if the article had been written by someone other than the leader of the Chicago school of economics and a front-runner for the Nobel Prize in Economics that was to come in 1976, that would have been the article’s fate. But instead this wild fantasy obtained widespread support as the new gospel of business.

People just wanted to believe…

The success of the article was not because the arguments were sound or powerful, but rather because people desperately wanted to believe. At the time, private sector firms were starting to feel the first pressures of global competition and executives were looking around for ways to increase their returns. The idea of focusing totally on making money, and forgetting about any concerns for employees, customers or society seemed like a promising avenue worth exploring, regardless of the argumentation.

In fact, the argument was so attractive that, six years later, it was dressed up in fancy mathematics to become one of the most famous and widely cited academic business articles of all time. In 1976, Finance professor Michael Jensen and Dean William Meckling of the Simon School of Business at the University of Rochester published their paper in the Journal of Financial Economics entitled

“Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure.”

Underneath impenetrable jargon and abstruse mathematics is the reality that whole intellectual edifice of the famous article rests on the same false assumption as Professor Friedman’s article, namely, that an organization is a legal fiction which doesn’t exist and that the organization’s money is owned by the stockholders.

Even better for executives, the article proposed that, to ensure that the firms would focus solely on making money for the shareholders, firms should turn the executives into major shareholders, by affording them generous compensation in the form of stock. In this way, the alleged tendency of executives to feather their own nests would be mobilized in the interests of the shareholders.

The money took over…

Sadly, as often happens with bad ideas that make some people a lot of money, shareholder value caught on and became the conventional wisdom. Not surprisingly, executives were only too happy to accept the generous stock compensation being offered. In due course, they even came to view it as an entitlement, independent of performance.

Politics also lent support. Ronald Reagan was elected in the US in 1980 with his message that government is “the problem”. In the UK, Margaret Thatcher became Prime Minister in 1979. These leaders preached “economic freedom” and urged a focus on making money as “the solution”. As the Michael Douglas character in the 1987 movie, Wall Street, pithily summarized the philosophy, greed was now good.

Moreover an apparent exemplar of the shareholder value theory emerged: Jack Welch. During his tenure as CEO of General Electric from 1981 to 2001, Jack Welch came to be seen–rightly or wrongly–as the outstanding implementer of the theory, as a result of his capacity to grow shareholder value and hit his numbers almost exactly. When Jack Welch retired, the company had gone from a market value of $14 billion to $484 billion at the time of his retirement, making it, according to the stock market, the most valuable and largest company in the world. In 1999 he was named “Manager of the Century” by Fortune magazine.

The disastrous consequences…

So for a time, it looked as though the magic of shareholder value was working. But once the financial tricks that were used to support it were uncovered, the underlying reality became apparent. The decline that Friedman and other sensed in 1970 turned out to be real and persistent. The rate of return on assets and on invested capital of US firms declined from 1965 to 2009 by three-quarters, as shown by the Shift Index, a study of 20,000 US firms.

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The shareholder value theory thus failed even on its own narrow terms: making money. The proponents of shareholder value and stock-based executive compensation hoped that their theories would focus executives on improving the real performance of their companies and thus increasing shareholder value over time. Yet, precisely the opposite occurred. In the period of shareholder capitalism since 1976, executive compensation has exploded while corporate performance declined.

Maximizing shareholder value thus turned out to be the disease of which it purported to be the cure. As Roger Martin in his book, Fixing the Game, noted, "between 1960 and 1980, CEO compensation per dollar of net income earned for the 365 biggest publicly traded American companies fell by 33 percent. CEOs earned more for their shareholders for steadily less and less relative compensation. By contrast, in the decade from 1980 to 1990, CEO compensation per dollar of net earnings produced doubled. From 1990 to 2000 it quadrupled."

Even Jack Welch sees the light…

Moreover in the years since Jack Welch retired from GE in 2001, GE’s stock price has not fared so well: in the decade following Welch's departure, GE lost around 60 percent of the market capitalization that Welch “created”. It turned out that the fabulous returns of GE during the Welch era were obtained in part by the risky financial leverage of GE Capital, which would have collapsed in 2008 if it had not been for a government bailout.

In due course, Jack Welch himself came to be one of the strongest critics of shareholder value. On March 12, 2009, he gave an interview with Francesco Guerrera of the Financial Times and said, “On the face of it, shareholder value is the dumbest idea in the world. Shareholder value is a result, not a strategy… your main constituencies are your employees, your customers and your products. Managers and investors should not set share price increases as their overarching goal… Short-term profits should be allied with an increase in the long-term value of a company.”

From shareholder value to hardball…

The supposed management dynamic of maximizing shareholder value was to make money, by whatever means are available.  Self-interest reigned supreme. The logic was continued in the perversely enlightening book, Hardball (2004), by George Stalk, Jr. and Rob Lachenauer. Firms should pursue shareholder value to “win” in the marketplace. These firms should be “willing to hurt their rivals”. They should be “ruthless” and “mean”. Exponents of the approach “enjoy watching their competitors squirm”. In an effort to win, they go up to the very edge of illegality or if they go over the line, get off with civil penalties that appear large in absolute terms but meager in relation to the illicit gains that are made.

In such a world, it is therefore hardly surprising, says Roger Martin in his book, Fixing the Game, that the corporate world is plagued by continuing scandals, such as the accounting scandals in 2001-2002 with Enron, WorldCom, Tyco International, Global Crossing, and Adelphia, the options backdating scandals of 2005-2006, and the subprime meltdown of 2007-2008. Banks and others have been gaming the system, both with practices that were shady but not strictly illegal and then with practices that were criminal. They include widespread insider trading, price fixing of LIBOR, abuses in foreclosure, money laundering for drug dealers and terrorists, assisting tax evasion and misleading clients with worthless securities.
Martin writes: “It isn’t just about the money for shareholders, or even the dubious CEO behavior that our theories encourage. It’s much bigger than that. Our theories of shareholder value maximization and stock-based compensation have the ability to destroy our economy and rot out the core of American capitalism. These theories underpin regulatory fixes instituted after each market bubble and crash. Because the fixes begin from the wrong premise, they will be ineffectual; until we change the theories, future crashes are inevitable.”

Peter Drucker got it right...

Not everyone agreed with the shareholder value theory, even in the early years. In 1973, Peter Drucker made a sustained argument against shareholder value in his classic book, Management. In his view, “There is only one valid definition of business purpose: to create a customer. . . . It is the customer who determines what a business is. It is the customer alone whose willingness to pay for a good or for a service converts economic resources into wealth, things into goods. . . . The customer is the foundation of a business and keeps it in existence.”
Similarly in 1979, Quaker Oats president Kenneth Mason, writing in Business Week, declared Friedman's profits-are-everything philosophy "a dreary and demeaning view of the role of business and business leaders in our society… Making a profit is no more the purpose of a corporation than getting enough to eat is the purpose of life. Getting enough to eat is a requirement of life; life's purpose, one would hope, is somewhat broader and more challenging. Likewise with business and profit."

The primacy of the customer…

Peter Drucker’s argument about the primacy of the customer didn’t have much effect until globalization and the Internet changed everything. Customers suddenly had real choices, access to instant reliable information and the ability to communicate with each other. Power in the marketplace shifted from seller to buyer. Customers started insisting on “better, cheaper, quicker and smaller,” along with “more convenient, reliable and personalized.” Continuous, even transformational, innovation became requirements for survival.

A whole set of organizations responded by doing things differently and focusing on delighting customers profitably, rather than a sole focus on shareholder value. These firms include Whole Foods [WFM], Apple [AAPL], Salesforce [CRM], Amazon [AMZN], Toyota [TM], Haier Group, Li & Fung and Zara along with thousands of lesser-known firms. The transition is happening not just in high tech, but also in manufacturing, books, music, household appliances, automobiles, groceries and clothing. This different way of managing turned out to be hugely profitable.

The common elements of what all these organizations are doing has now emerged. It’s not merely the application of new technology or a set of fixes or adjustments to hierarchical bureaucracy. It involves basic change in the way people think, talk and act in the workplace. It involves deep changes in attitudes, values, habits and beliefs.

The new management paradigm is capable of achieving both continuous innovation and transformation, along with disciplined execution, while also delighting those for whom the work is done and inspiring those doing the work. Organizations implementing it are moving the production frontier of what is possible.

The replacement for shareholder value is thus now identifiable. A set of books have appeared that spell out the elements of this canon of radically different management.

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In effect, shareholder value is obsolete. What we are seeing is a paradigm shift in management, in the strict sense laid down by Thomas Kuhn: a different mental model of how the world works.


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Tech Savvy: How Blockchains Could Transform Management 05-25

What’s happening this week at the intersection of management and technology.

Re-architecting the firm with blockchain: Is Craig Wright really Satoshi Nakamoto, the mysterious creator of Bitcoin? Who knows — and really, who cares? The bigger issue is blockchains, the distributed ledgers that underpin cryptocurrencies like Bitcoin.
Blockchain technology has so many uses that trying to summarize them can make veteran tech experts sound like PR hacks. “As such, it holds the potential for unleashing countless new applications and as yet unrealized capabilities that have the potential to change everything,” write Don Tapscott and his co-author and son Alex Tapscott in their new book, Blockchain Revolution:

How the Technology Behind Bitcoin is Changing Money, Business, and the World.

That might sound like hyperbole, but it seems like everywhere you turn these days you run into blockchains. Banks are trying to harness blockchain before its blows up their business models. IBM is betting on blockchains to give its revenues a bump. Disney has a blockchain team doing … well, who knows what.

What we haven’t heard very much about is how blockchain could fundamentally change how companies are managed and operate. That’s a good reason to take a closer look at Blockchain Revolution, in which the Tapscotts devote a chapter to the topic. “Blockchain technology is enabling new forms of economic organization and new portfolios of value,” they write. “There are distributed models of the firm emerging — ownership, structure, operations, reward, and governance — that go far beyond enhancing innovation, employee motivation, and collective action.”

Intrigued? If you’d like read more about how blockchains might change the everyday operation of a business, check out the excerpt from the chapter, reprinted with permission, below.
The innovation hub — same as it ever was? The Internet has wrought significant changes in how we work, but some things — innovation hubs, for example — remain remarkably durable. “For hundreds of years,” writes freelance journalist Emily Sohn in Nature, “regions developed specialities that often arose from access to a natural resource, but then intensified as people moved to the regions to be among the expertise. The Internet was supposed to change all that. Around-the-clock connectivity that allowed researchers and entrepreneurs to collaborate from anywhere at any time meant that distance would no longer be an issue, predicted popular economic theory of the early 2000s. A decade later, it hasn’t panned out that way.”

Sohn reports that global connectivity seems to have stimulated the growth of innovation hubs, like Silicon Valley, rather than shrunk them. “Innovators and PhD students are now clumped together in fewer places, often in big cities,” she says. “And collaborations are more likely to happen between researchers who live, or have lived, close to each other.”

New and existing companies can’t afford to buck this finding. Locating in innovation hubs gives them greater access to talent. It also boosts their performance: Sohn cites studies that show start-ups located in hubs are more likely to survive, and firms in hubs are more likely to file patents than companies outside hubs.

It turns out that no matter how easy it is to collaborate at a distance, proximity remains an essential element in stimulating innovation. It sets the stage for serendipitous meetings. Face-to-face interaction also creates feel-good reactions in our brains that promote trust and more effective collaboration.

It’s not that digital connectivity inhibits innovation. Far from it, reports Sohn. Rather, it stimulates the enhanced innovation that is already taking place within innovation hubs — in effect, supercharging it. It’s a finding worth keeping in mind that next time your company is considering where to locate a new business unit or research facility.

Putting data to work with knowledge graphs: A brief story popped up in The Seattle Times last week: A data analytics company named Maana announced it had raised $26 million in Series B funding from the investment arms of Saudi Aramco and Shell. In these (waning) days of billion-dollar start-up valuations, $26 million isn’t especially jaw-dropping. But the company does have has an interesting approach to data analytics, which uses “enterprise knowledge graphs.”
There are a couple of problems with data in big companies. First, there’s lots of it, and it’s often stashed in separate silos. “A single division could have over 60 different information systems that they work with,” CTO Donald Thompson told tech reporter Rachel Lerman. Second, you need to turn the data into useful insights and recommendations. Third, you have get those into the hands of people who can use them to enhance results.

Bearing in mind that I’m a layman at best, here’s how Maana approach works: Instead of placing the company’s data into a common pool, it sends out a search engine to crawl the various data silos in your company. Then, instead of simply delivering a list of results, it uses analytics and machine learning to construct knowledge graphs — kind of like the ones that Google introduced a few years back — that provide actionable recommendations based on the goals and needs of the business and delivers them to line-of-business applications. Maana has used cases on its website that show how this approach works and the results it has produced in operational settings in industrial and oil and gas companies.




Reproduced from MIT Sloan Management Review