Interesting Books In 2020

There have been a lot of things we haven’t been able to do during the last nine months. But it’s been a good time for reading ebooks and listening to audiobooks. So my on-again-off-again tradition of highlighting interesting books that I have read in the year is on again.

These books have not all been published during the last year, but are ones I’ve read this past year and thought worth mentioning to other folks who read this blog.  You’ll note that this is an eclectic combination of books on technology, government, the economy and other non-fiction – but that’s the range of topics that my blog is about.

Anyway, here’s my list for 2020 and a blurb as to why each book is on the list.  I have obviously eliminated from the list the many other books that I’ve read, which I would not recommend you spend your time on. ?

Technology, AI/Machine Learning and Science

  1. David Carmona – The AI Organization: Learn from Real Companies and Microsoft’s Journey How to Redefine Your Organization with AI (2019). Perhaps too many examples from Microsoft, but it is a really good book from A to Z on artificial intelligence.
  2. Cliff Kuang and Robert Fabricant – User Friendly: How the Hidden Rules of Design Are Changing the Way We Live, Work, and Play (2019). Very interesting review of the leading good (and sometimes bad) user interfaces.
  3. Matthew O. Jackson – The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors (2019). Good, understandable explanations of network measures and phenomena in various domains.
  4. Damon Centola – How Behavior Spreads: The Science of Complex Contagions (2018). Provides a nuanced view of the best time to use weak or strong ties, especially in leading changes in an organization or community.
  5. Eric Topol – Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again (2019). Although it is mostly about the ways that artificial intelligence can re-humanize the patient-doctor relationship, it even has a pretty good, understandable review of general artificial intelligence and machine learning concepts.
  6. Lisa Feldman Barrett – How Emotions Are Made: The Secret Life of the Brain (2017). The title highlights emotions, but this book is not just about emotions. It instead offers a paradigm shift about how the brain works.
  7. Jodie Archer and Matthew L. Jockers – The Bestseller Code: Anatomy of a Blockbuster Novel (2016). interesting book, better and more nuanced than the usual summaries about machine learning models to predict the success of books.
  8. Leonard Mlodinow – The Drunkard’s Walk: How Randomness Rules Our Lives (2009). Interesting explanations of the implications of probability theory and how most people get probability wrong.
  9. Scott Rigby and Richard M Ryan – Glued to games: how video games draw us in and hold us spellbound (2011). Good review of computer-based games, especially the psychological aspects.

Leadership And Business

  1. Jim McKelvey – The innovation stack: building an unbeatable business one crazy idea at a time (2020). Good, insightful and sometimes funny book by one of the co-founders of Square, with the proposition that success is the result of a chain (better word than stack) of innovations rather than just one big one.
  2. Scott Kupor – Secrets of Sand Hill Road: Venture Capital and How to Get It (2019). If you want to know how venture capitalists look at startups, this tells you how.
  3. Geoffrey G. Parker, Marshall W. Van Alstyne, Sangeet Paul Choudary – Platform Revolution: How Networked Markets Are Transforming the Economy – and How to Make Them Work for You (2017). While other books on the subject go more deeply into the broader policy implications of platforms, if you want to start a platform business, this is your best, almost required, user manual.
  4. Daniel Coyle – The Culture Code: The Secrets of Highly Successful Groups (2018). Culture is a frequently used word to explain the forces that drive behavior in organizations, but too often the concept is fuzzy. This book is one of the clearest and best on the subject.
  5. Dan Heath – Upstream: The Quest to Solve Problems Before They Happen (2020). Good, as usual for the Heath brothers, well written down to earth, but important concepts underneath and guidance at looking at the more fundamental part of problems that you are trying to solve.
  6. Matt Ridley – How Innovation Works: And Why It Flourishes in Freedom (2020). Includes many short histories of key innovations, not just invention, with an emphasis on the iterative and collaborative nature of the innovation process. Ridley advocates curtailing IP protections, thus providing more tolerance of risky experiments/innovations.
  7. Rita McGrath – Seeing Around Corners: How To Spot Inflection Points In Business Before They Happen (2019). Columbia Professor McGrath has made clear that no strategy is sustainable for a long time and in this book, she helps you figure out when you are at good or bad inflection points.

The Economy And Government

  1. Robert H. Frank – Under the Influence: Putting Peer Pressure to Work (2020). Frank is one of the most creative economists around and in this review of behavioral economics, he highlights how people pursue relative positions of wealth, rather than merely being rational maximizers of wealth.  He also offers a good discussion of public policies to pursue, that are based on this understanding of economic behavior.
  2. Stephanie Kelton – The Deficit Myth: Modern Monetary Theory and the Birth of the People’s Economy (2020). Well written, clear exposition of modern monetary theory and the positive and negative consequences of having completely fiat money (no gold standard or fixed currency exchanges). Professor Kelton is an increasingly influential economist and her ideas – whether or not she is given credit – have enabled the US Government to spend more with less angst than used to be the case.
  3. Abhijit V. Banerjee and Esther Duflo – Good Economics for Hard Times: Better Answers to Our Biggest Problems (2019). A review of economics research – and, more important, its limits – in addressing major socio-economic problems.
  4. Matthew Yglesias – One Billion Americans: The Case for Thinking Bigger (2020). Although no one (including me) will agree with everything he proposes, this is an interesting book with some original forward thinking – something we need more of as we face a very changed future.
  5. Michael Hallsworth and Elspeth Kirkman – Behavioral Insights (2020). This is a good overview of the application of behavior research to mostly public policy, especially about the UK.
  6. Paul Begala – You’re fired: the perfect guide to beating Donald Trump (2020). Smart and realistic proposals for the campaign to oppose Trump with many very funny lines.
  7. Jane Kleeb – Harvest the Vote: How Democrats Can Win Again in Rural America (2020). Along with Begala, explains her own success in rural America and more generally what needs to be done by Democrats to regain their old reputation as the party of the majority of people.
  8. Mark Lilla – The Once and Future Liberal: After Identity Politics (2017). Short review of how the Democratic party became dominated by identity politics and, for that reason, provides a bit of background for the previous too books.

Have a happy holiday season and a great, much better, year in 2021!

© 2020 Norman Jacknis, All Rights Reserved

Straight Lines And Hockey Sticks On The Road To A Cash Crunch

As a member of a couple of angel investor networks, a former software executive, and a teacher of a graduate course on new product/service creation, I have seen many financial projections from startup founders or even new product managers in large companies.

One very common pattern for sales projections is a straight rising line (simple linear trend). Here’s one that shows consistent growth in sales, with expenses following along in a similar path. Breakeven occurs around the fourth time period — perhaps that’s the second half of the second year.

More optimistic projections take the hockey stick approach. The folks with hockey stick graphs always show the long arm of the stick going up — this product is just going to take off and sales will go through the ceiling!

 

More often than these hopeful folks realize, the hockey stick goes the other way.

 

 

The underlying theory of sales growth in these charts is unclear — if there is even some kind of well-thought out model underlying them. Often there isn’t one and the creators of these projections are just playing with arithmetic.

An improvement over the simplistic linear or wishful hockey stick “model” is the four-stage product life cycle — launch, growth, maturity, decline. With that in mind, you might get to see sales projections that follow this pattern.

It’s a more nuanced, maybe even reasonable, basis for estimations in spreadsheets. But aside from launch, these phases are not easy to identify in real-time. Moreover, these phases are so general as to be generally useless in practice. And that is because this kind of curve has not been tied into what is known about patterns of adoption of new products or services, especially technology products.

There are better ways of thinking about sales projections for new products from startups or established companies. These better approaches are not original with me nor are they new, although they do not seem to have the popularity that you would expect.

Here then is a picture of the general pattern of adoption of technology products — what has long been called the diffusion of innovation or, with the notch between the second and third groups, the chasm that new products need to get across in order to be successful at scale.

While the speed of adoption and the actual distribution of various groups is not always a nice normal distribution, it provides a useful framework to identify when different kinds of people will adopt the new product or service. (This isn’t the place to go into the details about the characteristics of each of the five groups. More about that can be found in such classic books as Diffusion of Innovations by Everett M. Rogers and Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers by Geoffrey A. Moore.)

There has been a lot written on this S-curve, but curiously very little of it ties the S-curve to the financial demands of a startup or a new product launch in an established company. While many people seem to have a basic understanding of the S-curve for the adoption of new products/services, they don’t link that to the money they need. But the pattern of adoption has direct financial consequences.

What do these financial consequences look like? Consider this picture.

The model can be more granular, but this simple model illustrates the point. The expenses match the various stages of the adoption cycle, including the marketing costs of getting past the chasm. The model also reflects the fact that later adopters usually require more support in their use of products and even greater attention to an easy user interface than at the start. The model also reflects the need to create the product before there is any paying customer at all.

The technology adoption cycle provides the framework, but the actual pattern for a new product can be updated with real-time data that reflects how fast adoption is moving and into which groups.

With this kind of model in hand, the product planner can estimate the pattern of product adoption in the future. In turn, that enables better financial planning to estimate future profits.

It’s worth also saying that in startups, in particular, cash is king. No matter what your profits might be on paper, if there isn’t enough money to pay for essentials — say the salaries of key employees who are writing your software — then your startup is in deep trouble. In startups, misjudgments about the money coming into the business versus the money going out of it can be fatal. So startups are especially vulnerable to inaccurate sales projections.

Whether it’s a new product in a big company or a startup, even a sales projection (and updates) based on an understanding of the adoption of innovations does not necessarily guarantee a big success. But at the least this kind of smart sales projection will help avoid a nasty surprise that leads to a cash crunch.

© 2020 Norman Jacknis, All Rights Reserved

The Limits To Being Different

Product differentiation is often described as the key to business success. Companies are told that unless they really stand out from the crowd, their products or services will become “commoditized” — an undesirable position in the marketplace that results in little or no profit. This has been well-established guideline in the world of technology startups and even new technology-based product development in existing companies.

And that guidance is mostly right. Distinguishing your products from the crowd of competitors often results in greater than average profits. Consider Apple, with less market share than Android, but lots more profit than its smart phone competitors.

Of course, how to go about this is not so simple. One of the best and most inspiring books about how to differentiate — how to be really different — is Harvard Business School Professor Youngme Moon’s book, Different: Escaping the Competitive Herd — Standing Out In A World Where Conformity Reigns But Exceptions Rule.

These quotes summarize her forceful advice:

“What does it mean to be really different? Different in a way that makes a difference. It could mean doing the opposite of what everyone else is doing — going small when everyone else is going big…

“You could even say that breakaway brands revel in our stereotypes, since they make their living turning them upside down…

“These brands are the antithesis of well-behaved, and their mutiny is directed squarely at the category assumptions we bring to the table. And sometimes the transgression is more than a touch provocative; it’s a bit twisted as well. …

“What a breakaway positioning strategy offers is the opportunity to achieve a kind of differentiation that is sustainable over the long term. … it has no competitors; it remains sui generis.”

This advice applies not only to business, but can also apply to politics. That’s why I wrote a post four years ago called “The Breakaway Brand Of 2016” about the 2016 US Presidential election. Although I doubt that he read her book and his approach certainly didn’t please Professor Moon, Trump seemed to have been using it as his playbook for the 2016 election. His was the perfect exemplar of a breakaway brand in politics.

Now the 2020 Election also showed the limits of this approach. In a two-way election in the US, you need a majority (putting aside the Electoral College, for the moment).

 

It is also often the case that being different means you won’t get a majority, as both Apple and Trump have found out. For Apple, that’s not a problem. For Trump, it meant he lost the election.

While he did receive many votes, the limits of breaking too far away in politics was well stated by the most successful politician in American history, Franklin Roosevelt: “It is a terrible thing to look over your shoulder when you are trying to lead — and find no one there.”

The limits of extreme differentiation are clear enough in electoral contests. But the election result also reminded me that there are limits to being different in business too. I’m especially thinking of most established technology-based, multi-sided platform businesses (like Amazon) and other businesses that depend on direct network effects (like Facebook).

These businesses also need to have a majority (or even more) of the market. That’s because their value to customers depends a lot on network effects. Being too different for most people will mean you do not end up getting the majority of people as customers.

So, differentiating — even creating breakaway brands — is certainly good advice in general. But like any advice, it is not always appropriate. And the art of leadership is knowing when not to follow generally good advice and take a different road — even a different road about being different.

© 2020 Norman Jacknis, All Rights Reserved

Thinking About Something New: Brain Twisting Is Unnecessary

If you have a new product or service in mind, you know that you need to find a way to differentiate it from the alternatives that people are already using or could use.  But then maybe you have a hard time coming up with ways to make what you are offering really different and new.

This is a basically a challenge to your creativity. And many of us think we need to twist our brains to come up with good creative ideas, which is hard work we don’t feel we can do.

Although we have come to frequently expect new technology products, the challenge of creativity is especially hard for technologists.  They have lived in a world that demands no software bugs, no downtime and the like.  They are by training (as the A students many were in school) and maybe by nature perfectionists.

A perfectionist mindset undermines the kind of experimental approach and its possibility of failure which is necessary for innovation.  For that reason, creativity can seem to be an insurmountable, impossible challenge – to be both perfect and creative is a low probability occurrence.

Coming up with new ideas shouldn’t be such a challenge.  Consider just two of many authors.  Tina Seelig, Professor of Practice at Stanford, has written and spoken about creativity and innovation.  The titles of two of her books offer a quick summary of her themes — “InsightOut” Get Ideas Out Of Your Head and Into the World” and “inGenius: A Crash Course on Creativity” .

 

William Duggan of Columbia Business School has also written “Creative Strategy: A Handbook for Innovation” in which he champions the innovation matrix as a means of generating new ways of looking at the world. You break down what you’re trying to do into its parts and then search for any company that provides a model of how to do that part well. It’s a tool for what’s called recombinant innovation.

In addition to books on creativity, however, consider a methodology for analysis and software design from more than forty years ago that was named after its originators – Yourdon and DeMarco.  If it is remembered at all, it is for data flow diagrams.

 

That’s not what I want to emphasize here. Nor do I plan to lead an effort to revive the popularity of Yourdon-DeMarco structured analysis/design and the classic waterfall development lifecycle that it aimed to improve.  Nor am I advocating for the underlying idea that there could be a complete and correct design up front in that lifecycle.

Yourdon and DeMarco had even more important guidance for software designers, although that seems to have been lost in the history of software design.

That guidance:  think more conceptually, more abstractly.  They distinguished between the logical level (the “what”) and the physical level (the “how”).   At the physical level, you would describe the implementation.  At the logical level, traditionally, you would describe essentially what the organization is trying to do.  When thinking about a problem, separate out its implementation (how you see it operate) from its intention.

When it comes time to re-design a system or designing a new product, you first rearrange what is happening at the logical level.  Only after that makes sense to everyone do you worry about how it will be implemented.

By the way, this is not something that requires an excessive amount of writing upfront.  Instead, it is often better to explain this to someone else verbally.  Because you are trying to communicate clearly and concisely in conversation rather than impress someone with a document.

Look at what is happening and describe it in simple words, before you use a fancy name for it that you might have been taught.  Often the solution to a problem is obvious if you listen to yourself carefully.  (Maybe recording it helps.)  That’s what you should start with.

Thinking this way makes things clear and clarity yields insight. Sometimes the solution can be blindingly simple once you look at things conceptually. The ancient story of Alexander the Great and the Gordon knot is a good example. The knot only had to be broken. Instead of meticulously searching where to pull on it so it would unravel, he just cut it.

One often cited example of the reverse approach and of missed opportunities that result is in the transportation industry.  When airplanes and airlines first appeared, there was an opportunity for the railroads to invest and own the new industry.  Instead of thinking of themselves as the movers of people and goods over long distances (the higher conceptual level), they thought of themselves as the operators of railroads (the lower physical level).  As they say, the rest is history.

You don’t need to twist your brain to arrive at innovative solutions.  Actually, conventional thinking often requires more brain twisting than creative thinking.  Using the approaches that I’ve outlined here require less, not more, brain twisting to be creative.

© 2020 Norman Jacknis, All Rights Reserved

Digging Deeper Into Why There Is A Problem

Almost every pitch deck for a startup (or even a new corporate-funded initiative) starts with a customer problem. In some form or other, the entrepreneur/intrapreneur says: “Here is a customer problem. The customer’s problem is an opportunity for us because we know how to solve that problem.” And then they go on to ask for the money they need to bring their solution to life.

Having been on the receiving end of these pitches many times, I have often thought that the presenter too quickly jumped on the first problem they saw and it was not the real problem the potential customer had. So if they tried to fix the superficial problem, the entrepreneur/intrapreneur would not get the market traction they hoped for – and it wouldn’t be worth it for us to invest in an idea with no traction.

That’s why in my last post I reviewed the key points in Dan Heath’s book “Upstream: The Quest To Solve Problems Before They Happen”.  In a nutshell, his message is that you have to go upstream beyond the first problem (downstream) you see and find the root cause of that problem.

An example of thinking about a root cause can be found in the 500-year-old poem that is supposed to have been about the English King Richard III’s loss in 1485 at the Battle of Bosworth Field to Henry Tudor who then became king:

For want of a nail the shoe was lost. For want of a shoe the horse was lost. For want of a horse the rider was lost. For want of a rider the message was lost. For want of a message the battle was lost. For want of a battle the kingdom was lost. And all for the want of a horseshoe nail.

It isn’t always easy to figure out where upstream the problem is.  In post-mortems on fatal catastrophes, root cause analysis often starts with the Five Whys technique.

But you do not need a catastrophic failure to motivate you to use this method.  Anytime you want to understand better the problems that customers or constituents are facing, you can use the method.

It is quite easy to explain, although much harder for most people to do.  Here is a simple example.

Five Whys is especially useful in thinking about any new product or service you hope to bring into the world.  If you identify the root cause of the problem, you’ll be able to come up with the right solution.  If you identify a solution for the superficial complaint a customer has, you may well end up doing the right thing about the wrong thing.

A famous quote attributed to Henry Ford identifies how you can go astray: “If I had asked people what they wanted, they would have said faster horses.”  There were several root causes of the problem that annoyed Ford’s customers, none of which could have been fixed by getting horses to go faster.

As you can see from the 5 Whys picture of a restaurant’s problem, people often think about causes in a linear fashion.  Event A causes Event B, which causes Event C, etc.  So all you need to do is go back from where you started, say Event C.  This is sometimes called Event-Oriented thinking.

But life is more complicated than that.  In his book, eventually Dan Heath introduces the necessity of Systems Thinking, since upstream you may well find not a linear series of causes, but a set of interrelated factors.   This picture nicely summarizes the difference.

You may recognize the feeling of being caught in a loop, being in a “Catch-22” situation where you go in circles.  Since Catch-22 was originally about absurdity in wars and not an everyday experience, perhaps this Dilbert cartoon provides a better simple example.

Properly assessing the forces and their mutual reinforcement – in other words, doing systems thinking – is even harder than struggling with the 5 Whys of a simple linear chain of causes.  But it is necessary to really understand the world you are operating in.

Again, especially for those devising new products or services, it is that understanding which will help you avoid significant, strategic business errors.

© 2020 Norman Jacknis, All Rights Reserved

Are You Looking At The Wrong Part Of The Problem?

In business, we are frequently told that to build a successful company we have to find an answer to the customer’s problem. In government, the equivalent guidance to public officials is to solve the problems faced by constituents. This is good guidance, as far as it goes, except that we need to know what the problem really is before we can solve it.

Before those of us who are results-oriented, problem solvers jump into action, we need to make sure that we are looking at the right part of the problem. And that’s what Dan Heath’s new book, “Upstream: The Quest To Solve Problems Before They Happen” is all about.

Heath, along with his brother Chip, has brought us such useful books as “Made To Stick: Why Some Ideas Survive and Others Die” and “Switch: How to Change Things When Change Is Hard”.

As usual for a Heath book, it is well written and down to earth, but contains important concepts and research underneath the accessible writing.

He starts with a horrendous, if memorable, story about kids:

You and a friend are having a picnic by the side of a river. Suddenly you hear a shout from the direction of the water — a child is drowning. Without thinking, you both dive in, grab the child, and swim to shore. Before you can recover, you hear another child cry for help. You and your friend jump back in the river to rescue her as well. Then another struggling child drifts into sight…and another…and another. The two of you can barely keep up. Suddenly, you see your friend wading out of the water, seeming to leave you alone. “Where are you going?” you demand. Your friend answers, “I’m going upstream to tackle the guy who’s throwing all these kids in the water.”

 

Going upstream is necessary to solve the problem at its origin — hence the name of the book. The examples in the book range from important public, governmental problems to the problems of mid-sized businesses. While the most dramatic examples are about saving lives, the book is also useful for the less dramatic situations in business.

Heath’s theme is strongly, but politely, stated:

“So often we find ourselves reacting to problems, putting out fires, dealing with emergencies. We should shift our attention to preventing them.”

This reminds me of a less delicate reaction to this advice: “When you’re up to your waist in alligators, it’s hard to find time to drain the swamp”. And I often told my staff that unless you took some time to start draining the swamp, you are always going to be up to your waist in alligators.”

He elaborates and then asks a big question:

We put out fires. We deal with emergencies. We stay downstream, handling one problem after another, but we never make our way upstream to fix the systems that caused the problems. Firefighters extinguish flames in burning buildings, doctors treat patients with chronic illnesses, and call-center reps address customer complaints. But many fires, chronic illnesses, and customer complaints are preventable. So why do our efforts skew so heavily toward reaction rather than prevention?

His answer is that, in part, organizations have been designed to react — what I called some time ago the “inbox-outbox” view of a job. Get a problem, solve it, and then move to the next problem in the inbox.

Heath identifies three causes that lead people to focus downstream, not upstream where the real problem is.

  • Problem Blindness — “I don’t see the problem.”
  • A Lack of Ownership — “The problem isn’t mine to fix.”
  • Tunneling — “I can’t deal with the problem right now.”

In turn, these three primary causes lead to and are reinforced by a fatalistic attitude that bad things will happen and there is nothing you can do about that.

Ironically, success in fixing a problem downstream is often a mark of heroic achievement. Perhaps for that reason, people will jump in to own the emergency downstream, but there are fewer owners of the problem upstream.

…reactive efforts succeed when problems happen and they’re fixed. Preventive efforts succeed when nothing happens. Those who prevent problems get less recognition than those who “save the day” when the problem explodes in everyone’s faces.

Consider the all too common current retrospective on the Y2K problem. Since the problem didn’t turn out to be the disaster it could have been at the turn of the year 2000, some people have decided it wasn’t real after all. It was, but the issue was dealt with upstream by massive correction and replacement of out-of-date software.

Heath realizes that it is not simple for a leader with an upstream orientation to solve the problem there, rather than wait for the disaster downstream.

He asks leaders to first think about seven questions, which explores through many cases:

  • How will you get early warning of the problem?
  • How will you unite the right people to assess and solve the problem?
  • Where can you find a point of leverage?
  • Who will pay for what does not happen?
  • How will you change the system?
  • How will you know you’re succeeding?
  • How will you avoid doing harm?

Some of these questions and an understanding of what the upstream problem really is can start to be answered by the intelligent use of analytics. That too only complicates the issue for leaders, since an instinctive heroic reaction is much sexier than contemplating machine learning models and sexy usually beats out wisdom 🙂

Eventually Heath makes the argument that not only do we often focus on the wrong end of the problem, but that we think about the problem too simplistically. At that point in his argument, he introduces the necessity of systems thinking because, especially upstream, you may find a set of interrelated factors and not a simple one-way stream.

[To be continued in the next post.]

© 2020 Norman Jacknis, All Rights Reserved

Technology and Trust

A couple of weeks ago, along with the Intelligent Community Forum (ICF) co-founder, Robert Bell, I had the opportunity to be in a two-day discussion with the leaders of Tallinn, Estonia — via Zoom, of course. As part of ICF’s annual selection process for the most intelligent community of the year, the focus was on how and why they became an intelligent community.

They are doing many interesting things with technology both for e-government as well as more generally for the quality of life of their residents. One of their accomplishments, in particular, has laid the foundation for a few others — the strong digital identities (and associated digital signatures) that the Estonian government provides to their citizens. Among other things, this enables paperless city government transactions and interactions, online elections, COVID contact warnings along with protection/tracking of the use of personal data.

Most of the rest of the world, including the US, does not have strong, government-issued digital identities. The substitutes for that don’t come close — showing a driver’s license at a store in the US or using some third party logon.

Digital identities have also enabled an E-Residency program for non-Estonians, now used by more than 70,000 people around the world.

As they describe it, in this “new digital nation … E-Residency enables digital entrepreneurs to start and manage an EU-based company online … [with] a government-issued digital identity and status that provides access to Estonia’s transparent digital business environment”

This has also encouraged local economic growth because, as they say, “E-Residency allows digital entrepreneurs to manage business from anywhere, entirely online … to choose from a variety of trusted service providers that offer easy solutions for remote business administration.” The Tallinn city leaders also attribute the strength of a local innovation and startup ecosystem to this gathering of talent from around the world.

All this would be a great story, unusual in practice, although not unheard of in discussions among technologists — including this one. As impressive as that is, it was not what stood out most strongly in the discussion which was Tallinn’s unconventional perspective on the important issue of trust.

Trust among people is a well-known foundation for society and government in general. It is also essential for those who wish to lead change, especially the kind of changes that result from the innovations we are creating in this century.

I often hear various solutions to the problem of establishing trust through the use of better technology — in other words, the belief that technology can build trust.

In Tallinn’s successful experience with technology, cause-and-effect go more in the opposite direction. In Tallinn, successful technology is built on trust among people that had existed and is continually maintained regardless of technology.

While well-thought out good technology can also enhance trust to an extent, in Tallinn, trust comes first.

This is an important lesson to keep in mind for technologists who are going about changing the world and for government leaders who look on technology as some kind of magic wand.

More than once in our discussions, Tallinn’s leaders restated an old idea that preceded the birth of computers: few things are harder to earn and easier to lose than trust.

© 2020 Norman Jacknis, All Rights Reserved

When Strategic Thinking Needs A Refresh

This year I created a new, week-long, all-day course at Columbia University on Strategy and Analytics. The course focuses on how to think about strategy both for the organization as a whole as well as the analytics team. It also shows the ways that analytics can help determine the best strategy and assess how well that strategy is succeeding.

In designing the course, it was apparent that much of the established literature in strategy is based on ideas developed decades ago. Michael Porter, for example, is still the source of much thinking and teaching about strategy and competition.

Perhaps a dollop of Christensen’s disruptive innovation might be added into the mix, although that idea is not any longer new. Worse, the concept has become so popularly diluted that too often every change is mistakenly treated as disruptive.

Even the somewhat alternative perspective described in the book “Blue Ocean Strategy: How to Create Uncontested Market Space and Make Competition Irrelevant” is now more than ten years old.

Of the well-established business “gurus”, perhaps only Gary Hamel has adjusted his perspective in this century – see, for example, this presentation.

But the world has changed. Certainly, the growth of huge Internet-based companies has highlighted strategies that do not necessarily come out of the older ideas.

So, who are the new strategists worthy of inclusion in a graduate course in 2018?

The students were exposed to the work of fellow faculty at Columbia University, especially Leonard Sherman’s “If You’re in a Dogfight, Become a Cat! – Strategies for Long-Term Growth” and Rita Gunther McGrath’s “The End Of Competitive Advantage: How To Keep Your Strategy Moving As Fast As Your Business”.

But in this post, the emphasis in on strategic lessons drawn from this century’s business experience with the Internet, including multi-sided platforms and digital content traps. For that there is “Matchmakers – the new economics of multisided platforms” by David S Evans and Richard Schmalensee. And also Bharat Anand’s “The Content Trap: A Strategist’s Guide to Digital Change”.

For Porter and other earlier thinkers, the focus was mostly on the other players that they were competing against (or decided not to compete against). For Anand, the role of the customer and the network of customers becomes more central in determining strategy. For Evans and Schmalensee, getting a network of customers to succeed is not simple and requires a different kind of strategic framework than industrial competition.

Why emphasize these two books? It might seem that these books only focus on digital businesses, not the traditional manufacturers, retailers and service companies that previous strategists worked at.

But many now argue that all businesses are digital, just to varying degrees. For the last few year we’ve seen the repeated headline that “every business is now a digital business” (or some minor variation) from Forbes, Accenture, the Wharton School of the University of Pennsylvania, among others you may not have heard of. And about a year ago, we read that “Ford abruptly replaces CEO to target digital transformation”.

Consider then the case of GE, one of the USA’s great industrial giants, which offers a good illustration of the situation facing many companies. A couple of years ago, it expressed its desire to “Become a Digital Industrial Company”. Last week, Steve Lohr of the New York Times reported that “G.E. Makes a Sharp ‘Pivot’ on Digital” because of its difficulty making the transition to digital and especially making the transition a marketing success.

At least in part, the company’s lack of success could be blamed on its failure to fully embrace the intellectual shift from older strategic frameworks to the more digital 21st century strategy that thinkers like Anand, Evans and Schmalensee describe.

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