GUEST POST from Tim Kastelle

You probably heard about the contest that Netflix started in 2006 to crowdsource improvements in their recommendation algorithm. They offered a $1 million prize to anyone that could improve the accuracy of the recommendation algorithm by at least 10%. In 2009, a team of people hit the target, and won the prize.

Awesome, right? The team got their big check, Netflix got their performance improvement, and everyone ended up happy. Well, sort of.

It turns out that Netflix has never implemented the algorithm that won the prize. Mike Masnick has an excellent article outlining this surprising turn of events.

Here is part of what Netflix says about it:

“We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.”

Why not? Because the way their customers use the service has shifted:

“Streaming has not only changed the way our members interact with the service, but also the type of data available to use in our algorithms. For DVDs our goal is to help people fill their queue with titles to receive in the mail over the coming days and weeks; selection is distant in time from viewing, people select carefully because exchanging a DVD for another takes more than a day, and we get no feedback during viewing. For streaming members are looking for something great to watch right now; they can sample a few videos before settling on one, they can consume several in one session, and we can observe viewing statistics such as whether a video was watched fully or only partially.”

This is pretty amazing, and there are some very interesting lessons here:

1. It’s hard to fit new innovations into old business models: the recommendation algorithm was built for rentals, where the recommendations have to be right. So there was great value in making them more accurate. For streaming, when people can sample a movie before they watch all of it, there is much less pressure to get all of the recommendations absolutely correct. Trying to fit streaming into the old business model has been giving Netflix fits for most of the past year or two, and this is just more evidence of how hard it is to fit new innovations into old business models.

2. You have to break connections to make room for your new ideas: the engineering issue is an interesting one. Apparently a fair bit of effort was required to code the new algorithm into all of the existing processes. And that’s always the case with a new idea. You can’t just parachute new ideas into existing slots in the economy. First, you have to make space for them. You do this by breaking connections.

3. Optimising when your environment is changing is very dangerous: this is the big lesson. Netflix was optimising rental while the entire structure of the industry was changing. What they really needed to be doing was get the business model for streaming right. However, this wasn’t at all obvious when they started the contest in 2006. You always need to be aware of what’s going on around you. This has an enormous impact on what kind of ideas you should be testing out. If the environment is changing rapidly, then making yourself more efficient is risky – it’s quite possible that you’re perfecting a process that could be obsolete in a couple of years.

It’s been astonishing to watch Netflix struggle with the business model for streaming. They have been a textbook case of disruption for they way they changed the structure of the movie rental industry. And now they’re having huge problems adapting to the latest change in industry structure.

It’s kind of scary when things move this fast. That’s a big part of why being risk averse and not innovating is actually more risky than you think.

At least the guys that wrote the algorithm got their $1 million.

image credit: nytimes

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GUEST POST from Tim Kastelle

I don’t like focus groups. I’ve found the information that you get from them to be too shallow to be useful. However, this doesn’t mean that when we’re innovating we should just pursue whatever ideas drift across our minds.

Steve Jobs was quoted last year about how Apple doesn’t use focus groups. A number of people used this quote to justify being completely out of touch with their customers, which is a perversion of the main point. The reason that Apple can skip focus groups is that they are incredibly good at understanding what people are really trying to accomplish with technology. To do this, you have to develop a deep understanding of what the core issues in your field are. Here’s an analogy:

There is a chapter by the scientist/artist Jonathan Kingdon in the excellent new book Field Notes on Science & Nature, edited by Michael Canfield. There’s a fascinating section where Kingdon talks about drawing versus photography:

“In the age of instant digital photography it may seem perversely old-fashioned to put a value on the slow, primitive, and inaccurate techniques of manual drawing. Photography teaches us that the very act of putting a line around the edge of an observed object is an artifice. Such outlines rarely appear in photographs, or, for that matter, in nature, and yet… and yet? Contemporary research on the human brain shows that it does NOT process images as a neutral camera does. The brain finds edges and builds constructions that are at least partly based on previous experience 0 possibly including past contacts with artifacts such as “drawings” as well as previous knowledge of natural objects. Visual neurobiology is a discipline in its infancy, but it confirms that visual constructions are both complex and integral to cognitive development. This implies that even an outline sketch that bears little relationship to the so-called objectivity of a photograph might actually transmit information to another human being more selectively, sometimes even more usefully, than a photograph.
…
If the brain is unlike a camera in actively seeking outlines, there is a strong implication that “outline drawings” (just to take a single type of visual expression) can represent, in themselves, artifacts that may correspond more closely with what the brain seeks than the charts of light-fall that photographs represent.”

What does this mean in practice? It means that these drawings of a caracal by Kindgon may well transmit information to us that is more useful, more real, than what we could get from a series of photographs:

Those drawings do a great job of capturing something fundamental about the animal, as simple things often do. But to be able to draw them, you have to invest an enormous amount of time in observing the caracals, looking at what they do, in which contexts, to build up a deep knowledge of how their physical form expresses what they are trying to do.

You can’t ask a caracal (or even a house cat) what they are trying to express when they pin their ears back. But if you watch them long enough, the meaning becomes clear. Now, customers can answer questions more clearly than a caracal. Usually, at least… But sometimes, this greater ease of communication actually makes it harder to understand what they’re really trying to achieve.

It’s not an accident that the Apple products look like art. The essence of great design is to be able to communicate simply by stripping down an object or a process to it’s fundamentals – which is the same problem with which artists grapple. This is filtering, and it’s how we deal with the avalanche of information which sometimes overwhelms us.

To innovate well, we need the same kind of deep understanding of our customers that artists have of their subjects. This allows us to strip our offerings down to their essence – innovation through subtraction.

image credit: teachmath

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GUEST POST from Tim Kastelle

Are you creating something new or replacing something that’s already there? If you’re replacing, you need to do much different things than if you’re creating something new.

Every time you try to get your ideas to spread, you have to break connections. This is a lot harder if you’re trying to replace a deeply embedded idea.

Here’s how I’m thinking about it:

Replacement is like trying to knock out the red target in the middle of the diagram. The triangles might be suppliers, or complementary products, and the circles might be customers. The point is, the idea is really embedded.

If you’re trying to replace something, you have to come up with an idea that is MUCH better than the one you’re trying to replace. Here is how Stowe Boyd put it today talking about the new Microsoft Phone:

“The iPhone was easily an order-of-magnitude better that the shit phones we all tolerated when it launched. Microsoft had years to come up with something awesome, and it’s ok. Which means death, today.”

Replacement means being an order-of-magnitude better.

More academically, here is Clayton Christensen saying something similar in The Innovator’s Cookbook, and pretty good edited volume put together by Steven Johnson:

“Even if innovators succeed in cramming disruptive technology into an existing market application, the incumbents typically win. Digital photography, online consumer banking, and hybrid-electric vehicles are examples of potentially disruptive technologies that were deployed in such a sustaining fashion. Billions were spent on these innovation to beat out already acceptable and habitual technology; little net growth resulted, as sales of the new products cannibalized sales of the old; and the industry leaders maintained their rule.”

In short, replacement is very difficult. You have to stand out from the crowd, which is awfully hard, and it requires a quantum leap in functionality.

If you’re creating, you face a different set of problems. When you create something new, you don’t have any connections at all – you have to create them from nothing. This is tough.

However, the payoff to creating something new can be a lot higher than replacing. And there are some ideas that make this easier. Not all crazy ideas are great, but most great ideas are crazy: Fred Wilson says:

“When people ask me, ‘how do you know which companies and services are going to be the biggest successes?’, I usually tell them to look for the companies and services that are mocked and misunderstood. For some reason, that correlates highly with the biggest breakout successes.”

New ideas start out crappy: Greg Satell has a great interpretation of Christensen’s research, and he summarizes it by saying that disruptive innovations come through changing the basis of competition. To do this, you have to smart small, often with a new customer base, and with ideas that aren’t yet fully formed. This is completely different from how you approach replacement ideas.

For new ideas, you can use things like the lean start-up methodology: this includes the concept of the minimum viable product. The basic idea here is that you get a working version of your idea out as quickly as possible, so that you can learn what works and what doesn’t. This requires clear thinking about metrics, and a good business model, which you test all the way through.

Think about the difference between coming up with something that is an order-of-magnitude better than what’s currently out there, versus putting out a minimum viable product and experimenting. They are completely different. They require different skills, different mindsets, and different methods for experimenting and testing your assumptions.

That is why you have to be clear about whether you’re creating or replacing.

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GUEST POST from Tim Kastelle

One of the frustrating things about following politics is the idea, apparently deeply engrained, that you must never change your mind. If you do, you’re a flip-flopper, or wishy-washy, and you’re clearly not to be trusted.

The main problem with this line of thinking is that it is utterly and dangerously wrong. We live in a dynamic world, and our brains are dynamic – if you’re not changing your mind all the time, it’s a danger sign.

There are two very good reasons to change your mind: the facts have changed, or you have learned something.

Changing Facts.

To those of us that take innovation seriously, Joseph Schumpeter is the patron saint of economists. He was the first person to really articulate the importance of innovation and how central it is to economic growth.

One question that Schumpeter considered in his first groundbreaking book, The Theory of Economic Development, is this: which type of firm is more innovative – small or large? It’s a question he kept coming back to. Here is how Adrian Wooldridge put it in The Economist (and in another signal of the regard in which Schumpeter is held, his weekly column there is called “Schumpeter”):

“Joseph Schumpeter, after whom this column is named, argued both sides of the case. In 1909 he said that small companies were more inventive. In 1942 he reversed himself. Big firms have more incentive to invest in new products, he decided, because they can sell them to more people and reap greater rewards more quickly. In a competitive market, inventions are quickly imitated, so a small inventor’s investment often fails to pay off.”

Now, the big or small question is still interesting, but that’s not what I’m concerned with today. Instead, look at how he phrases this – “Schumpeter… argued both sides of the case.” This idea often comes up, and people usually try to say that Schumpeter was being slippery by trying to have things both ways.

But here’s the thing – Schumpeter changed his mind because the facts changed. In 1909, big firms didn’t innovate at all. The largest firms were mostly extractive. Nearly all new ideas came from smaller firms. Corporate R&D was just starting at the time, in Edison’s workshop and in the labs of the chemical companies that were trying to make new dyes for clothes.

A lot changed between then and the 1940s, including the innovation process. By the middle of the century, invention and innovation both were dominated by large corporate R&D. That was the birth of the mass market, an economic environment built by and favouring large firms. Schumpeter changed his mind because the facts changed.

Learning Something.

Here’s a quote attributed to John Maynard Keynes:

“When the facts change, I change my mind. What do you do, sir?”

One of the implications implicit in that quote is that Keynes was always right. Unfortunately, most of us aren’t as infallible as he was. So we have to learn by being wrong. This is a crucial innovation skill. We have a hypothesis about how we can make the world a better place – we have a great idea. The only way to turn it into an innovation is to experiment. Often, our initial assumptions are wrong. By experimenting, we figure out which ideas work, and which don’t –we learn. And by learning, we change our minds.

Dynamic Minds for Dynamic Times.

We live in a dynamic world. More importantly, we are learning machines. Both of these facts mean that we should be changing our minds all of the time. Rather than being a sign of weakness, a changed mind is a sign of someone that knows something more than they used to.

We should be learning all the time. Changing your mind is a sign of learning. We shouldn’t avoid it, we should seek it out. As Edward de Bono says:

“If you never change your mind, why have one?”

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GUEST POST from Tim Kastelle

Here’s just one of several examples from me today – I was completely wrong about the talk I gave this morning. I sent the slides through to the organization I was giving it for on Wednesday. Then I spent the entire 90 minute drive down to the venue re-thinking what should go in the talk.

I thought of about 10 slides that I had to add, and I was sure that if I didn’t add them, the talk would be a disaster. When it was time to set up, I looked on my usb stick to find the other talk that had the additional slides on it, but it wasn’t there. I had to go with my original ones.

So I did. And it ended up being the best public talk I’ve ever given.

I was completely wrong on the drive down about what had to change.

There all kinds of mistakes that we can make. We can overthink something like I did, or we can underthink it. We can try something that doesn’t work, or we can let fear keep us from trying something that would work. It was fear at work with me this morning. I had a talk that was quite different from others that I’ve given, and I wanted to add in some familiar slides so that I’d feel more comfortable. If I had, it would have ruined the talk. It’s good that I tried out the new talk – even if it hadn’t worked, I’d have learned something that would make the next one better, which wouldn’t have happened if I had gone with one of my standard talks.

Scott Berkun posted a great quote from Woody Allen from the American Masters documentary on him:

“There are a lot of surprises that happen between writing it, doing it, and seeing it on the screen, most surprises are negative. Most surprises are that you thought something was good, or funny, and it’s not. I’ve made just about 40 films in my life and so few of them have really been worth anything. Because it’s not easy – if it’s easy it wouldn’t be fun, it wouldn’t be valuable.”

The key point that I take from this is that we have to execute our ideas to see if they’ll work. The whole point of innovating is to experiment. Try something, find out what works, and learn from what doesn’t.

There always will be surprises, and not everything will work out as you plan. In fact, almost nothing will work out as you plan. That’s why we have to actually try things, because that’s the only way to find out what will actually work.

When was the last time you were wrong? I hope it was recently.

image credit: flickr/elycefeliz

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GUEST POST from Tim Kastelle

I missed writing about someone on Ada Lovelace Day this year because I was actually teaching my MBA class about a great technology heroine – Jane McGonigal.

I love the concept behind Ada Lovelace Day. In order to encourage more women to consider careers in science, technology, engineering and mathematics, the day is used to discuss women from these fields that have had an impact on your life. It’s named after Ada Lovelace, who was the world’s first computer programmer and an expert on Charles Babbage’s Difference Engine.

I talk about Lovelace in all of my classes too. But yesterday on Ada Lovelace Day, I played this video for my class:

When we discussed it in class, there were a few good innovation points that came up:

• McGonigal is innovating the business model for gaming by adding a key ingredient – purpose. If we look at the conclusions from Dan Pink’s drive, there are three factors that lead to satisfying work: autonomy, mastery and purpose. Video games currently provide players with the first two – autonomy and mastery. That’s why they’re so addictive. But the games that McGonigal designs also have a purpose – to save the world. That’s revolutionary.
• If you’re going to innovate something, you might as well innovate something that matters. There are plenty of people writing new video games. But writing games that are specifically designed to enable positive changes in the way people live is more rare. In her book Reality is Broken, McGonigal is saying that the games we play should be designed to have an impact in the world. In other words, we need more opportunities to feel what gamers feel, but that we should be achieving this in contexts that pertain to the real world. That’s why I love Evoke – it’s a game, but the real world outcomes are substantial (the website for Evoke is worth some of your time – it’s fascinating). There’s a nice review of RiB by Joe MacCarthy here.

That’s why Jane McGonigal is one of my technology heroes. She is fantastically creative, and she is using her skills and talents to try to create meaningful change. That makes her a great heroine on Ada Lovelace Day.

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GUEST POST from Tim Kastelle

Here is something I ran across yesterday that confuses me quite a bit. Take a look at this graph from The Economist:

The thing that confuses me is that I often hear from managers and others here in Australia that the reason that their organization isn’t very innovative is that they are risk averse. But if you look at that graph, Australians clearly aren’t risk averse at all.

How can it be that we embrace risk in gambling, an activity that only destroys value, but we avoid it in innovation, an activity that actively creates value?

Any thoughts?

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GUEST POST from Tim Kastelle

There are some interesting answers to this question in Why the West Rules, For Now by Ian Morris. As part of his research, Morris has developed a Social Development Index, which he uses to track the progress of civilizations from 14000 BC to present. The index tracks improvements in areas such as energy capture (both as food and as fuel), organizational capability, technology development, and information sharing capacity.

Here is what the graph shows (taken from this .pdf that summarizes the research):

The first big jump happened between 2000 and 1000 BC, indicated by the very crude arrow that I’ve added. What was the innovation that caused that jump?

The invention of Bureaucracy.

Morris (and many other historians) argue that it was the invention of bureaucracy that actually triggered the development of agriculture, written communication, and other tools that are necessary for people to undertake complex tasks.

Bureaucracy is one of the most important innovations in human history – without it, we’d still be in caves. So why does it get such a bad rap whenever we talk about innovation? It’s nearly impossible to discuss innovation within organisations without hearing complaints about bureaucracy and bureaucrats.

The problem isn’t actually with bureaucracy. Bureaucracy makes systems, supports the development of routines, and gives us some constraints – which are actually essential to innovation (see here and here for examples). We need all of these things to innovate.

The problem with bureaucracy is when we follow rules simply for the sake of following rules. This is another form of path dependence, which leads to lock-in on sub-optimal systems. The problem is with bureaucratic systems that don’t support strategy – these stifle innovation.

Bureaucracy is actually a neutral term, like aerodynamics. To call a car “aerodynamically designed” is a nonsense – all cars have aerodynamics. It’s just that Teslas and Porsches have excellent aerodynamics, while minivans and SUVs have terrible aerodynamics.

In the same way we can have excellent bureaucracy, which supports innovation, and terrible bureaucracy, which obstructs innovation.

Bureaucracy isn’t actually an innovation obstacle, but bad bureaucracy is.

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GUEST POST from Tim Kastelle

One of the major obstacles to innovation is switching costs. Here’s a story that shows why: after 120 years, the main library at Princeton University is finally converting all of it’s books to the Library of Congress book classification system.

This is remarkable for several reasons.

The main one is that the Library of Congress system itself is 113 years old!

Princeton’s system had been invented about 7 years earlier by University librarian Ernest Cushing Richardson. At the time, academic libraries had decided that the Dewey Decimal System, invented in 1876, was too simple for research libraries. Harvard, Yale and Princeton each developed their own (and I’m sure that today, each of them will tell you that theirs was first…).

Richardson developed his system in the early 1890s, and all of Princeton’s books were duly filed according to it.

The Library of Congress system was designed for research libraries was introduced in 1897, but then it was too late for Richardson. At the time, he said that he thought that eventually all libraries would use it. But he wouldn’t, because switching all of the books over would be expensive and time consuming. And, he’d just reclassified all of the books once to reflect his new system.

Princeton didn’t start using the LoC System until the late 1960s (around the same time that they finally got around to admitting women). So for the past 40+ years the library has held books using both classification systems. This has led to books on the same topic being filed in completely different physical locations – a problem. This is what motivated the final conversion over to LoC.

It’s an innovation diffusion story that has played out over 100 years – which illustrates a major problem in diffusing innovations – it is often costly to switch. Richardson acknowledged that the LoC System was better. It was simply to expensive to change to it.

It’s not enough to just come up with a great new idea, even one that is clearly better than whatever it replaces. You also have to get the idea to spread. Switching costs are one of the big obstacles to doing so.

And what about Richardson’s innovation? It’s not completely dead. Princeton has decided that it’s still too much trouble to convert all of books outside of the main library. So there will still be a few books around using his very long call numbers…

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GUEST POST from Tim Kastelle

I did a workshop last week with a group working on improving innovation within the Australian school system. I played my normal role of grenade-thrower, errr, thought-provoker on the topic of innovation, while working with eight other people that all have backgrounds in education. As the day went on, I noticed something interesting.

In sessions like this, people always pick up on different points that I make. This is one of the reasons that I try to make a wide variety of points – I never know for sure which ones will stick! On this day, one of the points that I made is that any time you have a gap between where you currently are and where you want to be, you have to innovate. You can’t bridge these gaps by simply doing more of what you’re currently doing.

This is a really important point when thinking about public sector innovation. In this context, all the justifications for innovating based on improving profit, market share, survival odds, and so on don’t really apply. And yet, innovation is still critical. Why? To bridge those gaps.

It was fascinating on this day to see how the teachers seized on this idea. The group included people that were passionate about trying to improve teaching, and it was clear that they have been looking for a way to support the case for innovation within their schools. For the rest of the day, as I eavesdropped on the breakout groups, everyone was talking about the gap – how do we identify the gap? What strategies can we use to bridge the gap?

The experience illustrates some important points:

• Organizations always have a gap to bridge, even if they’re not financially driven: I’ve found the idea of the gap to consistently work when I talk about innovation people from the public sector or non-profits. It seems to be a good way to motivate the case for innovation in these settings.
• We need different ways to talk about innovation: when we started the day, everyone in the room seemed to be wary of innovation. It wasn’t until we framed it in this way that they really started to respond – but once they did, they took off! There are many settings in which “innovation” might seem like a threatening concept. When we’re working in these settings, we need other ways to discuss the concept.
• Innovative teachers face many of the same problems that other innovators do: how can I get support for innovation? How can we make time in our organization to innovate? How can I get other people to buy into new ideas? How can we make innovation a sustainable process? These are some of the questions that I was asked in the course of the day. They don’t sound that different from what we hear on other organizations, right? Innovators face many of the same problems, regardless of their context.

It was an intense but fun day. It’s always interesting to see which concepts resonate with people. In this case, the idea was a simple one:

Mind the gap!

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