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March 30 2012

Top Stories: March 26-30, 2012

Here's a look at the top stories published across O'Reilly sites this week.

Designing great data products
Data scientists need a systematic design process to build increasingly sophisticated products. That's where the Drivetrain Approach comes in. (This report is also available as a free ebook.)


Five tough lessons I had to learn about health care
Despite the disappointments Andy Oram has experienced while learning about health care, he expects the system to change for the better.


A huge competitive advantage awaits bold publishers
"The Lean Startup" author Eric Ries talks about his experiences working with traditional publishing structures and how they can benefit from lean startup principles.

The Reading Glove engages senses and objects to tell a story
What if you mashed up a non-linear narrative, a tangible computing environment and a hint of a haunted house experience? You might get the Reading Glove, a novel way to experience a story.


Passwords and interviews
A candidate that forks over a social media password during an interview could become an employee that gives out a password in other situations. Employers aren't making that connection.


Fluent Conference: JavaScript & Beyond — Explore the changing worlds of JavaScript & HTML5 at the O'Reilly Fluent Conference, May 29 - 31 in San Francisco. Save 20% on registration with the code RADAR20.

Ambulance photo: Ambulance by plong, on Flickr

March 27 2012

A huge competitive advantage awaits bold publishers

In the video interview below, Eric Ries (@ericries), author of "The Lean Startup," sits down with O'Reilly online managing editor Mac Slocum to talk about the lean startup method and how it applies to publishing. Ries argues:

"When you're publishing a new book or any piece of media, you're actually an entrepreneur, whether it says that on your business card or not. It doesn't matter if you're an editor, a publisher or an author, you are an entrepreneur." (Discussed at 00:23.)

Ries talks about the lengthy process of producing a book and the inefficient business practices behind the slow iteration speeds:

"When I signed my publishing contract, I asked for the expedited process, which I was told was about 18 months. In those 18 months, how much time was actually spent on the editorial production of the book itself? Very little time. Most of the time was either me waiting for my editor or him waiting for me. It was dealing with all the intricacies of the publishing process — the catalog, figuring out the marketing campaign, tons of activities that are all important, but have nothing to do with the actual production of the book. [The 18 months is about] fitting a zillion books — far too many — into this crazy waterfall process.

"The reason we call this 'lean startup' is because of an insight that happened in manufacturing called lean manufacturing. Working in these supposedly efficient silos, where everyone is in their department and the work product is passed from department to department seems very efficient, but it's actually radically inefficient. The first publisher to restructure their process around these [lean] principles is going to have a huge competitive advantage over their rivals." (Discussed at 5:04.)

Ries also says that "the one realization that has not hit publishing yet is that if you make content, you're in the software business … if you look at the supply chain, who's accumulating all the power? It's software companies like Apple, Amazon and Google." (Discussed at 6:43.)

For more on how the lean startup methodology applies to publishing, check out the full interview in the following video:

The future of publishing has a busy schedule.
Stay up to date with Tools of Change for Publishing events, publications, research and resources. Visit us at oreilly.com/toc.

Associated photo on home and category pages: Eric Ries by O'Reilly Conferences, on Flickr

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February 17 2012

Top stories: February 13-17, 2012

Here's a look at the top stories published across O'Reilly sites this week.

The stories behind a few O'Reilly "classics"
Tim O'Reilly: "It's amazing to me how books I first published more than 20 years ago are still creating value for readers."

How to create a visualization
Creating a visualization requires more than just data and imagery. Pete Warden outlines the process and actions that drove his new Facebook visualization project.

Let's remember why we got into the publishing business
At the 2012 Tools of Change for Publishing Conference this week in New York City, keynoter LeVar Burton reminded the audience why storytelling will always matter.

There's Plan A, and then there's the plan that will become your business
Drawing from the Lean Startup and other methods, "Running Lean" helps entrepreneurs transform flawed Plan A ideas into viable companies. "Running Lean" author Ash Maurya explains the basics in this interview.

The bond between data and journalism grows stronger
This interview with Liliana Bounegru, project coordinator of Data Driven Journalism at the European Journalism Centre, offers more insight into why the importance of data journalism continues to grow in the age of big data.


Strata 2012, Feb. 28-March 1 in Santa Clara, Calif., will offer three full days of hands-on data training and information-rich sessions. Strata brings together the people, tools, and technologies you need to make data work. Save 20% on Strata registration with the code RADAR20.

February 13 2012

There's Plan A, and then there's the plan that will become your business

We need a word that captures the specific sort of pain entrepreneurs feel when their carefully developed startup ideas are met with blank indifference. All that time. All that effort. And it adds up to ... this?

"Running Lean" author Ash Maurya (@ashmaurya) doesn't have that word, but he may have something better: a method for avoiding the pain altogether. In the following interview, Maurya explains how the Running Lean process helps startups iterate from flawed "Plan A" ideas to products people want.

What is Running Lean?

Ash MauryaAsh Maurya: Running Lean is a systematic process for quickly vetting and building successful products. Most entrepreneurs start with an initial vision: their "Plan A." Unfortunately, most Plan A ideas don't work. Running Lean helps entrepreneurs iterate from their initial Plan A to one that works — before running out of resources.

What are the early signs that a Plan A idea isn't working?

Ash Maurya: A startup is about bringing bold, new ideas to the world. That naturally works to your advantage. Your initial goal is getting a strong signal (positive or negative) from customers. This typically doesn't require a large sample size. So, for instance, if you can't even get 10 strangers to say they want your product (or better yet, pay for your product), this problem is not going to go away by targeting 1,000 people. A strong negative signal indicates that your bold hypothesis most likely won't work. It lets you quickly refine or abandon it.

On the other hand, a strong positive signal doesn't necessarily mean it will scale up to a significant business. But it does give you permission to move forward on the hypothesis until it can be verified later through quantitative means.

Is there any value to writing a business plan?

Ash Maurya: Before you can start the process of iteration, you have to draw a line in the sand. You have to start by documenting your initial vision (or Plan A) and sharing it with at least one other person. Otherwise, it's too easy to endlessly iterate in your head and never be wrong.

Traditionally, business plans have been used for this purpose. But while writing a business plan is a good exercise for the entrepreneur, a business plan falls short of its intended purpose. Few people take the time to actually read business plans. More importantly, since many Plan As are likely to be proven wrong anyway, spending several weeks or months writing a 60-page business plan largely built on untested hypotheses is a form of waste.

I instead recommend using a one-page business model format called Lean Canvas. It captures the same core elements you find on a business plan, but because it fits on one page, it's a lot more concise, portable and readable.

Running Lean — This book demonstrates ways to apply and test techniques from the Customer Development, Lean Startup, and bootstrapping methods. Learn how to engage customers throughout the development cycle so you can build a product people will actually buy. (Note: This digital early release edition includes the author's raw and unedited content. You'll receive updates when significant changes are made, as well as the final ebook version.)

Why is it a bad idea to build products in stealth?

Ash Maurya: There is a fear, especially common among first-time entrepreneurs, that their great idea will be stolen by someone else. The truth is two-fold: First, most people are not capable of visualizing the potential of an idea at such an early stage; and second, they won't care. The initial challenge for most startups is getting noticed at all.

There is also a difference between stealth and obscurity. Stealth is bad because you build products in complete isolation only to find out later that you were optimizing a product no one wanted. On the other hand, obscurity is a gift. It allows you to test your product at micro-scale, getting it right, before attracting a lot of attention and scaling out.

So avoid stealth, but embrace obscurity.

Do the techniques in your book only apply to tech-centric startups?

Ash Maurya: Even though a lot of these concepts were recently popularized by tech-centric startups, I believe the principles they embody are universally applicable to products ranging from high-tech to no-tech. Several core principles in "Running Lean" date back to the last century when Taiichi Ohno and Shigeo Shingo were laying out the early groundwork for the Toyota Production System, which later became "lean manufacturing." I used these same techniques in the writing of my book, which I share as a case study in the book along with several other non-tech products.

What's the connection between Running Lean and the Lean Startup?

Ash Maurya: Running Lean is a synthesis of three methodologies: Lean Startup, Customer Development, and Bootstrapping. Of the three, Running Lean draws the most from Lean Startup. While the Lean Startup, created by Eric Ries, codifies the core principles, my goal with Running Lean was to create an actionable how-to guide for taking these principles to practice.

[Note: Eric Ries is the editor of the Lean Startup Series, which includes "Running Lean."]

Why did you decide to apply Lean Startup methods to your own work?

Ash Maurya: When I was first exposed to Lean Startup, I was already running a company and on my fifth product at the time. I had built products in stealth; attempted building a platform; dabbled with open sourcing; practiced release-early, release-often; embraced "less is more"; and even tried "more is more" — all with varying degrees of success.

I saw that acting on a vision can easily consume years of your life, and I was in search of a better, faster way of vetting and building products.

The key idea from Lean Startup that resonated with me was that of rapid iteration around customer learning. Specifically, that you could almost always test the riskiest parts of a vision without having to build the product first.

As I started internalizing these principles, I had more questions than answers. That prompted my own rigorous testing and application of these principles, which led to the book "Running Lean" and several other software products I am now building.

This interview was edited and condensed.


"Running Lean" author Ash Maurya will discuss the Running Lean methods in a free webcast on Feb. 14 at 10 am PT / 1 pm ET. Register to attend.



Related:


August 18 2011

The Meat to Math ratio

As we enter one of the biggest tech IPO seasons in recent history — LinkedIn, with Groupon, Pandora, Zillow, Dropbox, Zynga, and CafePress all lining up behind it — it's hard to know what will fly and what will flounder.

One indicator of a company's potential is how well it can scale its business independent of human intervention. This isn't simply the ability to automate tasks or replace workers with machines; rather, it's the ability to augment people with data and processes.

Call it the Meat to Math ratio.

First, a comparison

To explain what I mean, I've done some back-of-the-napkin math on six companies. Four are public, and two have impending IPOs. Of the four public ones, two are disruptors and two are the established incumbents they're beating to a fiscal pulp.

One common way to measure a company's productivity is the revenue per employee.

Revenue per employee across six companies

It's not just about revenue per employee, though. As Paul Strassman said before the first dot-com bubble in 1998, we shouldn't give industrial-age answers to information-age questions. Rather, it's about how well a company can leverage its employees over the long term. Companies with a good meat-to-math ratio should be able to do things like:

  • Automating processes at scale.
  • Maintaining genuine interactions with their customers despite a high number of customers per employee.
  • Finding new businesses from their own data exhaust through introspection and experimentation.

I want to look at each of these three in more detail.

Turking, then automating

I've spent the last year looking at a lot of new ventures, partly because of my involvement in a startup accelerator. Our accelerator uses lean startup methodologies. These are techniques for pushing the uncertainty to the front of the company's lifespan. Rather than getting your investment and business in place before launching, a lean model is all about doing the least amount of work to accurately predict whether a particular business will succeed. Then it's about iterating quickly to a fit between a set of product features and a target market. It's not a perfect science, but it's a good way to avoid losing a lot of money on a bad idea.

One lean startup trick is doing things by hand rather than wasting time programming. Consider, for example, that you're thinking of launching a search-by-email company. Rather than coding everything, you'd read users' emails, search for them, and respond in an email. You'd soon find out whether people wanted to search by email, without investing time in natural language parsing, email handling, and so on. You'd be "turking," a term that refers to the Turk (from which Amazon's people-as-a-function-call service gets its name.)

Turking takes many forms. It might mean drawing rudimentary user interfaces, then watching someone "use" them with their finger (a process called paper prototyping). Or it might mean replacing some complex function with a human (what we jokingly refer to as a Flesh-Based API). Or maybe it's creating landing pages for applications that don't exist, to see who signs up. One of our incubated companies didn't code for a month. Instead, they ran surveys and did customer development until they found something people cared about.

Early on, meat is cheaper than math.

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But if a company can't make a transition to math, it will have to turk at scale. Turking at scale is another way of describing the dirty business of managing people, with all of the chaos, uncertainty, and HR headaches it entails.

There's an old adage among investors that a change in order of magnitude means a change in leadership. If a company goes from $10M a year to $100M a year in revenue, it's time for a new leader. Similarly, if it goes from 100 to 1,000 customers, or from 10 to 100 employees, something has to change. Those customers and employees are meat, and meat is hard to scale.

Cloud computing and virtualization are a move from atoms to bits, replacing rack-and-stack with click-and-drag, and the resulting increases in IT productivity and server-to-administrator ratios are impressive. If you move to a virtualized, properly orchestrated IT architecture, you can survive an order-of-magnitude increase without throwing meat at the problem.

Put another way, meat is how you scale atoms — the kind that make up brick and mortar. Math is how you scale bits — the kind that make up big data businesses. Businesses that can scale bits are interesting, because bits don't have the coefficient of friction that atoms do.

Being genuine to the masses

There's a book called "The Clustering Of America." At one time, it was the Bible of marketing. It broke down, zip-code by zip-code, the population of the United States. It clustered people into simple and almost laughably stereotypical groups like "Blue-blood Estates," "Dodge Diplomats," and "Towns & Gowns."

It was the perfect book for a "Mad Men" era, where a pithy slogan and the right timeslot could open a million wallets. In the golden age of broadcasting, obedient audiences sat down as one around the family TV to watch a show at a time of the network's choosing.

Today, that world is a fading memory. DVRs, iTunes, and streaming have freed us from the tyranny of the o'clock. They've also made it easy for us to find our niche programming. Bruce Springsteen didn't think big enough: We have 57 million channels, and everything's on.

Traditional marketers hate this. They're hung over, recovering from a cheap cocktail of one-to-many, broadcast media purchases. They like buying things in big chunks, aimed at homogeneous clusters.

By contrast, modern marketing is about attention and engagement. It's about doing something interesting, and getting tailored messages to micro-markets that expect personal attention and engagement with the companies they love. Every brand has its Little Monsters, but unlike Lady Gaga, legacy businesses don't know how to interact with them.

Former Coca-Cola CMO Sergio Zyman describes marketing as "selling more stuff to more people more often for more money more efficiently." In other words, selling at scale. If modern, post-broadcast marketing is about being genuine, then marketers face the challenge of being genuine (meat) at scale (math).

Companies that can work this out will win. But it will take big data systems, next-generation customer relationship management, and machine learning to help augment front-line employees.

Mining your own exhaust

Netflix and Amazon have something in common beyond their destruction of incumbents: the ability to create new businesses from whole cloth while still generating revenue. Netflix managed to become the dominant paid streaming platform, using mail-based distribution to bootstrap itself. Amazon created a cloud service from what it learned about running large-scale IT infrastructure; introduced a digital reader that now outstrips book sales; and expanded from books into many other retail markets.

Blockbuster and Barnes & Noble could have done these things, but they didn't. Netflix and Amazon used their own data exhaust to innovate. They started new races in the middle of an existing one. As data-driven companies, they create volumes of data about their own operations, then recycle this into new insights and new businesses. Another reason for their agility is that meat has inertia: it's hard and time-consuming to hire, fire, and retrain people; it's easy to change an algorithm.

Back to those companies

So the meat-to-math ratio is vital for several things:

  • Scaling the company without adding messy atoms and the related overhead.
  • Scaling marketing without becoming disconnected from markets or customers.
  • Iterating into new markets and new services adjacent to a core business.

Comparing our six firms — four public, two soon to be — in this light, what does a good meat-to-math ratio mean for your business? Let's look closely at the two impending IPOs: Groupon and Dropbox.

Groupon's offering, initially valued at $30B, is taking a beating. It has 7,500 employees, and it's adding them fast. Despite the hiring binge, Groupon is seeing a promising increase in word-of-mouth sales, which is a sustainable model for scaling the business, and cost of customer acquisition is a key measure of whether a company can survive.

But Groupon has to sell to two groups: consumers looking for deals, and merchants willing to offer them. Humans have to call on small businesses directly. There are other reasons for Groupon's troubles — the company lacks sustainable barriers to entry, as shown by competitors like Google and LivingSocial — but the root of the issue is this: Groupon is throwing meat at the growth problem, when it should be throwing math at it.

Dropbox, on the other hand, has 74 employees (a number that's also growing fast, but is a hundred times smaller than Groupon.) Its IPO is valued at $5B, and it reportedly accepted a lower valuation in order to go with the banker it wanted. Dropbox has customer acquisition built into its model, a viral-marketing scheme where users invite their friends in return for extra free storage.

Assuming that the market values math over meat, how would you expect these two companies to compare on valuation per capita?

The way we keep score, like it or not, is market capitalization or IPO valuation. Let's compare these six companies' IPO values by company.

Market cap per employee across six companies

Clearly, if you're proving you can scale with math instead of meat, the market rewards you handsomely.

In a data-driven world, the true measure of any organization, from a regional government to a global conglomerate, is its meat-to-math ratio. This sounds like a cold statement, saying machines are better than people. That's not the point here: machines are better with people, and companies that can't augment their employees with data and tools, that cling to antiquated ideas like broadcast, and that can't turn their data exhaust into insight and innovation, are doomed.

Showing my math

The numbers I've used come from a variety of sources and time periods; they should be treated as illustrative, rather than hard data. In the interest of transparency, here's how I got the data. If you have better numbers, I'd love to hear them.

For Amazon: Amazon had $12.95B in Q410 revenues (This includes a variety of other revenues, most significantly non-media sales and computing services), and 33,700 employees, meaning a revenue per employee of $384,273. The company had a market cap of $91.8B in early August, 2011, and roughly 43,200 employees, for a value-per-employee of $2,125,694. Sources: Google Finance; Techflash, Blorge.

For Barnes & Noble: $1.91B in Q410 revenues, and 35,000 employees, meaning a revenue per employee of $54,571. The company had a market cap of $947M in early August, 2011, and roughly 30,000 employees, for a value-per-employee of $31,567. Sources: Hoovers says 30,000 in 2011, Wikipedia says there were 40,000 employees in 2008, and Zenobank says 35,000 today.

For Netflix: $444M in Q409 revenues, and 1,000 employees, meaning a revenue-per-employee of $444,000. The company had a market cap of $12.8B in early August, 2011, and 1,000 employees, for a value-per-employee of $595.92M . Sources: Home Media Magazine; Netflix's Adrian Cockroft tells me there are roughly 1,000 salaried contractors, plus hourly workers at distribution centres. I'm being conservative and using that same number for 2009, when it was certainly smaller.

For Blockbuster: $400M in Q409 revenues. The company peaked at 60,000 employees in 2009; I've assumed 55,000 by Q4, meaning a revenue-per-employee of $7,273. The company is not currently trading. Sources: Home Media Magazine, USNews.

For Groupon: Q211 revenues were $878M, with roughly 7,500 employees, for a revenue-per-employee of $117,067. The company's IPO filing was initially valued at $30B, but will likely be significantly lower; nevertheless, I'm using the original valuation. That means a value-per-employee of $4M. Sources: Groupon S-1 filing; Business Insider says Groupon had 3,000 employees in Q4 2010, and is adding headcount aggressively. SB Online says hiring costs have jumped. And the best guess on Quora puts the count at 7,500 employees.

For Dropbox: In Q211 Dropbox had $25M in revenues, and 74 employees, for a revenue per employee of $338K. The company is planning a $5B IPO, which means a value-per-employee ratio of $67.6M. Sources: There are 74 employees on Dropbox's website (they list them all.), TechCrunch suggests that the company chose a lower valuation than they could have in order to get the right investment bank. Business Insider estimates 2011 revenues at $100M total; I used 25 percent of these.



Related:


February 23 2010

Four short links: 23 February 2010

  1. SMS in Disaster Response -- Haitians SMS urgent needs to 4636, where they're translated through crowdsourcing and acted on. All based on the Uhsahidi SMS engine.
  2. Inside Open Source's Historic Victory -- open source developer wins against someone who took his work, added it to an open patent application, and then sued the open source developer for violating his patent.
  3. What's Wrong with Confidence (Pete Warden) -- the lean startup approach and the scientific method. Good read, with two magnificent quotes: "Strong opinions, weakly held, and Confidence is vital for getting things done, but it has to be a spur to test your theories, not a lazy substitute for gathering evidence.
  4. If You're a Pirate -- the user experience of legitimate DVDs is shite. That's not the only reason that people pirate, but it sure ain't helping.

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