Business

Tim O’Reilly talked about the “law of conservation of attractive profits” in a recent interview on the FLOSS Weekly podcast. Clayton Christensen explained this law in an HBR report in 2004. It says that when one thing becomes modular and commoditized, another thing becomes valuable.

O’Reilly argues that just as computers made out of commodity hardware made software more valuable, now commodity software and open standards have made data more valuable.

Taking this line of reasoning one step further, open data makes analysis more valuable. Good news for experts in statistics and machine learning.

The Hawthorne effect is the idea that people perform better when they’re being studied. The name comes from studies conducted at Western Electric’s Hawthorne Works facility. Increased lighting improved productivity in the plant. Later, lowering the lighting also increased productivity. The Hawthorne effect says that the productivity increase wasn’t due to changes in lighting per se but either the variety of changing something about the plant or the attention that workers got by being measured, a sort of placebo effect.

The Alternative Blog has a post this morning entitled Hawthorne effect debunked. The original Hawthorne effect was apparently due to a flaw in the study design; correcting for that flaw eliminates the effect.

The term “debunked” in the post title may imply too much. The effect in the original studies may have been debunked, but that does not necessarily mean there is no Hawthorne effect. Perhaps there are good examples of the Hawthorne effect elsewhere. On the other hand, I expect closer examination of the data could debunk other reported instances of the Hawthorne effect as well.

The Hawthorne effect makes sense. It has been ingrained in pop culture. I heard a reference to it on a podcast just this morning before reading the blog post mentioned above. Everyone knows it’s true. And maybe it is. But at a minimum, there is at least one example suggesting the effect is not as wide-spread as previously thought.

It would be interesting to track the popularity of the Hawthorne effect in scholarly literature and in pop culture. If the effect becomes less credible in scholarly circles, will it also become less credible in pop culture? And if so, how quickly will pop culture respond?

Check out this article from Jason Cohen: Starting a business isn’t as crazy and risky as they say. According to Cohen, popular ideas about failure rates for start-ups are based on misleading analysis of data. Statistics about business failures are muddled by two fundamental questions: (1) What is a business? and (2) What is a failure?

What is a business? There’s a big difference between a side business (a hobby or a casual source of extra income) and a primary business (main source of income) and yet statistics often lump these two together. Presumably failures are more common among side business, inflating the sense of how often serious businesses fail.

What is a failure? Common ideas about the frequency of failures are based on figures that simply track when a business goes out of existence. But a company can disappear for numerous reasons that are not failures. Maybe the company got bought out to the delight of the owner. Maybe the owner grew tired of the business and wanted to do something else. Maybe the owner retired. The figures are more encouraging when you sort out genuine failures from businesses that folded agreeably.

Related post: Plane crashes, software crashes, and business crashes

.NET Rocks episode 438 interviewed Patrick Hynds on why projects fail. One of the reasons is unclear expectations. He said in the interview that no matter what you say you’re going to do on a project, clients have additional implicit expectations. Hynds says that in order to have a successful project, you have to “destroy any hope” that you will deliver anything outside the specification. Here’s an excerpt from the podcast transcript, emphasis added.

… if I give you a spec I’m going to give you everything this document says and nothing more. In other words, if it’s not shown or described in detail in this document, it will not be done. … this is going to cost you X thousand dollars and if you expected something else to be in there, it won’t be. It sounds bad but you have to destroy any hope they have that you’re thinking the way that they’re thinking. …

I mean, in web projects we state explicitly what resolutions we will support and none others. What browser versions we will support and no others, what back-end database versions and libraries we will support and no others, that kind of thing. … I find for everything you say you’re going to do, you have to define one or two things you’re not going to do.

Hynds’ advice may sound adversarial, but everyone is happier in the long run when there are clear expectations up front.

Here’s another good quote from Hynds later in the interview.

There’s always someone out there willing to bid less to do a bad job.

Related posts:

Medieval software project management
Feasibility studies
Status report questions
Good, fast, or cheap: can you really pick two?

My frustration with personal productivity systems like GTD is that they’re all about projects and tasks. They leave out a third category: programs. GTD thinks of a project as something that can be broken into a manageable number of tasks and scratched off a list. But programs go on indefinitely and cannot be divided into a small number of one-time tasks.

I’m using the word “program” as in an “exercise program” or a “research program.” (I could think of my exercise program as a project, but it’s one I hope not to complete for a few more decades.) Sometimes there is a neat hierarchy where programs spawn off projects that can be divided into tasks. But sometimes you just have programs and tasks.

One of my frustrations with managing software development in an academic environment was the large number of programs disguised as projects. (Sorry, I know it’s confusing to talk about “programs” in the context of software development and not mean computer instructions.) You can’t manage programs as if they were projects. For example, you can’t talk about “after” project is done if it’s not really a project but a never-ending program. You have to either acknowledge that a program is really a program, or you have to have some way to make it into a finite project.

The Management Myth is a wonderfully cynical perspective on management theory from former management consult Matthew Stewart.

One strategy for increasing job security is to make yourself indispensable by never documenting anything. Deliberately following such a strategy is unethical. Passively falling into such a situation is more understandable, and more common, but it’s not very smart either.

If you’re indispensable , you can hold on to your job — maybe. But the flip side is that you can’t let go of your job either. You can never wash your hands of a project, never hand it over to someone else. You cannot be promoted. You’ll need to take your laptop with you on vacation, if you’re able to take vacation.

I’ve seen this play out in software projects that are never quite finished. The project minimally works, but only with the developer’s intervention. The developer isn’t trying to be indispensable. Quite the opposite: the developer desperately wants to get away from the project.  But the software isn’t stable. Bugs are discovered every time a new part of the code is exercised. These may be fixed quickly, but only by the original developer. Or maybe the code is stable, but only the original developer can reproduce the build. Or some part of the code ought to be configurable, but instead the developer has to constantly tweak the source code. For whatever reason, the project isn’t wrapped up and the developer cannot extricate himself from it.

The solution is to plan to make yourself dispensable from the beginning. Ask yourself throughout the project, “How am I going to be able to hand this over to someone else?” Or more graphically, “What if I get hit by a bus?”

Make yourself valuable for what you’re expected to accomplish in the future, not for what you’ve have accomplished in the past.

Related post:
Programming the last mile

The latest .NET Rocks podcast interviews Pat Hynds on why projects fail. Toward the end of his interview he mentions a simple template for status reports.

1. What did you work on?
2. What did you get done?
3. What did you do that you didn’t anticipate having to do?
4. What did you plan to do that you didn’t get done?
5. What do you plan to do?
6. What do you need from others?

When I started managing a group of programmers, I’d focus on #1 and #2. But in some ways #3 is the most important question. That question can alert you to a major time sink that’s not include in your project estimates. That question can let you know of problems beyond an individual developer’s ability to resolve. That question that can tell you it’s time to buy something you were planning on building yourself.

Imagine you’re on a highway with two lanes in each direction. Two cars are traveling side-by-side at exactly the speed limit. No one can pass, and so the cars immediately behind the lead pair go a little slower than the speed limit in order to maintain a safe distance. This process cascades until traffic slows down to a crawl miles behind the pair of cars responsible for the traffic jam.

Something similar can happen in organizations. Suppose the person at the top of a company is afraid to hire anyone smarter than himself. He wants to hire people who are talented, but not quite as talented as he is. If each of his subordinates follows his lead, the result can be a lack of talent at the bottom of the org chart.

A conversation at work this morning inspired a post on the Reproducible Ideas blog about preserving documents. Physically preserving documents may be the easy part. Keeping alive the memory that the documents exist can be much harder.

[The image above is a photograph of the Rosetta Disk, a disk preserving 13,000 pages of documentation about 1,500 human languages. The information is not encoded as bits; it is engraved. A thousand years from now, a scholar could read the disk using only a microscope. Click on the image for more information about the Rosetta Disk.]

Here are three ways to evaluate a person’s performance.

1. How good are they at their worst?
2. How good are they on average?
3. How good are they at their best?

Schools use the first two criteria, but the market uses the third.

Schools evaluate people at their worst

Teachers average grades to come up with semester grades, and semester grades feed into a grade point average. So in some sense schools evaluate average performance.

But in more subtle ways schools evaluate students by how good they are at their worst. To graduate, your lowest course grade in all your required courses must be passing. No amount of brilliance in one area can compensate for a failing grade in another area. Your creative writing grades are excellent, Mr. Shakespeare, but we cannot let you graduate until you pass physics.

How do you get on the honor roll? Your lowest grade has to be above a certain level. Again, what matters is how good you are at your worst.

How do you get to be valedictorian? Be good enough at every class to get an A. You have to be pretty good at everything, but you don’t have to be truly exceptional at anything.

Schools encourage perfectionism, not excellence. They encourage people to avoid mistakes, not to be creative.

Markets evaluate people at their best

Markets often evaluate people and products at their best.

If you write 100 obscure novels and one best-seller, you’re a best-selling author. If you consistently write moderately popular novels, you’re not. If you write one really good novel, you might get a Nobel prize. Imagine the Nobel committee evaluating a writer saying “Yeah, these two novels were brilliant, world-changing. But he also wrote this one novel that was mediocre. Let’s give the prize to someone whose books are consistently pretty good.”

The Ford F150 did poorly in focus groups. The average rating wasn’t good. But the people who liked it really liked it. And the F150 went on to be the most popular truck in history. All that matters in business is people who like your product enough to buy it. You don’t make any money by being everyone’s second choice.

If a company has one product that is a runaway success, the company is a success. If it has two or three runaway successes, even better. But a company can produce a few dismal failures (think Microsoft Bob or the Apple Newton) and still do quite well if their flagship products succeed.The same is true of the people behind these products. Someone can make a successful career with one big win even if they have a number of failures.

We all want others to see the best in us. There are ethical and economic reasons to look for the best in others. But years of education can incline us to look for the worst in others and in ourselves.

Related posts:

Quantity and quality
Four reasons we don’t apply the 80/20 rule
Gerald Weinberg’s law of twins

What is the distribution of StackOverflow user reputation scores? Here’s a graph of the number of users with reputations between 0 and 100, 100 and 200, …, 900 and 1000. (The scores go out much further, but the curve looks flat compared to the peak unless you zoom in further.)

This graph was based on a snapshot of the user reputations one day last week. The largest group, 15,219 users, had reputation less than 100. There were 2,494 users with reputation between 100 and 200, etc. The number of users in a 100-point reputation range generally decreases as the reputation score increases. The majority of users have reputation less than 100, and yet the top percentile have reputations over 4,800 and the highest reputation was 38,700. This sort of extreme disparity suggests a power law distribution.

The test for whether the reputation scores follow a power law is to plot the logarithms of the number of people with each score and look for a straight line. And after an initial steep drop off, the logs of the counts do fall roughly on a straight line.

(The graph goes out to scores below 7,700. Beyond that point, there are a few empty 100-point ranges. I stopped at 7,700 to avoid taking logs of zeros.)

The average reputation was 364, though the average does not mean much with a power law distribution. Instead of a bell shape centered around the average, there is a long tail. The average is not the middle because there is no middle to a power law.

Update: As pointed out in the comments, I should have plotted with the log of the reputation score to test for a power law distribution. Whether or not there is a power law here, however, there is a long tail and there’s no useful “middle.”

Other posts about StackOverflow:

Voting patterns on StackOverflow
StackOverflow question statistics

Other posts about power laws:

Networks and power laws
Rate of regularizing English verbs
Metabolism and power laws

In his book Secrets of Consulting, Gerald Weinberg tells the story of a woman who had several pairs of twins. Someone asked her if she and her husband got twins every time. She replied no, most of the time they got nothing at all. Just as intimacy doesn’t usually result in one child, much less two, most efforts in business don’t produce any significant results. Weinberg summarizes this observation in Weinberg’s Law of Twins:

Most of the time, for most of the world, no matter how hard people work at it, nothing of any significance happens.

Later he turns this around and states the principle more positively in Weinberg’s Law of Twins, Inverted:

Some of the time, in some places, significant change happens — especially when people aren’t working hard at it.

Related post: Four reasons we don’t apply the 80/20 rule.

Here’s a Washington Post article about an experiment. The newspaper asked world-famous violinist Joshua Bell to dress down and perform as a street musician at a DC metro stop. Hardly anyone paid attention. He earned $32.17 in tips. People routinely pay over $100 for a chance to listen to Joshua Bell in a concert hall, but he earned an average of about $0.03 per passer-by that day.

Suppose Bell were trying to support himself as a street musician. What might he say to himself after a disappointing day at the metro? That if only he had practiced a little harder he would have made more tips?