I installed a release preview of Windows 8 about 3 weeks about on my laptop.  I fully expected t really hate it.  And at first I did.  It was just ‘too dam different!’ from what I was used to.  Nothing in the right place.  Cursing MS for making me learn the Windows environment again.  Utterly confused about WHY they did this, or that.

I actually stopped using my laptop for a while out of sheer frustration and was about to write a really negative review of the system.

But then something happened.  I logged back in one day and just started looking at it anew.  As I played around with the system I realized that, yeah, this is actually pretty ‘fun’ – not a word I would normally use to describe a Windows experience.

I think there are three great design ideas in Windows 8 that just make it fun to use:

1. Getting rid of the ‘desktop’ – Ok, so it’s not completely gone in Win8 but it has one foot solidly out the door.  The ‘desktop’ in Win8 is basically like an app you use to run programs from.  You can switch between the ‘desktop’ and other Win8 apps but you can’t switch Win8 apps on the ‘desktop’ itself.  This takes some time to get used to.  It changes the basic concept of windows from this ‘two-dimensional place called the desktop where you can put things’ to this ‘three-dimensional place where things are stored, but you don’t need to know where they are stored to use them’.  It’s basically a further abstraction of the idea that programs/apps have a ‘home’.  They don’t need to.  They just need to be available when and where you want to use them.  To this end, you can pin anything to the Win8 start-page, you can pin to the old desktop taskbar, or you can search through all the apps with a very fast/zippy search function.  Or you can even alt-tab between running apps.  It’s a little bit like the Android recent app selection menu function, except all the ‘pinning’ makes it feel more ‘custom’.  I suspect the ‘desktop’ that does exist in Win8 is a result of extreme pressure to keep some semblance of consistency between versions.  I’d love to see it go completely – it’s simply a better experience without it.  Which is surprising.
2. Getting rid of ‘windows’ – If you use apps in Win8 you don’t really use ‘windows’ (as in the rectangles on the screen) anymore.  Apps are either full-screen or docked on the side of the screen.  You can of course use overlapping windows on the desktop (as usual), but not with apps.   Now this was something I initially thought was a step back – why reduce the flexibility users have when multi-tasking?  However, the more I used the side-docking (for things like a Twitter app), the more it made sense.  You see in Win8, an app knows when it is docked and hence reacts (or should react) appropriately to having a smaller screen space.  Unlike in the traditional desktop where most apps just scale down to uselessness when in a small window, the Win8 apps seem to seamlessly adjust.  Functions are hidden, screens reduce in size but still work.  Even new options appear when docked to allow you to customize what you see.  The only caveat on this is that it helps to have a wide-screen display, and I can see times when I will want some simple app (such as a calculator) to pop up over running apps for ease of entering numbers.  However, the design idea of docked apps that are ‘position aware’ is really good - kudos to MS.
3. It FAST! – Yes, again, not something we normally associate with Windows.  But right from boot-up, to interacting with the start screen, to searching, to switching between apps, Win8 is a zippy experience.  We probably have to give credit to Apple here for setting the bar for responsiveness for hardware based OS’s, it seems to be something MS has taken very seriously in Win8.  Hopefully this also translates into the Win8 tablet experience.  The only place you see a slow down is opening the old desktop.

As with anything Google does, they tend to bring a fresh perspective to an old problem and their Consumer Survey product is no different.

Instead of following in the footsteps of the DIY survey crowd (poster boy – Survey Monkey), they have rethought the whole process.  Google’s Consumer Surveys are short (two question maximum) surveys administered as access requirements to participating content sites.

The idea is beautifully simple.  Content providers who have large audiences get to monetize some select content while business with research needs get access to on-demand sample.  At only $0.10 per completed survey, that’s a ten to forty-fold saving over standard Panel rates (depending on who you want to talk to).

That’s a pretty disruptive business model in a market that needs a bit of disrupting.

However, if you want to use this service to do research, there are some things you need to know.  The kind of things you are only going to find in the finer details of what Google is offering.

1. You can only ask TWO questions of the SAME respondent – This is a pretty big limitation, but not surprising given the cost and methodology.  It means that if you have a ten question survey and you want to understand how people who answered question five also answered question nine, you might be out of luck.  Given that a lot of useful analysis is done between questions, this is a big restriction you need to be aware of.  NOTE: technically it’s two questions per ‘request’, so the same respondent might be asked more than 2 questions if they have multiple ‘requests’ for the same study (but it’s not clear if Google even tracks this).
2. Demographic information is inferred from IP address and browsing behavior - Given the two question limit you obviously can’t collect demographic data on each respondent.  Google gets around this by inferring demos from from IP address and using census tract information to compute average income, age, etc.  Another nice use of technology by Google, but there is really no discussion on how accurate this is.  In studies that I have done looking at IP address for survey responders, upwards of 20% of the sample may be taking the study at work.  Work IP address are different from home IP addresses and obviously not useful for inferring demos.  While Google does a lot of explaining in their White Paper around sampling accuracy, it is pretty much void of a discussion on demo inference accuracy – that’s something I’d love to see some more data on.  NOTE: I don’t consider the comparisons made in the sample accuracy work a direct measure of the accuracy of the demographics as it’s not clear how demographics interact with the questions they asked.
3. Your data may be weighted by the inferred demographic data  - Compounding the problem of potentially inaccurate demographic data is that this data is also used for post-stratification weighting just in case the sample is skewed is some way.  This makes sense to do, but again, there is not a lot of discussion on the accuracy of the demographic data so the compounding error when using this data for weighting is also unknown.  NOTE: The post-stratification average error in the Consumer Survey test was an improvement on the un-weighted data, but that’s for a single test of a US population sample – does this the accuracy still hold as you do more regionally targeted studies where the accuracy of IP demo inference might be more of an issue?
4. You don’t get access to the raw data – Not a big deal for most of the companies that will use this system, but annoying if you want to present results in a third party application (I expect Google will allow raw result extraction at some point in the future).
5. You can’t ask an open-end response – I couldn’t find any reference to open-end questions.  And I don’t really think this type of survey-wall approach lends itself to getting people to explain their thoughts/feelings.  So again, probably not a big limitation for the likely target audience for this product.

So despite some of these limitations, I think the product is well suited to smaller, DIY  projects that don’t require any type of relatively sophisticated analysis.  Which, if you’re honest about it, is probably 80% of most research requirements for small and medium sized businesses.

I also have to hand it to Google for at least doing some due diligence on the tool’s accuracy.  While I agree with their own assessment of the limitations, I don’t see these limitations particularly hurting the majority of studies.

People in Market Research tend to have a very inflated sense of self importance when it comes to collecting survey data.  If Google Consumer Surveys further reinforces the fact that data collection is simple, cheap and fast – that’s a good thing.  It should be.

Probably one of the most contentious, acrid debates going on anywhere over the last five to ten years has been on the topic of vaccine safety.  Go to any active forum that discusses children’s health and you will see some very heated arguments.  On one side, you have concerned parents relaying scary stories about developmental disorders that coincided to-the-day with vaccine doses.  And on the other side, screeds of scientific and medical research in support of vaccine safety and efficacy.

Just to give you an idea of how heated this really is, here is Penn & Teller in a YouTube video about vaccines (with some NSFW language).

Now, full disclosure.  I have a beautiful, healthy two year old daughter who has not been vaccinated.  However, I am only one part of the team that makes that decision.  My wife is firmly in the no-vaccine camp, whereas I am more on the fence (not surprisingly she has her way!).  But being on the fence has allowed me to take a more dispassionate view of the issues.  I’ve watched many pro and anti vaccine videos, and to be honest, neither side has convinced me one way or the other.

Firstly, there are just obvious misuses of data to prove a point.  Take the claim in the Penn & Teller video that thousands of children’s lives have been saved by vaccines.  The graph below of measles cases in the US since 1944 supports their claim (and is no doubt where it came from).  Since 1963 when the first measles vaccine was licensed in the US, the number of measles cases plummeted and is now all but eradicated compared to earlier in the century – pretty strong evidence for the effectiveness of vaccines.

 Now look at this graph representing the other side of the debate.

This is a graph of mortality rates for various diseases in the US since 1900.  The measles line is in red and starts off at around 10 deaths per 100,000 early in the century, dropping away to well below 1 death per 100,000 by the time the measles vaccine was introduced in 1963.  So contrary to Penn & Teller, vaccines didn’t actually save us from a disease infested society with children dying off left, right and center.  In fact, due to obvious improvements in health and sanitation, the mortality rates of most major diseases were negligible by the time vaccines were introduced.

So who who is right?  Well, it’s a matter of degree.  Penn & Teller are wrong when they equate vaccines with saving thousands of lives compared to non-vaccinated children.  The data tells us that most of those children weren’t dying from these diseases by the time vaccines were introduced.

However, the anti-vaccine camp is wrong in saying that vaccines haven’t been effective.  They have.  While they may not have drastically reduced the number of children dying from common diseases like measles, they have all but eradicated the incidence of these diseases.  If this in turn reduces mortality rates to almost zero, that’s still children’s lives being saved.  It may not be thousands of children, but if you’re a parent, one is enough.

So it’s fair to say vaccines are effective and they do save lives.  However, that’s not the end of the story.

In the anti-vaccine camp there is also a strong belief that vaccines are dangerous.  Particularly that they can cause, or are a significant factor in causing, autism in children.

This is where the data starts to get a bit murky – which will surprise many people who thought vaccines were completely safe and that vaccine-autism connections were just ‘junk science’.  In fact, there is actually no clear data indicating a link one way or another.  Which is worrying.

This is a quote from the Centers for Disease Control’s website (link here) rejecting a causal relationship between a common preservative in vaccines (thimerosal) and Autism (ASD).

Recent estimates from CDC’s Autism Developmental Disabilities Monitoring networkfound that about 1 in 150 children have ASD. This estimate is higher than estimates from the early 1990s. Some people believe increased exposure to thimerosal (from the addition of important new vaccines recommended for children) explains the higher prevalence in recent years. However, evidence from several studies examining trends in vaccine use and changes in autism frequency does not support such an association. Furthermore, a scientific review by the Institute of Medicine (IOM) concluded that “the evidence favors rejection of a causal relationship between thimerosal-containing vaccines an autism.” CDC supports the IOM conclusion.

This kind of finding always worries me because the devil is typically in the detail. So after actually downloading and reading through the IOM report, sure enough, while the ‘causal relationship’ between thimerosal containing vaccines and autism can’ t be substantiated, there were some interesting caveats.  They type of caveats that never get reported in media coverage.

Citing the shear amount of submissions they received on this topic:

The volume of correspondence to the committee on this issue is impassioned and impressive. There are, however, little data to shed light on how many families believe that vaccination actually caused their child’s autism, so that the magnitude of concern in the general population is uncertain. However, the committee concludes that because autism can be such a devastating disease, any speculation that links vaccines and autism means that this is a significant issue.

And then it goes on to say:

There are many examples in medicine of disorders defined by a constellation of symptoms that have multiple etiologies, and autism is likely to be among them. Determining a specific cause in the individual is impossible unless the etiology is known and there is a biological marker. Determining causality with population based methods such as epidemiological analyses requires either a well-defined at-risk population or a large effect in the general population. Absent biomarkers, well-defined risk factors, or large effect sizes, the committee cannot rule out, based on the epidemiological evidence, the possibility that vaccines contribute to autism in some small subset or very unusual circumstances. However, there is currently no evidence to support this hypothesis either. (emphasis mine)

That’s a pretty startling finding given that it’s in a paper on the CDC website.  It basically says that given the evidence, while there is nothing to support a vaccine-autism connection, that’s only because the effect in the population might not be large enough.

If you look at parents who believe their child became autistic due to a vaccine, the very definition of this group is ‘small subset or very unusual circumstance’.  It’s amazing to me that this paragraph isn’t cited more often in this debate.

So in short, vaccines are effective, but they aren’t cornerstones to a disease free society.  They have been responsible for (almost) eradicating many diseases that were already in decline due to natural improvements in health and sanitation.  However, their side-effects (particularly their connection to autism) aren’t well understood.  It’s possible that small sub-sets of the population are at risk.

It might not be thousands of children in that group, but as a parent, one is enough.

In one of the chapters, he opens with a quote from Tolstoy’s Anna Karenina:

“Happy families are all alike; every unhappy family is unhappy in its own way”.

He was illustrating a principle to do with the domestication of animals – throughout history, successful domestication relied more on the absence of any number of negative traits than the presence of positive ones.  Essentially, all domesticated animals were domesticated in the same way – like all happy families are alike.

You can read the wikipedia article on this here.  It’s a little unfinished but does give some history of similar principals (all the way back to Aristotle).

The thing I found fascinating about this principal when I first read about it in Guns, Germs and Steel was that it challenges our accepted ways of viewing a problem.  In business, we’re often asked to find out the ‘key reason’, the ‘driver’, the ‘thing we really need to win at’ in order to succeed.  But what if it simply doesn’t exist?  What if there isn’t any one key ‘thing’ we need to do?  Instead, there are many (possibly smaller) things we need to ensure we avoid.

This is a different mindset to solving a problem.  It doesn’t take away the importance of doing the right things correctly, it simply says those might not be enough.

This idea hit me the other day when I was talking to someone in the restaurant business.  They were trying to figure out what made a ‘great dining experience’.  They were looking at all the components – service, meal, atmosphere, etc.  Trying to figure out where to concentrate resource to really ‘wow’ the customer!

After thinking about it for a bit, it occurred to me that all great dining experiences are alike, while all bad ones are generally bad in their own way.

A great dining experience has a great meal, good service, good atmosphere, good company, good value.  You need all of these.  But you could have all of these and your hot drink might be cold.  Or the bathrooms could be dirty.  Or your order was wrong.  It only takes one or two small things to turn that dining experience bad.  MORE service, BETTER food, MORE atmosphere doesn’t make up for it.

Sometimes it’s just about removing obstacles.  There is no magic bullet.

One of the most interesting passages in the report deals with the ‘bundle’ – the packaging of content and advertising:

Digital disrupts the aggregation model that was so profitable for so long. Almost no one used to read the entire newspaper every morning, and audiences frequently tuned in and out of the network news at night. Yet, news organizations sold their advertising as if every page was turned and every moment was viewed

This is a great summary of the struggle so many media companies are going through.  It also has eerie parallels with the music industry – an industry also struggling to emerge from a time when content (songs) were bundled and sold as ‘physical things’ (CDs).

It’s also the reason I wouldn’t invest in Cable Companies right now – they sell bundled content in a way that assumes everyone watches most (if not all) channels and offerings.  When it’s increasingly plain most people don’t.

‘Bundling’ like this was important because you couldn’t separate content when it was physical or analog – no newspaper would produce a page for each story and sell those pages individually.  It wouldn’t make sense.

Digital content is obviously different.  It’s easy to separate and users can choose to only pay/view those things they are interested in.  Digital breaks the economics of the bundling model.  In the digital space users are determining what is ‘useful’ in a way that is almost a pure reflection of demand – “I like these three stories, this editorial and this cross-word puzzle, but the rest of the stuff you can have back”.

Just like on iTunes users determine that these three songs are worth it, but the rest of the album isn’t.  Or when you watch cable TV, your weekly viewing behavior is probably sending clear signals to the Cable Company on what you like and don’t like (and what, if given the choice, you would pay for).

Thinking about it this way, digital content hasn’t changed us, it’s just uncovered our true interests previoulsy  masked by content bundling.  For decades we’ve been paying for content in magazines and newspapers that, given the choice, we wouldn’t consume.  For decades media companies have been making excessive profits off of the back of these bundled offerings.

While I sympathize with the plight of the Professional Journalist and believe that there is still a place for good journalism in the digital world, I don’t lose any sleep over the dismantling of analog content bundling that masked true demand.  We’re far better off trying to figure out revenue models that rely on real demand and supply rather than protecting outdated business practices.

Cable companies, be warned.

On the face of it, it seems like a performance metric that is unquestionably accurate – if student’s don’t improve their test scores the teacher obviously hasn’t done their job.

That is until you read this quote from a NY Principle:

“If I thought they gave accurate information, I would take them more seriously,” the principal of P.S. 321, Elizabeth Phillips, said about the rankings. “But some of my best teachers have the absolute worst scores,” she said, adding that she had based her assessment of those teachers on “classroom observations, talking to the children and the number of parents begging me to put their kids in their classes.”

As it turns out there are not only a slew of data issues (accuracy and reliable collection and attribution), the correlation of how well a teacher performs in one year compared to the next is a miserly 0.3.

0.3.  Let’s put that in perspective.  That means that if you out-perform in one year – so you’re a great teacher and all of your kids improve out of sight – in the next year, you only have a 0.3 chance to be rated the same!  Unless in the school break teachers regularly binge drink themselves into a permanent skill reducing stupor (no doubt it’s happened), that’s an awful record for a performance metric!

Good teachers, you would think, should consistently out-perform, and bad teachers should consistently, well, be bad.

While this is how I remember my old teachers in school (the good ones tended to stay good), it turns out that statistically, this is very difficult to prove.

But as the article goes on to point out, shoddy correlations between performance metrics and performance are the norm, not the exception.  SAT scores correlate to college performance at a 0.35 rate.  Between season correlations of professional ball player batting averages is only 0.36.  In fact, the study cites a meta-study that found most complex performance based metrics fall between 0.33 and 0.40.

Rather than being astounded by this fact, I find it refreshing.  It corroborates much of what I have seen regarding business metrics in the corporate world.

When you get outside of the realm of finance and operations with simple relationships between things like profit, costs, revenue, throughput, etc.  and you get into the realm of return on innovation, marketing, customer service, it gets harder to predict outcomes.

That’s because these ‘softer’ measures of business represent more, not less, complex systems.  The problem with measuring them isn’t a dearth of data, it’s out inability to understand the myriad of interrelationships.

Business advertising like this drums home the idea that the more information we collect, the more we know.  It’s a false idea fast being perpetuated by our information crazy world.

It’s the idea that if we can describe it enough, and in enough detail, we can understand it.  Ask any really good teacher why this just doesn’t work.

The only excuse I can come up with is that in the past 6 months my wife and I have gone from renting childless couple to home-owning family of three.  Needless to say it’s been a lot of work.

We’ve moved up to Sonoma County, just north of San Francisco.  If you’ve never been here (or only been to its more popular neighbor Nappa) I highly recommend it.  I’d love to say I am sitting on a deck sipping Sonoma wine, but alas, the deck needs a lot of work, the grounds need re-landscaping and the house is in dire need of a make-over.  Yes, we bought a project.

Which is fine.  It’s a long-term project.  And it feels really good to finally have a house where I can drill holes in the walls without needing permission from the landowner!

Here’s a view from our front window.  You can just make out a vineyard across the road.  Pretty cool!

View from front window

I’ve got a ton of posts in my head right now so I will try and get some of those down on ‘paper’.  I might mix up the length a bit as well.  Part of the problem in keeping a blog going is the mental work in writing long posts.  I like it, but it’s taxing.  Some shorter ones might help remove the mental roadblock.

Hope everyone reading this is doing well!

I’ve just logged in and changed pretty much all of my privacy settings to make sure my information doesn’t end up in the hands of some distance third party of a third party who accessed it because someone I hadn’t seen in 20 years was my friend on Facebook.

Not that that can happen now, but it seems like a reasonable extension of where Mr Zuckerberg and friends are taking their social behemoth.

I’m not against Facebook, not against using the platform or the services.  But I am against someone using my personal data in the context of my ‘social graph’ for their own financial ends and disguising it as a business plan.

Facebook can’t make sufficient money for its user base because it’s fundamentally not a business. WPP’s chairman Martin Sorrell recently made a statement to this effect, that ‘Social Media is more a personal phenomenon than a business one’. Joseph Jaffe criticized Sorrell on his blog for saying this, but I think missed the core intent of the comment – there is no inherent value in being a medium for social connection.

I think Sorrell was making a statement about the viability of Facebook as a business more than he was commenting on the viability of using Facebook to run/market a business. These are two very different things. If I create a network of people using some fancy new technology, the value of those connections is held and realized by the individuals in the network and the savvy individuals/businesses that can utilize the network for their own means.

It’s not realized by the guy sitting on all the fancy technology used to connect the network in the first place.

Facebook is not worth some squared sum of the connections it facilitates. Up to now, it’s basically worth whatever advertisers want to pay to interrupt people writing daily updates on their virtual walls or playing with their virtual farms.

This being not enough, it now has its sights on using personal data to ‘augment the internet experience’. No thanks. I like your platform, like that it connects me to people I care about, but I can’t give you permission to use my personal information to build a business from. It’s not yours. It’s mine. I don’t care necessarily how private it is (it’s on the web after all, right?), but it’s not a way for you to make money.

This is the essence of the debate to me. It’s not about privacy, it’s about permission. The bargain was that you can bombard me with ads, services, third-party offers, it wasn’t that you could take my social data and use it to make money for yourself.

I can’t wait to see what 4 guys working night and day on only pizza and beer in NY can create!

On the first introduction to Google docs, there was a lot of kicking and screaming – what do you mean it can’t do that? How do you do this? That’s just stupid, why doesn’t it work like Excel? I knew this pain was coming, but I persevered because I knew that everything she wanted to accomplish could be done in Google, for free, and live permanently online (which was a bonus as the day before she accidentally deleted her entire My Documents folder – yes, she works in tech).

Sure enough, after a few weeks of working through and learning the new software, she is very content with her move online.

It interesting seeing this process first-hand, especially if you develop UIs (as I do now and then). A function that accomplishes an existing task in a different (but more efficient) way is normally loathed by the user. Especially if the efficiencies are ‘under the user radar’ – by that I mean small enough to not be individually noticed, yet in aggregate, meaningful.

When this happens you are trying to break a habit. Which is hard. Habits drive a considerable amount of our behavior because they are short-cuts we don’t need to think about. When you are forced to change a habit, you weigh the effort in changing against the perceived usefulness of the new approach. If the effort seems too much, you see a lot of kicking and screaming.

This is why you have to be careful with user feedback. Users want everything familiar, not necessarily better – because they don’t want to have to change their habits.

Sometimes you need to push through this barrier to a better place. Sometimes. It’s a fine line between functionality that improves the experience but breaks a habit, and functionality that’s simply different and annoying to users.

Either way, I suggest you try not to test too much on your wife.

More and more articles about the Long Tail tend to devolve the theory into a general comment on the state of industries affected by Internet economics.  Whereas Chris Anderson’s original argument is very ‘niche’ and specific – describing a set of circumstances that result in a specific outcome rather than a large, generalized theory of everything Internet related.

As I see more commentary on the Long Tail, it’s like a Long Tail of the Long Tail – arguments that tend to twist and turn the original idea to suit a set of observations or opinions.  Sometimes interesting, sometimes a stretch.

But alas, that is the destiny of many a theory.  Maybe the best ones tend to devolve to this kind of reference state – their specifics long ago forgotten, but their general idea intact well enough to guide curious minds who happen along their path.

The Tipping Point is another one that has achieved this ethereal status – although I can’t but help wonder if that was the point all along.

I can’t also help wonder if, by reducing the original Long Tail argument to a reference point, a lot of the original intent and usefulness is lost.

The Economist article has a section on the success of hit TV shows despite falling ratings from wandering eyeballs.  The argument being that even with reduced audiences numbers, we are still gravitating towards these ‘hits’ – as if there is comfort still in broad and common social discourse based on shared entertainment experiences.  Yet network TV in the US is in no way, shape, idea, structure, form, construction, similar in anyway, at all, to the basic tenants that underly the Long Tail theory.

The Long Tail adds nothing to the argument other than a reference for the general idea – that TV is generally full of more choice, that some of this is useful, but we still tend to choose similar things.

Chris Anderson has largely given up the fight to defend different interpretations of his theory.  I think this is a good thing.

It’s time to let the Long Tail go silently into the night.  With the realization that in death, it’s infinitely more powerful than in life.

The Long Tail, I predict, will truly become the Obi-Wan Kenobi of Internet theories.