Free as in beer

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Free wifi at the pub
Richard Stallman is famous for his recursive acronym GNU - standing for GNU’s Not Unix.  GNU is at the heart of the free software movement, leading Stallman even more famously to draw the distinction between free as in speech and free as in beer.  It is the former which is central to his concept of free software.  As a contribution to further confusion, a free beer movement has developed in tribute to Stallman, where the free beer is free as in speech, not free as in beer.

All of which is by way of rambling introduction to the fact that the pub at the corner of my road has suddenly sprouted a sign offering free wi-fi.  Free connections but not, I suspect, free beer.

Apple in our eyes

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Robert Scoble has two posts up following a conversation he had with Tom Conrad, now of Pandora, but more pertinently, formerly of Apple.

In the first, he tells an Apple joke:

If it’s 999 engineers who say “yes” to an idea vs. one who says “no” they’d score it as a tie and kill the idea. It wasn’t a funny joke. Apple before Jobs came back was paralysed and couldn’t get stuff done. I told him it sounded a lot like many groups at Microsoft. He explained why the joke was true. After all, the one engineer who said no was freaking smart. Probably had some degree from MIT and probably had invented something really killer.

He explained why Apple is so good now that Jobs is back. He was the tie breaker. All votes went his way.

And in the second he asks, ‘what will Steve Jobs kill next’?

Apple II? Didn’t have switches on the front.
Macintosh? No tape drive. No cursor keys.
Next? No disk drive.
iMac? No floppy drive.
iPod? No on/off button.
iPhone? No keyboard.

Where did I get this from? Tom Conrad, CTO of Pandora. He used to work at Apple. He tells me he asks himself what he should remove from his products to make them simpler. One thing he did on Pandora? Made the UI much smaller than a typical Web page. People asked him to add features. He said “they don’t fit.”

There is clearly a connection between the two – challenging consensus is likely to be a minority activity.  And if the score is 999-1 in a company famous for iconoclasm and innovation, what might it be like in, to take a random example, a large government department.  Who gets to play Steve Jobs?  And if we don’t have one of those, what’s our alternative?

First question: what’s missing?

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Discover Your Inner Economist: Use Incentives to Fall in Love, Survive Your Next Meeting, and Motivate Your Dentist – is the splendid title of a new book due to be out in the UK in August.  The author of Freakonomics has read an advance copy and pulls out an interesting paragraph:

A good intuitive economist approaches a practical problem by asking "What is the relevant scarcity hindering a better outcome?" If we
haven’t posed this query, and assembled at least the beginnings of an
answer, we may founder. For instance, we might make the mistake of
throwing more money at a problem, when money is not what is needed. By
identifying the relevant scarcity, we learn where to direct the
incentives.

Pretty clearly, that doesn’t just apply to financial decision making.  What are we short of that is stopping us doing better?

Segmenting internet users

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The Pew Internet and American Life project regularly publishes  interesting reports on various aspects of American internet usage.  They have a new report out with the not wildly snappy title, A Typology of Information and Communication Technology Users.  Not directly relevant to us, but certainly indicative of some significant trends.  The first thought which jumps out from their categorisation is that the early adopters aren’t any more – they have become the mainstream:

Elite Tech Users (31% of American adults)

Omnivores 8% –They have the most information gadgets and services, which they use voraciously to participate in cyberspace and express themselves online and do a range of Web 2.0 activities such as blogging or managing their own Web pages.

Connectors 7% — Between featured-packed cell phones and frequent online use, they connect to people and manage digital content using ICTs – all with high levels of satisfaction about how ICTs let them work with community groups and pursue hobbies.

Lackluster Veterans 8% — They are frequent users of the internet and less avid about cell phones. They are not thrilled with ICT-enabled connectivity.

Productivity Enhancers 8% — They have strongly positive views about how technology lets them keep up with others, do their jobs, and learn new things.

Middle-of-the-road Tech Users (20%)

Mobile Centrics 10% –They fully embrace the functionality of their cell phones. They use the internet, but not often, and like how ICTs connect them to others.

Connected But Hassled 10% — They have invested in a lot of technology, but they find the connectivity intrusive and information something of a burden.

Few Tech Assets (49%)

Inexperienced Experimenters 8% — They occasionally take advantage of interactivity, but if they had more experience, they might do more with ICTs.

Light But Satisfied 15% — They have some technology, but it does not play a central role in their daily lives. They are satisfied with what ICTs do for them.

Indifferents 11% — Despite having either cell phones or online access, these users use ICTs only intermittently and find connectivity annoying.

Off the Network 15% — Those with neither cell phones nor internet connectivity tend to be older adults who are content with old media.

The myth of the plan

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*

The success of planning in conquering both intellectual and popular opinion is really rather remarkable, particularly if one bears in mind that the idea of planning … only surfaced in the second decade of [the last] century. For the previous hundred years it had been ‘liberty’ which had been emblazoned on the banners of the international socialist movement. In the twentieth century, it is no exaggeration to say that planning has displaced liberty as the key slogan of socialism.

Peter Rutland, The Myth of the Plan (London:  Hutchinson, 1985)

New users start here

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We tend to have two contradictory assumptions about new channels – particularly online channels.

On one hand, they are the self-evident future, more straightforward in every respect than the cumbersome, time-consuming and bureaucratic processes they will replace – and so wholly unsurprisingly, our customers will seize on the new opportunities just as soon as we can make them available.

On the other, they are mysterious, unfamiliar, and a profound challenge to the channel conservatism which runs deep in everyone, and in our customers more than most. At best, online channels will be largely ignored; at worst they will generate so much confusion that they will increase the load on conventional channels, as we have to field vast numbers of phone calls triggered by the incomprehensibility of the online experience.

It is, of course, easy to forget how unfamiliar new things can be – which is the power behind my favourite definition of technology.  Even the book was new once, with user interface requirements very different from those comfortably familiar scrolls.  Some enterprising Danes (?) Norwegians have imagined the help desk call…

Scott Rosenberg makes the link between the video and some of the wider uncertainties which come from the introduction of a new format or channel.  He cites a conversation in which Geoffrey Bilder makes the point that the first instinct is to make the new look familiar to users of the old:

People were clearly uncomfortable moving from manuscripts to printed books. They’d print these books, and then they’d decorate them by hand. They’d add red capitals to the beginnings of paragraphs, and illuminate the margins, because they didn’t entirely trust this printed thing. It somehow felt of less quality, less formal, less official, less authoritative. And here we are, trying to make our online stuff more like printed stuff. This is the incunabula of the digital age that we’re creating at the moment. And it’s going to change.

So much of the apparatus that we take for granted when we look at a book – the table of contents, page numbers, running heads, footnotes – that wasn’t common currency. It got developed. Page numbers didn’t make much sense if there was only one edition of something. This kind of stuff got developed and adopted over a fairly long period of time.

If you treat Vannevar Bush as Gutenberg, we haven’t even gotten to Martin Luther yet, we haven’t even gotten to 1525. In fact, whereas people stopped trying to decorate manuscripts by 1501, we’re still trying to replicate print online. So in some ways they were way ahead of us in building new mechanisms for communicating, and new apparatus for the stuff they were dealing with.

As Rosenberg concludes:

It helps to think that what we’ve been doing here on the Web for several years is slowly, by trial error, inventing the online equivalents to “the apparatus that we take for granted when we look at a book.” And we’ve only just begun.

Arrows and loops

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The production of knowledge – or of insight – is often seen as a rational and linear progression.  There are various expressions of it.  One widely used version is:

Data -> Information -> Knowledge -> Wisdom

A variant developed closer to home is:

Data -> Information -> Insight -> Action

The second of those is, to my mind, an improvement on the first, not least because (as Marx didn’t quite say) the goal is not simply to understand the world, but to change it.  But it still strongly implies that the process is linear and that data exists independently of the insight and action we want to develop.

A moment’s thought makes clear that the world doesn’t really work that way:  without a framework for understanding the knowledge-wisdom/insight-action end of the process, there is no such thing as data, which is why it should not be surprising that when we change our desired insight or our intended action, the data and information needed to help us navigate proves simply not to exist (or not to be collected, organised and managed, which in practice comes to the same thing).

Some alternative ways of thinking about this come from the same sources which lie behind my post a few weeks ago on the three generations of knowledge management.

The first simply adds feedback loops and operators to each of the knowledge stages:

Poindexter model diagram

 

(and yes, the reference is to that Poindexter, but that’s by the by).  One step on from that is the OODA loop invented by the US military  – observe, orient, decide, act.  But as the picture shows,  the complexity of the feedback system and the components of orientation are considerably richer.

Observe, orient, decide, act

Dave Snowden discusses Poindexter’s approach,  partly in the context of OODA in this post, on which Patrick Lambe comments particularly pertinently:

I have also had my hair-pulling rants about the data-information-knowledge step model, mostly because it assumes (or
seems to represent) that data is a primitive of information is a primitive of knowledge. Naturalistically speaking, it seems to me that data is information that is purposefully decomposed for a variety of information and knowledge-driven manipulations ie it’s a sophisticated knowledge artefact. Data doesn’t exist in the wild.

Information is knowledge that is filtered and abstracted from specific contexts to make it communicable – ie so that it can be
liberated from the specific and can persist across time and space. Of course, within society and organisations we don’t start from a primitive state. We have a whole lot of baggage made up of the interplay of information and knowledge mostly, with occasional injections of data depending on our jobs.

However, I just don’t get the linearity bit in this model or ones like it. I just don’t experience linear DIK progressions and I rarely see anyone else work that way.

I might want to compile data on something. My purpose in doing so starts from a context of having knowledge and some knowledge objectives. I know I’ve got access to various information resources. So I’ll analyse what my data model needs to be either implicitly or explicitly. This is now a knowledge artefact, which sets up the specifications for the data I will pull from information available to me. I plough through my information sources or other data sources available to me to compile data into my model. I’ll play with it – using my knowledge – to generate new knowledge and probably some information to communicate to other people. Knowledge, information and data are all interacting with each other – indeed, feeding off each
other.

Now I agree that options, pathfinding and action are healthier extensions of the model than wisdom or intelligence are , but I just don’t see how the message of linear “progression” between elements, or the “A is a building block of B is a building block of C” can help anyone relate to knowledge and information use in the real world.

I am not entirely sure where that gets us – other than to the not entirely surprising conclusion that none of this is easy.  But at the very least, it must strongly reinforce two propositions which are matters of current debate:

  • information cannot be managed in a vacuum – and information managers are participants in a conversation, not guardians of a single truth
  • effective analysis and decision making is not and cannot be a linear process:  data needs to be based on intended action as much as action needs to be based on data.

 

A remarkably unremarkable phone call

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A dreadful, but no doubt utterly normal support call to HP – posted with captions commenting on what is going on.  It’s probably most depressing for being completely unsurprising.

Call summary:

  • proportion of call spent listening to hold music:  16%
  • proportion spent talking to a computer:  15%
  • proportion spent looking for and reading out serial numbers and model numbers:  44%
  • proportion spent talking about the customer’s problem: 16%
  • progress made towards resolving customer’s problem:  none

As ever, embedded video visible from proper computers, but not from DOI.