The beginning of the end of Serendipity

Almost everyone has experienced serendipity at some point in their lives. As a culture we have attached a romantic connotation to it, as evidenced by the romantic comedy of the same name. So it is with no great satisfaction that I suggest that we have now reached the beginning of the end of serendipity.

According to Wikipedia Serendipity means:

a "happy accident" or "pleasant surprise"; specifically, the accident of finding something good or useful without looking for it.

Consider for a moment the following scenario. I am working late in my office with a colleague, planning for an upcoming project. We are discussing possible candidates for one of the critical technical roles. At just that moment the perfect candidate, a colleague that neither of us knows directly, walks by on their way out of the building. Miraculously, my iPad notifies me just-in-time for me to jump out of my chair and chase them down.

How did this happen?

No, it was not one of the happy accidents of The Three Princes of Serendip.

The convergence of four emerging technologies, including; social, local, mobile, big data (analytics), enabled by the cloud, is rapidly changing the information landscape. As I suggested in my earlier posting, Too Much Buzz?, it may soon be the case that all that is known can be easily accessed on demand by anyone, anywhere, at any time – for free.

In fact, it was a context engine that allowed me to capitalize on the fortuitous arrival of the perfect candidate. It knows about me and my needs - my location, the project that I am working on and the roles that I need to fill. It also knows possible ways to meet my needs, including my network of colleagues and their network of connections, the skills and expertise of this extended network, and their real-time location.

All of this information is currently available or will be in the near future. Social platforms, such as Google+ or LinkedIn provide information about skills, networks and projects. Local and mobile tools, such as foursquare or Google Latitude on a smartphone, can provide real-time location information. A software agent could use big data capabilities to leverage this information and establish the context needed to alert me to act at just the right time.

Yesterday's announcement by Google of Search, plus Your World is a big step in this direction.

Before you bemoan the end of serendipity, consider the number of times when events failed to conspire to create a "happy accident" and you were left wanting.

Let me know what you think. I can't wait for a few pleasant surprises.

The context engine

I recently read a terrific book about translation, Is That a Fish in Your Ear?, that has inspired me to think about social business collaboration (SBC) in a new way. Amongst the wide range of topics covered by the author, David Bellos, is the relationship of words, context and meaning. He states something that we often take for granted "… For translation, and for us all, meaning is context." David goes on to say:

"The expression "One double macchiato to go" – an expression I utter most days, around 8 a.m. – means what it means when uttered in a coffee shop by a customer to a barista. The situation (the coffee shop) and the participants (customer and barista) are indispensable, inseparable parts of the meaning of the utterance. Imagine saying the same thing at 2 a.m., in bed, to your partner. … The words would be the same, but the meaning of their being said would be entirely different."

I believe that it is possible to relate David's assertion on the relationship of context and meaning to the world of information technology and social business collaboration with my own assertion that context is the bridge between data and information.

In my blog posting, Too Much Buzz?, I referred to a piece on Social Media that appeared in The Economist on December 31, 2011. They concluded the article with the following:

"As communication grows ever easier, the important thing is detecting whispers of useful information in a howling hurricane of noise. For speakers, the new world will be expensive. Companies will have to invest in ever more channels to capture the same number of ears. For listeners, it will be baffling. Everyone will need better filters—editors, analysts, middle managers and so on—to help them extract meaning from the blizzard of buzz."

There has been talk of late of the convergence of three separate but related emerging technologies – Social, Local and Mobile (SoLoMo). It is my contention that a fourth component, Big Data, will join with the other three to form "SoLoMoDat". By combining contextual information, such as who, what, where, when, why and how, SoLoMoDat will provide the basis for a context engine that will enable us to easily "…extract meaning from the blizzard of buzz".

We are already seeing commercial examples of this type of capability. Consider the value generated by intelligent recommendations made by services such as Pandora, Netflix, Amazon, Google Translate and Eureqa. If the context engine knows all about me – my skills, my interests, my projects, and my network of colleagues (and their skills etc.) then how far away is the capability to consider context when generating a social business activity stream that is meaningful to me?

For example, if I have the task of planning a project to solve a particular customer's business problem, then wouldn't a context engine be able to recommend the people to reach out to that have solved this problem before? My stream would become much more relevant and collaboration would become more agile and effective.

This may sound like fantasy, but I think that given the accelerating rate of technological advancement we will be working with these types of tools in a few short years if not months.

The real question is not the technology, it is the human element. Are you ready to do things differently?

 

 

From OWS to the Enterprise

The folks engaged in the “Occupy Wall Street” (OWS) movement have used the catch phrase “We are the 99%” to emphasize the income inequality that has arisen in the US in recent decades, as the income of the top 1% households accelerated, while it stagnated or declined for the majority of Americans.  According to a recent Congressional Budget Office report the share of total income that flowed to the top 1% increased to 60% by 2007, up from 50% in 1979.

While the popular movement focuses mostly on the top 1%, there is a broader perspective on the increasing polarization of wages and employment that is relevant for the Enterprise and the path forward as we adjust to new technologies and the new economic realities.

In their recent e-book, Race Against the Machine, authors McAfee and Brynjolfsson describe “How the Digital Revolution is Accelerating Innovation, Driving Productivity and Irreversible Transforming Employment and the Economy”.  It is an excellent book and a must read for anyone involved in information technology or interested in the economy.

In short, the book seeks to “reconcile two important facts. 1) Technology continues to progress rapidly. In fact, the past decade has seen the fastest productivity growth since the 1960s, but 2) median wages and employment have both stagnated, leaving millions of people worse off than before.”

For example, in the following chart we can see that real wage growth in recent decades (1980-2005) is lower than the post-War decades (1950-1980) for each of six occupation groups.  However, in the more recent data we also see that the real returns to the more skilled occupations are increasing while the returns to the less skilled occupations stagnate or decline.

Source: The Growth of Low Skill Service Jobs and the Polarization of the U.S. Labor Market, Autor and Dorn, June 2011.

It is well known that as the nation endured the transformation from an agrarian economy to an industrial economy in the 19^th century, more jobs were created than lost and employment opportunities increased for skilled and semi-skilled labor alike. The authors argue that in our most recent transformation from the industrial to the information age, digital technologies have eliminated jobs faster than enterprises have been able to adapt.  The majority of the resulting impact on wages and employment has been felt in the low to middle range of skills through the automation of routine tasks. 

As we go forward and digital technologies continue to improve we should expect to see even the more abstract work of skilled professionals become subject to automation.  The authors cite Google’s self-driving car and IBM’s Watson computer as examples and harbingers of what is to come.  As was the case with the mighty John Henry, men may win the battle against the machine for a time, but we may not win the war.

But the authors do not end on a sour note.  Instead they restate the problem from “Race Against the Machine” to” Race With the Machine”

“The John Henry legend shows us that, in many contexts, humans will eventually lose the head-to-head race against the machine. But the broader lesson of the first Industrial Revolution is more like the Indy 500 than John Henry: economic progress comes from constant innovation in which people race with the machines. Humans and machines collaborate together in a race to produce more, to capture markets, and to beat other teams of humans and machines.”

All of which brings me to my point.  We must take a very hard look at how we collaborate in the Enterprise.  We are in a race, but not against the machines.  Our race is with our competitors.  The factors driving the polarization of employment and wages will reach our shores before we know it.  We simply can’t stand by as our competitors out-innovate, out-deliver and out-compete us.