Network Effects, Compounding Interest and Inequality

I have seen a rash of "the rich are getting richer" stories in recent months, because, well, they are. But most articles I have consumed focus on tax policy and other mechanisms that have for the most part assisted in this shift of wealth over the last few decades.

This one took a different tact, and it got me thinking about the impact of the concepts of network effects and compounding interest on inequality.  This article focuses on the environmental effects of having wealth on a child's development.  Interesting, but there is a larger ecosystem at play that further expands the gap between rich and poor: the network.

I am fortunate in that I grew up in the US in an upper middle-class family.  I did not have a ready-made network for me to access, but I had other assets that have allowed me to develop my own network much more rapidly and effectively than many.  Principally, my network began because I had more flexibility as to how and when to earn an income.  Paying for college was not a worry, and my living expenses were subsidized (thanks Mom & Dad!). affording me to take an unpaid internship at The White House while in college.  This opportunity not only began my career, but also began the development of my network that continues to compound and pay dividends.  I have reconnected with several folks I met during those early years over the last year, as I develop my latest business.

Providing opportunity is one component of a much larger ecosystem.  Those in need also must have the means and flexibility to fully exploit opportunities.  Policy, philanthropy and other initiatives that attempt to address inequality have to look at the entire ecosystem.  Failing to do so may lead to exponentially less potent effects.

Industrial Internet

I have been following the "internet of things" idea as it has emerged over the past few years.  I own a Nest.  I want a Sonos.  Etc, etc., etc.  But, I've always struggled at understanding the underlying value of such systems.  I haven't seen real problems solved by these devices yet.  I have no doubt that it will come, but the space is just too immature at this point.  Enter the "industrial internet".

I had the fortune of attending the Colorado Innovation Summit in Denver back in August.  There, GE's Jeff Immelt spoke of their significant investment of what they call the industrial internet.  Essentially, this is the enterprise version of internet of things, or industrial IoT.

My "ah ha" moment was the realization that opportunities in industrial IoT can win simply with a clear ROI.  And win they are - Mr. Immelt spoke of several examples already in play among their businesses where they are applying industrial IoT to deliver double-digit returns.

The same is true on the consumer side, but the definition of return is so much more difficult given human behavior.  And how that return is delivered on the consumer side can take so many forms.

However, among key industries, incremental improvements in efficiency have significant impact on profitability, given the minimal cost of implementation of many industrial IoT concepts.  These investments will have a huge impact on every industry and the global economy in the coming years.

Source: GE Estimates

Phases of Data Intelligence

"Big Data" has been a thing for a few years now.  As with any new idea, the hype and promise of what it is can overshadow the effort required to actually deliver.  Big data is no exception.

Many focus on the promise of "predictive" intelligence without understanding the effort of basic data collection.  Others focus on dashboards and other tools without building the infrastructure needed to deliver those pretty pictures.

Through conversations with many, I see the following stages to data intelligence:

Stage 1*: Data Collection - many systems, processes, tools already through off a ton of data.  For most industries and applications, this data is often not stored, let alone organized for use. 

Stage 2: Data Infrastructure - for storage and organization, quality infrastructure is required.  This is where much of the foundational innovation has happened in the last 10 years or so, that has spurred idea of big data.  The idea of it being too costly or too difficult to store and organize vast amounts of data is no longer true.

Stage 3: Data Visualization and Interpretation - this is an area that some skip by either hubris or eagerness.  Hubris is when those not in the trenches believe they know the right path to extract intelligence from data, and build accordingly.  Eagerness manifests by going after predictive intelligence before knowing what data and information is available. 

Stage 4: Data Intelligence - this is the stage where real value is delivered.  The steps above are the plumbing to get you to this point of actually learning from the information gleaned from data. This is the stage where action is taken, given what is learned.

Predictive intelligence is an extension of data intelligence, whereby historical data is used to preemptively make decisions in the future.  As "cool" as it is to do, there is so much that can be learned and value uncovered by effectively developing intelligence from historical data.

*Given that there is so much data thrown by existing systems and processes, I take the data stream as a given.  This may not be true for some markets / industries, but it is fast becoming the norm that the data is there for the taking / analysis / employment...

LinkedIn and the New Economy

The following is either A) an insightful, educated observation of the true reality of the disruption underway of the middle class in the US, or B) a misguided diatribe that is a result of the availability heuristic and a "general" education.

For the purposes of this discussion, we will define the middle class as those that earn +/- 50% of the median household income within the US - currently ~$50K and change.  (This number has apparently been going down over the past decade or two.)

Over coffee with a friend yesterday morning, our conversation diverged to how our lives would be different had we had the tools and knowledge we have today even 10 years ago.  The world today is different, and the skills, tools and other resources necessary to thrive are different too.  My anecdote is that, had I had LinkedIn (and the attached awareness of the value of a nurtured network) while I was traveling the world on behalf of President Clinton, I would be in a different economic rung than I am now.   At the least, "change" would be "easier."

Don't get me wrong - my wife and I live well.  We are in the top 20% in terms of income, likely the top 10%.  I was born in at best the second 20%, and I grew up as our economic situation continued to

improve.  By the time I entered college, my family was well ensconced int he top 20%.  However, my take on the catalyst that enabled this rise within the socio-economic strata is that my Dad decided to make a change in 1980.  He switched careers, from being a school psychologist to pharmaceuticals.  And, with this change cam great opportunity, which he seized.

I remain in contact with many of my White House colleagues, with many reconnections made through my LinkedIn and Facebook accounts.  My point is that I cannot remember let alone contact people even half the people I worked with back in those days, people that likely could be helpful to me now and in the future.  Change is easy for me now, but it could be a lot easier with a wider, more diverse network.

My hypothesis is that the supposed "shrinking middle class" is less a result of a perilous economic attack, and more a result of the disruption of how to succeed - not unlike what has happened to music, is happening to journalism and television, and will soon happen to higher education, among other industries.  Lost in this disruption is a middle class that has a job for life, is promoted every 4 - 5 years, does roughly the same job for most of his / her career.  In it's place is an agile, undulating timeline of new roles, new responsibilities, new companies, and new colleagues.  There is no straight line in one's career trajectory, and those that understand that and equip themselves for that fact, prosper.  Those who do not stagnate.  Those that have built a network for change thrive.  Those that do not, wither.

What we are seeing in the census data is this shift.  The top 20% consists of those that understand and have adapted to this disruption, and are reaping the reward, hence the continued growth of their share of income.  As more understand this new world, more will prosper.  As our system adapts to the new reality, so too will the distribution of income.

Or not.  We shall see...

China

I had the fortunate opportunity to travel to China for the first time last week - what an amazing place.  I got on the plane alone, with no colleagues, and only an itinerary of my flight there and my flight home.  I was invited as a guest of a Chinese entrepreneur.  I cannot imagine a better way to see China than as a guest of a business leader.  And, being alone enabled me to consume the experience at all times - no opportunities to fall in to catty "American-centric" conversations.

I come away from the trip seeing more similarities than differences - among the people, within business, and even in how this Communist government operates.  Conversations were frank, direct, and untethered by political doctrine or even perceived social norms of Chinese culture (unlike my experience in Singapore).  The mode of business is one of service and immediate opportunity.  And, the government is trying to have the best interests of its people at heart, despite its often authoritarian ways.

I have a newfound sense of scale - there are 10 cities there larger than New York.  As I explored Dali City, a city roughly the size of Austin, my stomach churned at the site of what appeared as overbuilding to me at the time.  The bulk of the city appears to have been built int he 80s, if not prior.  It consists of Russian-style architecture, or Bali style native to the region.  Yet, there were several complexes recently completed, and by my count well over 50 projects underway - everything from a 5-star hotel to 5 - 10 building complexes.  It appeared that they are expecting the population to double overnight.

However, after discussing it with my Chinese friends, I realized how little is needed to fill these new apartment buildings, given the scale of growth underway and the vast population still living in the rural countryside.  I am still trying to understand how this scale impacts my view...

Hoffman Misses Key Asset of Current Diploma System

Reid Hoffman pens an interesting piece on how the diploma needs an upgrade.  Though I agree wholeheartedly with his premise, he misses one critical point that enables the current system to thrive - the value of fuzziness.

The root of any economic system is information - who has it and how good is it.  The value of a diploma from a 4-year institution is no different.  Hoffman describes well the pains some people have in clearly articulating the value of their education beyond the blunt instrument that is the diploma (the "sell" side of diploma value), and the tactics employers use to reduce their pools to the most likely candidates (the "buy" side).

However, he neglects the fact that this system provides access that otherwise would not be available for many, given the fuzzy reality of a given person's credentials due to this bluntness.  Think of the kid that skated by without going to class, or the one who only took the "easy" courses.  Would they have had the same opportunities if the system of measurement had been more fine-grained?  Will they engage in a system that penalizes them?

There is another similar issue with fuzziness that will stifle the embrace of a more modular system by the employer  - the fact that most employers do not have the information they need to hire well for a given role.  There is so much bias and prejudice that clouds the current hiring process in most firms that more information could hinder an already clunky process, rather than help. More information generated by a fine-grained system will take more effort from the hiring manager to sift and understand as well.  Roles will have to be broken down in more detail, and more time may be required to assess potential fits.

A critical key to any system that attempts to improve an imperfect but functioning system is to ensure these fuzziness issues are addressed on both the buy and sell sides.  Failure to address them adequately will stop any potential replacement of an archaic yet functioning economic system.

Data in Context

Garance Frank-Ruta clarifies the context surrounding the data "discovered" that show former IRS Commissioner Douglas Shulman visiting The White House 157 times during his tenure.  The short story is that the data used to make that claim is imperfect.  A large majority of the supposed visits were in fact unfulfilled invites.  Another claim made from the same dataset is that he visited more often than any cabinet member - another falsehood given that the system referenced is used primarily to allow access to those walking in to the complex - cabinet members, given their seniority, are able to drive on to the White House complex.

This is yet another example of how important it is to leverage data thoughtfully.  Intelligence requires deliberate thought, not quick assertions and grandiose conclusions.  Minimal effort would have reveled the imperfections of the data referenced.  (The system used was built to track appointments within the White House complex, but only for meetings and "typical events.  Access lists for larger events often forgo the use of this system, as do appointments involving more senior government officials cleared to drive in to the complex.)

To ensure one does not fall in to this trap, there are three questions you must first answer, before acting on the information gleaned from a particular dataset:

1. How was the data collected?
2. What specific data is included in the dataset?
3. And, most importantly, what specific data is NOT included in the dataset?

Only with such context can you begin to understand the information available...