Y2020 B3: the great baby boomer brain drain & machine learning

June 7, 2012

Methods, Research

Baby Boomer Retirement Brain Drain Crisis
The en masse retirement of Baby Boomer generation professionals underway is witnessing an unprecedented ‘brain drain’ from a host of industries in the industrialized world.  Can Machine Learning methods be used to capture some of this exiting expertise?
 Baby Boomer Retirement

The untimely, and arguably demographically linked, Global Economic Crisis underway has unfortunately pushed rigorous ‘expert succession planning’ at many corporations to the wayside, meaning many industries are employing an unsustainable stopgap approach: hiring Baby Boomers back as consultants to keep things running.

Recently, upon invitation from a colleague, I attended a professional petroleum engineering conference in Western Europe.  As an avid multi-disciplinarian at heart, I enjoy visiting scientific, engineering, and expert conferences.  Beyond the unique technical terms and procedures of any particular discipline, there are common concerns, codes and practices associated with expert industry bodies.  There is often a bit of jockeying for status, muted, long lingering arguments that raise their head, and universal frustrations associated with the snail’s pace with which innovation moves in large bureaucratic bodies (institutions and professional communities alike).

Via my commercial consulting at SARK7 and PhD research at Nyenrode Business University in the Netherlands, I am quite particularly interested in how groups of experts structure themselves and utilize analytics to make organizational decisions relevant to their particular sphere of expertise, be it animal, mineral, or vegetable.  However, a larger impression struck me at this particular conference: the overwhelming demographic preponderance of Western-educated engineers aged 55 and upwards.  As a body, this group could be considered the foremost experts on managing and maintaining oil pipeline infrastructure.  There was nary an intern or trainee, nor many under the age of 50, at this important annual industrial conference.

Indeed, I endeavored to weave the question of all these experts rapidly nearing retirement into a number of ‘qualitative conversations’, gaining nearly universal feedback that: this was indeed the case, that it verged on being a crisis, and that no group or business was seemingly dealing with the problem in a concerted manner.  In one case, an engineer mentioned that his employer, a major petrochemical player, had a mandatory retirement / pension date each year.  In 2011, on one particular Friday in June, 7,000 employees retired en masse at this company (the local baker must have had to prepare cakes for weeks!).  The great majority promptly returned to work on Monday as highly-paid consultants.  However, there was no plan to have them train or otherwise transmit their expertise, only to ensure that critical services would continue operating.  The feedback I heard took the form that: most of the people who know how to run things are aging and that, effectively, there is no plan to train suitable replacements.

Upon conducting rudimentary research as a follow-up, it appears that this problem is a demographic trend which afflicts industrial society at large across most all traditional industries.  The nature of the demographic situation is such that in North American and European industrialized economies, Baby Boomers (born 1946 – 1964) outnumber the successor generation, popularly known as Generation X  (born 1965 ~ 1983), by nearly two-to-one in terms of raw population.  The problem is well recognized as a crisis and is affecting not only expert succession, but the foundations of the global economy, as witnessed by this joint report from the Center for Strategic and International Studies and Citigroup.

The developing world is experiencing quite a different dramatic demographic shift:  a small relative older generation is being rapidly subsumed by a massive population of those 25 and younger seeking change, development, and rights.  Skewed demographic patterns have recently been cited as a driving factor in the so-called, still simmering Arab Spring revolutions.  For example, one can see a clear ‘fat-base’ pyramid in contemporary Egypt, highlighting the outsized proportion of under-25 Egyptians; it is academic to draw socio-political connections with the recent rapid casting away of the Mubarak regime.

As this is not a formal academic forum, but rather a place to discuss analytics practitioner topics and to raise potential research agendas, the main point is that demographics are rapidly shifting both industrialized and developing economies.  Related to the central topic of ‘generational professional brain drain’ in the industrialized world, the foundations are quite complex and intertwined, involving: an immense demographic population bubble of those born between 1946 – 64 approaching retirement (approx. 180 million across North America and Europe combined), the unraveling Global Economic Crisis and US / Euro Property Bubble (which has decimated the chosen retirement savings vehicle of this generation), an associated crisis in banking and pension funds (again, see above cited report), spiraling health care costs, monetary inflation (an inevitable byproduct of ongoing US and Eurozone bailouts and linked budget deficits), and the lack of a coordinated response on the part of governments and industry to plan adequately for ‘knowledge succession’ generally, exacerbated, in turn, by a lack of capital from the unwinding Financial Crisis.  It takes on the aspect of a frightening, sobering merry-go-round which, by all accounts, risks a continual downward spiral.

Essentially, many in the Baby Boomer generation are now facing the frightening prospect of not being able to retire due to pension and savings shortfalls.  The amount that many believed they would need for retirement a decade ago has either greatly reduced in the Economic Crisis (some having lost nearly all their savings), has dissolved in property devaluation, or is being steadily eaten up due to rising costs / inflationary pressures (a result of the aforementioned bail-outs and resulting deficits).  Understandably this jarring scenario may make Baby Boomer professionals and experts somewhat reluctant to pass on expert ‘trade knowledge’ if they have the opportunity to return to work via lucrative consulting contracts.  Cultural commentators have even opined that Baby Boomers themselves are potentially in a willful state of denial, combining a refusal to accept the existential ramifications of their own inevitable aging with an inability to plan adequately for a graceful exit from their traditional ‘central stage’ position in industrialized society.

Like the parable of the frog boiling in a slowly warming ‘hot tub’ (whereas the frog jumps out of rapidly heated water), governments and corporations seemingly are content to put Band-Aids on the evolving situation, hiring Baby Boomers as aged expert consultants to keep things running.  One might speculate at the ironic situation whereby those deciding to advance such contracts are, in the large, in the very same cohort, giving the tacit impression of an ‘I’ll scratch your back…’ scenario.  In any case, one can imagine that succession planning and training easily are cut from the budget when critical infrastructure and continuity must be maintained, whether we are discussing governments, industry associations, or corporations.

Being loath to frame hyperboles, I would characterize the mass knowledge drain of swiftly retiring expert Baby Boom generation professionals, in a broad range of industries, as a ‘slow burning, negative economic-demographic trend’, although the term ‘looming crisis’ is apt.  I have taken the liberty to coin the acronym “Y2020 B3”, meaning Year 2020 Baby Boomer Bug.  Y2020 B3 implies the generational, demographic ‘knowledge drain’ of retiring experts in a broad swath of industries and disciplines, which has already started, but which will hit crisis proportions at the demographic inflection point of 2020 when massive numbers of retiring Baby Boomer generation experts in industrialized economies reaches critical mass.

Without assigning blame, the scenario indeed has all the aspects of a ‘perfect storm’ in its monolithic and interconnected proportions.  There needs to be a clearer, more orchestrated strategy for ‘knowledge succession planning’.  There are simply not enough suitable industrialized economy Generation X mid-career experts to fill the gap, being at approximately 50% of the Baby Boomer demographic population level, and, as some charge, having had less opportunities to gain suitable expertise due to the overwhelming professional dominance of the preceding Baby Boomer generation in nearly all fields (IT, being a relatively new discipline, perhaps being somewhat of an exception).  The next alternative, a combination of Generation Y emerging experts (also known as the Millenials or Echo Boom generation, as the children of the Baby Boomers) and emerging nation experts is also less than suitable, facing a respective lack of raw on–the-job experience and a shortage of properly trained / educated professionals.

A stop-gap strategy is here proposed:  using advanced, emerging IT systems and methods to capture expert knowledge before it ungracefully exits, threatening to drag down critical infrastructure and services with it.  It is proposed that, in concert (likely via tax incentives), governments and large corporations, particularly those associated with industries overseeing critical infrastructure such as transport, energy, health care, and telecommunications, strongly consider making strategic investments in capturing expert knowledge via concerted Machine Learning campaigns.  Automated Decision Tree extrapolation (and supervised pruning) is in particular recommended, as the results are suitable to guiding decision making for both machines and humans.  The mass extrapolation of expert knowledge (from ‘Big Data’ matched with expert decisions) into Decision Trees would thus serve to document knowledge both for training younger, rising experts and to provide a foundation for the implementation of expert systems-based automation.

A solution must provide a cost-effective way to ‘record’ the expertise of retiring Boomers so that it is suitable both for the training and education of younger professionals and for the potential automation of decision making via expert systems.    My proposal here is a potential compromise route to ameliorate the pain:  a purposeful exertion on the part of corporate interests to engineer ‘expert systems’ to capture some portion of the expert knowledge rapidly reaching retirement, especially for critical infrastructure such as transport and energy supply chains.  Machine Learning is a potential method toward achieving these goals.  The methods and technologies are available; what is required is the will power to make commitments…

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About SARK7

Scott Allen Mongeau (@SARK7), an INFORMS Certified Analytics Professional (CAP), is a researcher, lecturer, and consulting Data Scientist. Scott has over 30 years of project-focused experience in data analytics across a range of industries, including IT, biotech, pharma, materials, insurance, law enforcement, financial services, and start-ups. Scott is a part-time lecturer and PhD (abd) researcher at Nyenrode Business University on the topic of data science. He holds a Global Executive MBA (OneMBA) and Masters in Financial Management from Erasmus Rotterdam School of Management (RSM). He has a Certificate in Finance from University of California at Berkeley Extension, a MA in Communication from the University of Texas at Austin, and a Graduate Degree (GD) in Applied Information Systems Management from the Royal Melbourne Institute of Technology (RMIT). He holds a BPhil from Miami University of Ohio. Having lived and worked in a number of countries, Scott is a dual American and Dutch citizen. He may be contacted at: webmaster@sark7.com LinkedIn: https://www.linkedin.com/in/smongeau/ Twitter: @sark7 Blog: sctr7.com Web: www.sark7.com All posts are copyright © 2020 SARK7 All external materials utilized imply no ownership rights and are presented purely for educational purposes.

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3 Comments on “Y2020 B3: the great baby boomer brain drain & machine learning”

  1. Joe Says:

    As a boomer and a manager of boomers in a government agency I recognize the coming drain of expertise as staff make plans for retirement. My plan is to have staff record critical processes in job specific procedures manuals. While this is a low tech solution, it has the potential of meeting my agencies needs.


    • sctr7 Says:

      Agreed – simple, straight-forward process documentation is the best for procedural environments. I can recommend ‘The Checklist Manifesto’ by Harvard Medical School Professor A. Gawande. He lays out a process for documenting proper protocol in high-impact environments.

      Concerning machine learning, based on research, this method is suitable for inherently complex infrastructure such as airplane operation, gas turbine installations, oil refineries, layered IT infrastructure, and the like. For linear procedures and processes, linear documentation is the way to go indeed.

      Machine learning is powerful in automatically extracting the complex logic people apply in associating X treatment for Y problem (diagnostics). The metaphor for complex diagnostic knowledge is someone who has owner a finicky car for many years and knows to pump the gas three times to get it started on a cold morning. While we could comprehensively interview this person to create a users manual for operating the old car, this would be prohibitively expensive. Rather, if we had a record of all the actions taken by the person given certain complex environmental factors (noting that such ambient data is increasingly available with sensor-based infrastructure and mass storage), decision-tree based machine learning can extract an automatic documentation of symptoms and treatments. Thus, for critical complex infrastructure, this is promising in automatic documentation in the face of mass retirements…



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