The Revolution Will Not Be Televised — The Sustainability Manifesto for Projects

While doing stuff and living life (which seems to take me away from writing) there were a good many interesting things written on project management.  The very insightful Dave Gordon at his blog, The Practicing IT Project Manager, provides a useful weekly list of the latest contributions to the literature that are of note.  If you haven’t checked it out please do so–I recommend it highly.

While I was away Dave posted to an interesting link on the concept of sustainability in project management.  Along those lines three PM professionals have proposed a Sustainability Manifesto for Projects.  As Dave points out in his own post on the topic, it rests on three basic principles:

  • Benefits realization over metrics limited to time, scope, and cost
  • Value for many over value of money
  • The long-term impact of our projects over their immediate results

These are worthy goals and no one needs to have me rain on their parade.  I would like to see these ethical principles, which is what they really are, incorporated into how we all conduct ourselves in business.  But then there is reality–the “is” over the “ought.”

For example, Dave and I have had some correspondence regarding the nature of the marketplace in which we operate through this blog.  Some time ago I wrote a series of posts here, here, and here providing an analysis of the markets in which we operate both in macroeconomic and microeconomic terms.

This came in response to one my colleagues making the counterfactual assertion that we operate in a “free market” based on the concept of “private enterprise.”  Apparently, such just-so stories are lies we have to tell ourselves to make the hypocrisy of daily life bearable.  But, to bring the point home, in talking about the concept of sustainability, what concrete measures will the authors of the manifesto bring to the table to counter the financialization of American business that has occurred of the past 35 years?

For example, the news lately has been replete with stories of companies moving plants from the United States to Mexico.  This despite rising and record corporate profits during a period of stagnating median working class incomes.  Free trade and globalization have been cited as the cause, but this involves more hand waving and the invocation of mantras, rather than analysis.  There has also been the predictable invocations of the Ayn Randian cult and the pseudoscience* of Social Darwinism.  Those on the opposite side of the debate characterize things as a morality play, with the public good versus greed being the main issue.  All of these explanations miss their mark, some more than others.

An article setting aside a few myths was recently published by Jonathan Rothwell at Brookings, which came to me via Mark Thoma’s blog, in the article, “Make elites compete: Why the 1% earn so much and what to do about it”.  Rothwell looks at the relative gains of the market over the last 40 years and finds that corporate profits, while doing well, have not been the driver of inequality that Robert Reich and other economists would have it be.  In looking at another myth that has been promulgated by Greg Mankiw, he finds that the rewards of one’s labors is not related to any special intelligence or skill.  On the contrary, one’s entry into the 1% is actually related to what industry one chooses to enter, regardless of all other factors.  This disparity is known as a “pay premium”.  As expected, petroleum and coal products, financial instruments, financial institutions, and lawyers, are at the top of the pay premium.  What is not, against all expectations of popular culture and popular economic writing, is the IT industry–hardware, software, etc.  Though they are the poster children of new technology, Bill Gates, Mark Zuckerburg, and others are the exception to the rule in an industry that is marked by a 90% failure rate.  Our most educated and talented people–those in science, engineering, the arts, and academia–are poorly paid–with negative pay premiums associated with their vocations.

The financialization of the economy is not a new or unnoticed phenomenon.  Kevin Phillips, in Wealth and Democracy, which was written in 2003, noted this trend.  There have been others.  What has not happened as a result is a national discussion on what to do about it, particularly in defining the term “sustainability”.

For those of us who have worked in the acquisition community, the practical impact of financialization and de-industrialization have made logistics challenging to say the least.  As a young contract negotiator and Navy Contracting Officer, I was challenged to support the fleet when any kind of fabrication or production was involved, especially in non-stocked machined spares of any significant complexity or size.  Oftentimes my search would find that the company that manufactured the items was out of business, its pieces sold off during Chapter 11, and most of the production work for those items still available done seasonally out of country.  My “out” at the time–during the height of the Cold War–was to take the technical specs, which were paid for and therefore owned by the government, to one of the Navy industrial activities for fabrication and production.  The skillset for such work was still fairly widespread, supported by the quality control provided by a fairly well-unionized and trade-based workforce–especially among machinists and other skilled workers.

Given the new and unique ways judges and lawyers have applied privatized IP law to items financed by the public, such opportunities to support our public institutions and infrastructure, as I was able, have been largely closed out.  Furthermore, the places to send such work, where possible, have also gotten vanishingly smaller.  Perhaps digital printing will be the savior for manufacturing that it is touted to be.  What it will not do is stitch back the social fabric that has been ripped apart in communities hollowed out by the loss of their economic base, which, when replaced, comes with lowered expectations and quality of life–and often shortened lives.

In the end, though, such “fixes” benefit a shrinkingly few individuals at the expense of the democratic enterprise.  Capitalism did not exist when the country was formed, despite the assertion of polemicists to link the economic system to our democratic government.  Smith did not write his pre-modern scientific tract until 1776, and much of what it meant was years off into the future, and its relevance given what we’ve learned over the last 240 years about human nature and our world is up for debate.  What was not part of such a discussion back then–and would not have been understood–was the concept of sustainability.  Sustainability in the study of healthy ecosystems usually involves the maintenance of great diversity and the flourishing of life that denotes health.  This is science.  Economics, despite Keynes and others, is still largely rooted in 18th and 19th century pseudoscience.

I know of no fix or commitment to a sustainability manifesto that includes global, environmental, and social sustainability that makes this possible short of a major intellectual, social or political movement willing to make a long-term commitment to incremental, achievable goals toward that ultimate end.  Otherwise it’s just the mental equivalent to camping out in Zuccotti Park.  The anger we note around us during this election year of 2016 (our year of discontent) is a natural human reaction to the end of an idea, which has outlived its explanatory power and, therefore, its usefulness.  Which way shall we lurch?

The Sustainability Manifesto for Projects, then, is a modest proposal.  It may also simply be a sign of the times, albeit a rational one.  As such, it leaves open a lot of questions, and most of these questions cannot be addressed or determined by the people to which it is targeted: project managers, who are usually simply employees of a larger enterprise.  People behave as they are treated–to the incentives and disincentives presented to them, oftentimes not completely apparent on the conscious level.  Thus, I’m not sure if this manifesto hits its mark or even the right one.

*This term is often misunderstood by non-scientists.  Pseudoscience means non-science, just as alternative medicine means non-medicine.  If any of the various hypotheses of pseudoscience are found true, given proper vetting and methodology, that proposition would simply be called science.  Just as alternative methods of treatment, if found effective and consistent, given proper controls, would simply be called medicine.

Sixteen Tons — Data Mining, Big Data, and the Asymmetry of variables and observations

Last Thursday I came upon what I can only interpret as an ironic comment at Mark Thoma’s Economist’s View blog entitled “Data Mining Can be a Productive Activity.”  I went to the link and it went to a VOX article by Castle and Hendry entitled “Data Mining with more variables than observations.”  All I could think after the opening line:  “A typical Oxford University econometrics exam question might take the form: ‘Data mining is bad, so mining with more candidate variables than observations must be pernicious. Discuss.'” was: are these people serious?

Data mining is a general term in high tech and not a specific approach to finding patterns and trends in large elements of data.  The authors–and I’m guessing that they are not alone in the econometrics profession–seem to be addressing a “Just Say No” approach to performing what for most of us who deal in statistical analysis and modeling of large datasets do every day, largely based on the fact that it involves these scary things called computers that run this mysterious thing behind the scenes called “code.”  Who knows what horrors may await us as we mistakenly draw causations from correlations by anything more than the use of Access or Excel spreadsheets?  It seems that Oxford dons need to get out more.

The use of microeconomic data mining has been in general use for quite some time in many businesses and business disciplines with a great deal of confidence and success (too much success in the medical insurance, financial services, and social networking fields to raise legal and ethical objections).  So the assertion that seems to be based on those of a single group of econometricians does seem to be odd.  In the end it seems to be a setup for a proprietary set of calculations placed within an Excel spreadsheet given the name “Autometrics.”  This largely argues for the proper approach to the organization of data rather than a criticism of data mining in general.

The discriminators among data mining and data mining-like technologies involve purpose, cost, ease of use, scalability, and sustainability.  New technologies are arising every year that allow for increased speed, more efficient grouping, and compression to allow organizations to handle what previously was thought to be “big data.”  Thus the concept of data mining and big data is a shifting one as our technologies drive toward greater capability in integrating and interpreting large datasets.  The authors cite the techniques of taking large data to prove anthropomorphic global warming as one of the success stories of large scale modeling based on large data.  Implicit in acknowledging this is that not every variable needs to be included in a model–only the relevant variables that drive and explain the results being produced.  There is no doubt that reification of statistical results is a common fallacy, but people had been doing that long before the development of silicon-based artificial intelligence.

There is no doubt that someday we will reach the limit of computational capabilities.  But for someone who lived through the nonexistent “crisis” of limited memory in the early ’90s followed not too after by the bogus Y2K “bug,” I am not quite ready to throw in the towel on the ability of data mining and modeling to effectively provide the tools for the more general discipline of econometrics.  We are only beginning to crack heuristic models in approaching big data and on the cusp of strong AI.