Ground Control from Major Tom — Breaking Radio Silence: New Perspectives on Project Management

Since I began this blog I have used it as a means of testing out and sharing ideas about project management, information systems, as well to cover occasional thoughts about music, the arts, and the meaning of wisdom.

My latest hiatus from writing was due to the fact that I was otherwise engaged in a different sort of writing–tech writing–and in exploring some mathematical explorations related to my chosen vocation, aside from running a business and–you know–living life.  There are only so many hours in the day.  Furthermore, when one writes over time about any one topic it seems that one tends to repeat oneself.  I needed to break that cycle so that I could concentrate on bringing something new to the table.  After all, it is not as if this blog attracts a massive audience–and purposely so.  The topics on which I write are highly specialized and the members of the community that tend to follow this blog and send comments tend to be specialized as well.  I air out thoughts here that are sometimes only vaguely conceived so that they can be further refined.

Now that that is out of the way, radio silence is ending until, well, the next contemplation or massive workload that turns into radio silence.

Over the past couple of months I’ve done quite a bit of traveling, and so have some new perspectives that and trends that I noted and would like to share, and which will be the basis (in all likelihood) of future, more in depth posts.  But here is a list that I have compiled:

a.  The time of niche analytical “tools” as acceptable solutions among forward-leaning businesses and enterprises is quickly drawing to a close.  Instead, more comprehensive solutions that integrate data across domains are taking the market and disrupting even large players that have not adapted to this new reality.  The economics are too strong to stay with the status quo.  In the past the barrier to integration of more diverse and larger sets of data was the high cost of traditional BI with its armies of data engineers and analysts providing marginal value that did not always square with the cost.  Now virtually any data can be accessed and visualized.  The best solutions, providing pre-built domain knowledge for targeted verticals, are the best and will lead and win the day.

b.  Along these same lines, apps and services designed around the bureaucratic end-of-month chart submission process are running into the new paradigm among project management leaders that this cycle is inadequate, inefficient, and ineffective.  The incentives are changing to reward actual project management in lieu of project administration.  The core fallacy of apps that provide standard charts based solely on user’s perceptions of looking at data is that they assume that the PM domain knows what it needs to see.  The new paradigm is instead to provide a range of options based on the knowledge that can be derived from data.  Thus, while the options in the new solutions provide the standard charts and reports that have always informed management, KDD (knowledge discovery in database) principles are opening up new perspectives in understanding project dynamics and behavior.

c.  Earned value is *not* the nexus of Integrated Project Management (IPM).  I’m sure many of my colleagues in the community will find this statement to be provocative, only because it is what they are thinking but have been hesitant to voice.  A big part of their hesitation is that the methodology is always under attack by those who wish to avoid accountability for program performance.  Thus, let me make a point about Earned Value Management (EVM) for clarity–it is an essential methodology in assessing project performance and the probability of meeting the constraints of the project budget.  It also contributes data essential to project predictive analytics.  What the data shows from a series of DoD studies (currently sadly unpublished), however, is that it is planning (via a Integrated Master Plan) and scheduling (via an Integrated Master Schedule) that first ties together the essential elements of the project, and will record the baking in of risk within the project.  Risk manifested in poorly tying contract requirements, technical performance measures, and milestones to the plan, and then manifested in poor execution will first be recorded in schedule (time-based) performance.  This is especially true for firms that apply resource-loading in their schedules.  By the time this risk translates and is recorded in EVM metrics, the project management team is performing risk handling and mitigation to blunt the impact on the performance management baseline (the money).  So this still raises the question: what is IPM?  I have a few ideas and will share those in other posts.

d.  Along these lines, there is a need for a Schedule (IMS) Gold Card that provides the essential basis of measurement of programmatic risk during project execution.  I am currently constructing one with collaboration and will put out a few ideas.

e.  Finally, there is still room for a lot of improvement in project management.  For all of the gurus, methodologies, consultants, body shops, and tools that are out there, according to PMI, more than a third of projects fail to meet project goals, almost half to meet budget expectations, less than half finished on time, and almost half experienced scope creep, which, I suspect, probably caused “failure” to be redefined and under-reported in their figures.  The assessment for IT projects is also consistent with this report, with CIO.com reporting that more than half of IT projects fail in terms of meeting performance, cost, and schedule goals.  From my own experience and those of my colleagues, the need to solve the standard 20-30% slippage in schedule and similar overrun in costs is an old refrain.  So too is the frustration that it need take 23 years to deploy a new aircraft.  A .5 CPI and SPI (to use EVM terminology) is not an indicator of success.  What this indicates, instead, is that there need to be some adjustments and improvements in how we do business.  The first would be to adjust incentives to encourage and reward the identification of risk in project performance.  The second is to deploy solutions that effectively access and provide information to the project team that enable them to address risk.  As with all of the points noted in this post, I have some other ideas in this area that I will share in future posts.

Onward and upward.

Post-Blogging NDIA Blues — The Latest News (Project Management Wonkish)

The National Defense Industrial Association’s Integrated Program Management Division (NDIA IPMD) just had its quarterly meeting here in sunny Orlando where we braved the depths of sub-60 degrees F temperatures to start out each day.

For those not in the know, these meetings are an essential coming together of policy makers, subject matter experts, and private industry practitioners regarding the practical and mundane state-of-the-practice in complex project management, particularly focused on the concerns of the the federal government and the Department of Defense.  The end result of these meetings is to publish white papers and recommendations regarding practice to support continuous process improvement and the practical application of project management practices–allowing for a cross-pollination of commercial and government lessons learned.  This is also the intersection where innovation among the large and small are given an equal vetting and an opportunity to introduce new concepts and solutions.  This is an idealized description, of course, and most of the petty personality conflicts, competition, and self-interest that plagues any group of individuals coming together under a common set of interests also plays out here.  But generally the days are long and the workshops generally produce good products that become the de facto standard of practice in the industry. Furthermore the control that keeps the more ruthless personalities in check is the fact that, while it is a large market, the complex project management community tends to be a relatively small one, which reinforces professionalism.

The “blues” in this case is not so much borne of frustration or disappointment but, instead, from the long and intense days that the sessions offer.  The biggest news from an IT project management and application perspective was twofold. The data stream used by the industry in sharing data in an open systems manner will be simplified.  The other was the announcement that the technology used to communicate will move from XML to JSON.

Human readable formatting to Data-focused formatting.  Under Kendall’s Better Buying Power 3.0 the goal of the Department of Defense (DoD) has been to incorporate better practices from private industry where they can be applied.  I don’t see initiatives for greater efficiency and reduction of duplication going away in the new Administration, regardless of what a new initiative is called.

In case this is news to you, the federal government buys a lot of materials and end items–billions of dollars worth.  Accountability must be put in place to ensure that the money is properly spent to acquire the things being purchased.  Where technology is pushed and where there are no commercial equivalents that can be bought off the shelf, as in the systems purchased by the Department of Defense, there are measures of progress and performance (given that the contract is under a specification) that are submitted to the oversight agency in DoD.  This is a lot of data and to be brutally frank the method and format of delivery has been somewhat chaotic, inefficient, and duplicative.  The Department moved to address this by a somewhat modest requirement of open systems submission of an application-neutral XML file under the standards established by the UN/CEFACT XML organization.  This was called the Integrated Program Management Report (IMPR).  This move garnered some improvement where it has been applied, but contracts are long-term, so incorporating improvements though new contractual requirements tends to take time.  Plus, there is always resistance to change.  The Department is moving to accelerate addressing these inefficiencies in their data streams by eliminating the unnecessary overhead associated with specifications of formatting data for paper forms and dealing with data as, well, data.  Great idea and bravo!  The rub here is that in making the change, the Department has proposed dropping XML as the technology used to transfer data and move to JSON.

XML to JSON. Before I spark another techie argument about the relative merits of each, there are some basics to understand here.  First, XML is a language, JSON is simply data exchange format.  This means that XML is specifically designed to deal with hierarchical and structured data that can be queried and where validation and fidelity checks within the data are inherent in the technology. Furthermore, XML is known to scale while maintaining the integrity of the data, which is intended for use in relational databases.  Furthermore, XML is hard to break.  It is meant for editing and will maintain its structure and integrity afterward.

The counter argument encountered is that JSON is new! and uses fewer characters! (which usually turns out to be inconsequential), and people are talking about it for Big Data and NoSQL! (but this happened after the fact and the reason for shoehorning it this way is discussed below).

So does it matter?  Yes and no.  As a supplier specializing in delivering solutions that normalize and rationalize data across proprietary file structures and leverage database capabilities, I don’t care.  I can adapt quickly and will have a proof-of-concept solution out within 30 days of receiving the schema.

The risk here, which applies to DoD and the industry, is that the decision to go to JSON is made only because it is the shiny new thing used by gamers and social networking developers.  There has also been a move to adapt to other uses because of the history of significant security risks that had been found in Java, so much so that an entire Wikipedia page is devoted to them.  Oracle just killed off Java applets, though Java hangs on.  JSON, of course, isn’t Java, but it was designed from birth as JavaScript Object Notation (hence the acronym JSON), with the purpose of handling relatively small bits of data across web servers in a number of proprietary settings.

To address JSON deficiencies relative to XML, a number of tools have been and are being developed to replicate the fidelity and reliability found in XML.  Whether this is sufficient to be effective against a structured LANGUAGE is to be seen.  Much of the overhead that technies complain about in XML is due to the native functionality related to the power it brings to the table.  No doubt, a bicycle is simpler than a Formula One racer–and this is an apt comparison.  Claiming “simpler” doesn’t pass the “So What?” test knowing the business processes involved.  The technology needs to be fit to the solution.  The purpose of data transmission using APIs is not only to make it easy to produce but for it to–you know–achieve the goals of normalization and rationalization so that it can be used on the receiving end which is where the consumer (which we usually consider to be the customer) sits.

At the end of the day the ability to scale and handle hierarchical, structured data will rely on the quality and strength of the schema and the tools that are published to enforce its fidelity and compliance.  Otherwise consuming organizations will be receiving a dozen different proprietary JSON files, and that does not address the present chaos but simply adds to it.  These issues were aired out during the meeting and it seems that everyone is aware of the risks and that they can be addressed.  Furthermore, as the schema is socialized across solutions providers, it will be apparent early if the technology will be able handle the project performance data resulting from the development of a high performance aircraft or a U.S. Navy destroyer.

The (Contract) is parent to the (Project)

It’s been a late spring filled with travel and tragedy.  Blogging had taken a hiatus, except for AITS.org, which I highly encourage you check out.  My next item will be posted there the first week of July.  The news from Orlando is that we are united and strong as a community, facing down both crackpots and opportunists, and so it is back to work.

At a recent conference one of the more interesting conversations surrounded the difference between contract and project management.  To many people this is one of the same–and a simple Google search reinforces this perception–but, I think, this is a misconception.

The context of the discussion was interesting in that it occurred during an earned value management-focused event.  EVM pitches itself as the glue that binds together the parts of project management that further constitutes integrated project management, but I respectfully disagree.  If we ignore the self-promotion of this position and like good engineers stick to our empiricist approach, we will find that EVM is a method of deriving the financial value of effort within a project.  It is also a pretty good indicator of cost risk manifestation.  This last shouldn’t be taken too far.

A recent DoD study, which is not yet published, demonstrated that early warning cannot be had by EVM even when diving into the details.  Instead, ensuring integration and traceability to the work package level tied to schedule activities could be traced to the slips in schedule (and the associated impact of the bow wave) against the integrated master schedule (IMS), which then served as the window to early warning.  So within the limited context of project performance, EVM itself is just one of many points of entry to eventually get to the answer.  This answer, of course, needs to be both timely and material.

Material in this case refers to the ability to understand the relevance and impact of the indicator.  The latest buzz-phrase to this condition is “actionable” but that’s just a marketing ploy to make a largely esoteric and mundane evolution sound more exciting.  No indicator by itself is ever actionable.  In some cases the best action is no action.  Furthermore, a seemingly insignificant effort may have asymmetrical impacts that threaten the project itself.  This is where risk enters the picture.

When speaking of risk, all too often the discussion comes down to simulated Monte Carlo analysis.  For the project professional situated within the earned value domain, this is a convenient way to pigeonhole the concept and keep it bounded within familiar pathways, but it does little to add new information.  When applied within this context the power of Monte Carlo is limited to a range of probable outcomes within the predictive capabilities of EVM and the IMS.  This is not to minimize the importance of applying the method to these artifacts but, instead, a realization that it is a limited application.

For risk also includes factors that are external to these measurements.  Oftentimes this is called qualitative risk, but that is an all too familiar categorization that makes it seem fuzzy.  These external factors are usually the driving environment factors that limit the ability of the project to adapt.  These factors also incorporate the framing assumptions underlying the justification for the project effort.  Thus, we are led to financing and the conditions needed to achieve the next milestone for financing.  In government project management, this is known as the budget hearing cycle, and it can be both contentious and risky.

Thus, as with the title of this post, the project is really the child of the contract.  Yet when speaking of contract management the terms or often intertwined, or are relegated to the prosaic legalese of contract clauses and, in government, to the Federal Acquisition Regulation (FAR).  But that does not constitute contract management.

This is where our discussions became interesting.  Because need invoke only one element not incorporated into consideration to prove the point.  Let’s take Contract Budget Base (CBB).  This number is made up of the negotiated contract cost (NCC) plus authorized unpriced work (AUW).  In order to take these elements into account, since existing systems act as if they are external to consideration, ephemeral tools or spreadsheets are used to augment the tracking and incorporation of AUW and its impact on the CBB, though the risk of incorrectly tracking and incorporating this work is immeasurably more risky than any single work package or control account in the more closely monitored program management baseline (PMB).  The same goes with management reserve (MR), and even within the PMB itself, undistributed budget (UB), work authorizations (WADs), and change order tracking and impact analysis are often afterthoughts.

But back to the contract itself, the highest elements of the contract are the total allocated budget (TAB) and profit/fee.  But this is simply shorthand for the other elements that affect the TAB.  For example, some contracts have contract clauses that provide incentives and/or penalties that are tied to technical achievement or milestones, yet our project systems act as if these conditions are unanticipated events that fall from the sky.  Only by augmenting project management indicators are these important contract management anticipated and their impacts assessed.

In my own experience, in looking at the total contract, I have seen projects fail for want of the right “color” of money being provided within the window for decisive impact on risk manifestation.  Thus, cashflow–and the manner in which cashflow is released to fund a project–enters the picture.  But more to the point, I have seen the decision regarding cashflow made based on inadequate or partial data that was collected at a level of the structure that was largely irrelevant.  When looking at the life-cycle management of a system–another level up in our hierarchy–our need for awareness–and the information systems that can augment that awareness–becomes that much more acute.

The point here is that, while we are increasingly concerned about the number of angels dancing on the head of the EVM pin, we are ignoring other essential elements of project success.  When speaking of integrated project management, we are speaking of slightly expanding our attention span in understanding the project ecosystem–and yet even those moderate efforts meet resistance.  Given new technology, it is time to begin incorporating those elements that go well beyond the integration of cost, schedule, and bounded schedule risk.

 

Don’t Know Much…–Knowledge Discovery in Data

A short while ago I found myself in an odd venue where a question was posed about my being an educated individual, as if it were an accusation.  Yes, I replied, but then, after giving it some thought, I made some qualifications to my response.  Educated regarding what?

It seems that, despite a little more than a century of public education and widespread advanced education having been adopted in the United States, along with the resulting advent of widespread literacy, that we haven’t entirely come to grips with what it means.  For the question of being an “educated person” has its roots in an outmoded concept–an artifact of the 18th and 19th century–where education was delineated, and availability determined, by class and profession.  Perhaps this is the basis for the large strain of anti-intellectualism and science denial in the society at large.

Virtually everyone today is educated in some way.  Being “educated” means nothing–it is a throwaway question, an affectation.  The question is whether the relevant education meets the needs of the subject being addressed.  An interesting discussion about this very topic is explored at Sam Harris’ blog in the discussion he held with amateur historian Dan Carlin.

In reviewing my own education, it is obvious that there are large holes in what I understand about the world around me, some of them ridiculously (and frustratingly) prosaic.  This shouldn’t be surprising.  For even the most well-read person is ignorant about–well–virtually everything in some manner.  Wisdom is reached, I think, when you accept that there are a few things that you know for certain (or have a high probability and level of confidence in knowing), and that there are a host of things that constitute the entire library of knowledge encompassing anything from a particular domain to that of the entire universe, which you don’t know.

To sort out a well read dilettante from someone who can largely be depended upon to speak with some authority on a topic, educational institutions, trade associations, trade unions, trade schools, governmental organizations, and professional organizations have established a system of credentials.  No system is entirely perfect and I am reminded (even discounting fraud and incompetence) that half of all doctors and lawyers–two professions that have effectively insulated themselves from rigorous scrutiny and accountability to the level of almost being a protected class–graduate in the bottom half of their class.  Still, we can sort out a real brain surgeon from someone who once took a course in brain physiology when we need medical care (to borrow an example from Sam Harris in the same link above).

Furthermore, in the less potentially life-threatening disciplines we find more variation.  There are credentialed individuals who constantly get things wrong.  Among economists, for example, I am more likely to follow those who got the last financial crisis and housing market crash right (Joe Stiglitz, Dean Baker, Paul Krugman, and others), and those who have adjusted their models based on that experience (Brad DeLong, Mark Thoma, etc.), than those who have maintained an ideological conformity and continuity despite evidence.  Science–both what are called the hard and soft sciences–demands careful analysis and corroborating evidence to be tied to any assertions in their most formalized contexts.  Even well accepted theories among a profession are contingent–open to new information and discovery that may modify, append, or displace them.  Furthermore, we can find polymaths and self-taught individuals who have equaled or exceeded credentialed peers.  In the end the proof is in the pudding.

My point here is threefold.  First, in most cases we don’t know what we don’t know.  Second, complete certainty is not something that exists in this universe, except perhaps at death.  Third, we are now entering a world where new technologies allow us to discover new insights in accessing previously unavailable or previously opaque data.

One must look back at the revolution in information over the last fifty years and its resulting effect on knowledge to see what this means in our day-to-day existence.  When I was a small boy in school we largely relied on the published written word.  Books and periodicals were the major means of imparting information, aside from collocated collaborative working environments, the spoken word, and the old media of magazines, radio, and television.  Information was hard to come by–libraries were limited in their collections and there were centers of particular domain knowledge segmented by geography.   Furthermore, after the introduction of television, society had developed  trusted sources and gatekeepers to keep the cranks and flimflam out.

Today, new media–including all forms of digitized information–has expanded and accelerated the means of transmitting information.  Unlike old media, books, and social networking, there are also fewer gatekeepers in new media: editors, fact checkers, domain experts, credentialed trusted sources, etc. that ensure quality control, reliability, fidelity of the information, and provide context.  It’s the wild west of information and those wooed by the voodoo of self-organization contribute to the high risk associated with relying on information provided through these sources.  Thus, organizations and individuals who wish to stay within the fact-based community have had to sort out reliable, trusted sources and, even in these cases, develop–for lack of a better shorthand–BS detectors.  There are two purposes to this exercise: to expand the use of the available data and leverage the speed afforded by new media, and to ensure that the data is reliable and can reliably tell us something important about our subject of interest.

At the level of the enterprise, the sector, or the project management organization, we similarly are faced with the situation in which the scope of data that can be converted into information is rapidly expanding.  Unlike the larger information market, this data on the microeconomic level is more controlled.  Given that data at this level suffers from significance because it records isolated events, or small sample sizes, the challenge has been to derive importance from data where sometimes significance is minimal.

Furthermore, our business systems, because of the limitations of the selected technology, have been self-limiting.  I come across organizations all the time who cannot imagine the incorporation and integration of additional data sets largely because the limitations of their chosen software solution has inculcated that approach–that belief–into the larger corporate culture.  We do not know what we do not know.

Unfortunately, it’s what you do not know that, more often than not, will play a significant role in your organization’s destiny, just as an individual that is more self-aware is better prepared to deal with the challenges that manifest themselves as risk and its resultant probabilities.  Organizations must become more aware and look at things differently, especially since so many of the more conventional means of determining risk and opportunities seems to be failing to keep up with the times, which is governed by the capabilities of new media.

This is the imperative of applying knowledge discovery in data at the organizational and enterprise level–and in shifting one’s worldview from focusing on the limitations of “tools”: how they paint a screen, whether data is displayed across the x or y axis, what shade of blue indicates good performance, how many keystrokes does it take to perform an operation, and all manner of glorified PowerPoint minutia–to a focus on data:  the ability of solutions to incorporate more data, more efficiently, more quickly, from a wider range of sources, and processed in a more effective manner, so that it is converted into information to be able to be used to inform decision making at the most decisive moment.

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.

The Monster Mash — Zombie Ideas in Project and Information Management

Just completed a number of meetings and discussions among thought leaders in the area of complex project management this week, and I was struck by a number of zombie ideas in project management, especially related to information, that just won’t die.  The use of the term zombie idea is usually attributed to the Nobel economist Paul Krugman from his excellent and highly engaging (as well as brutally honest) posts at the New York Times, but for those not familiar, a zombie idea is “a proposition that has been thoroughly refuted by analysis and evidence, and should be dead — but won’t stay dead because it serves a political purpose, appeals to prejudices, or both.”

The point is that to a techie–or anyone engaged in intellectual honesty–is that they are often posed in the form of question begging, that is, they advance invalid assumptions in the asking or the telling.  Most often they take the form of the assertive half of the same coin derived from “when did you stop beating your wife?”-type questions.  I’ve compiled a few of these for this post and it is important to understand the purpose for doing so.  It is not to take individuals to task or to bash non-techies–who have a valid reason to ask basic questions based on what they’ve heard–but propositions put forth by people who should know better based on their technical expertise or experience.  Furthermore, knowing and understanding technology and its economics is really essential today to anyone operating in the project management domain.

So here are a few zombies that seem to be most common:

a.  More data equals greater expense.  I dealt with this issue in more depth in a previous post, but it’s worth repeating here:  “When we inform Moore’s Law by Landauer’s Principle, that is, that the energy expended in each additional bit of computation becomes vanishingly small, it becomes clear that the difference in cost in transferring a MB of data as opposed to a KB of data is virtually TSTM (“too small to measure”).”  The real reason why we continue to deal with this assertion is both political in nature and also based in social human interaction.  People hate oversight and they hate to be micromanaged, especially to the point of disrupting the work at hand.  We see behavior, especially in regulatory and contractual relationships, where the reporting entity plays the game of “hiding the button.”  This behavior is usually justified by pointing to examples of dysfunction, particularly on the part of the checker, where information submissions lead to the abuse of discretion in oversight and management.  Needless to say, while such abuse does occur, no one has yet to point quantitatively to data (as opposed to anecdotally) that show how often this happens.

I would hazard to guess that virtually anyone with some experience has had to work for a bad boss; where every detail and nuance is microscopically interrogated to the point where it becomes hard to make progress on the task at hand.  Such individuals, who have been advanced under the Peter principle must, no doubt, be removed from such a position.  But this often happens in any organization, whether it be in private enterprise–especially in places where there is no oversight, check-and-balances, means of appeal, or accountability–or government–and is irrelevant to the assertion.  The expense item being described is bad management, not excess data.  Thus, such assertions are based on the antecedent assumption of bad management, which goes hand-in-hand with…

b. More information is the enemy of efficiency.  This is the other half of the economic argument to more data equals greater expense.  And I failed to mention that where the conflict has been engaged over these issues, some unjustifiable figure is given for the additional data that is certainly not supported by the high tech economics cited above.  Another aspect of both of these perspectives also comes from the conception of non-techies that more data and information is equivalent to pre-digital effort, especially in conceptualizing the work that often went into human-readable reports.  This is really an argument that supports the assertion that it is time to shift the focus from fixed report formatting functionality in software based on limited data to complete data, which can be formatted and processed as necessary.  If the right and sufficient information is provided up-front, then additional questions and interrogatories that demand supplemental data and information–with the attendant multiplication of data streams and data islands that truly do add cost and drive inefficiency–are at least significantly reduced, if not eliminated.

c.  Data size adds unmanageable complexity.  This was actually put forth by another software professional–and no doubt the non-techies in the room would have nodded their heads in agreement (particularly given a and b above), if opposing expert opinion hadn’t been offered.  Without putting too fine a point on it, a techie saying this to an open forum is equivalent to whining that your job is too hard.  This will get you ridiculed at development forums, where you will be viewed as an insufferable dilettante.  Digitized technology for well over 40 years has been operating under the phenomenon of Moore’s Law.  Under this law, computational and media storage capability doubles at least every two years under the original definition, though that equation has accelerated to somewhere between 12 and 24 months.  Thus, what was considered big data, say, in 1997 when NASA first coined the term, is not considered big data today.  No doubt, what is considered big data this year will not be considered big data two years from now.  Thus, the term itself is relative and may very well become archaic.  The manner in which data is managed–its rationalization and normalization–is important in successfully translating disparate data sources, but the assertion that big is scary is simply fear mongering because you don’t have the goods.

d.  Big data requires more expensive and sophisticated approaches.  This flows from item c above as well and is often self-serving.  Scare stories abound, often using big numbers which sound scary.  All data that has a common use across domains has to be rationalized at some point if they come from disparate sources, and there are a number of efficient software techniques for accomplishing this.  Furthermore, support for agnostic APIs and common industry standards, such as the UN/CEFACT XML, take much of the rationalization and normalization work out of a manual process.  Yet I have consistently seen suboptimized methods being put forth that essentially require an army of data scientists and coders to essentially engage in brute force data mining–a methodology that has been around for almost 30 years: except that now it carries with it the moniker of big data.  Needless to say this approach is probably the most expensive and slowest out there.  But then, the motivation for its use by IT shops is usually based in rice bowl and resource politics.  This is flimflam–an attempt to revive an old zombie under a new name.  When faced with such assertions, see Moore’s Law and keep on looking for the right answer.  It’s out there.

e.  Performance management and assessment is an unnecessary “regulatory” expense.  This one keeps coming up as part of a broader political agenda beyond just project management.  I’ve discussed in detail the issues of materiality and prescriptiveness in regulatory regimes here and here, and have addressed the obvious legitmacy of organizations to establish one in fiduciary, contractual, and governmental environments.

My usual response to the assertion of expense is to simply point to the unregulated derivatives market largely responsible for the financial collapse, and the resulting deep economic recession that followed once the housing bubble burst.  (And, aside from the cost of human suffering and joblessness, the expenses related to TARP).  Thus we know that the deregulation of banking had gone so well.  Even after the Band-Aid of Dodd-Frank the situation probably requires a bit more vigor, and should include the ratings agencies as well as the real estate market.  But here is the fact of the matter: such expenses cannot be monetized as additive because “regulatory” expenses usually represent an assessment of the day-to-day documentation, systems, and procedures required when performing normal business operations and due diligence in management.  I attended an excellent presentation last week where the speaker, tasked with finding unnecessary regulatory expenses, admitted as much.

Thus, what we are really talking about is an expense that is an essential prerequisite to entry in a particular vertical, especially where monopsony exists as a result of government action.  Moral hazard, then, is defined by the inherent risk assumed by contract type, and should be assessed on those terms.  Given the current trend is to raise thresholds, the question is going to be–in the government sphere–whether public opinion will be as forgiving in a situation where moral hazard assumes $100M in risk when things head south, as they often do with regularity in project management.  The way to reduce that moral hazard is through sufficiency of submitted data.  Thus, we return to my points in a and b above.

f.  Effective project assessment can be performed using high level data.  It appears that this view has its origins in both self-interest and a type of anti-intellectualism/anti-empiricism.

In the former case, the bias is usually based on the limitations of either individuals or the selected technology in providing sufficient information.  In the latter case, the argument results in a tautology that reinforces the fallacy that absence of evidence proves evidence of absence.  Here is how I have heard the justification for this assertion: identifying emerging trends in a project does not require that either trending or lower level data be assessed.  The projects in question are very high dollar value, complex projects.

Yes, I have represented this view correctly.  Aside from questions of competency, I think the fallacy here is self-evident.  Study after study (sadly not all online, but performed within OSD at PARCA and IDA over the last three years) have demonstrated that high level data averages out and masks indicators of risk manifestation, which could have been detected looking at data at the appropriate level, which is the intersection of work and assigned resources.  In plain language, this requires integration of the cost and schedule systems, with risk first being noted through consecutive schedule performance slips.  When combined with technical performance measures, and effective identification of qualitative and quantitative risk tied to schedule activities, the early warning is two to three months (and sometime more) before the risk is reflected in the cost measurement systems.  You’re not going to do this with an Excel spreadsheet.  But, for reference, see my post  Excel is not a Project Management Solution.

It’s time to kill the zombies with facts–and to behead them once and for all.

For What It’s Worth — More on the Materiality and Prescriptiveness Debate and How it Affects Technological Solutions

The underlying basis on the materiality vs. prescriptiveness debate that I previously wrote about lies in two areas:  contractual compliance, especially in the enforcement of public contracts, and the desired outcomes under the establishment of a regulatory regime within an industry.  Sometimes these purposes are in agreement and sometimes they are in conflict and work at cross-purposes to one another.

Within a simple commercial contractual relationship, there are terms and conditions established that are based on the expectation of the delivery of supplies and services.  In the natural course of business these transactions are usually cut-and-dried: there is a promise for a promise, a meeting of the minds, consideration, and performance.  Even in cases that are heavily reliant on services, where the terms are bit more “fuzzy,” the standard is that the work being performed be done in a “workmanlike” or “professional” manner, usually defined by the norms of the trade or profession involved.  There is some judgment here depending on the circumstances, and so disputes tend to be both contentious and justice oftentimes elusive where ambiguity reigns.

In research and development contracts the ambiguities and contractual risks are legion.  Thus, the type of work and the ability to definitize that work will, to the diligent contract negotiator, determine the contract type that is selected.  In most cases in the R&D world, especially in government, contract types reflect a sharing and handling of risk that is reflected in the use of cost-plus type contracts.

Under this contract type, the effort is reimbursed to the contractor, who must provide documentation on expenses, labor hours, and work accomplished.  Overhead, G&A, and profit is negotiated based on a determination of what is fair and reasonable against benchmarks in the industry, which will be ultimately determined through negotiation of the parties.  A series of phases and milestones are established to mark the type of work that is expected to be accomplished over time.  The ultimate goal is the produce a prototype end item application that meets the needs of the agency, whether that agency is the Department of Defense or some other civilian agency in the government.

The period of performance of the contracts in these cases, depending on the amount of risk in research and development in pushing the requisite technology, usually involving several years.  Thus, the areas of concern given the usually high dollar value, inherent risk, and extended periods, involve:

  1. The reliability, accuracy, quality, consistency, and traceability of the underlying systems that report expenditures, effort, and progress;
  2. Measures that are indicative of whether all of the aspects of the eventual end item will meet elements that constitute expectations and standards of effectiveness, performance, and technical achievement.  These measures are conducted within the overall cost and schedule constraints of the contracted effort;
  3. Assessment over the lifecycle of the contract regarding external, as well as internal technical, qualitative, and quantitative risks of the effort;
  4. The ability of items 1 through 3 above to provide an effective indication or early warning that the contractual vehicle will significantly vary from either the contractual obligations or the established elements outlining the physical, performance, and technical characteristics of the end item.
  5. The more mundane, but no less important, verification of contractual performance against the terms and conditions to avoid a condition of breach.

Were these the only considerations in public contracting related to project management our work in evaluating these relationships, while challenging, would be fairly cut-and-dried given that they would be looked at from a contracting perspective.  But there is also a systemic purpose for a regulatory regime.  These are often in conflict with one another.  Such requirements as compliance, surveillance, process improvement, and risk mitigation are looking at the same systems, but from different perspectives with, ultimately, differing reactions, levels of effectiveness, and results.  What none of these purposes includes is a punitive purpose or result–a line oftentimes overstepped, in particular, by private parties.  This does not mean that some regulations that require compliance with a law do not come with civil penalties, but we’ll get to that in a moment.

The underlying basis of any regulatory regime is established in law.  The sovereign–in our case the People of the United States through the antecedent documents of governance, including the U.S. Constitution and Constitutions of the various states, as well as common law–possesses an inherent right to regulate the health, safety, and welfare of the people.  The Preamble of the U.S. Constitution actually specifies this purpose in writing, but in broader terms.  Thus, the purposes of a regulatory regime when it comes to this specific issue are what are at issue.

The various reasons usually are as follows:

  1. To prevent an irreversible harm from occurring.
  2. To enforce a particular level of professional conduct.
  3. To ensure compliance with a set of regulations or laws, especially where ambiguities in civil and common law have yielded judicial uncertainty.
  4. To determine the level of surveillance of a regulated system that is needed based on a set of criteria.
  5. To encourage particular behaviors.
  6. To provide the basis for system process improvement.

Thus, in applying a regulation there are elements that go beyond the overarching prescriptiveness vs. materiality debate.  Materiality only speaks to relevance or significance, while prescriptiveness relates to “block checking”–the mindless work of the robotic auditor.

For example, let’s take the example of two high profile examples of regulation in the news today.

The first concerns the case of where Volkswagen falsified its emissions test results for a good many of its vehicles.  The role of the regulator in this case was to achieve a desired social end where the state has a compelling interest–the reduction of air pollution from automobiles.  The regulator–the Environmental Protection Agency (EPA)–found the discrepancy and issued a notice of violation of the Clean Air Act.  The EPA, however, did not come across this information on its own.  Since we are dealing with multinational products, the initial investigation occurred in Europe under a regulator there and the results passed to the EPA.  The corrective action is to recall the vehicles and “make the parties whole.”  But in this case the regulator’s remedy may only be the first line of product liability.  It will be hard to recall the pollutants released into the atmosphere and breach of implicit contract with the buyers of the automobiles.  Whether a direct harm can be proven is now up to the courts, but given that executives in an internal review (article already cited) admitted that executives knew about deception, the remedies now extend to fraud.  Regardless of the other legal issues,

The other high profile example is the highly toxic levels of lead in the drinking water of Flint, Michigan.  In this case the same regulator, the EPA, has issued a violation of federal law in relation to safe drinking water.  But as with the European case, the high levels of lead were first discovered by local medical personnel and citizens.  Once the discrepancy was found a number of actions were required to be taken to secure proper drinking water.  But the damage has been done.  Children in particular tend to absorb lead in their neurological systems with long term adverse results.  It is hard to see how the real damage that has been inflicted will make the damaged parties whole.

Thus, we can see two things.  First, the regulator is firmly within the tradition of regulating the health, safety, and welfare, particularly the first category and second categories.  Secondly, the regulatory regime is reactive.

While obviously the specific illnesses caused by the additional pollution form Volkswagen vehicles is probably not directly traceable to harm, the harm in the case of elevated lead levels in Flint’s water supply is both traceable and largely irreversible.

Thus, in comparing these two examples, we can see that there are other considerations than the black and white construct of materiality and prescriptiveness.  For example, there are considerations of irreversible harm, prevention, proportionality, judgment, and intentional results.

The first reason for regulation listed above speaks to irreversible harm.  In these cases proportionality and prevention are the main concerns.  Ensuring that those elements are in place that will prevent some catastrophic or irreversible harm through some event or series of events is the primary goal in these cases.  When I say harm I do not mean run of the mill, litigious, constructive “harm” in the legal or contractual sense, but great harm–life and death, resulting disability, loss of livelihood, catastrophic market failure, denial of civil rights, and property destruction kind of harm.  In enforcing such goals, these fall most in line with prescriptiveness–the establishment of particular controls which, if breached, would make it almost impossible to fully recover without a great deal of additional cost or effort.  Furthermore, when these failures occur a determination of culpability or non-culpability is determined.  The civil penalties in these cases, where not specified by statute, are ideally determined by proportionality of the damage.  Oftentimes civil remedies are not appropriate since these often involve violations of law.  This occurs, in real life, from the two main traditional approaches to audit and regulation being rooted in prescriptive and judgmental approaches.

The remainder of the reasons for regulation provide degrees of oversight and remedy that are not only proportional to the resulting findings and effects, but also to the goal of the regulation and its intended behavioral response.  Once again, apart from the rare and restricted violations given in the first category above, these regulations are not intended to be enforced in a punitive manner, though there can be penalties for non-compliance.  Thus, proportionality, purpose, and reasonableness are additional considerations to take into account.  These oftentimes fall within the general category of materiality.

Furthermore, going beyond prescriptiveness and materiality, a paper entitled Applying Systems-Thinking to Reduce Check-the-Box Decisions in the Audit of Complex Estimates, by Anthony Bucaro at the University of Illinois at Urbana-Champaign, proposes an alternative auditing approach that also is applicable to other types of regulation, including contract management.  The issue that he is addressing is the fact that today, in using data, a new approach is needed to shift the emphasis to judgment and other considerations in whether a discrepancy warrants a finding of some sort.

This leads us, then, to the reason why I went down this line of inquiry.  Within project management, either a contractual or management prerogative already exists to apply a set of audits and procedures to ensure compliance with established business processes.  Particular markets are also governed by statutes regulating private conduct of a public nature.  In the government sphere, there is an added layer of statutes that prescribe a set of legal and administrative guidance.  The purposes of these various rules varies.  Obviously breaking a statute will garner the most severe and consequential penalties.  But the set of regulatory and administrative standards often act at cross purposes, and in their effect, do not rise to the level of breaking a law, unless they are necessary elements in complying with that law.

Thus, a whole host of financial and performance data assessing what, at the core, is a very difficult “thing” to execute (R&D leading to a new end item), offers some additional hazards under these rules.  The underlying question, outside of statute, concerns what the primary purpose should be in ensuring their compliance.  Does it pass the so-what? test if a particular administrative procedure is not followed to the letter?

Taking a broader approach, including a data-driven and analytical one, removes much of the arbitrariness when judgment and not box-checking is the appropriate approach.  Absent a consistent and wide pattern that demonstrates a lack of fidelity and traceability of data within the systems that have been established, auditors and public policymakers must look at the way that behavior is affected.  Are there incentives to hide or avoid issues, and are there sufficient incentives to find and correct deficiencies?  Are the costs associated with dishonest conclusions adequately addressed, and are there ways of instituting a regime that encourages honesty?

At the core is technology–both for the regulated and the regulator.  If the data that provides the indicators of compliance come, unhindered, from systems of record, then dysfunctional behaviors are minimized.  If that data is used in the proper manner by the regulator in driving a greater understanding of the systemic conditions underlying the project, as well as minimizing subjectivity, then the basis for trust is established in determining the most appropriate means of correcting a deficiency.  The devil is in the details, of course.  If the applied technology simply reproduces the check-block mentality, then nothing has been accomplished.  Business intelligence and systems intelligence must be applied in order to achieve the purposes that I outlined earlier.