Money for Nothing — Project Performance Data and Efficiencies in Timeliness

I operate in a well regulated industry focused on project management. What this means practically is that there are data streams that flow from the R&D activities, recording planning and progress, via control and analytical systems to both management and customer. The contract type in most cases is Cost Plus, with cost and schedule risk often flowing to the customer in the form of cost overruns and schedule slippages.

Among the methodologies used to determine progress and project eventual outcomes is earned value management (EVM). Of course, this is not the only type of data that flows in performance management streams, but oftentimes EVM is used as shorthand to describe all of the data captured and submitted to customers in performance management. Other planning and performance management data includes time-phased scheduling of tasks and activities, cost and schedule risk assessments, and technical performance.

Previously in my critique regarding the differences between project monitoring and project management (before Hurricane Irma created some minor rearranging of my priorities), I pointed out that “looking in the rear view mirror” was often used as an excuse for by-passing unwelcome business intelligence. I followed this up with an intro to the synergistic economics of properly integrated data. In the first case I answered the critique demonstrating that it is based on an old concept that no longer applies. In the second case I surveyed the economics of data that drives efficiencies. In both cases, new technology is key to understanding the art of the possible.

As I have visited sites in both government and private industry, I find that old ways of doing things still persist. The reason for this is multivariate. First, technology is developing so quickly that there is fear that one’s job will be eliminated with the introduction of technology. Second, the methodology of change agents in introducing new technology often lacks proper socialization across the various centers of power that inevitably exist in any organization. Third, the proper foundation to clearly articulate the need for change is not made. This last is particularly important when stakeholders perform a non-rational assessment in their minds of cost-benefit. They see many downsides and cannot accept the benefits, even when they are obvious. For more on this and insight into other socioeconomic phenomena I strongly recommend Daniel Kahneman’s Thinking Fast and Slow. There are other reasons as well, but these are the ones that are most obvious when I speak with individuals in the field.

The Past is Prologue

For now I will restrict myself to the one benefit of new technology that addresses the “looking in the rear window” critique. It is important to do so because the critique is correct in application (for purposes that I will outline) if incorrect in its cause-and-effect. It is also important to focus on it because the critique is so ubiquitous.

As I indicated above, there are many sources of data in project management. They derive from the following systems (in brief):

a. The planning and scheduling applications, which measure performance through time in the form of discrete activities and events. In the most sophisticated implementations, these applications will include the assignment of resources, which requires the integration of these systems with resource management. Sometimes simple costs are also assigned and tracked through time as well.

b. The cost performance (earned value) applications, which ideally are aligned with the planning and scheduling applications, providing cross-integration with WBS and OBS structures, but focused on work accomplishment defined by the value of work completed against a baseline plan. These performance figures are tied to work accomplishment through expended effort collected by and, ideally, integrated with the financial management system. It involves the proper application of labor rates and resource expenditures in the accomplishment of the work to not only provide an statistical assessment of performance to date, but a projection of likely cost performance outcomes at completion of the effort.

c. Risk assessment applications which, depending of their sophistication and ease of use, provide analysis of possible cost and schedule outcomes, identify the sensitivity of particular activities and tasks, provide an assessment of alternative driving and critical paths, and apply different models of baseline performance to predict future outcomes.

d. Systems engineering applications that provide an assessment of technical performance to date and the likely achievement of technical parameters within the scope of the effort.

e. The financial management applications that provide an accounting of funds allocation, cash-flow, and expenditure, including planning information regarding expenditures under contract and planned expenditures in the future.

These are the core systems of record upon which performance information is derived. There are others as well, depending on the maturity of the project such as ERP systems and MRP systems. But for purposes of this post, we will bound the discussion to these standard sources of data.

In the near past, our ability to understand the significance of the data derived from these systems required manual processing. I am not referring to the sophistication of human computers of 1960s and before, dramatized to great effect in the uplifting movie Hidden Figures. Since we are dealing with business systems, these methodologies were based on simple business metrics and other statistical methods, including those that extended the concept of earned value management.

With the introduction of PCs in the workplace in the 1980s, desktop spreadsheet applications allowed this data to be entered, usually from printed reports. Each analyst not only used standard methods common in the discipline, but also developed their own methods to process and derive importance from the data, transforming it into information and useful intelligence.

Shortly after this development simple analytical applications were introduced to the market that allowed for pairing back the amount of data deriving from some of these systems and performing basic standard calculations, rendering redundant calculations unnecessary. Thus, for example, instead of a person having to calculate multiple estimates to complete, the application could perform those calculations as part of its functionality and deliver them to the analyst for use in, hopefully, their own more extensive assessments.

But even in this case, the data flow was limited to the EVM silo. The data streams relating to schedule, risk, SE, and FM were left to their own devices, oftentimes requiring manual methods or, in the best of cases, cut-and-paste, to incorporate data from reports derived from these systems. In the most extreme cases, for project oversight organizations, this caused analysts to acquire a multiplicity of individual applications (with the concomitant overhead and complexity of understanding differing lexicons and software application idiosyncrasies) in order to read proprietary data types from the various sources just to perform simple assessments of the data before even considering integrating it properly into the context of all of the other project performance data that was being collected.

The bottom line of outlining these processes is to note that, given a combination of manual and basic automated tools, that putting together and reporting on this data takes time, and time, as Mr. Benjamin Franklin noted, is money.

By itself the critique that “looking in the rear view mirror” has no value and attributing it to one particular type of information (EVM) is specious. After all, one must know where one has been and presently is before you can figure out where you need to go and how to get there and EVM is just one dimension of a multidimensional space.

But there is a utility value associated with the timing and locality of intelligence and that is the issue.

Contributors to time

Time when expended to produce something is a form of entropy. For purposes of this discussion at this level of existence, I am defining entropy as availability of the energy in a system to do work. The work in this case is the processing and transformation of data into information, and the further transformation of information into usable intelligence.

There are different levels and sub-levels when evaluating the data stream related to project management. These are:

a. Within the supplier/developer/manufacturer

(1) First tier personnel such as Control Account Managers, Schedulers (if separate), Systems Engineers, Financial Managers, and Procurement personnel among other actually recording and verifying the work accomplishment;

(2) Second tier personnel that includes various levels of management, either across teams or in typical line-and-staff organizations.

b. Within customer and oversight organizations

(1) Reporting and oversight personnel tasks with evaluating the fidelity of specific business systems;

(2) Counterpart project or program officer personnel tasked with evaluating progress, risk, and any factors related to scope execution;

(3) Staff organizations designed to supplement and organize the individual project teams, providing a portfolio perspective to project management issues that may be affected by other factors outside of the individual project ecosystem;

(4) Senior management at various levels of the organization.

Given the multiplicity of data streams it appears that the issue of economies is vast until it is understood that the data that underlies the consumers of the information is highly structured and specific to each of the domains and sub-domains. Thus there are several opportunities for economies.

For example, cost performance and scheduling data have a direct correlation and are closely tied. Thus, these separate streams in the A&D industry were combined under a common schema, first using the UN/CEFACT XML, and now transitioning to a more streamlined JSON schema. Financial management has gone through a similar transition. Risk and SE data are partially incorporated into project performance schemas, but the data is also highly structured and possesses commonalities to be directly accessed using technologies that effectively leverage APIs.

Back to the Future

The current state, despite advances in the data formats that allow for easy rationalization and normalization of data that breaks through propriety barriers, still largely is based a slightly modified model of using a combination of manual processing augmented by domain-specific analytical tools. (Actually sub-domain analytical tools that support sub-optimization of data that are a barrier to incorporation of cross-domain integration necessary to create credible project intelligence).

Thus, it is not unusual at the customer level to see project teams still accepting a combination of proprietary files, hard copy reports, and standard schema reports. Usually the data in these sources is manually entered into Excel spreadsheets or a combination of Excel and some domain-specific analytical tool (and oftentimes several sub-specialty analytical tools). After processing, the data is oftentimes exported or built in PowerPoint in the form of graphs or standard reporting formats. This is information management by Excel and PowerPoint.

In sum, in all too many cases the project management domain, in terms of data and business intelligence, continues to party like it is 1995. This condition also fosters and reinforces insular organizational domains, as if the project team is disconnected from and can possess goals antithetical and/or in opposition to the efficient operation of the larger organization.

A typical timeline goes like this:

a. Supplier provides project performance data 15-30 days after the close of a period. (Some contract clauses give more time). Let’s say the period closed at the end of July. We are now effectively in late August or early September.

b. Analysts incorporate stove-piped domain data into their Excel spreadsheets and other systems another week or so after submittal.

c. Analysts complete processing and analyzing data and submit in standard reporting formats (Excel and PowerPoint) for program review four to six weeks after incorporation of the data.

Items a through c now put a typical project office at project review for July information at the end of September or beginning of October. Furthermore, this information is focused on individual domains, and given the lack of cross-domain knowledge, can be contradictory.

This system is broken.

Even suppliers who have direct access to systems of record all too often rely on domain-specific solutions to be able to derive significance from the processing of project management data. The larger suppliers seem to have recognized this problem and have been moving to address it, requiring greater integration across solutions. But the existence of a 15-30 day reconciliation period after the end of a period, and formalized in contract clauses, is indicative of an opportunity for greater efficiency in that process as well.

The Way Forward

But there is another way.

The opportunities for economy in the form of improvements in time and effort are in the following areas, given the application of the right technology:

  1. In the submission of data, especially by finding data commonalities and combining previously separate domain data streams to satisfy multiple customers;
  2. In retrieving all data so that it is easily accessible to the organization at the level of detailed required by the task at hand;
  3. In processing this data so that it can converted by the analyst into usable intelligence;
  4. In properly accessing, displaying, and reporting properly integrated data across domains, as appropriate, to each level of the organization regardless of originating data stream.

Furthermore, there opportunities to realizing business value by improving these processes:

  1. By extending expertise beyond a limited number of people who tend to monopolize innovations;
  2. By improving organizational knowledge by incorporating innovation into the common system;
  3. By gaining greater insight into more reliable predictors of project performance across domains instead of the “traditional” domain-specific indices that have marginal utility;
  4. By developing a project focused organization that breaks down domain-centric thinking;
  5. By developing a culture that ties cross-domain project knowledge to larger picture metrics that will determine the health of the overarching organization.

It is interesting that when I visit the field how often it is asserted that “the technology doesn’t matter, it’s process that matters”.

Wrong. Technology defines the art of the possible. There is no doubt that in an ideal world we would optimize our systems prior to the introduction of new technology. But that assumes that the most effective organization (MEO) is achievable without technological improvements to drive the change. If one cannot efficiently integrate all submitted cross-domain information effectively and efficiently using Excel in any scenario (after all, it’s a lot of data), then the key is the introduction of new technology that can do that very thing.

So what technologies will achieve efficiency in the use of this data? Let’s go through the usual suspects:

a. Will more effective use of PowerPoint reduce these timelines? No.

b. Will a more robust set of Excel workbooks reduce these timelines? No.

c. Will an updated form of a domain-specific analytical tool reduce these timelines? No.

d. Will a NoSQL solution reduce these timelines? Yes, given that we can afford the customization.

e. Will a COTS BI application that accepts a combination of common schemas and APIs reduce these timelines? Yes.

The technological solution must be fitted to its purpose and time. Technology matters because we cannot avoid the expenditure of time or energy (entropy) in the processing of information. We can perform these operations using a large amount of energy in the form of time and effort, or we can conserve time and effort by substituting the power of computing and information processing. While we will never get to the point where we completely eliminate entropy, our application of appropriate technology makes it seem as if effort in the form of time is significantly reduced. It’s not quite money for nothing, but it’s as close as we can come and is an obvious area of improvement that can be made for a relatively small investment.

Synergy — The Economics of Integrated Project Management

The hot topic lately in meetings and the odd conference on Integrated Project Management (IPM) often focuses on the mechanics of achieving that state, bound by the implied definition of current regulation, which has also become–not surprisingly–practice. I think this is a laudable goal, particularly given both the casual resistance to change (which always there by definition to some extent) and in the most extreme cases a kind of apathy.

I addressed the latter condition in my last post by an appeal to professionalism, particularly on the part of those in public administration. But there is a more elemental issue here than the concerns of project analysts, systems engineers, and the associated information managers. While this level of expertise is essential in the development of innovation, relying too heavily on this level in the organization creates an internal organizational conflict that creates the risk that the innovation is transient and rests on a slender thread. Association with any one manager also leaves innovation vulnerable due to the “not invented here” tact taken by many new managers in viewing the initiatives of a predecessor. In business this (usually self-defeating) approach becomes more extreme the higher one goes in the chain of command (the recent Sears business model anyone?).

The key, of course, is to engage senior managers and project/program managers in participating in the development of this important part of business intelligence. A few suggestions on how to do this follow, but the bottom line is this: money and economics makes the implementation of IPM an essential component of business intelligence.

Data, Information, and Intelligence – Analysis vs. Reporting

Many years ago using manual techniques, I was employed in activities that required that I seek and document data from disparate sources, seemingly unconnected, and find the appropriate connections. The initial connection was made with a key. It could be a key word, topic, individual, technology, or government. The key, however, wasn’t the end of the process. The validity of the relationship needed to be verified as more than mere coincidence. This is a process well known in the community specializing in such processes, and two good sources to understand how this was done can be found here and here.

It is a well trod path to distinguish between the elements that eventually make up intelligence so I will not abuse the reader in going over it. Needless to say that a bit of data is the smallest element of the process, with information following. For project management what is often (mis)tagged as predictive analytics and analysis is really merely information. Thus, when project managers and decision makers look at the various charts and graphs employed by their analysts they are usually greeted with a collective yawn. Raw projections of cost variance, cost to complete, schedule variance, schedule slippage, baseline execution, Monte Carlo risk, etc. are all building blocks to employing business intelligence. But in and of themselves they are not intelligence because these indicators require analysis, weighting, logic testing, and, in the end, an assessment that is directly tied to the purpose of the organization.

The role and application of digitization is to make what was labor intensive less so. In most cases this allows us to apply digital technology to its strength–calculation and processing of large amounts of data to create information. Furthermore, digitization now allows for effective lateral integration among datasets given a common key, even if there are multiple keys that act in a chain from dataset to dataset.

At the end of the line what we are left with is a strong correlation of data integrated across a number of domains that contribute to a picture of how an effort is performing. Still, even given the most powerful heuristics, a person–the consumer–must validate the data to determine if the results possess validity and fidelity. For project management this process is not as challenging as, say, someone using raw social networking data. Project management data, since it is derived from underlying systems that through their processing mimic highly structured processes and procedures, tends to be “small”, even when it can be considered Big Data form the shear perspective of size. It is small Big Data.

Once data has been accumulated, however, it must be assessed so as to ensure that the parts cohere. This is done by assessing the significance and materiality of those parts. Once this is accomplished the overall assessment must then be constructed so that it follows logically from the data. That is what constitutes “actionable intelligence”: analysis of present condition, projected probable outcomes, recommended actions with alternatives. The elements of this analysis–charts, graphs, etc., are essential in reporting, but reporting these indices is not the purpose of the process. The added value of an analyst lies in the expertise one possesses. Without this dimension a machine could do the work. The takeaway from this point, however, isn’t to substitute the work with software. It is to develop analytical expertise.

What is Integrated Project Management?

In my last post I summed up what IPM is, but some elaboration and refinement is necessary.

I propose that Integrated Project Management is defined as that information necessary to derive actionable intelligence from all of the relevant cross-domain information involved in the project organization. This includes cost performance, schedule performance, financial performance and execution, contract implementation, milestone achievement, resource management, and technical performance. Actionable intelligence in this context, as indicated above, is that information that is relevant to the project decision-making authority which effectively identifies specific probable qualitative and quantitative risks, risk impact, and risk handling necessary to make project trade-offs, project re-baselining or re-scope, cost-as-an-independent variable (CAIV), or project cancellation decisions. Underlying all of this are feedback loop systems assessments to ensure that there is integrity and fidelity in our business systems–both human and digital.

The data upon which IPM is derived comes from a finite number of sources. Thus, project management data lends itself to solutions that break down proprietary syntax and terminology. This is really the key to achieving IPM and one that has garnered some discussion when discussing the process of data normalization and rationalization with other IT professionals. The path can be a long one: using APIs to perform data-mining directly against existing tables or against a data repository (or warehouse or lake), or pre-normalizing the data in a schema (given both the finite nature of the data and the finite–and structured–elements of the processes being documented in data).

Achieving normalization and rationalization in this case is not a notional discussion–in my vocation I provide solutions that achieve this goal. In order to do so one must expand their notion of the architecture of the appropriate software solution. The mindset of “tools” is at the core of what tends to hold back progress in integration, that is, the concept of a “tool” is one that is really based on an archaic approach to computing. It assumes that a particular piece of software must limit itself to performing limited operations focused on a particular domain. In business this is known as sub-optimization.

Oftentimes this view is supported by the organization itself where the project management team is widely dispersed and domains hoard information. The rice bowl mentality has long been a bane of organizational effectiveness. Organizations have long attempted to break through these barriers using various techniques: cross-domain teams, integrated product teams, and others.

No doubt some operations of a business must be firewalled in such a way. The financial management of the enterprise comes to mind. But when it comes to business operations, the tools and rice bowl mindset is a self-limiting one. This is why many in IT push the concept of a solution–and the analogue is this: a tool can perform a particular operation (turn a screw, hammer a nail, crimp a wire, etc.); a solution achieves a goal of the system that consists of a series of operations, which are often complex (build the wall, install the wiring, etc.). Software can be a tool or a solution. Software built as a solution contains the elements of many tools.

Given a solution that supports IPM, a pathway is put in place that facilitates breaking down the barriers that currently block effective communication between and within project teams.

The necessity of IPM

An oft-cited aphorism in business is that purpose drives profit. For those in public administration purpose drives success. What this means is that in order to become successful in any endeavor that the organization must define itself. It is the nature of the project–a planned set of interrelated tasks separately organized and financed from the larger enterprise, which is given a finite time and budget specifically to achieve a goal of research, development, production, or end state–that defines an organization’s purpose: building aircraft, dams, ships, software, roads, bridges, etc.

A small business is not so different from a project organization in a larger enterprise. Small events can have oversized effects. What this means in very real terms is that the core rules of economics will come to bear with great weight on the activities of project management. In the world in which we operate, the economics underlying both enterprises and projects punishes inefficiency. Software “tools” that support sub-optimization are inefficient and the organizations that employ them bear unnecessary risk.

The information and technology sectors have changed what is considered to be inefficient in terms of economics. At its core, information has changed the way we view and leverage information. Back in 1997 economists Brad DeLong and Michael Froomkin identified the nature of information and its impact on economics. Their concepts and observations have had incredible staying power if, for no other reason, because what they predicted has come to pass. The economic elements of excludability, rivalry, transparency have transformed how the enterprise achieves optimization.

An enterprise that is willfully ignorant of its condition is one that is at risk. Given that many projects will determine the success of the enterprise, a project that is willfully ignorant of its condition threatens the financial health and purpose of the larger organization. Businesses and public sector agencies can no longer afford not to have cohesive and actionable intelligence built on all of the elements that contribute to determining that condition. In this way IPM becomes not only essential but its deployment necessary.

In the end the reason for doing this comes down to profit on the one hand, and success on the other. Given the increasing transparency of information and the continued existence of rivalry, the trend in the economy will be to reward those that harness the potentials for information integration that have real consequences in the management of the enterprise, and to punish those who do not.

All Along the Watch Tower — Project Monitoring vs. Project Management

My two month summer blogging hiatus has come to a close. Along the way I have gathered a good bit of practical knowledge related to introducing and implementing process and technological improvements into complex project management environments. More specifically, my experience is in introducing new adaptive technologies that support the integration of essential data across the project environment–integrated project management in short–and do so by focusing on knowledge discovery in databases (KDD).

An issue that arose during these various opportunities reminded me of the commercial where a group of armed bank robbers enter a bank and have everyone lay on the floor. One of the victims whispers to a uniformed security officer, “Hey, do something!” The security officer replies, “Oh, I’m not a security guard, I’m a security monitor. I only notify people if there is a robbery.” He looks to the robbers who have a hostage and then turns back to the victim and says calmly, “There’s a robbery.”

We oftentimes face the same issues in providing project management solutions. New technologies have expanded the depth and breadth of information that is available to project management professionals. Oftentimes the implementation of these solutions get to the heart as to whether people considers themselves project managers or project monitors.

Technology, Information, and Cognitive Dissonance

This perceptual conflict oftentimes plays itself out in resistance to change in automated systems. In today’s world the question of acceptance is a bit different than when I first provided automated solutions into organizations more than 30 years ago. At that time, which represented the first modern wave of digitization, focused on simply automating previously manual functions that supported existing line-and-staff organizations. Software solutions were constructed to fit into the architecture of the social or business systems being served, regardless of whether those systems were inefficient or sub-optimal.

The challenge is a bit different today. Oftentimes new technology is paired with process changes that will transform an organization–and quite often is used as the leading edge in that initiative. The impact on work is transformative, shifting the way that the job and the system itself is perceived given the new information.

Leon Festinger in his work A Theory of Cognitive Dissonance (1957) stated that people seek psychological consistency in order to function in the real world. When faced with information or a situation that is contradictory to consistency, individuals will experience psychological discomfort. The individual can then simply adapt to the new condition by either accepting the change, adding rationalizations to connect their present perceptions to the change, or to challenge the change–either by attacking it as valid, by rejecting its conclusions, or by avoidance.

The most problematic of the reactions that can be encountered in IT project management are the last two. When I have introduced a new technology paired with process change this manifestation has usually been justified by the refrains that:

a. The new solution is too hard to understand;

b. The new solution is too detailed;

c. The new solution is too different from the incumbent technology;

d. The solution is unrelated to “my job of printing out one PowerPoint chart”;

e. “Why can’t I just continue to use my own Excel workbooks/Access database/solution”;

f. “Earned value/schedule/risk management/(add PM methodology here) doesn’t tell me what I don’t already know/looks in the rear view mirror/doesn’t add enough value/is too expensive/etc.”

For someone new to this kind of process the objections often seem daunting. But some perspective always helps. To date, I have introduced and implemented three waves of technology over the course of my career and all initially encountered resistance, only to eventually be embraced. In a paradoxical twist (some would call it divine justice, karma, or universal irony), oftentimes the previous technology I championed, which sits as the incumbent, is used as a defense against the latest innovation.

A reasonable and diligent person involved in the implementation of any technology which, after all, is also project management, must learn to monitor conditions to determine if there is good reason for resistance, or if it is a typical reaction to relatively rapid change in a traditionally static environment. The point, of course, is not only to meet organizational needs, but to achieve a high level of acceptance in software deployment–thus maximizing ROI for the organization and improving organizational effectiveness.

If process improvement is involved, an effective pairing and coordination with stakeholders is important. But such objections, while oftentimes a reaction to people receiving information they prefer not to have, are ignored at one’s own peril. This is where such change processes require both an analytical and leadership-based approach.

Technology and Cultural Change – Spock vs. Kirk

In looking at resistance one must determine whether the issue is one of technology or some reason of culture or management. Testing the intuitiveness of the UI, for example, is best accomplished by beta testing among SMEs. Clock speeds latency, reliability, accuracy, and fidelity in data, and other technological characteristics are easily measured and documented. This is the Mr. Spock side of the equation, where, in an ideal world, rationality and logic should lead one to success. Once these processes are successfully completed, however, the job is still not done.

Every successful deployment still contains within it pockets of resistance. This is the emotional part of technological innovation that oftentimes is either ignored or that managers hope to paper or plow over, usually to their sorrow. It is here that we need to focus our attention. This is the Captain Kirk part of the equation.

The most vulnerable portion of an IT project deployment happens within the initial period of inception. Rolling wave implementations that achieve quick success will often find that there is more resistance over time as each new portion of the organization is brought into the fold. There are many reasons for this.

New personnel may be going by what they observed from the initial embrace of the technology and not like the results. Perhaps buy-in was not obtained by the next group prior to their inclusion, or senior management is not fully on-board. Perhaps there is a perceived or real fear of job loss, or job transformation that was not socialized in advance. It is possible that the implementation focused too heavily on the needs of the initial group of personnel brought under the new technology, which caused the technology to lag in addressing the needs of the next wave. It could also be that the technology is sufficiently different as to represent a “culture shock”, which causes an immediate defensive reaction. If there are outsourced positions, the subcontractor may feel that its interests are threatened by the introduction of the technology. Some SMEs, having created “irreplaceable asset” barriers, may feel that their position would be eroded if they were to have to share expertise and information with other areas of the organization. Lower level employees fear that management will have unfettered access to information prior to vetting. The technology may be oversold as a panacea, rather as a means of addressing organizational or information management deficiencies. All of these reasons, and others, are motivations to explore.

There is an extensive literature on the ways to address the concerns listed above, and others. Good examples can be found here and here.

Adaptive COTS or Business Intelligence technologies, as well as rapid response teams based on Agile, go a long way in addressing and handling barriers to acceptance on the technology side. But additional efforts at socialization and senior management buy-in are essential and will be the difference maker. No amount of argumentation or will persuade people otherwise inclined to defend the status quo, even when benefits are self-evident. Leadership by information consumers–both internal and external–as well as decision-makers will win the day.

Process and Technology – Integrated Project Management and Big(ger) Data

The first wave of automation digitized simple manual efforts (word processing, charts, graphs). This resulted in an incremental increase in productivity but, more importantly, it shifted work so that administrative overhead was eliminated. There are no secretarial pools or positions as there were when I first entered the workforce.

The second and succeeding waves tackled transactional systems based on line and staff organizational structures, and work definitions. Thus, in project management, EVM systems were designed for cost analysts, scheduling apps for planners and schedulers, risk analysis software for systems engineers, and so on.

All of these waves had a focus on functionality of hard-coded software solutions. The software determined what data was important and what information could be processed from it.

The new paradigm shift is a focus on data. We see this through the buzz phrase “Big Data”.  But what does that mean? It means that all of the data that the organization or enterprise collects has information value. Deriving that information value, and then determining its relevance and whether it provides actionable intelligence, is of importance to the organization.

Thus, implementations of data-focused solutions represent not only a shift in the way that work is performed, but also how information is used, and how the health and performance of the organization is assessed. Horizontal information integration across domains provides insights that were not apparent in the past when data was served to satisfy the needs of specialized domains and SMEs. New vulnerabilities and risks are uncovered through integration. This is particularly clear when implementing integrated project management (IPM) solutions.

A pause in providing a definition is in order, especially since IPM is gaining traction, and so large lazy and entrenched incumbents adjust their marketing in the hope of muddying the waters to fit their square peg focused and hard-coded solutions into the round hole of flexible IPM solutions.

Integrated Project Management are the processes and integration of information necessary to derive actionable intelligence from all of the relevant cross-domain information involved in the project organization. This includes cost performance, schedule performance, financial performance and execution, contract implementation, milestone achievement, resource management, and technical performance. Actionable intelligence is that information that is relevant to the project decision-making authority which effectively identifies specific probable qualitative and quantitative risks, risk impact, and risk handling necessary to make project trade-offs, project re-baselining or re-scope, cost-as-an-independent variable (CAIV), or project cancellation decisions. Underlying all of this are feedback loop systems assessments to ensure that there is integrity and fidelity in our business systems–both human and digital.

No doubt, we have a ways to go to get to this condition, but organizations are getting there. What it will take is a change the way leadership views its role, in rewriting traditional project management job descriptions, cross-domain training and mentoring, and in enforcing both for ourselves and in others the dedication to the ethics that are necessary to do the job.

Practice and Ethics in Project Management within Public Administration

The final aspect of implementations of project management systems that is often overlooked, and which oftentimes frames the environment that we are attempting to transform, concerns ethical behavior in project management. It is an aspect of project success as necessary as any performance metric, and it is one for which leadership within an organization sets the tone.

My own expertise in project management has concerned itself in most cases with project management in the field of public administration, though as a businessman I also have experience in the commercial world. Let’s take public administration first since, I think, it is the most straightforward.

When I wore a uniform as a commissioned Naval officer I realized that in my position and duties that I was merely an instrument of the U.S. Navy, and its constitutional and legal underpinnings. My own interests were separate from, and needed to be firewalled from, the execution of my official duties. When I have observed deficiencies in the behavior of others in similar positions, this is the dichotomy that often fails to be inculcated in the individual.

When enlisted personnel salute a commissioned officer they are not saluting the person, they are saluting and showing respect to the rank and position. The officer must earn respect as an individual. Having risen from the enlisted ranks, these were the aspects of leadership that were driven home to me in observing this dynamic: in order to become a good leader, one must first have been a good follower; you must demonstrate trust and respect to earn trust and respect. One must act ethically.

Oftentimes officials in other governmental entities–elected officials (especially), judges, and law enforcement–often fail to understand this point and hence fail this very basic rule of public behavior. The law and their position deserves respect. The behavior and actions of the individuals in their office will determine whether they personally should be shown respect. If an individual abuses their position or the exercise of discretion, they are not worthy of respect, with the danger that they will delegitimize and bring discredit to the office or position.

But earning respect is only one aspect of this understanding in ethical behavior in public administration. It also means that one will make decisions based on the law, ethical principles, and public policy regardless of whether one personally agrees or disagrees with the resulting conclusion of those criteria. That an individual will also apply a similar criteria whether or not the decision will adversely impact their own personal interests or those of associates, friends, or family is also part of weight of ethical behavior.

Finally, in applying the ethical test rule, one must also accept responsibility and accountability in executing one’s duties. This means being diligent, constantly striving for excellence and improvement, leading by example, and to always represent the public interest. Note that ego, personal preference, opinion, or bias, self-interest, or other such concerns have no place in the ethical exercise of public administration.

So what does that mean for project management? The answer goes to the heart of whether one views himself or herself as a project manager or project monitor. In public administration the program manager has a unique set of responsibilities tied to the acquisition of technologies that is rarely replicated in private industry. Oftentimes this involves shepherding a complex effort via contractual agreements that involve large specialized businesses–and often a number of subcontractors–across several years of research and development before a final product is ready for production and deployment.

The primary role in this case is to ensure that the effort is making progress and executing the program toward the goal, ensuring accountability of the funds being expended, which were appropriated for the specific effort by Congress, to ensure that the effort intended by those expenditures through the contractual agreements are in compliance, to identify and handle risks that may manifest to bring the effort into line with the cost, schedule, and technical baselines, all the while staying within the program’s framing assumptions. In addition, the program manager must coordinate with operational managers who are anticipating the deployment of the end item being developed, manage expectations, and determine how best to plan for sustainability once the effort goes to production and deployment. This is, of course, a brief summary of the extensive duties involved.

Meeting these responsibilities requires diligence, information that provides actionable intelligence, and a great deal of subject matter expertise. Finding and handling risks, determining if the baseline is executable, maintaining the integrity of the effort–all require leadership and skill. This is known as project management.

Project monitoring, by contrast, is acceptance of information provided by self-interested parties without verification, of limiting the consumption and processing of essential project performance information, of demurring to any information of a negative nature regarding project performance or risk, of settling for less than an optimal management environment, and using these tactics to, euphemistically, kick the ball down the court to the next project manager in the hope that the impact of negligence falls on someone else’s watch. Project monitoring is unethical behavior in public administration.

Practice and Ethics in Project Management within Private Industry

The focus in private industry is a bit different since self-interest abounds and is rewarded. But there are ethical rules that apply, and which a business person in project management would be well-served to apply.

The responsibility of the executive or officers in a business is to the uphold the interests of the enterprise’s customers, its employees, and its shareholders. Oftentimes business owners will place unequal weighting to these interests, but the best businesses view these responsibilities as being in fine balance.

For example, aside from the legal issues, ethics demands that in making a commitment in providing supplies and services there are a host of obligations that go along with that transaction–honest representation, warranty, and a commitment to provide what was promised. For employees, the commitments made regarding the conditions of employment and to reward employees appropriately for their contribution to the enterprise. For stockholders it is to conduct the business in such as way as to avoid placing its fiduciary position and its ability to act as a going concern in avoidable danger.

For project managers the responsibility within these ethical constraints is to honestly assess and communicate to the enterprise’s officers project performance, whether the effort will achieve the desired qualitative results within budgetary and time constraints, and, from a private industry perspective, handle most of the issues articulated for the project manager in the section on public administration above. The customer is different in this scenario, oftentimes internal, especially when eliminating companies that serve the project management verticals in public administration. Oftentimes the issues and supporting systems are less complex because the scale is, on the whole, smaller.

There are exceptions, of course, to the issue of scaling. Some construction, shipbuilding, and energy projects approach the complexity of some public sector programs. Space X and other efforts are other examples. But the focus there is financial from the perspective of the profit motive–not from the perspective of meeting the goals of some public interest involving health, safety, or welfare, and so the measures of measurement will be different, though the need for accountability and diligence is no less urgent. In may ways such behavior is more urgent given that failure may result in the failure of the entire enterprise.

Yet, the basic issue is the same: are you a project manager or a project monitor? Diligence, leadership, and ethical behavior (which is essential to leadership) are the keys. Project monitoring most often results in failure, and with good reason. It is a failure of both practice and ethics.

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 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.

Rear View Mirror — Correcting a Project Management Fallacy

“The past is never dead. It’s not even past.” —  William Faulkner, Requiem for a Nun

Over the years I and others have briefed project managers on project performance using KPPs, earned value management, schedule analysis, business analytics, and what we now call predictive analytics. Oftentimes, some set of figures will be critiqued as being ineffective or unhelpful; that the analytics “only look in the rear view mirror” and that they “tell me what I already know.”

In approaching this critique, it is useful to understand Faulkner’s oft-cited quote above.  When we walk down a street, let us say it is a busy city street in any community of good size, we are walking in the past.  The moment we experience something it is in the past.  If we note the present condition of our city street we will see that for every building, park, sidewalk, and individual that we pass on that sidewalk, each has a history.  These structures and the people are as much driven by their pasts as their expectations for the future.

Now let us take a snapshot of our street.  In doing so we can determine population density, ethnic demographics, property values, crime rate, and numerous other indices and parameters regarding what is there.  No doubt, if we stop here we are just “looking in the rear view mirror” and noting what we may or may not know, however certain our anecdotal filter.

Now, let us say that we have an affinity for this street and may want to live there.  We will take the present indices and parameters that noted above, which describe our geographical environment, and trend it.  We may find that housing pricing are rising or falling, that crime is rising or falling, etc.  If we delve into the street’s ownership history we may find that one individual or family possesses more than one structure, or that there is a great deal of diversity.  We may find that a Superfund site is not too far away.  We may find that economic demographics are pointing to stagnation of the local economy, or that the neighborhood is becoming gentrified.  Just by time-phasing and delving into history–by mapping out the trends and noting the significant historical background–provides us with enough information to inform us about whether our affinity is grounded in reality or practicality.

But let us say that, despite negatives, we feel that this is the next up-and-coming neighborhood.  We would need signs to make that determination.  For example, what kinds of businesses have moved into the neighborhood and what is their number?  What demographic do they target?  There are many other questions that can be asked to see if our economic analysis is valid–and that analysis would need to be informed by risk.

The fact of the matter is that we are always living with the past: the cumulative effect of the past actions of numerous individuals, including our own, and organizations, groups of individuals, and institutions; not to mention larger economic forces well beyond our control.  Any desired change in the trajectory of the system being evaluated must identify those elements that can be impacted or influenced, and an analysis of the effort that must be expended to bring about the change, is also essential.

This is a scientific fact, proven countless times by physics, biology, and other disciplines.  A deterministic universe, which provides for some uncertainty at any given point at our level of existence, drives the possible within very small limits of possibility and even smaller limits of probability.  What this means in plain language is that the future is usually a function of the past.

Any one number or index, no doubt, does not necessarily tell us something important.  But it could if it is relevant, material, and prompts further inquiry essential to project performance.

For example, let us look at an integrated master schedule that underlies a typical medium-sized project.


We will select a couple of metrics that indicates project schedule performance.  In the case below we are looking at task hits and misses and Baseline Execution Index, a popular index that determines efficiency in meeting baseline schedule planning.

Note that the chart above plots the performance over time.  What will it take to improve our efficiency?  So as a quick logic check on realism, let’s take a look at the work to date with all of the late starts and finishes.

Our bow waves track the cumulative effort to date.  As we work to clear missed starts or missed finishes in a project we also must devote resources to the accomplishment of current work that is still in line with the baseline.  What this means is that additional resources may need to be devoted to particular areas of work accomplishment or risk handling.

This is not, of course, the limit to our analysis that should be undertaken.  The point here is that at every point in history in every system we stand at a point of the cumulative efforts, risk, failure, success, and actions of everyone who came before us.  At the microeconomic level this is also true within our project management systems.  There are also external constraints and influences that will define the framing assumptions and range of possibilities and probabilities involved in project outcomes.

The shear magnitude of the bow waves that we face in all endeavors will often be too great to fully overcome.  As an analogy, a bow wave in complex systems is more akin to a tsunami as opposed to the tidal waves that crash along our shores.  All of the force of all of the collective actions that have preceded present time will drive our trajectory.

This is known as inertia.

Identifying and understanding the contributors to the inertia that is driving our performance is important to knowing what to do.  Thus, looking in the rear view mirror is important and not a valid argument for ignoring an inconvenient metric that may only require additional context.  Furthermore, knowing where we sit is important and not insignificant.  Knowing the factors that put us where we are–and the effort that it will take to influence our destiny–will guide what is possible and not possible in our future actions.

Note:  All charted data is notional and is not from an actual project.

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.

Takin’ Care of Business — Information Economics in Project Management

Neoclassical economics abhors inefficiency, and yet inefficiencies exist.  Among the core issues that create inefficiencies is the asymmetrical nature of information.  Asymmetry is an accepted cornerstone of economics that leads to inefficiency.  We can see in our daily lives and employment the effects of one party in a transaction having more information than the other:  knowing whether the used car you are buying is a lemon, measuring risk in the purchase of an investment and, apropos to this post, identifying how our information systems allow us to manage complex projects.

Regarding this last proposition we can peel this onion down through its various levels: the asymmetry in the information between the customer and the supplier, the asymmetry in information between the board and stockholders, the asymmetry in information between management and labor, the asymmetry in information between individual SMEs and the project team, etc.–it’s elephants all the way down.

This asymmetry, which drives inefficiency, is exacerbated in markets that are dominated by monopoly, monopsony, and oligopoly power.  When informed by the work of Hart and Holmström regarding contract theory, which recently garnered the Nobel in economics, we have a basis for understanding the internal dynamics of projects in seeking efficiency and productivity.  What is interesting about contract theory is that it incorporates the concept of asymmetrical information (labeled as adverse selection), but expands this concept in human transactions at the microeconomic level to include considerations of moral hazard and the utility of signalling.

The state of asymmetry and inefficiency is exacerbated by the patchwork quilt of “tools”–software applications that are designed to address only a very restricted portion of the total contract and project management system–that are currently deployed as the state of the art.  These tend to require the insertion of a new class of SME to manage data by essentially reversing the efficiencies in automation, involving direct effort to reconcile differences in data from differing tools. This is a sub-optimized system.  It discourages optimization of information across the project, reinforces asymmetry, and is economically and practically unsustainable.

The key in all of this is ensuring that sub-optimal behavior is discouraged, and that those activities and behaviors that are supportive of more transparent sharing of information and, therefore, contribute to greater efficiency and productivity are rewarded.  It should be noted that more transparent organizations tend to be more sustainable, healthier, and with a higher degree of employee commitment.

The path forward where there is monopsony power, where there is a dominant buyer, is to impose the conditions for normative behavior that would otherwise be leveraged through practice in a more open market.  For open markets not dominated by one player as either supplier or seller, instituting practices that reward behavior that reduces the effects of asymmetrical information, and contracting disincentives in business transactions on the open market is the key.

In the information management market as a whole the trends that are working against asymmetry and inefficiency involve the reduction of data streams, the construction of cross-domain data repositories (or reservoirs) that allow for the satisfaction of multiple business stakeholders, and the introduction of systems that are more open and adaptable to the needs of the project system in lieu of a limited portion of the project team.  These solutions exist, yet their adoption is hindered because of the long-term infrastructure that is put in place in complex project management.  This infrastructure is supported by incumbents that are reinforcing to the status quo.  Because of this, from the time a market innovation is introduced to the time that it is adopted in project-focused organizations usually involves the expenditure of several years.

This argues for establishing an environment that is more nimble.  This involves the adoption of a series of approaches to achieve the goals of broader information symmetry and efficiency in the project organization.  These are:

a. Instituting contractual relationships, both internally and externally, that encourage project personnel to identify risk.  This would include incentives to kill efforts that have breached their framing assumptions, or to consolidate progress that the project has achieved to date–sending it as it is to production–while killing further effort that would breach framing assumptions.

b. Institute policy and incentives on the data supply end to reduce the number of data streams.  Toward this end both acquisition and contracting practices should move to discourage proprietary data dead ends by encouraging normalized and rationalized data schemas that describe the environment using a common or, at least, compatible lexicon.  This reduces the inefficiency derived from opaqueness as it relates to software and data.

c.  Institute policy and incentives on the data consumer end to leverage the economies derived from the increased computing power from Moore’s Law by scaling data to construct interrelated datasets across multiple domains that will provide a more cohesive and expansive view of project performance.  This involves the warehousing of data into a common repository or reduced set of repositories.  The goal is to satisfy multiple project stakeholders from multiple domains using as few streams as necessary and encourage KDD (Knowledge Discovery in Databases).  This reduces the inefficiency derived from data opaqueness, but also from the traditional line-and-staff organization that has tended to stovepipe expertise and information.

d.  Institute acquisition and market incentives that encourage software manufacturers to engage in positive signalling behavior that reduces the opaqueness of the solutions being offered to the marketplace.

In summary, the current state of project data is one that is characterized by “best-of-breed” patchwork quilt solutions that tend to increase direct labor, reduces and limits productivity, and drives up cost.  At the end of the day the ability of the project to handle risk and adapt to technical challenges rests on the reliability and efficiency of its information systems.  A patchwork system fails to meet the needs of the organization as a whole and at the end of the day is not “takin’ care of business.”