Take Me To The River, Part 2, Schedule Elements–A Digital Inventory of Integrated Program Management Elements

Recent attendance at various forums to speak has interrupted the flow of this series on IPM elements. Among these venues I was engaged in discussions regarding this topic, as well as the effects of acquisition reform on the IT, program, and project management communities in the DoD and A&D marketplace.

For this post I will restrict the topic to what are often called schedule elements, though that is a nebulous term. Also, one should not draw a conclusion that because I am dealing with this topic following cost elements, that it is somehow inferior in importance to those elements. On the contrary, planning and scheduling are integral to applying resources and costs, in tracking cost performance, and in our systemic analysis its activities, artifacts, and elements are antecedent to cost element considerations.

The Relative Position of Schedule

But the takeaway here is this: under no circumstances should any program or project manager believe that cost and schedule systems represent a dichotomy, nor a hierarchy, of disciplines. They are interdependent and the behavior noted in one will be manifested in the other.

This is important to keep in mind, because the software industry, more than any other, has been responsible for reinforcing and solidifying this (erroneous) perspective. During the first generation of desktop application development, software solutions were built to automate the functions of traditional line and staff functions. This made a great deal of sense.

From a sales and revenue perspective, it is easier to sell a limited niche software “tool” to an established customer base that will ensure both quick acceptance and immediate realization of productivity and labor savings. The connection from the purchase to ROI was easily traceable in the time span and at the level of the person performing their workaday tasks.

Thus, solutions were built to satisfy the needs of cost analysts, schedule analysts, systems engineers, cost estimators, and others. Where specific solutions left gaps, such spreadsheet solutions such as Microsoft Excel were employed to fill them. It was in no one’s interest to go beyond their core competency. Once a dominant or set of dominant incumbents (a monoposony) inhabited a niche, they employed the usual strategies for “stickiness” to defend territory and raise barriers to new entries.

What was not anticipated by many organizations was the fact that once you automate a function that the nature of the system, if one is to implement the most effective organizational structure, is transformed to conform to the most efficient flow and use of data–and its resulting transformation into information and intelligence. Oftentimes the skill set to use the intelligence does not exist because the resulting insights and synergy involved in taking larger and more comprehensive datasets which themselves are more credible and accurate was not anticipated in adjusting the organizational structure.

This is changing and must change, because the old way of using limited sets of data in the age of big(ger) data that provide a more comprehensive view of business conditions is not tenable. At least, not if a company or organization wants to stay relevant or profitable.

Characteristics and Basic Elements of the Project Schedule

If one were to perform a Google search of project schedule while reading this post, you would find a number of definitions, some of which overlap. For example, the PMBOK defines a schedule as, quite simply, “the planned dates for performing activities and the planned dates for meeting milestones.”

Thus our elements include planned dates, activities, and milestones. But is that all? Under this definition, any kind of plan, from a minor household renovation or upgrade to building an aircraft carrier would contain only these elements.

I don’t think so.

For complex projects and programs, which is the focus on this blog, our definition of a project schedule is a bit more comprehensive. If you go to the aforementioned A Guide for DoD Program Managers mentioned in my last post, you will find even less specificity.

The reason for this is that what we define as a project schedule is part and parcel of the planning phase of a project, which is then further specified in the specific time-phased planning elements for execution of the project through its lifespan into production. It is the schedule that ties together all of the disciplines in putting together a project–acquisition, systems engineering, cost estimating, and project performance management.

In attending scheduled-focused conferences over the years and in talking to program management colleagues is the refrain that:

a. It is hard to find a good scheduler, and

b. Constructing a schedule is more of an art than a science.

I can only say that this cedes the field to a small cadre of personnel who perform an essential function, but who do so with few objective tests of effectiveness or accountability–until it is too late.

But the reality is quite different from the fuzzy perception of schedule that is often assumed. All critical path method (CPM) schedules describe the same phenomena, though the lexicon will vary based on the specific proprietary application employed.

In government-focused and large commercial projects, the schedule is heart of planning and execution. In the DoD world it is known as the Integrated Master Schedule (IMS), which utilize the inherent bottom-up relationships of elements to determine the critical path. The main sources regarding the IMS have a great deal of overlap, but tend to be either aspirational (and unfortunately not prescriptive in defining the basic characteristics of an IMS) or reflect the “art over science” approach. For those following along these are the DoD Integrated Master Plan and Integrated Master Schedule Preparation and Use Guide of 21 October 2005, the NAVAIR Integrated Master Schedule (IMS) Guidebook of February 2010, and the NDIA Planning and Scheduling Excellence Guide (PASEG) of 9 March 2016 (unfortunately no current direct link).

The key elements that comprise an IMS, in addition to what we identified under the PMBOK are that it is networked schedule consisting of specific durations that are assigned to specific work tasks that must be accomplished in discrete work packages. In most cases these durations will be derived by some kind of either fixed, manual method or through the inherent optimization algorithm being applied by the CPM application. More on this below. But these work packages are discrete, meaning that they represent the full scope of the work that must be accomplished to during the specified duration for the creation of an end product. Discrete work is distinguished from level of effort (LOE) work, the latter being effort that is always expended, such as administrative and management tasks, that are not directly tied to the accomplishment of an end product.

These work packages are tied together to illustrate antecedent and progressive work that show predecessor and successor relationships. Long term planning activities, which cannot be fleshed out until more immediate work is completed are set aside as placeholders called planning packages. Each of the elements that are tracked in the IMS are based on the presentation of established criteria that define completion, events, and specific accomplishments.

The most comprehensive IMSs consist of detailed planning that include resources and elements of cost.

Detailed Elements of the IMS

Given these general elements, the best source of identifying the key elements of detailed schedules is also found in Department of Defense documents. The core document in this case is the Data Item Description for the IMS numbered as DI-MGMT-81650. The latest one is dated March 30, 2005. There are a minimum of 32 data elements, some of these already mentioned and which I will not repeat in this post since they are pretty well listed and identified in the source document.

For those not familiar with these documents, Data Item Descriptions (or DiDs–gotta love acronyms) represent the detailed technical documents for artifacts involved in the management of DoD-related operations. Thus, this provides us with a pretty good inventory of elements to source. But there are others that are implied.

For example, the 81650 DiD identifies an element known as “methodology.” What this means is that each scheduling application has an optimization engine where the true differences in schedule construction and intellectual property reside. Elements that affect these calculations are time-based, duration-based, float, and slack, and those related to resources.

These time-based elements consist of early start, early finish, late start, late finish. Duration-based elements consist of shortest time, longest time, greatest rank weight. An additional element related to schedule float identifies minimum slack. Resources are further delineated by the greatest work content and the greatest cumulative resource content.

I would note that the NDIA PASEG adds some sub-elements to this list that are based on the algorithmic result of the schedule engines and, thus, tends to ignore the antecedent salient elements of validating the optimization engine found above. These additional sub-elements are total float, free float, soft constraints, hard constraints, and–also found in the aforementioned DiD–program, task, and resource calendars.

Normally, this is where a survey would end–with schedule-specific data elements focused on the details of the schedule. But we’re going to challenge our assumptions a bit more.

Framing Assumptions of Schedules and Programs

The essential document that provides a definition of the term “framing assumption” was published by RAND Corporation in 2014 entitled Identifying Acquisition Framing Assumptions Through Structured Deliberation by Mark V. Arena and Lauren A. Mayer.  The definition of a framing assumption is “any explicit or implicit assumption that is central is shaping cost, schedule, or performance expectations.”

As I have explored in my prior post, the use of the term “cost” is a fuzzy one. To some it means earned value management, which measures a small part of the costs of development and ownership of a system. To others it means total cost of ownership. Schedule is an implicit part of this definition, and then we have performance expectations, which I will deal with in a separate post.

But we can apply the concept of framing assumptions in two ways.

The first applies to the assumed purpose of the schedule. What do we construct one? This goes back to my earlier statement that “…the schedule…ties together all of the disciplines in putting together a project–acquisition, systems engineering, cost estimating, and project performance management.”

For the NDIA PASEG the IMS is a “tool, not just a report” that “provides an ever-changing window into the progress (or lack of it) of current work effort. The strategic mission of the schedule is to point out future risks and opportunities.”

For the NAVAIR IMS Guide the IMS “At a top level…contain(ing) the networked, detailed tasks necessary to ensure successful program execution…” that “capture(s) project tasks and task relationships”, “show(s) the magnitude and how long each task will take”, “show(s) resources, durations, and constraints for each task” and “show(s) the critical path.”

For the DiD 81650 “The Integrated Master Schedule (IMS) is an integrated schedule containing the networked, detailed tasks necessary to ensure successful program execution.”

But the most comprehensive definition that goes to the core of the purpose of an IMS can be found in paragraph 1.2 of the DoD Integrated Master Plan and Integrated Master Schedule Preparation and Use Guide (IMP/IMS Guide). The elements of this purpose is worth transcribing, because if we have a requirement and cannot ask the “So What?” question, that is, if we cannot effectively determine why something must be done, then it probably does not need to be done (or we need to apply rigor in the development our expertise).

For what the IMP/IMS Guide does is clearly tie the schedule to the programmatic framing assumptions (used in the context in which RAND meant it) from initial acquisition through planning. Thus, the Integrated Master Plan (IMP) is firmly established as an antecedent and intermediate planning process (not merely an artifact or tool), that results in the program R&D execution process.

Taken in whole these processes and the resulting artifacts of the processes provide:

a. Provides offerors and acquiring activities with detailed execution planning, organization, and scheduling information that sets realistic expectations for the resulting contract action.

b. Serves as the execution plan for how the supplier will meet the contract’s performance requirements within cost and schedule constraints.

c. Provides a basis for integrating all of the functions involved in development and deployment of the system being acquired and, after award, sets the framing assumptions of the program.

d. Provides the basis for determining and assessing progress, identifying risks, determining the basis for contractual award fees and penalties, assess progress on Key Performance Parameters (KPPs) and Technical Performance Measures (TPMs), determine alternative paths to project completion, and determine opportunities for innovation and new acquisitions not apparent at the time of the award.

What all of this means is that the Integrated Master Schedule is too important to be left to the master scheduler. Yes, the schedule is a “tool” to those at the most basic tactical level in work execution. Yes, it is also an artifact and record.

But, more importantly, it is the comprehensive notional representation of the project’s or program’s scope, effort, progress, and assessment.

Private and Government-focused Industry Practice

A word has to mentioned here about the difference in practice between purely private industry practice in managing large projects and programs, and the skewing in the posts that focus on those industries that focus on public sector acquisition.

In the listing of schedule elements listed earlier there is reference to resources and elements of cost, yet here is an area that standard practice diverges. In private industry the application of resource assignments to specific work is standard practice and found in the IMS.

In companies focused on the public sector and DoD, the practice is to establish a different set of data outside of the schedule to manage resources. Needless to say this creates problems of validation of data across disparate systems related to the lowest level of planning and execution of a project or program. The basis for it, I think, relates to viewing the schedule as a “tool” and not the basis for project execution. This “tool” mindset also allows for separate “earned value engines” that oftentimes do not synchronize with the execution of the schedule, not only undermining the practical value of both, but also creating systems complexity and inefficiency where none need exist.

Another gap found in many areas of public acquisition concerns the development of an integrated master plan antecedent to the integrated master schedule. The cause here, once again, I believe is viewing the discipline of systems engineering separate; one that is somehow walled off from the continuing assessment of program execution, though that assumption is not supported by program phasing and milestone planning and achievement.

From the perspective of Integrated Program/Project Management, these considerations cannot be ignored, and so our inventory of essential data elements must include elements from these practices.

But Wait! There’s More!

Most discussions at conferences and professional meetings will usually stop at this point–viewing cost and schedule integration as the essence of IPM–with “cost’ limited to EVM. Some will add some “oh by the ways” such as technical performance and risk. I will address these in the next post as well.

But there are also other systems and processes that are relevant to our inventory. But what I have covered thus far in this series should challenge you if you have been paying attention.

I tackled cost first because of the assumptions implicit in equating it with EVM, and then went on to demonstrate that there are other elements of cost that provide a more comprehensive view. This is not denigrate the value of EVM, since it is an essential process in project management, but to demonstrate that its analytics are not comprehensive and, as with any complex system, require the contribution of additional information, depending on the level and type of work performance and progress being recorded and assessed.

In this post I have tacked the IMS, and have demonstrated that it is not supplementary process, but central to all other processes and actions being taken in the execution of the project or program. Many times people enter the schedule from an assessment of cost performance–tracing cost drivers to specific schedule activities and then tasks. But this has it backwards, based on the best technology available sometime in the late 1990s.

It is the schedule that brings together all relevant information from our execution and control processes and systems. It seems to me that perhaps the first place one goes is the schedule, that the first element to trace are those related to schedule slippage and unexpected resource consumption, and then to trace these to contract cost impact.

But, of course, there is more–and these other elements may turn out to be of greater consequence than just cost and schedule considerations. More on these in my next post.

In Closing: Battle Rhythm and the Plans of the Day and Week

When I was on active duty in the Navy we planned our days and weeks around a Plan of the Day or Plan of the Week. This is a posted agenda so that the entire ship or command understands the major events that affect its operations. It establishes focus on the main events at hand and fosters communication both laterally and vertically within the chain of command.

As one rises in rank and responsibility it is important to understand the operational tempo of the unit or ship, its systems, and subsystems. This is important in avoiding crisis management.This is known as Battle Rhythm.

Baked into the schedule (assuming proper construction and effective integrated product teaming) are the major events, milestones, and expected achievement of the program or project. Thus, there are events that should be planned around and anticipation of these items on a daily, weekly, biweekly, monthly, quarterly, and major milestone basis.

Given an effective battle rhythm, a PM should never complain about performance and progress indicators “looking into the rear view mirror”. If that is the case then perhaps the PM should look at the effectiveness and timeliness of the underlying project and program systems.Thus, when a PMO complains of information and intelligence being too late to be actionable, it is actually describing a condition of ineffective, latent, and disjointed information and intelligence systems.

Thus, our next step in our next post is to identify more salient IPM elements that cut to the heart of the matter.

Take Me to the River, Part 1, Cost Elements – A Digital Inventory of Integrated Program Management Elements

In a previous post I recommended a venue focused on program managers to define what constitutes integrated program management. Since that time I have been engaged with thought leaders and influencers in both government and industry, many of whom came to a similar conclusion independently, agree in this proposition and who are working to bring it about.

My own interest in this discussion is from the perspective of maximization of the information ecosystem that underlies and describes the systems known as projects and programs. But what do I mean by this? This is more than a gratuitous question, because oftentimes the information essential to defining project and program performance and behavior are intermixed, and therefore diluted and obfuscated, by confusion with those of the overall enterprise.

Project vs. Program

What a mean by the term project in this context is an organization that is established around a defined effort of fixed duration (a defined beginning and projected end) that is specifically planned and organized for the development and deployment of a particular end item, state, or result, with an identified set of resources assigned and allocated to achieve its goals.

A program is defined as a set of interrelated projects and sub-projects which is also of fixed duration that is specifically planned and organized not only for the development and deployment, but also the continues this role through sustainment (including configuration control), of a particular end item, state, or result, with an identified set of resources assigned and allocated to achieve its goals. As such, the program management team also is the first level life-cycle manager of the end item, state, or result, and participates with other levels of the organization in these activities. (More on life-cycle costs below).

Note the difference in scope and perspective, though oftentimes we use these terms interchangeably.

For shorthand, a small project of short duration operates at the tactical level of planning. A larger project, which because of size, complexity, duration, and risk approaches the definition of a program, operates at the operational level, as do most programs. Larger and more complex programs that will affect the core framing assumptions of the enterprise align their goals to the strategic level of planning. Thus, there are differences in scale, complexity and, hence, data points that can be captured at these various levels.

Another aspect of the question of establishing an integrated digital project and program management environment is sufficiency of data, which relates directly to scale. Sufficiency in this regard is defined as whether there is enough data to establish a valid correlation and, hopefully, draw a causation. Micro-economic foundations–and models–often fail because of insufficient data. This is important to keep in mind as we inventory the type of data available to us and its significance. Oftentimes additional data points can make up for those cases where there is insufficiency in the depth and quality of a more limited set of data points. Doing so will also mitigate subjectivity, especially in smaller efforts.

Thus, in constructing a project or program, regardless of its level of planning, we often begin by monitoring the most basic elements. These are usually described as cost, schedule, performance, and risk, though I will discuss and identify other contributors that can be indexed.

This first post will concentrate on the first set of elements–those that constitute cost. In looking at these, however, we will find that the elements within this category are a bit broader than what is currently used in determining project and program performance.

Contract Costs

When we refer to costs in project and program management we oftentimes are referring to those direct and indirect costs expended by the supplier over the course of the effort, particular in Cost Plus contractual efforts. The breakout of cost from a data perspective places it in subcategories:

Note that these are costs within the contract itself, as a cohesive, self-identifying entity. But there are other costs associated with our contracts which feed into program and project management. These are necessary to identify and capture if we are to take an holistic approach to these disciplines.

The costs that are anticipated by the contract are based on cost estimates, which need to be funded. These funded costs will be allocated to particular lines in the contract (CLINs), whether these be supporting contract efforts or deliverables. Thus, additional elements of our digital inventory include these items but lead us to our next categories.

Cost Estimates, Colors of Money, and Cash Flow

Cost estimates are the basis for determining the entire contract effort, and eventually make it into the project and program cost plan. Once cost estimates are applied and progress is tracked through the collection of actual costs, these elements are further traced to project and program activities, products, commodities, and other business categories, such as the indirect costs identified on the right hand side of the chart above.

Our cost plans need to be financed, as with any business entity. Though the most complex projects often are financed by some government entity because of their scale and impact, private industry–even among the largest companies–must obtain financing for the efforts at hand, whether these come from internal or external sources.

Thus two more elements present themselves: “colors” of money, that is, money that is provided for a specific purpose within the project and program cost plan which could also be made available for only some limited period of time, and the availability of that money sufficient to execute particular portions of the project or program, that is, cash flow.

The phase of the project or program will determine the type of money that is made available. These are also contained in the costs that are identified in the next section, but include, from a government financing perspective, Research, Development, Test and Evaluation (RDT&E) money, Procurement, Operations and Maintenance (O&M), and Military Construction (MILCON) dollars. By Congressional appropriation and authorization, each of these types of money may be provided for particular programs, and each type of authorization has a specific period in which they can be committed, obligated, and expended before they expire. The type of money provided also aligns with the phase of the project or program: whether it still be in development, production, deployment and acquisition, sustainment, or retirement.

These costs will be reflected in reporting that reflects actual and projected rates of expenditure, that will be tied to procurement, material management, and resource management systems.

Additional Relative Costs

As with all efforts, the supplier is not the only entity to incur costs on a development project or program. The customer also incurs costs, which must be taken into account in determining the total cost of the effort.

For anyone who has undergone any kind of major effort on their home, or even had to get things other workaday things done, like deciding when to change the tires on the car or when to get to the dentist implicitly understand that there is more effort in timing and determining the completion of these items than the cost of new kitchen cabinets, tires, or a filling. One must decide to take time off from work. One must look to their own cash flow to see if they have sufficient funds not only for the merchant, but for all of the sundry and associated tasks that must be done in preparation for and after the task’s completion. To choose to do one thing is to choose not to do another–an opportunity cost. Other people may be involved in the decision. Perhaps children are in the household and a babysitter is required. Perhaps the home life is so disrupted that another temporary abode is necessary on a short term basis.

All of these are costs that one must take into account, and at the individual level we do these calculations and plan these activities as a matter of fact.

In customer-supplier relationships the former incurs costs above the contract costs, which must be taken into account by the customer project or program executive. In the Department of Defense an associated element is called program management administration (PMA). For private entities this falls into allocated G&A and Overhead costs, aside from direct labor and material costs, but in all cases these are costs that have come about due to the decision to undertake the specific effort.

Other elements of cost on the customer side are contractually furnished facilities, property, material or equipment, and testing and evaluation costs.

Contract Cost Performance: Earned Value Management

I will further discuss EVM in more detail a later installment of this element inventory, but mention must be made of EVM since to exclude it is to be grossly remiss.

At core EVM is a financial measure of value against what has been physically achieved against a performance management baseline (PMB), which ties actual costs and completion of work through a work breakdown structure (WBS). It is focused on the contract level of performance, which in some cases may constitute the entire project, though not necessarily the entire effort for the program.

Linkages to the other cost elements I have delineated elsewhere in this post ranges from strong to non-existent. Thus, while an essential means of linking contractual achievement to work accomplishment that, at various levels of fidelity, is linked to actual technical achievement, it does not capture all of the costs in our data inventory.

An essential overview in understanding what it does capture is best summed up in the following diagram taken from the Defense Acquisition University (DAU) site:

Commercial EVM elements, while not necessarily using the same terminology or highly structured process, possess a similar structure in allocating costs and achievement against baseline costs in developmental efforts to work packages (oftentimes schedule tasks in resource-loaded schedules) under an integrated WBS structure with Management Reserve not included as part of the baseline.

Also note that commercial efforts often include their internal costs as part of the overall contractual effort in assessing earned value against actual work achievement, while government contracting efforts tend to exclude these inherent costs. That being said, it is not that there is no cost control in these elements, since strict ceilings often apply to PMA and other such costs, it is that contract cost performance does not take these costs, among others, into account.

Furthermore, the chart above provides us with additional sub-elements in our inventory that are essential in capturing data at the appropriate level of our project and program hierarchy.

Thus, for IPM, EVM is one of many elements that are part our digital inventory–and one that provides a linkage to other non-cost elements (WBS). But in no way should it be viewed as capturing all essential costs associated with a contractual effort, aside from the more expansive project or program effort.

Portfolio Management and Life-Cycle Costs

There is another level of management that is essential in thinking about project and program management, and that is the program executive level. In the U.S. military services these are called Program Executive Officers (PEOs). In private industry they are often product managers, CIOs, and other positions that often represent the link between the program management teams and the business operations side of the organization. Thus, this is also the level of management organized to oversee a number of individual projects and programs that are interrelated based on mission, commodity, or purpose. As such, this level of management often concentrates on issues across the portfolio of projects and programs.

The main purpose of the portfolio management level is to ensure that project and program efforts are aligned with the strategic goals of the organization, which includes an understanding of the total cost of ownership.

In performing this purpose one of the functions of portfolio management is to identify risks that may manifest within projects and programs, and to determine the most productive use of limited resources across them, since they are essentially competing for the same dollars. This includes cost estimates and re-allocations to address ontological, aleatory, and epistemic risk.

Furthermore, the portfolio level is also concerned with the life-cycle factors of the item under development, so that there is effective hand-off at the production and sustainment phases. The key here is to ensure that each project or program, which is focused on the more immediate goals of project and program execution, continues to meet the goals of the organization in terms of life-cycle costs, and its effectiveness in meeting the established goals essential to the project or program’s framing assumptions.

But here we are focusing on cost, and so the costs involved are trade-off costs and opportunities, assessments of return on investment, and the aforementioned total cost of ownership of the end item or system. The costs that contribute to the total cost of ownership include all of the development costs, external and internal program management costs, procurement costs, operations and support costs, maintenance and life extension costs, and system retirement costs.

Conclusion

I believe that the survey of cost elements presented in this initial post illustrates that present digital project and program management systems are limited and immature–capturing and evaluating only a small portion of the total amount of available data.

These gaps make it impossible, for example, to determine the relative significance any one element–and the analytics that can derived from it–over another; not to mention the inability to provide the linkage among these absent elements that would garner insights into cause-and-effect and predictive behavior so that we have enough time to influence the outcome.

It is also clear that, when we strive to define what constitutes integrated project and program management, that we must learn what is of most importance to the PM in performing those duties that are viewed as essential to success, and which are not yet captured in our analytical and predictive systems.

Only when our systems reach the level of cohesiveness and comprehensiveness in providing organizational insight and intelligence essential to project or program management will PMs ignore them at their own risk. In getting there we must first identify what can be captured from the activities that contribute to our efforts.

My next post will identify essential elements related to planning and scheduling.

 

Note: I am indebted to Defense Acquisition University’s resources in my research across many of my postings and link to them for the edification of the reader. For more insight into many of the points raised in this post I would recommend that readers familiarize themselves with A Guide for DoD Program Managers.

 

Don’t Stop Thinking About Tomorrow–Post-Workshop Blogging…and some Low Comedy

It’s been a while since I posted to my blog due to meetings and–well–day job, but some interesting things occurred during the latest Integrated Program Management (IPMD) of the National Defense Industrial Association (NDIA) meeting that I think are of interest. (You have to love acronyms to be part of this community).

Program Management and Integrated Program Management

First off is the initiative by the Program Management Working Group to gain greater participation by program managers with an eye to more clearly define what constitutes integrated program management. As readers of this blog know, this is a topic that I’ve recently written about.

The Systems Engineering discipline is holding their 21st Annual Systems Engineering Conference in Tampa this year from October 22nd to the 25th. IPMD will collaborate and will be giving a track dedicated to program management. The organizations have issued a call for papers and topics of interest. (Full disclosure: I volunteered this past week to participate as a member of the PM Working Group).

My interest in this topic is based on my belief from my years of wide-ranging experience in duties from having served as a warranted government contracting officer, program manager, business manager, CIO, staff officer, and logistics officer that there is much more to the equation in defining IPM that transcends doing so through the prism of any particular discipline. Furthermore, doing so will require collaboration and cooperation among a number of project management disciplines.

This is a big topic where, I believe, no one group or individual has all of the answers. I’m excited to see where this work goes.

Integrated Digital Environment

Another area of interest that I’ve written about in the past involved two different–but related–initiatives on the part of the Department of Defense to collect information from their suppliers that is necessary in their oversight role not only to ensure accountability of public expenditures, but also to assist in project cost and schedule control, risk management, and assist in cost estimation, particularly as it relates to risk sharing cost-type R&D contracted project efforts.

Two major staffs in the Offices of the Undersecretary of Defense have decided to go with a JSON-type schema for, on the one hand, cost estimating data, and on the other, integrated cost performance, schedule, and risk data. Each initiative seeks to replace the existing schemas in place.

Both have been wrapped around the axle on getting industry to move from form-based reporting and data sharing to a data-agnostic solution that meet the goals of reducing redundancy in data transmission, reducing the number of submissions and data streams, and moving toward one version of truth that allows for SMEs on both sides of the table to concentrate on data analysis and interpretation in jointly working toward the goal of successful project completion and end-item deployment.

As with the first item, I am not a disinterested individual in this topic. Back when I wore a uniform I helped to construct DoD policy to create an integrated digital environment. I’ve written about this experience previously in this blog, so I won’t bore with details, but the need for data sharing on cost-type efforts acknowledges the reality of the linkage between our defense economic and industrial base and the art of the possible in deploying defense-related end items. The same relationship exists for civilian federal agencies with the non-defense portion of the U.S. economy. Needless to say, a good many commercial firms unrelated to defense are going the same way.

The issue here is two-fold, I think, from speaking with individuals working these issues.

The first is, I think, that too much deference is being given to solution providers and some industry stakeholders, influenced by those providers, in “working the refs” through the data. The effect of doing so not only slows down the train and protects entrenched interests, it also gets in the way of innovation, allowing the slowest among the group to hold up the train in favor of–to put it bluntly–learning their jobs on the job at the expense of efficiency and effectiveness. As I expressed in a side conversion with an industry leader, all too often companies–who, after all, are the customer–have allowed themselves to view the possible by the limitations and inflexibility of their solution providers. At some point that dysfunctional relationship must end–and in the case of comments clearly identified as working the refs–they should be ignored. Put your stake in the ground and let innovation and market competition sort it out.

Secondly, cost estimating, which is closely tied to accounting and financial management, is new and considered tangential to other, more mature, performance management systems. My own firm is involved in producing a solution in support of this process, collecting data related to these reports (known collectively in DoD as the 1921 reports), and even after working to place that data in a common data lake, exploring with organizations what it tells us, since we are only now learning what it tells us. This is classical KDD–Knowledge Discovery in Data–and a worthwhile exercise.

I’ve also advocated going one step further in favor of the collection of financial performance data (known as the Contract Funds Status Report), which is an essential reporting requirement, but am frustrated to find no one willing to take ownership of the guidance regarding data collection. The tragedy here is that cost performance, known broadly as Earned Value Management, is a technique related to the value of work performance against other financial and project planning measures (a baseline and actuals). But in a business (or any enterprise), the fuel that drives the engine are finance-related, and two essential measures are margin and cash-flow. The CFSR is a report of program cash-flow and financial execution. It is an early measure of whether a program will execute its work in any given time-frame, and provides a reality check on the statistical measures of performance against baseline. It is also a necessary logic check for comptrollers and other budget decision-makers.

Thus, as it relates to data, there has been some push-back against a settled schema, where the government accepts flat files and converts the data to the appropriate format. I see this as an acceptable transient solution, but not an ultimate one. It is essential to collect both cost estimating and contract funds status information to perform any number of operations that relate to “actionable” intelligence: having the right executable money at the right time, a reality check against statistical and predictive measures, value analysis, and measures of ROI in development, just to name a few.

I look forward to continuing this conversation.

To Be or Not to Be Agile

The Section 809 Panel, which is the latest iteration of acquisition reform panels, has recommended that performance management using earned value not be mandated for efforts using Agile. It goes on, however, to assert that program executive “should approve appropriate project monitoring and control methods, which may include EVM, that provide faith in the quality of data and, at a minimum, track schedule, cost, and estimate at completion.”

Okay…the panel is then mute on what those monitoring and control measure will be. Significantly, if only subtly, the #NoEstimates crowd took a hit since the panel recommends and specifies data quality, schedule, cost and EAC. Sounds a lot like a form of EVM to me.

I must admit to be a skeptic when it comes to swallowing the Agile doctrine whole. Its micro-economic foundations are weak and much of it sounds like ideology–bad ideology at best and disproved ideology at worst (specifically related to the woo-woo about self-organization…think of the last speculative bubble and resulting financial crisis and depression along these lines).

When it comes to named methodologies I am somewhat from Missouri. I apply (and have in previous efforts in the Dark Ages back when I wore a uniform) applied Kanban, teaming, adaptive development (enhanced greatly today by using modern low-code technology), and short sprints that result in releasable modules. But keep in mind that these things were out there long before they were grouped under a common heading.

Perhaps Agile is now a convenient catch-all for best practices. But if that is the case then software development projects using this redefined version of Agile deserve no special dispensation. But I was schooled a bit by an Agile program manager during a side conversation and am always open to understanding things better and revising my perspectives. It’s just that there was never a Waterfall/Agile dichotomy just as there never really was a Spiral/Waterfall dichotomy. These were simply convenient development models to describe a process that were geared to the technology of the moment.

There are very good people on the job exploring these issues on the Agile Working Group in the IPMD and I look forward to seeing what they continue to come up with.

Rip Van Winkle Speaks!

The only disappointing presentation occurred on the second and last day of the meeting. It seemed we were treated by a voice from somewhere around the year 2003 that, in what can only be described as performance art involving free association, talked about wandering the desert, achieving certification for a piece of software (which virtually all of the software providers in the room have successfully navigated at one time or another), discovering that cost and schedule performance data can be integrated (ignoring the work of the last ten years on the part of, well, a good many people in the room), that there was this process known as the Integrated Baseline Review (which, again, a good many people in the room had collaborated on to both define and make workable), and–lo and behold–the software industry uses schemas and APIs to capture data (known in Software Development 101 as ETL). He then topped off his meander by an unethical excursion into product endorsement, selected through an opaque process.

For this last, the speaker was either unaware or didn’t care (usually called tone-deafness) that the event’s expenses were sponsored by a software solution provider (not mine). But it is also as if the individual speaking was completely unaware of the work behind the various many topics that I’ve listed above this subsection, ignoring and undermining the hard work of the other stakeholders that make up our community.

On the whole an entertaining bit of poppycock, which leads me to…

A Word about the Role of Professional Organizations (Somewhat Inside Baseball)

In this blog, and in my interactions with other professionals at–well–professional conferences–I check my self-interest in at the door and publicly take a non-commercial stance. It is a position that is expected and, I think, appreciated. For those who follow me on social networking like LinkedIn, posts from my WordPress blog originate from a separate source from the commercial announcements that are linked to my page that originate from my company.

If there are exhibitor areas, as some conferences and workshops do have, that is one thing. That’s where we compete and play; and in private side conversations customers and strategic partners will sometimes use the opportunity as a convenience to discuss future plans and specific issues that are clearly business-related. But these are the exceptions to the general rule, and there are a couple of reasons for this, especially at this venue.

One is because, given that while it is a large market, it is a small community, and virtually everyone at the regular meetings and conferences I attend already know that I am the CEO and owner of a small software company. But the IPMD is neutral ground. It is a place where government and industry stakeholders, who in other roles and circumstances are in a contractual or competing relationship, come to work out the best way of hashing out processes and procedures that will hopefully improve the discipline of program and project management. It is also a place of discovery, where policies, new ideas, and technologies can be vetted in an environment of collaboration.

Another reason for taking a neutral stance is simply because it is both the most ethical and productive one. Twenty years ago–and even in some of the intervening years–self-serving behavior was acceptable at the IPMD meetings where both leadership and membership used the venue as a basis for advancing personal agendas or those of their friends, often involving backbiting and character assassination. Some of those people, few in number, still attend these meetings.

I am not unfamiliar with the last–having been a target at one point by a couple of them but, at the end of the day, such assertions turned out to be without merit, undermining the credibility of the individuals involved, rightfully calling into question the quality of their character. Such actions cannot help but undermine the credibility and pollute the atmosphere of the organization in which they associate, as well.

Finally, the companies and organizations that sponsor these meetings–which are not cheap to organize, which I know from having done so in the past–deserve to have the benefit of acknowledgment. It’s just good manners to play nice when someone else is footing the bill–you gotta dance with those that brung you. I know my competitors and respect them (with perhaps one or two exceptions). We even occasionally socialize with each other and continue long-term friendships and friendly associations. Burning bridges is just not my thing.

On the whole, however, the NDIA IPMD meetings–and this one, in particular–was a productive and positive one, focused on the future and in professional development. That’s where, I think, that as a community we need to be and need to stay. I always learn something new and get my dose of reality from a broad-based perspective. In getting here the leadership of the organization (and the vast majority of the membership) is to be commended, as well as the recent past and current members of the Department of Defense, especially since the formation of the Performance Assessments and Root Cause Analysis (PARCA) office.

In closing, there were other items of note discussed, along with what can only be described as the best pair of keynote addresses that I’ve heard in one meeting. I’ll have more to say about some of the concepts and ideas that were presented there in future posts.

Move It On Over — Third and Fourth Generation Software: A Primer

While presenting to organizations regarding business intelligence and project management solutions I often find myself explaining the current state of programming and what current technology brings to the table. Among these discussions is the difference between third and fourth generation software, not just from the perspective of programming–or the Wikipedia definition (which is quite good, see the links below)–but from a practical perspective.

Recently I ran into someone who asserted that their third-generation software solution was advantageous over a fourth generation one because it was “purpose built.” My response was that a fourth generation application provides multiple “purpose built” solutions from one common platform in a more agile and customer-responsive environment. For those unfamiliar with the differences, however, this simply sounded like a war of words rather than the substantive debate that it was.

For anyone who has used a software application they are usually not aware of the three basic logical layers that make up the solution. These are the business logic layer, the application layer, and the database structure. The user interface delivers the result of the interaction of these three layers to the user–what is seen on the screen.

Back during the early advent of the widespread use of PCs and distributed computing on centralized systems, a group of powerful languages were produced that allowed the machine operations to be handled by an operating system and for software developers to write code to focus on “purpose built” solutions.

Initially these efforts concentrated on automated highly labor-intensive activities to achieve maximum productivity gains in an organization, and to leverage those existing systems to distribute information that previously would require many hours of manual effort in terms of mathematical and statistical calculation and visualization. The solutions written were based on what were referred to as third generation languages, and they are familiar even to non-technical people: Fortran, Cobol, C+, C++, C#, and Java, among others. These languages are highly structured and require a good bit of expertise to correctly program.

In third generation environments, the coder specifies operations that the software must perform based on data structure, application logic, and pre-coded business logic.These three levels of highly integrated and any change in one of them requires that the programmer trace the impact of that change to ensure that the operations in the other two layers are not affected. Oftentimes, the change has a butterfly effect, requiring detailed adjustments to take into the account the subtleties in processing. It is this highly structured, interdependent, “purpose built” structure that causes unanticipated software bugs to pop up in most applications. It is also the reason why software development and upgrade configuration control is also highly structured and time-consuming–requiring long lead-times to even deliver what most users view as relatively mundane changes and upgrades, like a new chart or graph.

In contrast, fourth generation applications separate the three levels and control the underlying behavior of the operating environment by leveraging a standard framework, such as .NET. The .NET operating environment, for example, controls both a library of interoperability across programming languages (known as a Framework Class Library or FCL), and virtual machine that handles exception handling, memory management, and other common functions (known as Common Language Runtime or CLR).

With the three layers separated, with many of the more mundane background tasks being controlled by the .NET framework, a great deal of freedom is provided to the software developer that provides real benefits to customers and users.

For example, the database layer is freed from specific coding from the application layer, since the operating environment allows libraries of industry standard APIs to be leveraged, making the solution agnostic to data. Furthermore, the business logic/UI layer allows for table-driven and object-oriented configuration that creates a low code environment, which not only allows for rapid roll-out of new features and functionality (since hard-coding across all three layers is eschewed), but also allows for more precise targeting of functionality based on the needs of user groups (or any particular user).

This is what is meant in previous posts by new technology putting the SME back in the driver’s seat, since pre-defined reports and objects (GUIs) at the application layer allow for immediate delivery of functionality. Oftentimes data from disparate data sources can be bound together through simple query languages and SQL, particularly if the application layer table and object functionality is built well enough.

When domain knowledge is incorporated into the business logic layer, the distinction between generic BI and COTS is obliterated. Instead, what we have is a hybrid approach that provides the domain specificity of COTS (‘purpose built”), with the power of BI that reduces the response time between solution design and delivery. More and better data can also be accessed, establishing an environment of discovery-driven management.

Needless to say, properly designed Fourth Generation applications are perfectly suited to rapid application development and deployment approaches such as Agile. They also provide the integration potential, given the agnosticism to data, that Third Generation “purpose built” applications can only achieve through data transfer and reconciliation across separate applications that never truly achieve integration. Instead, Fourth Generation applications can subsume the specific “purpose built” functionality found in stand-alone applications and deliver it via a single platform that provides one source of truth, still allowing for different interpretations of the data through the application of differing analytical approaches.

So move it on over nice (third generation) dog, a big fat (fourth generation) dog is moving in.

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.

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.

Like Tinker to Evers to Chance: BI to BA to KDD

It’s spring training time in sunny Florida, as well as other areas of the country with mild weather and baseball.  For those of you new to the allusion, it comes from a poem by Franklin Pierce Adams and is also known as “Baseball’s Sad Lexicon”.  Tinker, Evers, and Chance were the double play combination of the 1910 Chicago Cubs (shortstop, second base, and first base).  Because of their effectiveness on the field these Cubs players were worthy opponents of the old New York Giants, for whom Adams was a fan, and who were the kings of baseball during most of the first fifth of a century of the modern era (1901-1922).  That is, until they were suddenly overtaken by their crosstown rivals, the Yankees, who came to dominate baseball for the next 40 years, beginning with the arrival of Babe Ruth.

The analogy here is that the Cubs infielders, while individuals, didn’t think of their roles as completely separate.  They had common goals and, in order to win on the field, needed to act as a unit.  In the case of executing the double play, they were a very effective unit.  So why do we have these dichotomies in information management when the goals are the same?

Much has been written both academically and commercially about Business Intelligence, Business Analytics, and Knowledge Discovery in Databases.  I’ve surveyed the literature and for good and bad, and what I find is that these terms are thrown around, mostly by commercial firms in either information technology or consulting, all with the purpose of attempting to provide a discriminator for their technology or service.  Many times the concepts are used interchangeably, or one is set up as a strawman to push an agenda or product.  Thus, it seems some hard definitions are in order.

According to Technopedia:

Business Intelligence (BI) is the use of computing technologies for the identification, discovery and analysis of business data – like sales revenue, products, costs and incomes.

Business analytics (BA) refers to all the methods and techniques that are used by an organization to measure performance. Business analytics are made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company.

Knowledge Discover in Databases (KDD) is the process of discovering useful knowledge from a collection of data. This widely used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior knowledge on data sets and interpreting accurate solutions from the observed results.

As with much of computing in its first phases, these functions were seen to be separate.

The perception of BI, based largely on the manner in which it has been implemented in its first incarnations, is viewed as a means of gathering data into relational data warehouses or data marts and then building out decision support systems.  These methods have usually involved a great deal of overhead in both computing and personnel, since practical elements of gathering, sorting, and delivering data involved additional coding and highly structured user interfaces.  The advantage of BI is its emphasis on integration.  The disadvantage from the enterprise perspective, is that the method and mode of implementation is phlegmatic at best.

BA is BI’s younger cousin.  Applications were developed and sold as “analytical tools” focused on a niche of data within the enterprise’s requirements.  In this manner decision makers could avoid having to wait for the overarching and ponderous BI system to get to their needs, if ever.  This led many companies to knit together specialized tools in so-called “best-of-breed” configurations to achieve some measure of integration across domains.  Of course, given the plethora of innovative tools, much data import and reconciliation has had to be inserted into the process.  Thus, the advantages of BA in the market have been to reward innovation and focus on the needs of the domain subject matter expert (SME).  The disadvantages are the insertion of manual intervention in an automated process due to lack of integration, which is further exacerbated by so-called SMEs in data reconciliation–a form of rent seeking behavior that only rewards body shop consulting, unnecessarily driving up overhead.  The panacea applied to this last disadvantage has been the adoption of non-proprietary XML schemas across entire industries that reduce both the overhead and data silos found in the BA market.

KDD is our both our oldster and youngster–grandpa and the grandson hanging out.  It is a term that describes a necessary function of insight–allowing one to determine what the data tells us are needed for analytics rather than relying on a “canned” solution to determine how to approach a particular set of data.  But it does so, oftentimes, using an older approach that predates BI, known as data mining.  You will often find KDD linked to arguments in favor of flat file schemas, NoSQL (meaning flat non-relational databases), and free use of the term Big Data, which is becoming more meaningless each year that it is used, given Moore’s Law.  The advantage of KDD is that it allows for surveying across datasets to pick up patterns and interrelationships within our systems that are otherwise unknown, particularly given the way in which the human mind can fool itself into reifying an invalid assumption.  The disadvantage, of course, is that KDD will have us go backward in terms of identifying and categorizing data by employing Data Mining, which is an older concept from early in computing in which a team of data scientists and data managers develop solutions to identify, categorize, and use that data–manually doing what automation was designed to do.  Understanding these limitations, companies focused on KDD have developed heuristics (cognitive computing) that identify patterns and possible linkages, removing a portion of the overhead associated with Data Mining.

Keep in mind that you never get anything for nothing–the Second Law of Thermodynamics ensures that energy must be borrowed from somewhere in order to produce something–and its corollaries place limits on expected efficiencies.  While computing itself comes as close to providing us with Maxwell’s Demon as any technology, even in this case entropy is being realized elsewhere (in the software developer and the hardware manufacturing process), even though it is not fully apparent in the observed data processing.

Thus, manual effort must be expended somewhere along the way.  In any sense, all of these methods are addressing the same problem–the conversion of data into information.  It is information that people can consume, understand, place into context, and act upon.

As my colleague Dave Gordon has pointed out to me several times that there are also additional methods that have been developed across all of these methods to make our use of data more effective.  These include more powerful APIs, the aforementioned cognitive computing, and searching based on the anticipated questions of the user as is used by search engines.

Technology, however, is moving very rapidly and so the lines between BI, BA and KDD are becoming blurred.  Fourth generation technology that leverages API libraries to be agnostic to underlying data, and flexible and adaptive UI technology can provide a  comprehensive systemic solution to bring together the goals of these approaches to data. With the ability to leverage internal relational database tools and flat schemas for non-relational databases, the application layer, which is oftentimes a barrier to delivery of information, becomes open as well, putting the SME back in the driver’s seat.  Being able to integrate data across domain silos provide insight into systems behavior and performance not previously available with “canned” applications written to handle and display data a particular way, opening up knowledge discovery in the data.

What this means practically is that those organizations that are sensitive to these changes will understand the practical application of sunk cost when it comes to aging systems being provided by ponderous behemoths that lack agility in their ability to introduce more flexible, less costly, and lower overhead software technologies.  It means that information management can be democratized within the organization among the essential consumers and decision makers.

Productivity and effectiveness are the goals.