Money for Nothing — Project Performance Data and Efficiencies in Timeliness

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

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

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

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

The Past is Prologue

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Contributors to time

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

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

a. Within the supplier/developer/manufacturer

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

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

b. Within customer and oversight organizations

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

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

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

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

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

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

Back to the Future

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

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

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

A typical timeline goes like this:

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

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

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

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

This system is broken.

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

The Way Forward

But there is another way.

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

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

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

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

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

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

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

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

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

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

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

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

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

Synergy — The Economics of Integrated Project Management

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

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

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

Data, Information, and Intelligence – Analysis vs. Reporting

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

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

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

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

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

What is Integrated Project Management?

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

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

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

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

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

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

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

The necessity of IPM

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Onward and upward.

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.

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

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

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

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

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

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

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

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

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

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

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

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

Something New (Again)– Top Project Management Trends 2017

Atif Qureshi at Tasque, which I learned via Dave Gordon’s blog, went out to LinkedIn’s Project Management Community to ask for the latest tends in project management.  You can find the raw responses to his inquiry at his blog here.  What is interesting is that some of these latest trends are much like the old trends which, given continuity makes sense.  But it is instructive to summarize the ones that came up most often.  Note that while Mr. Qureshi was looking for ten trends, and taken together he definitely lists more than ten, there is a lot of overlap.  In total the major issues seem to the five areas listed below.

a.  Agile, its hybrids, and its practical application.

It should not surprise anyone that the latest buzzword is Agile.  But what exactly is it in its present incarnation?  There is a great deal of rising criticism, much of it valid, that it is a way for developers and software PMs to avoid accountability. Anyone ready Glen Alleman’s Herding Cat’s Blog is aware of the issues regarding #NoEstimates advocates.  As a result, there are a number hybrid implementations of Agile that has Agile purists howling and non-purists adapting as they always do.  From my observations, however, there is an Ur-Agile that is out there common to all good implementations and wrote about them previously in this blog back in 2015.  Given the time, I think it useful to repeat it here.

The best articulation of Agile that I have read recently comes from Neil Killick, whom I have expressed some disagreement on the #NoEstimates debate and the more cultish aspects of Agile in past posts, but who published an excellent post back in July (2015) entitled “12 questions to find out: Are you doing Agile Software Development?”

Here are Neil’s questions:

  1. Do you want to do Agile Software Development? Yes – go to 2. No – GOODBYE.
  2. Is your team regularly reflecting on how to improve? Yes – go to 3. No – regularly meet with your team to reflect on how to improve, go to 2.
  3. Can you deliver shippable software frequently, at least every 2 weeks? Yes – go to 4. No – remove impediments to delivering a shippable increment every 2 weeks, go to 3.
  4. Do you work daily with your customer? Yes – go to 5. No – start working daily with your customer, go to 4.
  5. Do you consistently satisfy your customer? Yes – go to 6. No – find out why your customer isn’t happy, fix it, go to 5.
  6. Do you feel motivated? Yes – go to 7. No – work for someone who trusts and supports you, go to 2.
  7. Do you talk with your team and stakeholders every day? Yes – go to 8. No – start talking with your team and stakeholders every day, go to 7.
  8. Do you primarily measure progress with working software? Yes – go to 9. No – start measuring progress with working software, go to 8.
  9. Can you maintain pace of development indefinitely? Yes – go to 10. No – take on fewer things in next iteration, go to 9.
  10. Are you paying continuous attention to technical excellence and good design? Yes – go to 11. No – start paying continuous attention to technical excellent and good design, go to 10.
  11. Are you keeping things simple and maximising the amount of work not done? Yes – go to 12. No – start keeping things simple and writing as little code as possible to satisfy the customer, go to 11.
  12. Is your team self-organising? Yes – YOU’RE DOING AGILE SOFTWARE DEVELOPMENT!! No – don’t assign tasks to people and let the team figure out together how best to satisfy the customer, go to 12.

Note that even in software development based on Agile you are still “provid(ing) value by independently developing IP based on customer requirements.”  Only you are doing it faster and more effectively.

With the possible exception of the “self-organizing” meme, I find that items through 11 are valid ways of identifying Agile.  Given that the list says nothing about establishing closed-loop analysis of progress says nothing about estimates or the need to monitor progress, especially on complex projects.  As a matter of fact one of the biggest impediments noted elsewhere in industry is the inability of Agile to scale.  This limitations exists in its most simplistic form because Agile is fine in the development of well-defined limited COTS applications and smartphone applications.  It doesn’t work so well when one is pushing technology while developing software, especially for a complex project involving hundreds of stakeholders.  One other note–the unmentioned emphasis in Agile is technical performance measurement, since progress is based on satisfying customer requirements.  TPM, when placed in the context of a world of limited resources, is the best measure of all.

b.  The integration of new technology into PM and how to upload the existing PM corporate knowledge into that technology.

This is two sides of the same coin.  There is always  debate about the introduction of new technologies within an organization and this debate places in stark contrast the differences between risk aversion and risk management.

Project managers, especially in the complex project management environment of aerospace & defense tend, in general, to be a hardy lot.  Consisting mostly of engineers they love to push the envelope on technology development.  But there is also a stripe of engineers among them that do not apply this same approach of measured risk to their project management and business analysis system.  When it comes to tracking progress, resource management, programmatic risk, and accountability they frequently enter the risk aversion mode–believing that the less eyes on what they do the more leeway they have in achieving the technical milestones.  No doubt this is true in a world of unlimited time and resources, but that is not the world in which we live.

Aside from sub-optimized self-interest, the seeds of risk aversion come from the fact that many of the disciplines developed around performance management originated in the financial management community, and many organizations still come at project management efforts from perspective of the CFO organization.  Such rice bowl mentality, however, works against both the project and the organization.

Much has been made of the wall of honor for those CIA officers that have given their lives for their country, which lies to the right of the Langley headquarters entrance.  What has not gotten as much publicity is the verse inscribed on the wall to the left:

“And ye shall know the truth and the truth shall make you free.”

      John VIII-XXXII

In many ways those of us in the project management community apply this creed to the best of our ability to our day-to-day jobs, and it lies as the basis for all of the management improvement from Deming’s concept of continuous process improvement, through the application of Six Sigma and other management improvement methods.  What is not part of this concept is that one will apply improvement only when a customer demands it, though they have asked politely for some time.  The more information we have about what is happening in our systems, the better the project manager and the project team is armed with applying the expertise which qualified the individuals for their jobs to begin with.

When it comes to continual process improvement one does not need to wait to apply those technologies that will improve project management systems.  As a senior management (and well-respected engineer) when I worked in Navy told me; “if my program managers are doing their job virtually every element should be in the yellow, for only then do I know that they are managing risk and pushing the technology.”

But there are some practical issues that all managers must consider when managing the risks in introducing new technology and determining how to bring that technology into existing business systems without completely disrupting the organization.  This takes–good project management practices that, for information systems, includes good initial systems analysis, identification of those small portions of the organization ripe for initial entry in piloting, and a plan of data normalization and rationalization so that corporate knowledge is not lost.  Adopting systems that support more open systems that militate against proprietary barriers also helps.

c.  The intersection of project management and business analysis and its effects.

As data becomes more transparent through methods of normalization and rationalization–and the focus shifts from “tools” to the knowledge that can be derived from data–the clear separation that delineated project management from business analysis in line-and-staff organization becomes further blurred.  Even within the project management discipline, the separation in categorization of schedule analysts from cost analysts from financial analyst are becoming impediments in fully exploiting the advantages in looking at all data that is captured and which affects project performance.

d.  The manner of handling Big Data, business intelligence, and analytics that result.

Software technologies are rapidly developing that break the barriers of self-contained applications that perform one or two focused operations or a highly restricted group of operations that provide functionality focused on a single or limited set of business processes through high level languages that are hard-coded.  These new technologies, as stated in the previous section, allow users to focus on access to data, making the interface between the user and the application highly adaptable and customizable.  As these technologies are deployed against larger datasets that allow for integration of data across traditional line-and-staff organizations, they will provide insight that will garner businesses competitive advantages and productivity gains against their contemporaries.  Because of these technologies, highly labor-intensive data mining and data engineering projects that were thought to be necessary to access Big Data will find themselves displaced as their cost and lack of agility is exposed.  Internal or contracted out custom software development devoted along these same lines will also be displaced just as COTS has displaced the high overhead associated with these efforts in other areas.  This is due to the fact that hardware and processes developments are constantly shifting the definition of “Big Data” to larger and larger datasets to the point where the term will soon have no practical meaning.

e.  The role of the SME given all of the above.

The result of the trends regarding technology will be to put the subject matter expert back into the driver’s seat.  Given adaptive technology and data–and a redefinition of the analyst’s role to a more expansive one–we will find that the ability to meet the needs of functionality and the user experience is almost immediate.  Thus, when it comes to business and project management systems, the role of Agile, while these developments reinforce the characteristics that I outlined above are made real, the weakness of its applicability to more complex and technical projects is also revealed.  It is technology that will reduce the risk associated with contract negotiation, processes, documentation, and planning.  Walking away from these necessary components to project management obfuscates and avoids the hard facts that oftentimes must be addressed.

One final item that Mr. Qureshi mentions in a follow-up post–and which I have seen elsewhere in similar forums–concerns operational security.  In deployment of new technologies a gatekeeper must be aware of whether that technology will not open the organization’s corporate knowledge to compromise.  Given the greater and more integrated information and knowledge garnered by new technology, as good managers it is incumbent to ensure these improvements do not translate into undermining the organization.

Over at — In Defense of Empiricism

What is the responsibility of those in high tech for ensuring that that their products are used in an ethical manner?  That information management is a product of empiricism is self-evident.  Project and business managers who would delude themselves by relying on invalid information usually find themselves facing hard reality in a most unpleasant manner.  How do we separate out the fanciful from the real when information is flattened, highlighted by the issue, raised over the last year, of fake news?  These are the issues that I address in my latest post at  Please check it out.