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

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

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

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

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

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

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

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

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

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

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

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

Takin’ Care of Business — Information Economics in Project Management

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

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

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

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

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

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

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

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

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

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

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

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

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

Living in the Material World — A Call for Open Data from Materials Science

Since advocating for transparent and open data schemas and data repositories, I’ve run into other high tech professionals who have opined that such data requirements are only applicable to my core competency in the U.S. Aerospace & Defense vertical.  Now, in the 26 November on-line edition of the journal Science, comes an opinion piece from a number of Chinese corrosion scientists under the title “Materials science: Share corrosion data” have advocated the development of open data infrastructures to make the age and life of existing metallurgic infrastructure easily accessible to technology in a non-proprietary format.

As the article states, “corrosion costs six cents for every dollar of gross domestic product in the United States. Globally, that amounts to more than US$4 trillion a year — equivalent to damages from 40 Hurricane Katrinas. Half of that cost is in corrosion prevention and control, the other half in damages and lost productivity.”

In the software technology industry, the recent issues regarding the Trans-Pacific Partnership (TPP) has revealed strains in U.S.-Chinese relations on intellectual property and proprietary systems.  So the danger is always that the source of the advocacy will be ignored, despite the clear economic argument in favor of what the editorial proposes.

But the concern is transnational in nature.  As the piece points out, the U.S. Department of Energy has initiated its own program in alignment with the U.S. National Institute of Standards and Technology (NIST) Materials Genome Initiative (MGI) to establish open repositories of data in support of the alternative energy industry.  Given the aging U.S. infrastructure and the dangers from sudden failure from these engineering systems, it seems wise to track and share data for materials corrosion and lifespans.  Think of the sudden bridge failures that have hit headlines over the last several years.

Since the cost of not doing so can be measured in lives as well as economic cost, such data normalization and rationalization in this area takes on the role of being an essential element of governance.

Over at Dave Gordon takes me to task on data normalization — and I respond with Data Neutrality

Dave Gordon at takes me to task on my post regarding recommending using common schemas for certain project management data.  Dave’s alternative is to specify common APIs instead.   I am not one to dismiss alternative methods of reconciling disparate and, in their natural state, non-normalized data to find the most elegant solution.  My initial impression, though, is: been there, done that.

Regardless of the method used to derive significance from disparate sources of data that is of a common type, one still must obtain the cooperation of the players involved.  The ANSI X12 standard has been in use in the transportation industry for quite some time and has worked quite well, leaving the preference of proprietary solution up to the individual shippers.  The rule has been, however, that if you are going to write solutions for that industry that you need to allow the shipping info needed by any receiver to conform to a particular format so that it can be read regardless of the software involved.

Recently the U.S. Department of Defense, which had used certain ANSI X12 formats for particular data for quite some time has published and required a new set of schemas for a broader set of data under the rubric of the UN/CEFACT XML.  Thus, it has established the same approach as the transportation industry: taking an agnostic stand regarding software preferences while specifying that submitted data must conform to a common schema so that a proprietary file type is not given preference over another.

A little background is useful.  In developing major systems contractors are required to provide project performance data in order to ensure that public funds are being expended properly for the contracted effort.  This is the oversight responsibility portion of the equation.  The other side concerns project and program management.  Given the usual cost-plus contract type most often used, the government program management office in cooperation with its commercial counterpart looks to identify the manifestation of cost, schedule, and/or technical risk early enough to allow that risk to be handled as necessary.   Also, at the end of this process, which is only now being explored, is the usefulness of years of historical data across contract types, technologies, and suppliers that can be used to benefit the public interest by demonstrating which contractors perform better, to show the inherent risk associated with particular technologies through parametric methods, and a host of insights that can be derived through econometric project management trending and modeling.

So let’s assume that we can specify APIs in requesting the data in lieu of specifying that the customer can receive an application-agnostic file that can be read by any application that conforms with the data standard.  What is the difference?  My immediate observation is that is reverses the relationship in who owns the data.  In the case of the API the proprietary application becomes the gatekeeper.  In the case of an agnostic file structure it is open to everyone and the consumer owns the data.

In the API scenario large players can do what they want to limit competition and extensions to their functionality.  Since they can block box the manner in which data is structured, it also becomes increasingly difficult to make qualitative selections from the data.  The very example that Dave uses–the plethora of one-off mobile apps–usually must exist only in their own ecosystem.

So it seems to me that the real issue isn’t that Big Brother wants to control data structure.  What it comes down to is that specifying an open data structure defeats the ability of one or a group of solution providers from controlling the market through restrictions on accessing data.  This encourages maximum competition and innovation in the marketplace–Data Neutrality.

I look forward to additional information from Dave on this issue.  Each of the methods of achieving the end of Data Neutrality isn’t an end in itself.  Any method that is less structured and provides more flexibility is welcome.  I’m just not sure that we’re there yet with APIs.