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.

The Water is Wide — Data Streams and Data Reservoirs

I’ll have an article that elaborates on some of the ramifications of data streams and data reservoirs on AITS.org, so stay tuned there.  In the meantime, I’ve had a lot of opportunities lately, in a practical way, to focus on data quality and approaches to data.  There is some criticism in our industry about using metaphors to describe concepts in computing.

Like any form of literature, however, there are good and bad metaphors.  Opposing them in general, I think, is contrarian posing.  Metaphors, after all, often allow us to discover insights into an otherwise opaque process, clarifying in our mind’s eye what is being observed through the process of deriving similarities to something more familiar.  Strong metaphors allow us to identify analogues among the phenomena being observed, providing a ready path to establishing a hypothesis.  Having served this purpose, we can test that hypothesis to see if the metaphor serves our purposes in contributing to understanding.

I think we have a strong set of metaphors in the case of data streams and data reservoirs.  So let’s define our terms.

Traditionally a data stream in communications theory is a set of data packets that are submitted in sequence.  For the purpose of systems theory, a data stream is data that is submitted between two entities either on a sequential real time or on a regular periodic basis.  A data reservoir is just what it sounds like it is.  Streams can be diverted to feed a reservoir, which diverts data for a specific purpose.  Thus, data in the reservoir is a repository of all data from the selected streams, and any alternative streams, that includes legacy data.  The usefulness of the metaphors are found in the way in which we treat these data.

So, for example, data streams in practical terms in project and business management are the artifacts that represent the work that is being performed.  This can be data relating to planning, production, financial management and execution, earned value, scheduling, technical performance, and risk for each period of measurement.  This data, then, requires real time analysis, inference, and distribution to decision makers.  Over time, this data provides trending and other important information that measures the inertia of the efforts in providing leading and predictive indicators.

Efficiencies can be realized by identifying duplication in data streams, especially if the data being provided into the streams are derived from a common dataset.  Streams can be modified to expand the data that is submitted, so as to eliminate alternative streams of data that add little value on their own, that is, that are stovepiped and suboptimized contrary to the maximum efficiency of the system.

In the case of data reservoirs, what these contain is somewhat different than the large repositories of metadata that must be mined.  On the contrary, a data reservoir contains a finite set of data, since what is contained in the reservoir is derived from the streams.  As such, these reservoirs contain much essential historical information to derive parametrics and sufficient data from which to derive organizational knowledge and lessons learned.  Rather than processing data in real time, the handling of data reservoirs are done to append the historical record of existing efforts to provide a fuller picture of performance and trending, and of closed out efforts that can inform systems approaches to similar future efforts.  While not quite fitting into the category of Big Data, such reservoirs can probably best be classified as Small Big Data.

Efficiencies from the streams into the reservoir can be realized if the data can be further definitized through the application of structured schemas, combined with flexible Data Exchange Instructions (DEIs) that standardize the lexicon, allowing for both data normalization and rationalization.  Still, there may be data that is not incorporated into such schemas, especially if the legacy metadata predates the schema specified for the applicable data streams.  In this case, data rationalization must be undertaken combined with standard APIs to provide consistency and structure to the data.  Even in this case, however, given the finite set since the data is specific to a system that uses a fairly standard lexicon, such rationalization will yield results that are valid.

Needless to say, applications that are agnostic to data and that provide on-the-fly flexibility in UI configuration by calling standard operating environment objects–also known as fourth generation software–have the greatest applicability to this new data paradigm.  This is because they most effectively leverage both flexibility in the evolution of the data streams to reach maximum efficiency, and in leveraging the lessons learned that are derived from the integration of data that was previously walled off from complementary data that will identify and clarify systems interdependencies.