project management data

Big Data and the Repository of Babel

In 1941, the Argentine writer Jorge Luis Borges (1899-1986) published a short story entitled “The Library of Babel.” In the story Borges imagines a universe, known as the Library, which is described by the story’s narrator as made up of adjacent hexagonal rooms.

Each of the rooms of the library is poorly lit, with one side acting as the entrance and exit, and four of the five remaining walls of the rooms containing bookshelves whose books are placed in a completely uniform style, though the books’ contents are completely random.

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The Need for an Integrated Digital Environment (IDE) Strategy in Project Management*

Putting the Pieces Together

To be an effective project manager, one must possess a number of skills in order to successfully guide the project to completion. This includes having a working knowledge of the information coming from multiple sources and the ability to make sense of that information in a cohesive manner. This is so that, when brought together, it provides an accurate picture of where the project has been, where it is in its present state, and what actions must be taken to keep it (or bring it back) on track.

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Shake it Out – Embracing the Future of Program Management – Part Two: Private Industry Program and Project Management in Aerospace, Space, and Defense

In my previous post, I focused on Program and Project Management in the Public Interest, and the characteristics of its environment, especially from the perspective of the government program and acquisition disciplines. The purpose of this exploration is to lay the groundwork for understanding the future of program management—and the resulting technological and organizational challenges that are required to support that change.

The next part of this exploration is to define the motivations, characteristics, and disciplines of private industry equivalencies. Here there are commonalities, but also significant differences, that relate to the relationship and interplay between public investment, policy and acquisition, and private business interests.

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Innervisions: The Connection Between Data and Organizational Vision

During my day job I provide a number of fairly large customers with support to determine their needs for software that meets the criteria from my last post. That is, I provide software that takes an open data systems approach to data transformation and integration. My team and I deliver this capability with an open user interface based on Windows and .NET components augmented by time-phased and data management functionality that puts SMEs back in the driver’s seat of what they need in terms of analysis and data visualization. In virtually all cases our technology obviates the need for the extensive, time consuming, and costly services of a data scientist or software developer.

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Potato, Potahto, Tomato, Tomahto: Data Normalization vs. Standardization, Why the Difference Matters

In my vocation I run a technology company devoted to program management solutions that is primarily concerned with taking data and converting it into information to establish a knowledge-based environment. Similarly, in my avocation I deal with the meaning of information and how to turn it into insight and knowledge. This latter activity concerns the subject areas of history, sociology, and science.

In my travels just prior to and since the New Year, I have come upon a number of experts and fellow enthusiasts in these respective fields. The overwhelming numbers of these encounters have been productive, educational, and cordial. We respectfully disagree in some cases about the significance of a particular approach, governance when it comes to project and program management policy, but generally there is a great deal of agreement, particularly on basic facts and terminology. But some areas of disagreement–particularly those that come from left field–tend to be the most interesting because they create an opportunity to clarify a larger issue.

In a recent venue I encountered this last example where the issue was the use of the phrase data normalization. The issue at hand was that the use of “data normalization” suggested some statistical methodology in reconciling data into a standard schema. Instead, it was suggested, the term “data standardization” was more appropriate.

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Ring Out the Old, Ring in the New: Data Transformation Podcasting

Robin Williams at Innovate IPM interviewed me a few weeks ago and has a new podcast up to cap off the year. The main thrust of our discussion, as it turned out, which began as a wide-ranging one, settled on digital transformation and the changes and developments that I’ve seen in this area over the last three decades.

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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.

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Rear View Mirror — Correcting a Project Management Fallacy

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

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

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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.

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River Deep, Mountain High — A Matrix of Project Data

Been attending conferences and meetings of late and came upon a discussion of the means of reducing data streams while leveraging Moore’s Law to provide more, better data.  During a discussion with colleagues over lunch they asked if asking for more detailed data would provide greater insight.  This led to a discussion of the qualitative differences in data depending on what information is being sought.  My response to more detailed data was to respond: “well there has to be a pony in there somewhere.”  This was greeted by laughter, but then I finished the point: more detailed data doesn’t necessarily yield greater insight (though it could and only actually looking at it will tell you that, particularly in applying the principle of KDD).  But more detailed data that is based on a hierarchical structure will, at the least, provide greater reliability and pinpoint areas of intersection to detect areas of risk manifestation that is otherwise averaged out–and therefore hidden–at the summary levels.

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