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.
data-driven project management
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.
(more…)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.
(more…)For What It’s Worth — More on the Materiality and Prescriptiveness Debate and How it Affects Technological Solutions
The underlying basis on the materiality vs. prescriptiveness debate that I previously wrote about lies in two areas: contractual compliance, especially in the enforcement of public contracts, and the desired outcomes under the establishment of a regulatory regime within an industry. Sometimes these purposes are in agreement and sometimes they are in conflict and work at cross-purposes to one another.
(more…)New Directions — Fourth Generation apps, Agile, and the New Paradigm
The world is moving forward and Moore’s Law is accelerating in interesting ways on the technology side, which opens new opportunities, especially in software. In the past I have spoken of the flexibility of Fourth Generation software, that is, software that doesn’t rely on structured hardcoding, but instead, is focused on the data to deliver information to the user in more interesting and essential ways. I work in this area for my day job, and so using such technology has tipped over more than a few rice bowls.
(more…)Do You Believe in Magic? — Big Data, Buzz Phrases, and Keeping Feet Planted Firmly on the Ground
My alternative title for this post was “Money for Nothing,” which is along the same lines. I have been engaged in discussions regarding Big Data, which has become a bit of a buzz phrase of late in both business and government. Under the current drive to maximize the value of existing data, every data source, stream, lake, and repository (and the list goes on) has been subsumed by this concept. So, at the risk of being a killjoy, let me point out that not all large collections of data is “Big Data.” Furthermore, once a category of data gets tagged as Big Data, the further one seems to depart from the world of reality in determining how to approach and use the data. So for of you who find yourself in this situation, let’s take a collective deep breath and engage our critical thinking skills.
(more…)Rise of the Machines — Drivers of Change in Business and Project Management
Last week I found myself in business development mode, as I often am, in explaining to a prospective client our future plans in terms of software development. The point that I was making was that it was not our goal to simply reproduce the functionality that every other software solution provider offered, but to improve how the industry does business by making the drive for change through the application of appropriate technology so compelling through efficiencies, elimination of redundancy, and improved productivity, that not making the change would be deemed foolish. In sum, we are out to take a process and improve on it through the application of disruptive technology. I highlighted my point by stating: “It is not our goal to simply reproduce functionality so we can party like it’s 1998, it’s been eight software generations since that time and technology has provided us smarter and better ways of doing things.”
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Life, Project Management, and Everything 