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.
Knowledge Discovery in Databases
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…)Both Sides Now — The Value of Data Exploration
Over the last several months I have authored a number of stillborn articles that just did not live up to the standards that I set for this blog site. After all, sometimes we just have nothing important to add to the conversation. In a world dominated by narcissism, it is not necessary to constantly have something to say. Some reflection and consideration are necessary, especially if one is to be as succinct as possible.
A quote ascribed to Woodrow Wilson, which may be apocryphal, though it does appear in two of his biographies, was in response to being lauded by someone for making a number of short, succinct, and informative speeches. When asked how he was able to do this, President Wilson is supposed to have replied:
“It depends. If I am to speak ten minutes, I need a week for preparation; if fifteen minutes, three days; if half an hour, two days; if an hour, I am ready now.”
An undisciplined mind has a lot to say about nothing in particular with varying degrees of fidelity to fact or truth. When in normal conversation we most often free ourselves from the discipline expected for more rigorous thinking. This is not necessarily a bad thing if we are saying nothing of consequence and there are gradations, of course. Even the most disciplined mind gets things wrong. We all need editors and fact checkers.
(more…)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.
(more…)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.
(more…)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.
(more…)Don’t Know Much…–Knowledge Discovery in Data
A short while ago I found myself in an odd venue where a question was posed about my being an educated individual, as if it were an accusation. Yes, I replied, but then, after giving it some thought, I made some qualifications to my response. Educated regarding what?
It seems that, despite a little more than a century of public education and widespread advanced education having been adopted in the United States, along with the resulting advent of widespread literacy, that we haven’t entirely come to grips with what it means. For the question of being an “educated person” has its roots in an outmoded concept–an artifact of the 18th and 19th century–where education was delineated, and availability determined, by class and profession. Perhaps this is the basis for the large strain of anti-intellectualism and science denial in the society at large.
(more…)I Can See Clearly Now — Knowledge Discovery in Databases, Data Scalability, and Data Relevance
I recently returned from a travel and much of the discussion revolved around the issues of scalability and the use of data. What is clear is that the conversation at the project manager level is shifting from a long-running focus on reports and metrics to one focused on data and what can be learned from it. As with any technology, information technology exploits what is presented before it. Most recently, accelerated improvements in hardware and communications technology has allowed us to begin to collect and use ever larger sets of data.
(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.
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Life, Project Management, and Everything 