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.
(more…)program management software
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…)Back in the Saddle Again — Putting the SME into the UI Which Equals UX
“Any customer can have a car painted any colour that he wants so long as it is black.” — Statement by Henry Ford in “My Life and Work”, by Henry Ford, in collaboration with Samuel Crowther, 1922, page 72
The Henry Ford quote, which he made half-jokingly to his sales staff in 1909, is relevant to this discussion because the information sector has developed along the lines of the auto and many other industries. The statement was only half-joking because Ford’s cars could be had in three colors. But in 1909 Henry Ford had found a massive market niche that would allow him to sell inexpensive cars to the masses. His competition wasn’t so much as other auto manufacturers, many of whom catered to the whims of the rich and more affluent members of society, but against the main means of individualized transportation at the time–the horse and buggy. The color was not so much important to this market as was the need for simplicity and utility.
(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.”
(more…)The Water is Wide — Data Streams and Data Reservoirs
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.
(more…)Let’s Get Physical — Pondering the Physics of Big Data
As a primer a useful commentary on the ethical uses of Big Data was published today at Salon.com in an excerpt from Jacob Silverman’s book, Terms of Service: Social Media and the Price of Constant Connection. Silverman takes a different approach from the one that I outline in my article, but he tackles the economics of new media that were identified years ago by Brad DeLong and A. Michael Froomkin back in the late 1990s and first decade of the 21st century. This article on First Monday from 2000 regarding speculative microeconomics emerging from new media nicely summarizes their thesis. Silverman rejects reforming the system in economic terms, entering the same ethical terrain on personal data collection that was explored by Rebecca Skloot on the medical profession’s genetic collection and use of tissue during biopsies in the book, The Immortal Life of Henrietta Lacks.
(more…)The Song Remains the Same (But the Paradigm Is Shifting) — Data Driven Assessment and Better Software in Project Management
Probably the biggest DoD-centric project management news this past week was the unofficial announcement by Frank Kendall, who is the Undersecretary of Defense for Acquisition, Technology, and Logistics USD(AT&L), that thresholds would be raised for mandatory detailed surveillance of programs to $100M from the present requirement of $20M. While earned value management implementation and reporting will still be required on programs based on dollar value, risk, and other key factors, especially the $20M threshold for R&D-type projects, the raising of the threshold for mandatory surveillance reviews was seen as good news all around for reducing some regulatory burden. The big proviso in this announcement, however, was that it is to go into effect later this summer and that, if the data in reporting submissions show inconsistencies and other anomalies that call into question the validity of performance management data, then all bets are off and the surveillance regime is once again imposed, though by exception.
(more…)
Life, Project Management, and Everything 
You must be logged in to post a comment.