data

The Medium Controls the Present: Is it Too Late to Stop a Digital Dark Age?

“He who controls the past controls the future. He who controls the present controls the past.” ― George Orwell, 1984

A few short pre-Covid years ago, Google Vice President Vint Cerf turned some heads at the annual meeting of the American Association for the Advancement of Science in San Jose, warning the attending scientists that the digitization of the artifacts of civilization may create a digital dark age. “If we’re thinking 1,000 years, 3,000 years ahead in the future, we have to ask ourselves, how do we preserve all the bits that we need in order to correctly interpret the digital objects we create?” Cerf’s concerns are that today’s technology will become obsolete at some future time, with the information of our own times locked in a technological prison.

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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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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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Big Time — Elements of Data Size in Scaling

I’ve run into additional questions about scalability.  It is significant to understand the concept in terms of assessing software against data size, since there are actually various aspect of approaching the issue.

Unlike situations where data is already sorted and structured as part of the core functionality of the software service being provided, this is in dealing in an environment where there are many third-party software “tools” that put data into proprietary silos.  These act as barriers to optimizing data use and gaining corporate intelligence.  The goal here is to apply in real terms the concept that the customers generating the data (or stakeholders who pay for the data) own the data and should have full use of it across domains.  In project management and corporate governance this is an essential capability.

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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.

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The Future — Data Focus vs. “Tools” Focus

The title in this case is from the Leonard Cohen song.

Over the last few months I’ve come across this issue quite a bit and it goes to the heart of where software technology is leading us.  The basic question that underlies this issue can be boiled down into the issue of whether software should be thought of as a set of “tools” or an overarching solution that can handle data in a way that the organization requires.  It is a fundamental question because what we call Big Data–despite all of the hoopla–is really a relative term that changes with hardware, storage, and software scalability.  What was Big Data in 1997 is not Big Data in 2016, and will not be Big Data in 2030.

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Stay Open — Open and Proprietary Databases (and Why It Matters)

The last couple of weeks have been fairly intense workwise and so blogging has lagged a bit.  Along the way the matter of databases came up at a customer site and what constitutes open data and what comprises proprietary data.  The reason why this issue matters to customers rests of several foundations.

First, in any particular industry or niche there is a wide variety of specialized apps that have blossomed.  This is largely due to Moore’s Law.  Looking at the number of hosted and web apps alone can be quite overwhelming, particularly given the opaqueness of what one is buying at any particular time when it comes to software technology.

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