Information and Value

We have talked of representation previously, in the context of task management. Common representation is essential to achieving common ground when human interactions are time or space-shifted. Understanding representation is fundamental to understanding information.

Humans have been representing information since at least the creation of writing. As early as 3000 BCE, the ancient Sumerians used cuneiform to record information. Cuneiform was created by pressing wedge-shaped sticks into wet clay. A particular impression or set of impressions corresponded to a citizen, or how much grain they grew, or how much beer they received. Certainly, the symbols shown are not the same as the beer, or the grain, or the long-dead Sumerian. This may seem obvious, but it can be tempting to confuse the representation with the reality. This is known as the reification fallacy.

All information management can be understood as a reduction in uncertainty. And we can and should quantify the value of having the information versus the cost of capturing and maintaining it.

Doug Hubbard, in the classic How to Measure Anything [Hubbard 2014], asks the following questions when the measurement is proposed:

As he states: “All measurements that have value must reduce the uncertainty of something that affects some decision with economic consequences”. While Hubbard is proposing these questions in the context of particular analysis initiatives, they are also excellent questions to ask of any proposal to manage information or data.

Information management, in the context of digital systems, adds value through improving efficiency, effectiveness, and optimizing risk (our three primary categories of value). Since digital systems started off primarily as efficiency aids, we will discuss efficiency first.

We have periodically discussed historical aspects of computing and digital systems, but not yet covered some of the fundamental motivations for their invention and development.

As technology progressed through the late 19th and early 20th centuries, applied mathematics became increasingly important in a wide variety of areas such as:

Calculations were performed by “computers”. These were not automated devices, but rather people, often women, tasked with endless, repetitive operation of simple adding machines, by which they manually executed tedious calculations to compile, for example, tables of trigonometric angles.

It was apparent, at least since the mid-19^th century, that it would be possible to automate such calculation. In fact, mathematical devices had long existed; for example, the abacus, Napiers' Bones, and the slide rule. But such devices had many limitations. The vision of automating digital calculations first came to practical realization through the work of Charles Babbage and Ada Lovelace, who took significant steps through the design and creation of the Difference and Analytical Engines.

After Babbage, the development of automated computation encountered a hiatus. Purely mechanical approaches based on gears and rods could not scale, and the manufacturing technology of Babbage’s day was inadequate to his visions – the necessary precision and power could not be achieved by implementing a general-purpose computer using his legions of gears, cams, and drive shafts. However, mathematicians continued to explore these areas, culminating in the work of Alan Turing who established both the potential and the limits of computing, initially as a by-product of investigations into certain mathematical problems of interest at the time.

Around the same time, the legendary telecommunications engineer Claude Shannon had developed essential underpinning engineering in expressing Boolean logic in terms of electronic circuits, and rigorous mathematical theory describing the fundamental characteristics and limitations of information transmission (e.g., the physical limits of copying one bit of data from one location from another) [Shannon 1948]. Advances in materials and manufacturing techniques resulted in the vacuum tube, ideally suited to the combination of Shannon digital logic with Turing’s theories of computation, and thus the computer was born. It is generally recognized that the first practical general-purpose computer was developed by the German, Konrad Zuse.

Turing and a fast-growing cohort of peers driven by (among other things) the necessities of World War II developed both the theory and the necessary practical understandings to automate digital computation. The earliest machines were used to calculate artillery trajectories. During World War II, mathematicians and physicists such as John von Neumann recognized the potential of automated computation, and so computers were soon also used to simulate nuclear explosions. This was a critical leap beyond the limits of manual “computers” pounding out calculations on adding machines.

The business world was also attentive to the development of computers. Punched cards had been used for storing data for decades preceding the invention of automated computers. Record-keeping at scale has always been challenging – the number of Sumerian clay tablets still in existence testifies to that! Industrial-era banks, insurers, and counting-houses managed massive repositories of paper journals and files, at great cost. A new form of professional emerged: the “white collar worker”.

Any means of reducing the cost of this record-keeping was of keen interest. Paper files were replaced by punched cards. Laborious manual tabulation was replaced by mechanical and electro-mechanical techniques, that could, for example, calculate sums and averages across a stack of punched cards, or sort through the stack, compare it against a control card, and sort the cards accordingly.

During World War II, many business professionals found themselves in the military, and some encountered the new electronic computers being used to calculate artillery trajectories or decrypt enemy messages. Edmund Berkeley, the first secretary of the Association for Computing Machinery, was one such professional who grasped the potential of the new technology [Akera 2007]. After the war, Berkeley advocated for the use of these machines to the leadership of the Prudential insurance company in the US, while others did the same with firms in the UK.

What is the legacy of Babbage and Lovelace and their successors in terms of today’s digital economy? The reality is that digital value for the first 60 years of fully automated computing systems was primarily in service of efficiency. In particular, record-keeping was a key concern. Business computing (as distinct from research computing had one primary driver: efficiency. Existing business models were simply accelerated with the computer. 300 clerks could be replaced by a $10 million machine and a staff of 20 to run it (at least, that was what the sales representative promised). And while there were notable failures, the value proposition held up such that computer technology continued to attract the necessary R&D spending, and new generations of computers started to march forth from the laboratories of Univac^®, IBM, Hewlett-Packard™, Control Data™, Burroughs™, and others.

Efficiency, ultimately, is only part of the business value. Digital technology relentlessly wrings out manual effort, and this process of automation is now so familiar and widespread that it is not necessarily a competitive advantage. Harvard Business Review editor Nicholas Carr became aware of this in 2003. He wrote a widely discussed article “IT Doesn’t Matter” in which he argued that: “When a resource becomes essential to competition but inconsequential to strategy, the risks its creates become more important than the advantages it provides” [Carr 2003]. Carr compared IT to electricity, noting that companies in the early 20^th century had vice-presidents of electricity and predicting the same for CIOs. His article provoked much discussion at the time and remains important and insightful. Certainly, to the extent IT’s value proposition is coupled only to efficiency (e.g., automating clerical operations), IT is probably less important than strategy.

But, as we have discussed throughout this document, IT is permeating business operations, and the traditional CIO role is in question as mainstream product development becomes increasingly digital. The value of correctly and carefully applied digital technology is more variable than the value of electricity. At this 2018 writing, the five largest companies by market capitalization – Apple, Amazon, Google, Facebook, and Microsoft – are digital firms, based on digital products, the result of digital strategies based on correct understanding and creative application of digital resources.

In this world, information enables effectiveness as much as, or even more than, efficiency.

Information management as we will discuss in the rest of this Competency Area arises from the large-scale absorption of data into highly efficient, miniaturized, automated digital infrastructures with capacity orders of magnitude greater than anything previously known. However, cuneiform and quipu, hash marks on paper, financial ledgers, punched cards, vacuum tubes, transistors, and hard disks represent a continuum, not a disconnected list. Whether we are looking at a scratch on a clay tablet or the magnetic state of some atoms in a solid state drive, there is one essential question:

What do we mean by that?

Consider the state of those atoms on a solid state drive. They represent the numbers 547. But without context, that number is meaningless. It could be:

The state of this data may have significant consequences. A destination address might be wrong, a tax return mis-identified. A credit card might be accepted or declined. A mortgage might be approved or denied. Or the full force of the law may be imposed on an offender, including criminal penalties resulting from a conviction on the evidence stored in the computer.

The COBIT Enabling Information guide [ISACA 2013] proposes a layered approach to this problem, as shown in COBIT Enabling Information layers.

Without all these layers, the magnetic state of those atoms is irrelevant.

The physical, empirical, and syntactic layers (hardware and lowest-level software) are in general out of scope for this document. They are the concern of broad and deep fields of theory, research, development, market activity, and standards; Digital Infrastructure on infrastructure is the most closely related.

A similar, but simpler hierarchy is:

Data is the context-less raw material.

Information is data + context, which makes it meaningful and actionable.

Knowledge is the situational awareness to make use of information.

Semantic, pragmatic, and social concerns (information and knowledge) are fundamental to this document and Competency Area. At digital scale – terabytes, petabytes, exabytes – establishing the meaning and social consequence of data is a massive undertaking. Data management and records management are two practices by which such meaning is developed and managed operationally. We will start by examining data management as a practice.

Evidence of Notability

Information management and its related value is the basis of computing and IT. Its notability is evidenced in the history of the human race’s approaches to managing it, from cuneiform to the present day.

Limitations

Information tends to be static and passive, where process is dynamic.

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