The Trouble With Information
In solving the information problem, we have created a new problem: information glut, incoherence and meaninglessness…our technological ingenuity transformed it into a form of garbage and ourselves as garbage collectors
— Neil Postman NY communication guruWe now skim the surface rather than dive deeply. And most of what impacts on our consciousness is essentially irrelevant to us.
—John Carr Communication Studies, UTS
In the milestone 1948 treatise, The Mathematical Theory of Communication, authors Claude Shannon and Warren Weaver define information in terms that bring us to where we are today in technology. Our computer age would be impossible without it. However, the technologists’ idea of information is not what other people think of as information.
The accepted definition of information offered by Shannon and Weaver, the forerunners of information science, is insufficient for semantic meaning and content. Information theory defines information by message length, complexity, and signal integrity which is ironically enough sometimes wrongly called understanding.
We can make some assumptions based on information theory. A random string of numbers can have a high scientific information value, but practically no human information value. All the journals in the world don’t hold the information value of a season of Jerry Springer. Clearly, not all bits have equal value.
…survey responses overwhelmingly point to the need for improved information, tools, and incentives for decisionmakers in all parts of the organization.
While these assumptions solve important problems for capturing, storing, processing and transmitting data, they are not as useful for information as humans understand it. Humans don’t classify information value by length, complexity, or raw signal reception. In fact a compelling argument could be made Shannon–based information factors are opposite human–based information factors. The problem I have with Shannon’s theory of information is that it is more descriptive of bits. Information, according to this theory, is about the work of computers — their storage and transmission of bits — rather than human objectives or even basic work tasks.
Bits Don’t Equal Information
Companies have perfected automation and labor substitution. They are quite good at process streamlining and standardization to drive efficiency and productivity. Few, however, have a clue how to improve and sustain the productivity of high–value knowledge workers.
—Are you ready for the next workplace revolution? by Tony DiRomualdo
Web analytics regularly show who visited, linked, or converted into a sale externally. Suggest pointing analytic tools inward and those generating computer activity and output will insist otherwise. Yet moving to feedback informed demand–pull turns computer activity into information work.
Terms like information anxiety, data smog, technostress and multitasking madness describe a design failure. Application development still favors task efficiency. Doing with great efficiency what should be eliminated degrades information work effectiveness.
Related Articles:
Information Work: What Is Context Worth?
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Resources
- “Somewhere in these last 10 years the IT industry got so enamored with technology for technology’s sake that we forgot that it’s actually about solving business problems,” Rahmani told the audience. “We’ve got to start thinking about this from the customer point of view.” IBM’s Innovative Approaches to Bolster Growth. HBR Strategy+Business; Profiles in Organizational DNA Research and Remedies And Are you ready for the next workplace revolution? by Tony DiRomualdo all point to the trouble with information and technology getting together.
- “Blyth noted that IT is easily replicated and “any advantage gained quickly erodes over time as competitors catch up.” Even knowing this, companies still put as much as 85% of their IT investments into infrastructure and only 15% into innovation. It took competitors six weeks to imitate Intel’s latest chip technology, he said, and yet one hears CIOs evaluating IT proposals based on 10–year paybacks.” Why So Many Big IT Investments Do So Little for Shareholder Value
- The PDF document Toward a Systemic Notion of Information: Practical Consequences by Nigib Callaos and Belkis Callaos is one of the few balanced considerations of what information has to be for an information age. Their conclusion is that an IS system must include the technological and human systems, working together. I=0 (Information has no intrinsic meaning) argues against many of the claims made for information technology.
- Real world information systems face a laundry list of problems:
- People say one thing, often do another
- People don’t have the time, patience, or motivation to implement sophisticated metadata schemes. And even when they do, a simple thought experiment can make a good argument other considerations matter
- People make mistakes, especially when predicting beforehand how data should be organized and put to use (indexer’s paradox)
- People resist change which interferes with their priorities
- Technologists generally look askance at those factors hinting at internal human drives. But when Toy companies use Ebay trades as an information map of trend momentum more accurate than many focus groups, it’s time to rethink those assumptions.
- The Next Information Revolution, Peter Drucker interview, Forbes.
- In InformationAnxiety2, Paul Kaufman is quoted as saying “This is the kind of information that engineers are rightly proud of: pulses and signals zipping along through optical fibers, rather indifferent to the meaning of it all. However, to use information productively, (toward some valued end or purpose), people must know what they are doing and why.” (page 20)
- Technology Review backgrounder about Claude Shannon. And Information Content, Compressibility and Meaning by Gert Korthof proposes the neat solution of renaming scientific information Compressibility.
- Vital Definition: Data is a representation of something using numbers, symbols, words, graphics and/or sound. That something can be a physical object, idea, event or process. The data, however, is not the thing it represents. Data is descriptive and so has a bias for observation and reductionism. Data has pattern, information has context. Recognition of pattern does not mean cognition of the contexts and meaning. Without context data processing is biased toward accumulating more data, an infinite loop of paralysis by analysis. Nearly all of what is commonly thought of as information overload is, in fact, data overload.
- Vital Definition: Information. A definition suitable for both technology and business use. What guidelines should we give for development of a definition? One, any definition should point out how information is different from data, and fundamentally so. Two, it should be accessible to and meaningful for the average person. Three, while it may concern how structures in software are developed, it should be independent of and transcendent of structures. The objective is to raise the technology to the level of the definition. Not to lower the bar until any and every technology developed automatically qualifies. A suitable definition would seem to rule out technology built around data being indistinguishable from and interchangeable with technology built around information. Or that data technology would simply turn into information technology over time in the absence of a clear idea how the two differ. Check out Information Work Tactics to learn more.