Thoughts on Information Logistics – Part 1

By: George Spafford

March 22, 2007

Information technology professionals frequently feel challenged about how to communicate with business people and senior management. Social psychologists estimate that anywhere from 40-60% of intended meaning is lost during the communication process. If you take this and factor in that 75% of the average person’s day is spent in some form of communication, be it speaking, writing or reading, then we have some very real lost productivity not to mention potential consequences arising from misunderstandings. If we look at communication, we can define it as the exchange of information for a purpose. To create a baseline of understanding about communication we need to define some base terms around the lifecycle of information.

Data

A term we often use, and misuse, is “data”. It can be thought of as raw unprocessed historical fact devoid of any biases or errors. Simply put, it is precisely what happened in the past.

In this respect, data is interesting because it is always historical. There is always a delay from occurrence to recognition so concepts like “real time data acquisition” are a tad misleading because you are always collecting what happened in the past. To counter this example, IT must understand what the business needs truly are and work from there. Decreasing acquisition times reaches a point of diminishing returns wherein each additional dollar spent results in incrementally fewer improvements in timing. In other words, the faster you try to acquire data the more you will spend and eventually the costs become astronomical with little perceived improvement.

Noise

No matter how hard we may try, errors ranging from subtle to catastrophic will creep in to the process and this is what we term “noise”. The accuracy of our senses and remote sensors, our mental state, processing algorithms, storage and retrieval are all opportunities for biases and errors to enter. Some people think themselves infallible. Let me tell you, human error creates a lot of problems and so does thinking that removing people from the equation will solve matters! The latter will not – it merely changes the potential and introduces new challenges. For example, if someone records an audio signal thinking that some target sound can be acquired without a person missing it may run into challenges with the quality of the microphone and its sensitivity, the sampling rate, the storage method, the ability to find the right recording and the right point in time of the recording, the quality of playback, etc.

In addition, there are two broad categories of noise. Intentional noise is purposefully injected into the environment to mislead. For example, a competitor may launch a disinformation campaign to throw off a competitor’s intelligence gathering activities or to identify the source of intelligence leakage.

Unintentional noise is essentially everything else. If someone misunderstands something and then reports the error as fact then this perpetuates the noise. The recording errors in the example above could all be unintentional provided that the noise was not premeditated.

Content

Once we begin to manipulate data we are creating content. Essentially data is theoretical as our very act of recognition can alter it. From a practical perspective, content is essentially comprised of data and noise. In some cases noise will be zero for all intents and purposes. In other cases there may be all noise and no data, which would indicate a falsehood.

Content is an intermediary phase between data and information is all to often where groups attempting to deliver value to the consumer stall. It is often said that we drowning in information but nothing can be farther from the truth – we are drowning in content. All too often, it is content that we are providing to consumers and its value may likely be suboptimal. With our automation we can generate millions of pages of content and deliver it to inboxes and portals in very little time. But is this what the consumer needs? Did anybody actually ask the consumer directly what he/she needs? Is this really information? The truth may range from irrelevancy to valuable but the results risk being variable and less than ideal.

Information

As you may surmise, content is not necessarily information. Only by understanding the consumer’s needs and delivering content processed to those specifications can we come close. This is because the only person who can say whether something is information or not is the consumer. If the consumer reads, watches or listens to something, then fails to have that “ah ha” moment wherein something is meaningful and adds value then information was not delivered.

For IT and the business to evolve, there must be a fundamental shift away from “technology push” wherein IT shoves out new services, including reports, to “technology pull” wherein IT and the business work together to define what the needs of the business are and then implement solutions, perhaps in an incremental fashion, that meet the needs of the organization.

In IT’s quest to deliver value, data, noise, content and information are all in play. Now, we must determine how to craft information. To do so we will review the logistics of information in our next article.