By ANDY ANDREWSLos Alamos World Futures Institute
Data, information and knowledge are obviously related. A simple statement and one could stop at this point.
Today, however, we are inundated with all three and it is useful to examine them more closely and explore their implications in our everyday lives and decision making about the future. Let us begin with data.
Data is the plural of datum, at least it was in the 20th century. Today the English language has evolved and we use “data” for both the singular and the plural. Originally used in English around 1640, our perception of data changed in 1946 when it was used to mean the storing and transmission of computer bits and bytes. While one can argue with the precision of this statement (is it an analog?), there has been an evolution of our personal perception of data. Go to your online banking account or ATM and you can see your balance – a data point to you. Behind it, however, there are thousands, millions, and even many, many more data points, usually represented as ones and zeros.
Looking at it another way, consider an automobile with a tachometer, the device that conveys to you how fast the engine is spinning. Not all cars have tachometers, but this example does. Further, the car has a six cylinder engine and the tachometer says the engine is spinning at a rate of 2,400 revolutions per minute (RPM). This means that every cylinder is going up and down 2,400 times per minute. The spark plug is firing every other time the cylinder goes up and down (or it should). Theoretically, you could have a display on your dash board telling you how many times per second a given spark plug is firing. Or it could show the elapsed time between firings. This would be providing you the raw data you need for something – maybe.
If you are an engine design engineer, that data might be useful. And if you are a spark plug designer it could establish the capability your design must achieve. But if your goal is simply to avoid a traffic ticket while driving to work, you probably do not care. In fact, you probably do not even need a tachometer since the metric actually used is either miles or kilometers per hour.
In the spark plug example, data is measured as elapsed time between firing, collected in a sensor, and the result is displayed or represented to you. Then you, the human computer, uses it. Your computer brain reasons or analyzes the data and responds or dismisses it as useless or meaningless. In contrast, the data from the speedometer might be noted and your computer brain, using training and algorithms, has other parts of your body react – ease off the gas.
In 1948, Claude Shannon published a paper about information entropy. In the scientific world, entropy is the measure of disorder. In the universe of data it can also represent disorder. First, there is the source of data. Something happens or does not – the spark plug firing. Then it travels through a communication channel – the electronics of the car. And then it is received – by the driver or observer. If any of these “malfunction,” the data is useless and adds to the disorder in the data universe. While we can easily place data such as this in the trash bin – more disorder – how do we identify where it is useful and should we include it in the order of things? More importantly, how do you identify needed, orderly data in what might appear to be a hodgepodge?
Usually, when we think of data in a collective manner, it is an amalgam of raw data. In the scientific world this might be a collection of responses to stimulation in order to infer insight into an observed or postulated process. But in other domains, data leads to other insights. For instance, in the business world data is collected on sales, revenues, profits and stock prices. Businesses themselves want data on consumer preferences, what do consumers want to buy. In government we need data about crime, unemployment, and literacy in order to make hopefully better legislative decisions. From the data we can infer what is working and what is not. We want to know what processes are good and what needs to be replaced.
The amount of data available is enormous. If we wanted to determine the temperature of a room we could measure the energy in every molecule in the room, assuming the measuring process does not change the energy level of the measured particle. While one might say that this is a stupid, article-type suggestion (and I would agree), it does highlight the need for what we really want to know. We can determine the temperature of the room with a thermostat from a sample of the data theoretically available. This provides us with what we really want – information. But is it good or bad information or somewhere in between?
Til next time….
Los Alamos World Futures Institute website is LAWorldFutures.org. Feedback, volunteers and donations (501.c.3) are welcome. Email andy.andrews@laworldfutures.org or email bob.nolen@laworldfutures.org. Previously published columns can be found at www.ladailypost.com or www.laworldfutures.org.