When it comes to looking at data over time, there is another interesting phenomenon that
occurs. That phenomenon is that over long periods of time, the integrity of data
“degrades.” Perhaps, the term degrades is not appropriate because there is a pejorative
sense to “degrades.” And—as used here—the term “degrades” has no such pejorative
connotations. Instead, as used here, the term “degrades” simply means that there is a
natural and normal decay of meaning of data over time.
Fig. 1.6.9 shows the degradation of integrity of data over time.
Fig. 1.6.9 The degradation of data over time.In order to understand the degradation of integrity over time, let's look at some examples.
Let's consider the price of meat—say hamburger—over time. In 1850, hamburger was
0.05 cents a pound. In 1950, the price of hamburger was 0.95 cents a pound. And in
2015, the price of hamburger is $2.75 a pound. Does this comparison of the price of
hamburger over time make sense? The answer is it sort of makes sense. The problem is
not in the measurement of the price of hamburger. The problem is in the currency by
which hamburger is measured. Even the meaning of what is a dollar is different in 1850
than what a dollar is in 2015.
Now, let's consider another example. The stock price of one share of International
Business Machines (IBM) was $35 in 1950, and the price of that same share of stock in
2015 is $200 a share. Is the comparison of a stock price over time a valid comparison?
The answer is sort of. IBM in 2015 is not the same company as it was in 1950, in terms of
products, in terms of customers and revenues, and in terms of the value of the dollar. In a
hundred ways, doing the examination of IBM in 1950 compared with IBM in 2015, there
simply is no comparison. Over time, the very definition of the data has changed. So while
a comparison of IBM's stock price in 1950 versus the stock price in 2015 is an interesting
number, it is a completely relative number, because the very meaning of the number has
Chapter 1.6: The Life Cycle of Data: Understanding Data Over Time