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Avoiding a Digital Dark Age

Data longevity depends on both the storage medium and the ability to decipher the information

Kurt D. Bollacker


Unlike the many venerable institutions that have for centuries refined their techniques for preserving analog data on clay, stone, ceramic or paper, we have no corresponding reservoir of historical wisdom to teach us how to save our digital data. That does not mean there is nothing to learn from the past, only that we must work a little harder to find it. We can start by briefly looking at the historical trends and advances in data representation in human history. We can also turn to nature for a few important lessons.

The earliest known human records are millennia-old physical scrapings on whatever hard materials were available. This medium was often stone, dried clay, bone, bamboo strips or even tortoise shells. These substances were very durable—indeed, some specimens have survived for more than 5,000 years. However, stone tablets were heavy and bulky, and thus not very practical.

Possibly the first big advance in data representation was the invention of papyrus in Egypt about 5,500 years ago. Paper was lighter and easier to make, and it took up considerably less space. It worked so well that paper and its variants, such as parchment and vellum, served as the primary repositories for most of the world’s information until the advent of the technological revolution of the 20th century.

Technology brought us photographic film, analog phonographic records, magnetic tapes and disks, optical recording, and a myriad of exotic, experimental and often short-lived data media. These technologies were able to represent data for which paper cannot easily be used (video, for example). The successful ones were also usually smaller, faster, cheaper and easier to use for their intended applications. In the last half of the 20th century, a large part of this advancement included a transition from analog to digital representations of data.

2010-03CompSciBollackerFA.jpgClick to Enlarge ImageEven a brief investigation into a small sampling of information-storage media technologies throughout history quickly uncovers much dispute regarding how long a single piece of each type of media might survive. Such uncertainty cannot be settled without a time machine, but we can make reasonable guesses based on several sources of varying reliability. If we look at the time of invention, the estimated lifespan of a single piece of each type of media and the encoding method (analog or digital) for each type of data storage (see the table at right), we can see that new media types tend to have shorter lifespans than older ones, and digital types have shorter lifespans than analog ones. Why are these new media types less durable? Shouldn’t technology be getting better rather than worse? This mystery clamors for a little investigation.

2010-03CompSciBollackerFB.jpgClick to Enlarge ImageTo better understand the nature of and differences between analog and digital data encoding, let us use the example of magnetic tape, because it is one of the oldest media that has been used in both analog and digital domains. First, let’s look at the relationship between information density and data-loss risk. A standard 90-minute analog compact cassette is 0.00381 meters wide by about 129 meters long, and a typical digital audio tape (DAT) is 0.004 meters wide by 60 meters long. For audio encodings of similar quality (such as 16 bit, 44.1 kilohertz for digital, or 47.6 millimeters per second for analog), the DAT can record 500 minutes of stereo audio data per square meter of recordable surface, whereas the analog cassette can record 184 minutes per square meter. This means the DAT holds data about 2.7 times more densely than the cassette. The second table (right) gives this comparison for several common consumer audio-recording media types. Furthermore, disk technologies tend to hold data more densely than tapes, so it is no surprise that magnetic tape has all but disappeared from the consumer marketplace.

However, enhanced recording density is a double-edged sword. Assume that for each medium a square millimeter of surface is completely corrupted. Common sense tells us that media that hold more data in this square millimeter would experience more actual data loss; thus for a given amount of lost physical medium, more data will be lost from digital formats. There is a way to design digital encoding with a lower data density so as to avoid this problem, but it is not often used. Why? Cost and efficiency: It is usually cheaper to store data on digital media because of the increased density.

2010-03CompSciBollackerFC.jpgClick to Enlarge ImageA possibly more important difference between digital and analog media comes from the intrinsic techniques that comprise their data representations. Analog is simply that—a physical analog of the data recorded. In the case of analog audio recordings on tape, the amplitude of the audio signal is represented as an amplitude in the magnetization of a point on the tape. If the tape is damaged, we hear a distortion, or “noise,” in the signal as it is played back. In general, the worse the damage, the worse the noise, but it is a smooth transition known as graceful degradation. This is a common property of a system that exhibits fault tolerance, so that partial failure of a system does not mean total failure.

Unlike in the analog world, digital data representations do not inherently degrade gracefully, because digital encoding methods represent data as a string of binary digits (“bits”). In all digital symbol number systems, some digits are worth more than others. A common digital encoding mechanism, pulse code modulation (PCM), represents the total amplitude value of an audio signal as a binary number, so damage to a random bit causes an unpredictable amount of actual damage to the signal.

Let’s use software to concoct a simulated experiment that demonstrates this difference. We will compare analog and PCM encoding responses to random damage to a theoretically perfect audiotape and playback system. The first graph in the third figure (above) shows analog and PCM representations of a single audio tone, represented as a simple sine wave. In our perfect system, the original audio source signal is identical to the analog encoding. The PCM encoding has a stepped shape showing what is known as quantization error, which results from turning a continuous analog signal into a discrete digital signal. This class of error is usually imperceptible in a well-designed system, so we will ignore it for now.

For our comparison, we then randomly damage one-eighth of the simulated perfect tape so that the damaged parts have a random amplitude response. The second graph in the third figure (above) shows the effect of the damage on the analog and digital encoding schemes. We use a common device called a low-pass filter to help minimize the effect of the damage on our simulated output. Comparing the original undamaged audio signal to the reconstructions of the damaged analog and digital signals shows that, although both the analog and digital recordings are distorted, the digital recording has wilder swings and higher error peaks than the analog one.

But digital media are supposed to be better, so what’s wrong here? The answer is that analog data-encoding techniques are intrinsically more robust in cases of media damage than are naive digital-encoding schemes because of their inherent redundancy—there’s more to them, because they’re continuous signals. That does not mean digital encodings are worse; rather, it’s just that we have to do more work to build a better system. Luckily, that is not too hard. A very common way to do this is to use a binary-number representation that does not mind if a few bits are missing or broken.

2010-03CompSciBollackerFD.jpgClick to Enlarge ImageOne important example where this technique is used is known as an error correcting code (ECC). A commonly used ECC is the U.S. Postal Service’s POSTNET (Postal Numeric Encoding Technique), which represents ZIP codes on the front of posted envelopes. In this scheme, each decimal digit is represented as five binary digits, shown as long or short printed bars (right). If any single bar for any decimal digit were missing or incorrect, the representation would still not be confused with that of any other digit. For example, in the rightmost column of the table, the middle bar for each number has been erased, yet none of the numbers is mistakable for any of the others.

Although there are limits to any specific ECC, in general, any digital- encoding scheme can be made as robust as desired against random errors by choosing an appropriate ECC. This is a basic result from the field of information theory, pioneered by Claude Shannon in the middle of the 20th century. However, whichever ECC we choose, there is an economic tradeoff: More redundancy usually means less efficiency.

Nature can also serve as a guide to the preservation of digital data. The digital data represented in the DNA of living creatures is copied into descendents, with only very rare errors when they reproduce. Bad copies (with destructive mutations) do not tend to survive. Similarly, we can copy digital data from medium to medium with very little or no error over a large number of generations. We can use easy and effective techniques to see whether a copy has errors, and if so, we can make another copy. For instance, a common error-catching program is called a checksum function: The algorithm breaks the data into binary numbers of arbitrary length and then adds them in some fashion to create a total, which can be compared to the total in the copied data. If the totals don’t match, there was likely an accidental error in copying. Error-free copying is not possible with analog data: Each generation of copies is worse than the one before, as I learned from my father’s reel-to-reel audiotapes.

Because any single piece of digital media tends to have a relatively short lifetime, we will have to make copies far more often than has been historically required of analog media. Like species in nature, a copy of data that is more easily “reproduced” before it dies makes the data more likely to survive. This notion of data promiscuousness is helpful in thinking about preserving our own data. As an example, compare storage on a typical PC hard drive to that of a magnetic tape. Typically, hard drives are installed in a PC and used frequently until they die or are replaced. Tapes are usually written to only a few times (often as a backup, ironically) and then placed on a shelf. If a hard drive starts to fail, the user is likely to notice and can quickly make a copy. If a tape on a shelf starts to die, there is no easy way for the user to know, so very often the data on the tape perishes silently, likely to the future disappointment of the user.

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