Sometimes Worse Data Quality is Better

Continuing a theme from three previous posts, which discussed when it’s okay to call data quality as good as it needs to get, the occasional times when perfect data quality is necessary, and the costs and profits of poor data quality, in this blog post I want to provide three examples of when the world of consumer electronics proved that sometimes worse data quality is better.

 

When the Betamax Bet on Video Busted

While it seems like a long time ago in a galaxy far, far away, during the 1970s and 1980s a videotape format war waged between Betamax and VHS.  Betamax was widely believed to provide superior video data quality.

But a blank Betamax tape allowed users to record up to two hours of high-quality video, whereas a VHS tape allowed users to record up to four hours of slightly lower quality video.  Consumers consistently chose quantity over quality — and especially since lower quality also meant a lower price.  Betamax tapes and machines remained more expensive based on betting that consumers would be willing to pay a premium for higher-quality video.

The VHS victory demonstrated how people often choose quantity over quality, so it doesn’t always pay to have better data quality.

 

When Lossless Lost to Lossy Audio

Much to the dismay of those working in the data quality profession, most people do not care about the quality of their data unless it becomes bad enough for them to pay attention to — and complain about.

An excellent example is bitrate, which refers to the number of bits — or the amount of data — that are processed over a certain amount of time.  In his article Does Bitrate Really Make a Difference In My Music?, Whitson Gordon examined the common debate about lossless versus lossy audio formats.

Using the example of ripping a track from a CD to a hard drive, a lossless format means the track is not compressed to the point where any of its data is lost, retaining, for all intents and purposes, the same audio data quality as the original CD track.

By contrast, a lossy format compresses the track so that it takes up less space by intentionally deleting some of its data, reducing audio data quality.  Audiophiles often claim anything other than vinyl records sound lousy because they are so lossy.

However, like truth, beauty, and art, data quality can be said to be in the eyes — or the ears — of the beholder.  So, if your favorite music sounds fine to you in MP3 file format, then not only do you not need vinyl records, audio tapes, and CDs anymore, but if you consider MP3 files good enough, then you will not pay more attention to (or pay more money for) audio data quality.

 

When Digital Killed the Photograph Star

The Eastman Kodak Company, commonly known as Kodak, which was founded by George Eastman in 1888 and dominated the photograph industry for most of the 20th century, filed for bankruptcy in January 2012.  The primary reason was that Kodak, which had previously pioneered innovations like celluloid film and color photography, failed to embrace the industry’s transition to digital photography, despite the fact that Kodak invented some of the core technology used in current digital cameras.

Why?  Because Kodak believed that the data quality of digital photographs would be generally unacceptable to consumers as a replacement for film photographs.  In much the same way that Betamax assumed consumers wanted higher-quality video, Kodak assumed consumers would always want to use higher-quality photographs to capture their “Kodak moments.”

In fairness to Kodak, mobile devices are causing a massive — and rapid — disruption to many well-established business models, creating a brave new digital world, and obviously not just for photography.  However, when digital killed the photograph star, it proved, once again, that sometimes worse data quality is better.

  

Related Posts

Data Quality and the OK Plateau

When Poor Data Quality Kills

The Costs and Profits of Poor Data Quality

Promoting Poor Data Quality

Data Quality and the Cupertino Effect

The Data Quality Wager

How Data Cleansing Saves Lives

The Dichotomy Paradox, Data Quality and Zero Defects

Data Quality and Miracle Exceptions

Data Quality: Quo Vadimus?

The Seventh Law of Data Quality

A Tale of Two Q’s

Paleolithic Rhythm and Data Quality

Groundhog Data Quality Day

Data Quality and The Middle Way

Stop Poor Data Quality STOP

When Poor Data Quality Calls

Freudian Data Quality

Predictably Poor Data Quality

Satisficing Data Quality