Shining a Social Light on Data Quality

Last week, when I published my blog post Lightning Strikes the Cloud, I unintentionally demonstrated three important things about data quality.

The first thing I demonstrated was even an obsessive-compulsive data quality geek is capable of data defects, since I initially published the post with the title Lightening Strikes the Cloud, which is an excellent example of the difference between validity and accuracy caused by the Cupertino Effect, since although lightening is valid (i.e., a correctly spelled word), it isn’t contextually accurate.

The second thing I demonstrated was the value of shining a social light on data quality — the value of using collaborative tools like social media to crowd-source data quality improvements.  Thankfully, Julian Schwarzenbach quickly noticed my error on Twitter.  “Did you mean lightning?  The concept of lightening clouds could be worth exploring further,” Julian humorously tweeted.  “Might be interesting to consider what happens if the cloud gets so light that it floats away.”  To which I replied that if the cloud gets so light that it floats away, it could become Interstellar Computing or, as Julian suggested, the start of the Intergalactic Net, which I suppose is where we will eventually have to store all of that big data we keep hearing so much about these days.

The third thing I demonstrated was the potential dark side of data cleansing, since the only remaining trace of my data defect is a broken URL.  This is an example of not providing a well-documented audit trail, which is necessary within an organization to communicate data quality issues and resolutions.

Communication and collaboration are essential to finding our way with data quality.  And social media can help us by providing more immediate and expanded access to our collective knowledge, experience, and wisdom, and by shining a social light that illuminates the shadows cast upon data quality issues when a perception filter or bystander effect gets the better of our individual attention or undermines our collective best intentions — which, as I recently demonstrated, occasionally happens to all of us.

Related Posts

Data Quality and the Cupertino Effect

Are you turning Ugly Data into Cute Information?

The Importance of Envelopes

The Algebra of Collaboration

Finding Data Quality

The Wisdom of the Social Media Crowd

Perception Filters and Data Quality

Data Quality and the Bystander Effect

The Family Circus and Data Quality

Data Quality and the Q Test

Metadata, Data Quality, and the Stroop Test

The Three Most Important Letters in Data Governance

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Comments

[Julian Schwarzenbach]

Jim,

You beat me to it! I had also considered a blog post on how this discussion/series of events demonstrates different aspects of data quality. I had considered adding in examples such as:

* The work of e e cummings whereby capitalization and punctuation are not used
* The example where text has the first and last letters of a word correctly placed but all intervening letters mixed up
* The effect of missing words on meaning

Although these could all be used as examples of poor quality data, they also demonstrate that even with poor quality data, if there is a certain level of consistency to data, then the original meaning can (generally) still be determined.

Julian