In philosophy (according to Wikipedia), the term qualia is used to describe the subjective quality of conscious experience.
Examples of qualia are the pain of a headache, the taste of wine, or the redness of an evening sky. As Daniel Dennett explains:
“Qualia is an unfamiliar term for something that could not be more familiar to each of us:
The ways things seem to us.”
Like truth, beauty, and singing ability, data quality is in the eyes of the beholder, or since data quality is most commonly defined as fitness for the purpose of use, we could say that data quality is in the eyes of the user.
However, most data has both multiple uses and multiple users. Data of sufficient quality for one use or one user may not be of sufficient quality for other uses and other users. Quite often these diverse data needs and divergent data quality perspectives make it a daunting challenge to provide meaningful data quality metrics to the organization.
Recently on the Data Roundtable, Dylan Jones of Data Quality Pro discussed the need to create data quality reports that matter, explaining that if you’re relying on canned data profiling reports (i.e., column statistics and data quality metrics at an attribute, table, and system level), then you are measuring data quality in isolation of how the business is performing.
Instead, data quality metrics must measure data qualia—the subjective quality of the user’s business experience with data:
“Data Qualia is an unfamiliar term for something that must become more familiar to the organization:
The ways data quality impact business performance.”