Information quality, like many information and technology subspecialties, seems to drift in and out of focus. Sometimes I find it difficult to understand why the subspecialty exists.
A recent epiphany concerning words and language has sent me on an outward-bound trajectory. I used to chafe at the "definition" of quality in a data context--the one that defines quality as "fit for purpose." I am now ready to break with it completely.
My epiphany had to do with the fact that meaning is not packaged up in words and phrases. Rather meaning is hiding behind and beneath them. Well-chosen words can be used as markers to stake out the boundaries of meaning or even, if we're careful, to constrain meaning much like a fence. When we focus on the stakes or the enclosure we risk losing the meaning that's inside.
We have all sat in high school (or college) literature classes and debated what the author or poet meant--what was the meaning that he or she had captured within the fence of language they had constructed. What we have failed to see in our information technology context is that business leaders, managers, consultants and others are exactly like those authors (only usually not nearly as careful in their use of language). We always have to ask what they meant and when we investigate, we invariably find that they didn't really know.
For more than 30 years we have labored to constrain the use of terms and encourage (or even enforce) the standardization of terminology. The issue seems to have taken on even more importance with the widespread adoption of newer reporting (business intelligence) technology that brings the data full circle. The leaders who were unable to stay focused long enough to generate good requirements are now launching their BI desktop and seeing the result.
I think that if we are honest with ourselves we will realize that though we now have better titles for what we do and we are getting paid better to do it, we would have to admit that our goal is not attainable. We are being held accountable for The Quality of Data because that's how we have labeled ourselves. We haven't even learned from the tribulations suffered by the other quality disciplines. At least we should begin calling it data quality control or data quality assurance. We have to focus on improvement in processes rather than improvement in data.
Look inside the fence I have built here and see if you can find some meaning that will help you.