In a blog post yesterday on the Melissa Data blog Elliot King wrote about Classifying Data Quality Problems. The post suggests that there are three different kinds of data quality issues:
This classification revolves around the root cause of bad data.
As examined in my post yesterday sometimes bad data quality isn’t bad data. A good deal of problems doesn’t relate to the raw data itself, but is linked to how data are structured,for example in data models, and how data are categorized, for example by (not) using metadata.
Flaws in data structure seem to have similar root causes as the suggested categorization, for example:
- Operational: Data are structured and labeled to fit capturing systems which may not fit further downstream purposes of use.
- Conceptual: The term conceptual data models (or similar approaches) pop up here. We miss them, not at least the enterprise-wide ones, very much in IT landscapes made up by popular off-the-shelf software.
- Organizational: We are usually not very well in talking the same language about the same data.
By the way: One good book about overcoming these challenges I read recently is by Thomas Frisendal and is called Design Thinking Business Analysis.