Mind the Gap: Data Quality Is Not “Fit for Purpose”
Posted: Tue Feb 11, 2025 6:20 am
Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored the rise of the data product. This month, we’ll look at data quality vs. data fitness.
Everybody likes a pithy definition. Marketers describe them as “sticky,” or easy to remember. Of course, that doesn’t always mean they’re useful or completely accurate. Information management has a couple. Metadata is switzerland whatsapp number data almost universally described as “data about data,” but I’d be willing to bet that you rolled your eyes just now. How many times have we seen metadata introduced in that way, with the presenter or author immediately apologizing and then moving on to a more useful description.
Similarly, the data quality bumper sticker reads “fit for purpose.” You can probably already guess that I’m not a fan. Let’s pull out our DMBoK and see what it says:
The term data quality refers both to the characteristics associated with high quality data and the processes used to measure or improve the quality of data. [DMBoK-2, 644]
Characteristics and processes. Sounds good so far. Continuing:
Data is of high quality to the degree that it meets the expectations and needs of data consumers. That is, if the data is fit for the purposes to which they want to apply it. It is of low quality if it is not fit for those purposes. Data quality is thus dependent on context and on the needs of the data consumer.
Everybody likes a pithy definition. Marketers describe them as “sticky,” or easy to remember. Of course, that doesn’t always mean they’re useful or completely accurate. Information management has a couple. Metadata is switzerland whatsapp number data almost universally described as “data about data,” but I’d be willing to bet that you rolled your eyes just now. How many times have we seen metadata introduced in that way, with the presenter or author immediately apologizing and then moving on to a more useful description.
Similarly, the data quality bumper sticker reads “fit for purpose.” You can probably already guess that I’m not a fan. Let’s pull out our DMBoK and see what it says:
The term data quality refers both to the characteristics associated with high quality data and the processes used to measure or improve the quality of data. [DMBoK-2, 644]
Characteristics and processes. Sounds good so far. Continuing:
Data is of high quality to the degree that it meets the expectations and needs of data consumers. That is, if the data is fit for the purposes to which they want to apply it. It is of low quality if it is not fit for those purposes. Data quality is thus dependent on context and on the needs of the data consumer.