Data is often defined as “Facts about things” and Information is often defined as “Facts about things in a context”.
From Lwanga Yonke (IAIDQ Advisor and one of the visionaries behind the CIQP certification) comes this great example of where, without consistent application of context, it is possible for the Data to give rise to poor quality and misleading information.
Image linked from "thepocket.com"
What we see in the sign opposite are three distinct contexts:
- A count of the population (562)
- The height of the town above sealevel (2150)
- The year the town was founded (1951)
And of course, when we see a column of figures our instinct (since our earliest school days) is to add them all up… to give us 4663.
Of course, that figure is meaningless as information, and is also poor quality data.
I have personally experienced similar “challenges of context” in tracking back root cause analyses in Regulatory Compliance projects.. the stakeholder pulling the incident reports together didn’t consider context and as such was comparing apples with ostrich eggs (if he’d been comparing apples to oranges at least they’d both have been fruit).
I’d love to hear your stories of Contextual conundrums that have lead to poor quality data and erroneous Information.
Lack of definitions (or poor use of abbreviations) for metrics is a major problem. Presentation is a big part of understanding data and I think that is easy to forget when one is focused on producing a metric.