This definition has deep roots in the field of quality management, incorporating concepts articulated by some of its giants, including Joseph Juran (“fitness for use”), Philip Crosby (“conformance to requirements”), and W. Edwards Deming (“meeting or exceeding the customer’s expectation”). Their common thread is the focus on consumption, driven by precise and complete specifications. Quality improves as requirements and processes improve. Discipline in this approach is common with engineered and manufactured items like missiles, automobiles, and consumer goods.
It is less common with data.
Far be it for me to challenge the accumulated knowledge switzerland whatsapp number data of our field, but I very strongly disagree with defining data quality as “fit for purpose.”
Imagine you’re looking to purchase a used car to get you back and forth to work. You don’t have a lot of money, but you have to drive only a couple miles each way. You find a car that’s extremely inexpensive, but the engine overheats after running for about a half hour. problem because it satisfies your requirements: really low price and five-mile commute. It is fit for your purpose, and therefore, by the DMBoK definition, it is “high quality.”
One day, you want to visit family a couple hundred miles away. You set out in your “high-quality” car and haven’t even completed 10% of the trip when you have to stop and let the engine cool. At this rate, the journey will take days. You curse this piece of junk. The car is now “low quality” because it does not satisfy the new purpose to which you wanted to apply it.