Is Data Quality the Same As Data Accuracy?

Accurate data can be of poor quality if it doesn’t suit its intended purpose

We are living in the digital age, and data have a universal presence that directly or indirectly affects our lives, even when we’re not aware of it. Hence, data quality is an important topic of discussion. Data quality isn’t only an aspect of data that determines their fitness for use, but is also a function or subdiscipline of data management.

Data quality can be defined as evaluating data’s fitness to use (that is, serve their purpose) in a given context. Sustaining high quality data is a challenge that most organizations face, and the data quality arena is surrounded by its own set of myths. This misleads people when it comes to making data quality management-related decisions. These myths can slow down, hinder, or put a stop to an organization’s data quality management efforts or the deployment of data quality projects or initiatives.¹

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Comments

Data Pedigree

I also recommend to read "Show Me the Pedigree", Quality Progress, January 2019.

Best regards

Three Comments

Good article. Here are my coments:

For years, I have had on my desk the statement by Gagetalker, " Without data, it's just another opinion" Needs to e good data.
My colleague used to say "Knowledge is the ability to direct energy". But again the knowledge has to be based on good quality data.
And as to timeliness, I worked as Quality Manager for a plant manager who wanted to know information about recent quality of products for our largest customer, in terms of conformance to specifications. I told him I would get the information within the half hour. He was exasperated and asked for a statement off the top of my head. I refused saying I could give an exact comprehensive answer within the suggested time. Returning about 20 minutes later he was still unhappy but I was not going to guess when I could do better. You are right about the need for balancing and trade-offs.