What is Data Quality? - Definition & Importance

Instructor: David Gloag
Data Quality is an important area in today's world. It tells us how well we've accomplished something and whether we've done it the right way. In this lesson, we'll take a look at its various aspects, and uses.

Quality - An Underlying Theme

Quality is constantly on the tips of our tongues. We don't often think about it, but we apply it to everything. This is a quality car, that is a high quality handbag, or there is a quality establishment. We inherently do it, and there is an implied comparison. In other words, how does this item match up to those that are similar?

What is Quality?

Quality is a measure of how successfully something was made. From a more formal perspective, there are 2 basic aspects to quality: validation and verification. Validation deals with outputs, or results. The question this aspect is asking is, 'Have I created what I started out to create?' For example, if I started out to create a blue car, is that car indeed blue? Is it the right shade of blue? Is it the right car? Obviously, there are many more questions you could ask, but you get the idea.

Verification, on the other hand, deals with process. The question this aspect is asking is, 'Have I created, what I started out to create, in the correct fashion?' For example, if there were 20 steps required to make the blue car in the example above, were those steps performed? Were they performed in the correct sequence? Was each step executed correctly? As with the validation example, there are many more questions that could be asked.

How Does Quality Apply to Data?

If we apply these ideas to data, then data quality is a measure of how successfully the data can be used for its intended purpose. From the validation aspect, this is directly related to how accurate, current, and complete the data is. Usually, this is confirmed through comparison with expected values. From the verification aspect, it is directly related to how the data is gathered, ordered, analyzed, and stored. Or specifically, how successful each of these processes is. This is confirmed by examining the steps for each of these processes, and comparing them with expected values. In either case, the expected values would be determined from requirements, research, and/or prototypes.

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