Blog: Claudia Imhoff

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Data Quality or Data Integration - which is more difficult?

I read an interesting article in the Business Intelligence Pipeline Newsletter recently asking which was the more difficult challenge - assuring data quality or integrating data from across your organization. They have a voting booth set up so you can cast your vote for which you believe is the more difficult task. I have my own opinion as well.

I voted for assuring data quality and, at the time of my vote, it appeared that the majority of voters agreed with me. Why? In my opinion, it is because of the assuring part of the task.

Data integration seems to be a much more straightforward task with more mature technologies, methodology, and practical expertise in the data integrators. Even the definition of data integration seems to be cut and dried. (Not always but at least you have a solid standard to go from -- a single version of the truth...)

I think we are still feeling our way through what it means to assure data quality. While there certainly is useful technology to help with data quality, so much of the assurance part is still heavily dependent on the human being (in this case, usually a business person)eyeballing the cleaned up data to verify its "quality". There don't seem to be very clear, standard methodologies or processes to follow either. And what are the metrics of quality? When to we reach a state of "quality"? And what exactly does quality data even mean?

Without answers to these fundamental questions, it seems to me that we will continue to struggle with this challenge more so than with that faced by data integrators.

Your thoughts?

Yours in BI success,

Claudia

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