From the course: Data Quality: Core Concepts
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Defining data quality
From the course: Data Quality: Core Concepts
Defining data quality
- [Mark] In the previous video, we talked about the impact of data quality. And now that we know why it's so important to account for, what exactly is data quality? And this is actually a really hard thing to define. If you think about it, data quality's been around for centuries. Since people recorded data, there has been data quality. And there's been decades from the beginning of computers and all that research in the seventies, people thinking about data quality. It's a huge ask to come up with a single term. And actually, I wrote a book on data quality, and I spent months going through literature trying to figure out a correct way to define it. And so I'm going to provide you three different definitions that I like, and then from there go into what to consider for data quality in defining it. And so these are the three kind of pieces of literature that I'm going to be pulling from. "Data Quality Fundamentals" by Barr Moses, Lior Gavish, and Molly Vorwerck. Another book, "Data…
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Contents
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Data quality introduction13s
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Impact of poor data quality3m 40s
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(Locked)
Defining data quality4m 48s
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Data quality dimensions: Intro1m 1s
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DQ dimensions: Validity, completeness, consistency2m 36s
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DQ dimensions: Integrity, timeliness, currency2m 41s
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DQ dimensions: Reasonableness, uniqueness, accuracy3m
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Common data quality assessment frameworks2m 26s
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Connecting data quality to business outcomes: Intro1m 22s
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Thought exercise: Ecommerce41s
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Thought exercise: Understand the business model44s
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Thought exercise: Map your data lifecycle1m 32s
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Thought exercise: Identify your stakeholders1m 12s
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Thought exercise: Evaluate how stakeholders drive revenue41s
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Thought exercise: Assess how DQ impacts revenue and risk48s
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Thought exercise: Synthesize research and communicate ROI3m 15s
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