From the course: Data Quality: Core Concepts
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Data quality dimensions: Intro
From the course: Data Quality: Core Concepts
Data quality dimensions: Intro
- [Instructor] In previous videos, I hinted at there's these data quality dimensions that DAMA talk about. We're now going to be going over them in the next few videos at a time where I'll provide kind of a high level example of each one, as well as examples from my experience in my career, seeing these data quality issues pop up. DAMA defines these data quality dimensions as validity, completeness, consistency, integrity, timeliness, currency, reasonableness, uniqueness, and accuracy. Now, there are a whole bunch of other different aspects of data quality that are not included in this. There's so many different directions you can go, so many different ways you can define it. I like the DAMA ones 'cause it starts as a good foundation to start off with before you start exploring more. And this book is what people many times reference for when they're thinking about data governance strategy or data quality strategy. In the next set of videos, we'll go over three at a time, where you can…
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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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(Locked)
Data quality dimensions: Intro1m 1s
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(Locked)
DQ dimensions: Validity, completeness, consistency2m 36s
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(Locked)
DQ dimensions: Integrity, timeliness, currency2m 41s
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(Locked)
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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(Locked)
Thought exercise: Synthesize research and communicate ROI3m 15s
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