Introductory to data quality with cookies and ultra marathons | iData

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Getting Started with Data Quality

Introduction - Alasdair Moore

What you'll learn in this course (0:50)
Introduction to iData Quality instructors (3:04)
iData Quality Academy Slack Group

Importance of data quality - Jordan Morrow

Introductory to data quality with cookies and ultra marathons (3:18)
Data quality - defined (3:02)
Data quality and data literacy (3:54)

Data profiling - George Firican

Introduction (0:23)
Data profiling defined
What is data profiling? (2:25)
Data profiling techniques (1:00)
Column profiling example #1 (2:44)
Column profiling example #2 (4:41)
Column profiling example #3 (1:43)
Table profiling example (2:20)
Cross-table profiling example (3:17)
Conclusion (0:59)

Data profiling - Stuart Reyner

iData demo - data profiling (5:29)

Data preparation - Kevin Jackson & Stuart Reyner

Introduction to Kevin (0:29)
Introduction to data preparation (2:08)
Data preparation context (1:09)
Reformatting (3:50)
Reformatting with iData (5:40)
Reformatting continued (1:25)
Data cleansing (5:25)
Cleansing with iData (3:36)
Data cleansing continued (1:28)
Cleansing names and addresses with iData (4:53)
Data cleansing - standardizing values (1:25)
Data cleansing wrap up (0:32)
Introduction to data deduplication (1:19)
Identify duplicate data (2:18)
Data deduplication with iData (2:29)
Data deduplication wrap up (1:39)
Conclusion (1:45)
Thank you (0:30)

Data transformation & assurance - T. Scott Clendaniel

Introduction (0:12)
Scenario for ETL (1:34)
Big data - ETL (2:12)
Impact of bad ETL (3:23)
ETL process overview (7:33)
Data quality assurance with ETL (3:42)
ETL best practices (2:21)

Data quality impact on AI & ML - Susan Walsh

Introduction (1:18)
How AI & machine learning works (2:39)
Consequences of bad data on AI & machine learning (8:40)
How to check data for errors (3:23)
Conclusion (2:28)

Data obfuscation - Kate Strachnyi & Stuart Reyner

Introduction (0:50)
Data obfuscation techniques (1:11)
Data obfuscation best practices (1:58)
Benefits of data obfuscation (2:33)
Challenges of data obfuscation (1:42)
Need for data obfuscation (1:44)
iData demo - data obfuscation (3:20)
Conclusion (0:26)

The cost of poor data quality - George Firican

Introduction (1:01)
Data is the new oil (2:17)
How important is data quality - real-life examples (6:45)
How costly is bad data quality (3:55)
Calculating the cost of bad data quality (4:27)
Conclusion (0:36)

Relationship between data quality & data visualization - Kate Strachnyi

Introduction (1:02)
Relationship between data quality & data visualization (1:00)
Methods of using data visualization to uncover poor data quality (1:16)
An example of using data visualization for identifying data quality (1:20)
Conclusion (0:32)

Selling data quality to the business - Scott Taylor

Introduction (2:02)
What the business cares about & truth before meaning (3:18)
The golden rule of data (1:04)
The 5 pillars of master data (3:28)
Data quality is macro-trend agnostic (1:09)
Align data quality with the business strategy (0:28)
If I had one minute with your CEO (0:58)
Ways to talk about data in business terms (3:21)
Conclusion (1:49)

Conclusion - James Briers

Congratulations (0:56)
Feedback
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Introductory to data quality with cookies and ultra marathons

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