Data Quality Fundamentals

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Sid Inf

2:46:47

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1 - Data Quality

1 - What is Data Quality.mp4

05:55

2 - Example of Data Quality.mp4

07:53

3 - Can we achieve 100 Data Quality.mp4

08:21

4 - What can be done to achieve 100 Data Quality.mp4

07:42

5 - How can we measure Data Quality.mp4

03:24

2 - Data Quality Dimensions

6 - What are Data Quality Dimensions.mp4

01:39

7 - Consistency Data Quality Dimension.mp4

02:26

8 - Completeness Data Quality Dimension.mp4

03:11

9 - Timeliness Data Quality Dimension.mp4

02:51

10 - Uniqueness Data Quality Dimension.mp4

01:52

11 - Validity Data Quality Dimension.mp4

01:31

12 - Accuracy Data Quality Dimension.mp4

01:18

13 - Example of Data Quality Dimension.mp4

02:16

3 - Data Quality Vs Data Governance

14 - Data Quality Vs Data Governance.mp4

03:41

4 - Data Life Cycle

15 - Introduction to the End to End Data Life Cycle with a case study.mp4

04:50

16 - Data Maintenance.mp4

01:53

17 - Data Derivation.mp4

02:30

18 - Data Usage.mp4

02:17

19 - Data Publication.mp4

02:18

20 - Data Archival.mp4

02:04

21 - Data Purging.mp4

01:53

5 - Data Quality Life Cycle

22 - Data Quality Life Cycle.mp4

10:56

6 - Data Profiling

23 - What is Data Profiling.mp4

04:26

24 - Commonly used data types during Data Profiling.mp4

07:58

25 - Data Profiling Vs Data Mining.mp4

01:39

26 - What are the different types of Data Profiling.mp4

06:04

7 - Business Expectations and Impacts of Low Data Quality

27 - Business Expectations on Data Quality.mp4

11:51

28 - Impacts and Costs of Low Data Quality Part 1.mp4

04:05

29 - Impacts and Costs of Low Data Quality Part 2.mp4

07:07

30 - How to correct the existing errors in the Data Warehouse.mp4

11:06

31 - How does the Enhance Transform and Calculate phase or the ETL phase help.mp4

07:57

32 - Data Standardization.mp4

06:52

33 - Complete and Corrected Data.mp4

05:56

34 - Match and Consolidate the Data.mp4

03:35

8 - Data Quality Roles

35 - Different Data Quality Roles in an Enterprise.mp4

05:30

Description

Understand key concepts, principles and terminology related to Data Quality.

What You'll Learn?

Determine data quality requirements by studying business functions, gathering information, evaluating output requirements and formats.
Profile select data sets to ensure quality and develop the data visualizations necessary to both manage and communicate data quality.
Coordinate business efforts to deliver data that is fit for use for use in critical processes, analysis and reports.
Collaborate with business application team to document information architecture requirements as needed
Serve as a subject matter expert and perform data quality related functions for urgent, high visibility, high profile, and strategic projects while meeting challenging deadlines.

Who is this for?

Data Scientists

Solution Architects

Big Data Developers/Administrator

Data Quality Consultants

Data Analysts

Data Stewards

Project Managers

ETL Developers

ETL Testers

What You Need to Know?

Basic understanding of Enterprise Data Management

Basic understanding of Data Warehouse Concepts

More details

Description
Data quality is not necessarily data that is devoid of errors. Incorrect data is only one part of the data quality equation. Managing data quality is a never ending process. Even if a company gets all the pieces in place to handle todayâ€™s data quality problems, there will be new and different challenges tomorrow. Thatâ€™s because business processes, customer expectations, source systems, and business rules all change continuously. To ensure high quality data, companies need to gain broad commitment to data quality management principles and develop processes and programs that reduce data defects over time.
Much like any other important endeavor, success in data quality depends on having the right people in the right jobs. This course helps you understand key concepts, principles and terminology related to data quality and other areas in data management.Â
Who this course is for:
Data Scientists
Solution Architects
Big Data Developers/Administrator
Data Quality Consultants
Data Analysts
Data Stewards
Project Managers
ETL Developers
ETL Testers

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Business Intelligence Consultant and Trainer with 14+ years of extensive work experience on various client engagements. Delivered many large data warehousing projects and trained numerous professionals on business intelligence technologies. Extensively worked on all facets of data warehousing including requirement gathering, gap analysis, database design, data integration, data modeling, enterprise reporting, data analytics, data quality, data visualization, OLAP. Has worked on broad range of business verticals and hold exceptional expertise on various ETL tools like Informatica Powercenter, SSIS, ODI and IDQ, Data Virtualization, DVO, MDM.

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