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Data Quality Fundamentals

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Data Quality Management

Business Analytics & Intelligence

Data Quality Fundamentals

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

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2511 enrolled on this course

Last updated 11/2021

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₹ 3499.0

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Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

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 today’s data quality problems, there will be new and different challenges tomorrow. That’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.
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

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.

Requirements

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Data Quality

1.1-What is Data Quality?

1.2-Example of Data Quality

1.3-Can we achieve 100 % Data Quality?

1.4-What can be done to achieve 100% Data Quality?

1.5-How can we measure Data Quality?

2-Data Quality Dimensions

2.1-What are Data Quality Dimensions?

2.2-Consistency Data Quality Dimension

2.3-Completeness Data Quality Dimension

2.4-Timeliness Data Quality Dimension

2.5-Uniqueness Data Quality Dimension

2.6-Validity Data Quality Dimension

2.7-Accuracy Data Quality Dimension

2.8-Example of Data Quality Dimension

3-Data Quality Vs Data Governance

3.1-Data Quality Vs Data Governance

4-Data Life Cycle

4.1-Introduction to the End to End Data Life Cycle with a case study

4.2-Data Maintenance

4.3-Data Derivation

4.4-Data Usage

4.5-Data Publication

4.6-Data Archival

4.7-Data Purging

5-Data Quality Life Cycle

5.1-Data Quality Life Cycle

6-Data Profiling

6.1-What is Data Profiling?

6.2-Commonly used data types during Data Profiling

6.3-Data Profiling Vs Data Mining

6.4-What are the different types of Data Profiling?

7-Business Expectations and Impacts of Low Data Quality

7.1-Business Expectations on Data Quality

7.2-Impacts and Costs of Low Data Quality - Part 1

7.3-Impacts and Costs of Low Data Quality - Part 2

7.4-How to correct the existing errors in the Data Warehouse?

7.5-How does the Enhance, Transform and Calculate phase or the ETL phase help?

7.6-Data Standardization

7.7-Complete and Corrected Data

7.8-Match and Consolidate the Data

8-Data Quality Roles

8.1-Different Data Quality Roles in an Enterprise

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