Data Quality Fundamentals - Udemy

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.
Requirements
Basic understanding of Enterprise Data Management
Basic understanding of Data Warehouse Concepts
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.
Overview
Section 1: Data Quality
Lecture 1 What is Data Quality?
Lecture 2 Example of Data Quality
Lecture 3 Can we achieve 100 % Data Quality?
Lecture 4 What can be done to achieve 100% Data Quality?
Lecture 5 How can we measure Data Quality?
Section 2: Data Quality Dimensions
Lecture 6 What are Data Quality Dimensions?
Lecture 7 Consistency Data Quality Dimension
Lecture 8 Completeness Data Quality Dimension
Lecture 9 Timeliness Data Quality Dimension
Lecture 10 Uniqueness Data Quality Dimension
Lecture 11 Validity Data Quality Dimension
Lecture 12 Accuracy Data Quality Dimension
Lecture 13 Example of Data Quality Dimension
Section 3: Data Quality Vs Data Governance
Lecture 14 Data Quality Vs Data Governance
Section 4: Data Life Cycle
Lecture 15 Introduction to the End to End Data Life Cycle with a case study
Lecture 16 Data Maintenance
Lecture 17 Data Derivation
Lecture 18 Data Usage
Lecture 19 Data Publication
Lecture 20 Data Archival
Lecture 21 Data Purging
Section 5: Data Quality Life Cycle
Lecture 22 Data Quality Life Cycle
Section 6: Data Profiling
Lecture 23 What is Data Profiling?
Lecture 24 Commonly used data types during Data Profiling
Lecture 25 Data Profiling Vs Data Mining
Lecture 26 What are the different types of Data Profiling?
Section 7: Business Expectations and Impacts of Low Data Quality
Lecture 27 Business Expectations on Data Quality
Lecture 28 Impacts and Costs of Low Data Quality - Part 1
Lecture 29 Impacts and Costs of Low Data Quality - Part 2
Lecture 30 How to correct the existing errors in the Data Warehouse?
Lecture 31 How does the Enhance, Transform and Calculate phase or the ETL phase help?
Lecture 32 Data Standardization
Lecture 33 Complete and Corrected Data
Lecture 34 Match and Consolidate the Data
Section 8: Data Quality Roles
Lecture 35 Different Data Quality Roles in an Enterprise
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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https://www.udemy.com/course/data-quality-fundamentals/

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