Data Quality Fundamentals

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