The Total Data Quality Framework

University of Michigan via Coursera

Go to Course: https://www.coursera.org/learn/the-total-data-quality-framework

Introduction

Identify the essential differences between designed and gathered data.

Summarize the key dimensions of the Total Data Quality (TDQ) Framework.

Describe why data analysis defines an important dimension of the Total Data Quality framework.

Define the three measurement dimensions of the Total Data Quality framework.

Syllabus

Introduction, Different Types of Data and the Total Data Quality Framework

Welcome to the Total Data Quality Framework Course! This is the first course in the Total Data Quality Specialization. This week, you’ll get to know your instructors after reviewing the course syllabus and the learning goals. We will then introduce you to the basic components of the Total Data Quality (TDQ) Framework through a series of video lectures, including Designed Data, Gathered Data, and Hybrid Data. Next, we’ll provide a high-level overview of the TDQ Framework and incorporate the perspectives of global TDQ experts in both a lecture and an interview. We’ll then wrap up the week with a short quiz about measurement and representation concepts.

Measurement Dimensions of Total Data Quality: Validity, Data Origin, and Data Processing

In Week 2, we’ll explore the concepts of validity, data origin, and data processing. First, we’ll define validity and discuss threats to validity for designed data and gathered data. We’ll also explore validity through an interview, a real-world application, and a case study. After taking a short quiz to test your knowledge of validity, you’ll then move to the data origin module. We’ll define data processing and explore data origin threats for designed and gathered data through a series of video lectures and case studies. The data processing module will conclude with a short quiz. Week 2 will conclude with an exploration of data processing; data processing threats for designed and gathered data; case studies; and a quiz to check your understanding of data processing.

Representation Dimensions of Total Data Quality: Data Access, Data Source, and Data Missingness

This week, we’ll be exploring three representation dimensions of the TDQ framework along with potential threats to data quality. First, we’ll define and discuss data access - as well as data access threats for gathered and designed data - through a series of video lectures, readings, and case studies. After you complete a quiz on data access, we’ll then define data sources and explore data threats for designed and gathered data, along with two case studies. Lastly, we’ll define data missingness along with data missingness threats for designed and gathered data, and then conclude the week with a quiz.

Data Analysis as an Important Aspect of TDQ

We’ll be wrapping up the Total Data Quality Framework course this week. We’ll be discussing why data analysis is a critical dimension of the TDQ framework and threats to data analysis quality for designed and gathered data. You’ll also be reviewing several case studies and will be able to complete an optional tutorial using free R software. After a short quiz on data analysis threats, we’ll conclude the course with a list of references from across Course 1 and we’ll ask you to complete a course survey.