Data Quality

Description

This class introduces common data quality issues, including missing, incomplete, duplicated, inconsistent, and inaccurate data. Understanding data quality is important because analytical results are only useful when the underlying data appropriately represent the activities being analyzed.

Motivation

Can we rely on the raw data we have been given?

Review

The prior class introduced how accountants use data to answer questions and support decisions. This class introduces an important first step in that process by asking whether the data available for analysis are appropriate and reliable.

Preparation

• Review the data quality and ETL concepts introduced in Class 1.

Class plan

• We will review the prior class and continue to discuss data quality and how it impacts the ETL process conceptually.
• We will work in class on short exercises to identify data quality concerns and errors.
• We will discuss how modern systems aim to avoid data quality issues, and how to solve unavoidable data quality issues as part of the ETL process.

Concepts

• Data types
• Data Analysis Errors
• Validation and reconciliation

Data

Data will be delivered in class using git clone, the data can then be extracted from ACCTG320/data_quality.