How confident are you in the quality of the data you’re using for your audits? How would you know if the data is accurate and complete, even record by record and field by field?
Data analytics can help auditors examine entire populations, identify unusual transactions, and uncover risks that traditional testing might miss. But even the most sophisticated analysis can produce misleading results when the underlying data is incomplete, inaccurate, improperly formatted, or otherwise unreliable. Issues could exist at the source system level or be introduced during extraction, conversion, transmission, and import. Something as simple as a missing leading zero, an incorrectly defined date field, or a shifted column can affect analytical results—and potentially lead auditors to the wrong conclusions.
In this practical, two-hour session, Dave Coderre and Paul Chaney will explore the importance of data quality and demonstrate techniques for assessing both the overall integrity of a data file and the quality of individual data fields. Using real-world examples and Arbutus Analyzer, they will examine common problems involving numeric, date, and character fields, including invalid values, missing information, inconsistent formats, and unusual patterns.
Learning objectives
By the end of this session, participants will be able to:
- Identify common data quality attributes and explain how poor-quality data can affect audit conclusions and management
decisions. - Recognize potential sources of data quality problems during data entry, extraction, conversion, transmission, and import.
- Evaluate the completeness, relevance, and structural integrity of data files using record counts, control totals, and
meta data. - Apply Arbutus Analyzer techniques to identify invalid values, missing data, inconsistent formats, and unusual patterns in
numeric, date, and character fields. - Assess data quality exceptions and determine appropriate follow-up actions to support reliable audit testing.
Field of Study: Computer Software & Applications (2.0 CPE) ~ Program Knowledge: Intermediate


Day Sales Outstanding with Python (1 CPE) 
