DAY 1

Introduction to Data Analysis

This session will introduce the reader to the principles of information flow within organizations as well as data analytic methodologies and terminology. The focus will be on developing an understanding of where critical data exists for analysis, the obtaining of access and the selection of the appropriate analytical techniques.
We will examine the differences between a given set of data in the standard benchmark in terms of central tendency, variation and shape of curve. In addition we will examine the fundamental principles of Baysian probability theory. In general, this is a methodology used to trying to clarify the relationship between theory and evidence. It attempts to demonstrate how the probability that the theory is true is affected by a new piece of evidence. This can be critical to auditors in drawing conclusions about large populations based upon small samples drawn.

Understanding Sampling

This session will cover the fundamental assumptions underlying the use of sampling techniques, the nature of populations and the use of variables. Distribution frequencies and central tendency measurement will be covered as well as the impact on analysis of distribution characteristics. It will also examine the differences between judgmental and statistical sampling, the applicability of both in audit practice, and the dangers inherent in confusing the two.
The differences in selection methods will be covered as well as their impact on the analysis and interpretation possible within the sampling methods.

• Judgmental vs Statistical Sampling
• Probability theory in Data Analysis
• Types of Evidence
• Population Analysis
• Correlations and Regressions

DAY 2

Applying Differing Analytical Techniques

This session will examine typical CAATs in common use in the selection of the appropriate technique based upon the type of evidence and the audit objective. Included will be the dangers to the auditor inherent in pre-judgment of expected results and the subsequent distortion of the analysis based upon these pre-conceptions. It will also examine the audit methodologies for the analysis of Big data. Big data is a term given to large datasets containing a variety of data types. Big data analysis allows the auditor to seek for hidden patterns and identify concealed correlations, market trends and other data inter-relationships which can indicate areas for improved operational efficiencies within business processes.

Topics covered in this session include:

• Big Data Structures
• OLAP
• Statistical Analysis and Big Data
• Substantive Analytical Procedures
• Validation
• Questionnaire Analysis and Likert Scales
• Statistical Reliability Analysis

DAY 3