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| Date | Venue | Fee |
|---|---|---|
| 14-16 May | 18-20 June | The Protea Balalaika Hotel, Sandton, Johannesburg | ZAR: 11999.00 |
| Date | Venue | Fee |
|---|---|---|
| 14-16 May | 18-20 June | Online via ZOOM | ZAR: 8999.00 |
A practical hands-on approach to conducting Data Analytics has become a necessity since the spread of IT systems has made it a pre-requisite that auditors as well as management have the ability to examine high volumes of data and transaction in order to determine patterns and trends. In addition, the increasing need to continuously monitor and audit IT systems has created an imperative for the effective use of appropriate data mining tools.
While a variety of powerful tools are readily available today, the skills required to utilize such tools are not. Not only must the correct testing techniques be selected but the effective interpretation of outcomes presented by the software is essential in the drawing of appropriate conclusions based on the data analysis.
This means that the users of such tools must gain skills not only in the technical implementation of the software, but also in the understanding of structures and meanings of corporate data including the ability to determine the information requirements for the effective management of business
You will leave this course with an understanding of:
This session will introduce the delegate 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.
This session looks at specific characteristics of data that make it useful for decision making. It looks at:
In this session, you will learn to recognize the importance of making room for empirical enquiry in decision making. You will explore characteristics of an analytical mindset in business and accounting contexts, and link those to your core courses. You will then evaluate a framework for making data-driven decisions using big data.
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.
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:
This session will examine the techniques available to the auditor in order to identify the red flags and indicators that fraud may be occurring or may have occurred in the past as well as the obtaining of forensically acceptable data analytical evidence.
Topics covered in this session include:
This session will examine the various types of evidence available to the auditor in financial systems in order to evaluate both the adequacy and effectiveness of the system of internal controls. This will include the identification of population types and the division into sub-populations for analytic purposes. Differing collection types and evidence sources will also be identified.
Topics covered in this session include:
This session will cover the assessment of IT software in order to determine the appropriateness of use in evidentiary analysis as well as the techniques the auditor may use in order to obtain, extract and, if necessary, transform such data to facilitate analysis.
Topics covered in this session include:
With Sophisticated analysis, the audit department is frequently judged on the quality of its primary output, the Audit Report and this can affect the perception of the professionalism of the whole department. In many cases the effectiveness of the analysis can be affected by the non-acceptability of the Audit Report.
Topics covered in this session include: