What is the current state and future trends of the audit clients regarding digitization and data analytics? Impact on the audit?

Digitization is two things: Automation; i.e. robots, computers, etc. taking over manual jobs performed by people. Second; the use of Big Data; i.e. access to enormous amount of data that may be used in analysis and decision-making.
However, research and audit experience show that the use of robotics and data analytics is not advanced in the accounting departments. Often, accounting staff and controllers work in subsidiaries on their own systems or own versions in the main system, and create their own reports and accounts, and upload numbers to consolidation reports to the group more or less manually. There are still a lot of manual reconciliation tasks, calculations in Excel spreadsheets, 'clip and paste' etc.
We expect however many changes in this area in the future. This will have a major impact for auditing.

In the course, we will review the status of the clients. Consultants will teach their experience. The students will learn to build their own little robot that can automatically perform reconciliation. The students will see artificial intelligence and machine learning in practice in the field of accounting.

How to obtain data, store and 'clean' data in a proper manner for use in audit?

In the new audit world, the auditor will extract and analyze large amounts of customer data and external data. However, it is important for data to be handled correctly in order not to distort the conclusion. The students must be able to ask questions e.g.: How do SQL databases work? Are data received complete, accurate and not manipulated? How to extract and store large quantities of data by using SAP Connector, Hadoop or similar tools? How to clean 'dirty' data without destroying key inputs?

Implementation of data analysis.
Today's audit is often based on sampling techniques and manual reconciliations, reperformance, recalculations etc.

Future audit is expected to test 100% of the data. Auditor's computer systems download all relevant data in a selected area and perform testing of all of these data.
Data analysis means using statistical methods such as regression and decision trees. Through case assignments, students learn how to use and interpret tools like Excel, 'R' and Tableau.

Most important is to find patterns in data, risk areas, outliers, develop hypotheses and draw the right conclusions. This is the key elements of part three.

But is it audit evidence?
Audit standards today are one partly suited for a new, digital world of auditing. Supervisory authorities seems somewhat skeptical to new methods. This means that audit firms must prove that new methods are (at least) as good evidence as today's methods. This may cause audit firms to be reluctant to implement more effective and equally good methodology.

Students must understand whether the conclusions from data analytics may be used as audit evidence. This is discussed based on theory and practical cases.