5 September 2017

Auditing and data analytics: A winning combination with huge benefits

Our new data analytics director, Martin Langosch, discusses how data analytics provides a huge benefit compared to the traditional method of auditing.

For years, auditors have been using office tools, such as spreadsheets and word processing software, to support and possibly automate typically complex processes and tasks. However, this only touches the surface. As such, within the audit profession, Computer Assisted Auditing Techniques (CAAT) are becoming increasingly popular. More and more auditors are now making use of data analytics and business intelligence tools to be able to analyse and visualize larger amounts of data. Gone are the days where naked figures alone are considered to be sufficient. We’ve moved on to integrating colourful charts and plots to show, for example, year-to-date statistics or spot outliers in data.

Data analytics vs. traditional auditing

Integrating data analytics has a huge benefit compared to the traditional method of auditing. Rather than using just a small sample of the available data, with data analytics you can get insights by looking at the entire population. Many standard tests, such as checking for duplicates in invoice numbers or outliers in employee claim amounts, cannot provide enough assurance when we look into only samples. This is where data analytics provides real benefit. Certain steps can be automated and the time to perform single tests can be drastically decreased, allowing the auditor to perform much more analysis in the same period of time.

How do we see data analytics becoming a key element in audit?

In our opinion, there are a couple of approaches. The introduction of dedicated, user-friendly CAAT software is definitely a step in the right direction. There are a lot of vendors now who tackle specific challenges arising during an audit process and this software allows a tailored, accurate review. What’s more, users can be trained quickly; the software is very intuitive and there’s usually no coding required.

The other approach is using statistical and analytical software, such as R and Python, for data analysis. These tools are freely available and contain all the functionality you might ever need. Although coding is required and the learning curve can be quite steep, we can help you with that.

We find a combination of both of these will deliver the best outcome. However, whatever approach you decide to take, both CAAT and analytical programming languages need to be able to complete the following tasks:

  • Data import – raw data can be available in many formats – you need to ensure the tools you use can handle them.
  • Data cleansing – data quality is often an issue and clean data is crucial for analysis. This step often highlights general issues that need to be addressed.
  • Merging and matching – when looking into a combination of data sources, we often gain better insights by looking at the bigger picture.
  • Summaries and aggregations – simple summaries and aggregations of columns can give us an indication of which area to focus on.
  • Analysis – additional findings can be collected by applying statistical analysis, for example frequency analysis or Benford’s law.
  • Data export – in order to provide data to others for in-depth analysis we often need to use sampling. Exporting the data in different formats will increase usability and acceptance.
  • Reporting and audit – we can create meaningful reports quickly, saving all audit steps to be reviewed and approved, in order to give better assurance.

Our recommendation

If you would like to find out more about how data analytics can benefit your business, please do not hesitate to reach out to our data analytics director, Martin Langosch.  

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