Data analytics and visualizations tools: A primer for your digital audit transformation

It’s become common expectation for audit practitioners to leverage technology and analytics to deliver a more innovative audit experience. Gone are the days of manual calculations and tick-marking hard-copy documents – if not completely extinct, at least in the critically endangered stage of its lifecycle. Data analytics and visualization technologies can be used to bring more value and insight to the audit by enhancing risk assessments, enabling more thorough analysis of bigger data sets and reducing inefficiencies.

There are many different data analytics, visualization and automation tools in the market. The volume of tools and technologies can be overwhelming when trying to understand what is out there, what should be on your radar as an auditor and where you or your organization can start. While this article does not cover every tool that exists, it is intended to provide a primer on some of the tools being used in practice.

Read this blog to learn:

• the common types of software used for audit data analytics
• tips for getting started with ad-hoc data analytics 

*Information about the tools presented in this article is based on the experiences of the author and is not intended to be an endorsement of any tools by CPA Canada.

Audit Data Analytics and Automated Tools & Techniques

Audit data analytics (ADA), which use automated tools and techniques, can quickly get complex; knowing where to start to choose the right tools to design and deploy ADAs is an important first step in any CPA’s digital audit journey. Automated tools and techniques can range in complexity from the basic use of spreadsheet software such as Microsoft Excel for simple calculations, all the way to using advanced machine learning (ML) and artificial intelligence (AI) models to automate business processes. Choosing the right analytics tool for the job depends on the type of automation and analytics you are looking to perform. The more unique and customized the business requirements are for the tool, likely the more complex the level of technical expertise required from the user (or the builder) of that tool. 

To read more about ADAs and automated tools and techniques, check out these resources:

• Resources for using audit data analytics
• Recent guidance on using automated tools and techniques in the audit

Common types of software used for ADAs

Audit documentation and off-the-shelf ADAs

You will likely have heard of audit documentation software or have already been using one system or another for many years. Examples such as Caseware, TeamMate (Wolters Kluwer) and Inflo have been around for a while. Over time, most audit documentation software has advanced to incorporate automation of audit activities including documentation, calculations, sampling, different types of analytics (e.g., statistical analysis, pattern and outlier analysis, trend analysis, benchmarking) and visualization.

A more recent example of software designed for use by accountants and auditors for financial transaction analysis and fraud risk analytics is MindBridge AI. This type of software comes equipped with canned analytics specific to accounting transactions that are designed to be used without the need to write any complex programming.

Choosing the right software for the job depends on the capabilities of the tool, how you want the tool to integrate with your current suite of audit applications and, realistically, how much you are looking to invest.

Ad-hoc analytics

There are often opportunities to create automations or analytics that are very specific to a client, a unique business process, or a unique data set that the above types of audit analytics software are not easily equipped to adapt to. These are the ad-hoc types of analytics that need to be designed and built from the ground up. While that may sound daunting, and it very often can be, there are several low-code and no-code options available that can be used for these very types of analysis.

What are low-code or no-code data analytics and automation platforms? These are software tools that allow anyone to create data analytics, visualizations and automations without having to write time-consuming and complex technical code (or, for low code, relying on a basic level of coding knowledge). These are the types of accessible platforms that auditors can leverage for creating ADAs from scratch without having to hold a degree in programming or data science (although one certainly couldn’t hurt). Below are a few examples of low or no code software audit practitioners in Canada are currently using.

Microsoft Power Platform

Power BI Desktop, + more

Power BI (Business Intelligence) is a business analytics service that delivers insights for analyzing data. It can share those insights through data visualizations which are comprised of reports and dashboards to enable fast, informed decisions.

Potential uses: Automate business intelligence, import large data sets, real-time data analysis, data transformation, data collaboration, and no-code data visualization. Power BI uses its own programming language for more complex functions known as Data Analysis Expressions (DAX).

Tableau (Salesforce) Platform

Tableau Prep Builder, Tableau Desktop, + more

Tableau Prep and Tableau Desktop allow users to combine, clean, and visualize data. It can be used to quickly build powerful calculations from existing data, ask questions, and perform analysis. Tableau Prep enables the extraction, combining and cleaning process for data without writing code.

Potential uses: Automate business intelligence, importing large data sets, real-time data analysis, data transformation, data collaboration, and no-code data visualization. Tableau uses its own coding language, VisQL, which visually expresses data by translating drag and drop actions into data queries through an easy-to-use interface.

Alteryx Platform

Alteryx Designer

Alteryx Designer is an end-to-end data analytics platform that allows users to prepare, blend and analyze data in a drag and drop user interface.

Potential uses: Automate analytics, including data preparation, blending, reporting, and predictive analytics, and manage importing large data sets through a self-service platform using 250+ code-free tools.

Caveats to the above

The above only scratches the surface of the low-code and no-code applications in the market. There are Robotics Process Automation tools (e.g., UI Path), Process Mining tools (e.g., Celonis, Blue Prism), AI and ML applications. There’s a long list, and it’s changing every day with the advent of new technologies (hello, Chat GPT).

Tips for getting started with ad-hoc data analytics

Here are some quick lessons learned from using tools such as Alteryx, Power BI and Tableau:

• Start small – Find one use case to start with; solve a pain-point or automate a repeatable task or analysis.
• Transformation is a marathon, not a sprint – Don’t plan for these tools to be your silver bullet; it takes time to build up expertise and success. Learn from failed attempts and build on successful ones.
• Your plan will change – Often, when we get into the data, we find problems. Data clean-up can be 90 per cent of the battle when building data analytics and automation tools. Be adaptable to change in the planning, design and execution of your tool to account for these unknowns.
• Don’t forget quality – Build in documentation and quality control upfront. Nothing is worse than an amazing tool that can’t be used because it’s too complicated, not well-documented, and/or hasn’t been properly tested.
• There’s a 99 per cent chance you can Google it – Chances are that someone has done the same analysis or automation you are looking to create and has written a blog post about it, written it in a forum, or made a YouTube tutorial on it. There is a huge online community that makes using these tools much more accessible to the average user. 

Disclaimer

The views and opinions expressed in this article are those of the author and do not necessarily reflect that of CPA Canada.