Great audit methodologies can enhance your data analytics
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Great audit methodologies can enhance your data analytics

2 months ago · 2 min read · AICPA Insights Blog

Data analytics is big business. This is nothing new in the accounting and finance world but applying data analytics within an audit is an entirely different game.

In this insightful webcast, AICPA® employee and data analytics expert Jimmy Young detailed the value of a data analytics focused audit in the one-off webcast, Data Analytics in Financial Statement Audits.

Here are some key takeaways from that presentation:

Testing complete sets of data – not samples— can improve your audit

A deep dive of data, from recent contracts right to the nuances of each transaction, will typically deliver greater results — and potentially unearth some hidden anomalies.

Always earmark anomalies and trends within your data

The more transactions a company experiences, the more likely it is that key trends and patterns will go unnoticed.

Audit-relevant data identifies the trends and risks that matter.

Having a clear identification of trends and anomalies can elevate the data analytics drive and boost the investigative effort of the process.

A good audit should be backed up by the correct data modelling so the processes and controls are continually improved.

When combined with traditional auditing techniques, data analytics allows you to better understand your clients

Audit data analytics methods go beyond traditional analysis.

They assess individual and overall risk by analyzing data to discover patterns, correlations, and fluctuations from models that tech programs may not be able to do on their own.

Such methods give auditors fresh insights and improve the quality of the analytical procedures in each stage of the audit.

Tools and technologies— Power BI, IDEA and more — can bolster your data analytics audit

While audit data analytics can discover anomalies and patterns to unearth valuable nuggets of information, a data analytics audit boosts the overall quality of the process, thus increasing its efficiency and cost.

The results don’t only depend on one particular person. An auditor will often use advanced tools, such as Power BI.

Technologies allow audit procedures (bank confirmations, journal-entry testing, etc.) to be outsourced to remote teams of experts and third-party providers. This allows auditors to focus on other areas of the data, including higher-risk areas of the company.

Identifying critical trends and patterns in data is now critical to company growth

Data is big business and is changing how companies generate revenue.

Young emphasizes why unique and novel ways of identifying data with the help of an expert audit can unleash a goldmine of future sales, from customer behaviors to macroeconomic shifts in emerging markets.

Ultimately, these discoveries will boost the quality of a company’s products and services. However, this requires a carefully crafted on-premises system or a subscription to a data analytics-focused service.

To succeed in this growing space, developing the necessary know-how is key

The No. 1 goal in any data analytics audit is to obtain a thorough business understanding of the company so its mid-to-long-term objectives can be met.

The correct ADA guidance (the sources of data used, the steps taken to access the data, etc.) knowledge on top of a rudimentary understanding of some of the many 230 standard AU-C documents is vital.

Competing in this field requires, at the very least, an entry-level understanding of data analytics auditing and a respect for how data analytics has revolutionized sales and marketing.

Take the next step with the AICPA’s Guide to Data Analytics, a comprehensive overview for beginners.

It’s just one of many products designed to accelerate your understanding of this critical field and guide your ascent to data analytics nirvana.

Part two of this intriguing, profession-leading webcast can be found here.

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