Data analytics is becoming a cornerstone of the audit profession and data-enabled audit techniques are growing in prominence.

As artificial intelligence (AI) garners more of the headlines, data capabilities are often overlooked, but to fully capitalise on the potential of AI, auditors must first develop key data skills, according to ICAEW’s Head of Data Analytics and Tech, Ian Pay.

“AI will undoubtedly bring seismic changes to the audit profession, but there are still obstacles to overcome,” says Pay. “Many auditors are cautious about AI, particularly generative AI, due to concerns about its output consistency and risks, such as hallucinations.”

However, Pay adds that more traditional AI capabilities, such as machine learning, may offer far greater potential. “These techniques are tried and tested in their ability to support areas such as anomaly detection, and data analytics will play a pivotal role in driving these changes.”

Avoiding common pitfalls in data analytics adoption

Auditors can face several challenges when adopting data analytics tools. Underestimating the importance of obtaining high-quality data from clients can be one such issue, says Pay. Another is failing to secure client buy-in from the start, which can create resistance later in the process.

Poor planning and last-minute implementation also frequently cause problems, as does relying too heavily on tools without adequate human oversight. Additionally, auditors sometimes expect immediate results without recognising that effective implementation takes time and requires patience, often over multiple audit cycles.

To navigate these challenges, firms should adopt a phased, strategic approach when introducing new tools. This means starting with simpler cases or lower-stakes audits to ensure successful implementation, before moving on to more complex situations.

Pay recommends trialling data analytics solutions on clients based on proper risk profiling, considering the complexity of the client’s IT systems, their amenability and strength of relationship, and always testing out the approach well in advance of the audit year-end.

The five essential skills

As firms incorporate data analytics into their audit processes, auditors need to develop five key competencies to maximise its effectiveness:

1. Basic data literacy: An understanding of fundamental data principles and how to work with data effectively.
2. Data acquisition: The ability to collect complete, accurate and reliable data from clients – a skill often undervalued.
3. Data quality assessment: The ability to recognise good versus bad data and to identify quality issues early, which will prevent delays in audits.
4. Data ethics: Ensuring integrity, objectivity and confidentiality when handling data to foster trust in analytics-driven audits.
5. Awareness of the ‘art of the possible’: An understanding of the potential of data analytics, even if auditors lack the technical expertise to develop the solutions themselves. This is where collaboration with specialists can bridge the gap.

Technology and audit standards

Looking ahead, auditors should monitor developments in technology-related auditing standards. “The International Auditing and Assurance Standards Board (IAASB) is increasingly engaging with audit technology, suggesting shifts in regulatory expectations. Firms that embrace technology proactively will be better positioned to adapt,” Pay says.

Smaller firms and individual practitioners may assume that adopting data analytics requires expensive software or advanced coding skills. “In reality, many effective tools are available at little or no cost. Excel with Power Query is a robust solution when used well, and open-source, low-code platforms such as KNIME provide an accessible entry point for more advanced capabilities without programming expertise.

“By leveraging these tools, smaller firms can enhance audit quality and efficiency without significant financial investment,” Pay explains.