A decade after ICAEW first examined the potential of data analytics in external audit, find out how adoption has evolved across UK firms of different sizes. Explore the growing role of platforms, vendors, new delivery models, and emerging AI technologies as well as the practical barriers to adoption that remain.
ICAEW’s new report and accompanying webinar look at these themes.
Key takeaways
- Analytics is becoming embedded in audit workflows. Firms are moving away from standalone tools towards integrated platforms. that support analytics across the audit cycle.
- Firm size influences adoption but does not determine it. Larger firms often have more resources to build bespoke solutions, while mid-tier and smaller firms benefit from a growing vendor market and more accessible tools.
- Data quality remains a major barrier. Extracting, transforming and validating client data is still one of the most time-consuming and technically challenging parts of the analytics process.
- Methodology alignment is critical. Firms are more confident using analytics when tools are clearly linked to audit methodology and approach.
- Skills need to evolve. Auditors do not need to become data specialists, but they do need the confidence using tools, including AI, to understand when to use tools, how they work, and how to interpret results.
- Regulatory uncertainty can slow innovation. Many firms want more guidance and practical examples showing how analytics can be used to obtain sufficient appropriate audit evidence.
When ICAEW published its initial publication Data analytics for external auditors in 2016, the discussion largely focused on whether data analytics could transform audit. Now in 2026, the conversation has shifted towards how analytics can be embedded into audit platforms, methodologies, training programmes and regulatory frameworks.
While firms still face practical barriers around data quality, methodology alignment, and regulatory uncertainty, staying up to date with analytics and the evolving landscape of AI is vital. Understanding the risks and opportunities for analytics across the audit life cycle is important for firms, vendors, methodology providers, regulators and standard-setters.
What’s changed in the last decade?
Analytics have moved from being separate, optional activities to being embedded in audit platforms, workflows and methodologies. This means analytics are increasingly part of how audits are won and lost, planned and performed and can form a substantial part of the findings reported. Larger firms have advanced internal platforms whereas mid-tier and smaller firms are increasingly adopting analytics through third-party vendors, integrated audit platforms and tools.
A broader market for analytics
In 2016, analytics at scale was associated with the largest firms. 10 years on, a growing vendor market has made analytics tools more accessible to mid-tier and smaller firms. Solutions such as Inflo, MindBridge, Validis, DataSnipper, and Alteryx offer a range of capabilities that allow firms to adopt analytics without developing extensive internal systems.
New delivery models
Firms have adopted a range of approaches to deliver audit analytics. Some firms empower auditors directly through self-service tools, while others use specialist data teams to support engagement teams. In a self-service model, auditors use analytics tools directly and only bring in specialists for more complex work. In a two-team model, the audit team decides when analytics should be used, but data specialists usually handle data extraction, transformation and analysis before the audit team interprets the results. Hybrid approaches are also emerging, combining auditor experience with specialist skills.
Embedded workflows
The most significant development since 2016 is the integration of analytics into the audit process. Today, many larger firms have embedded analytics within audit platforms, allowing data to flow through the planning, execution, and completion stages. Larger firm examples include EY Helix within EY Canvas, KPMG Clara and Deloitte Omnia. Moore Kingston Smith's CARE platform is also an example of internally developed capabilities in the mid-tier. Rather than relying entirely on external specialists, the firm has developed a self-service analytics environment that allows audit teams to access and analyse client data directly.
Azets' approach, on the other hand, shows that firms do not need a proprietary platform to embed analytics into audits, as they use externally sourced tools such as Alteryx and Power BI to support audit procedures and data analysis.
Skills now needed
Technology and data skills have become increasingly important, but auditors are not expected to become data scientists. The key skills now needed are more practical: knowing when analytics should be used, understanding how tools are configured, interpreting outputs responsibly, and applying professional judgement to decide whether the results provide useful audit evidence. This is particularly important as analytics moves into risk assessment, journals testing, substantive procedures and AI-enabled audit work.
ICAEW is supporting this shift in several ways, for example, by recognising the role it plays in improving auditor training and awareness around analytics. This includes embedding data analytics software into the Next Generation ACA and introducing a Technology Learning Hub. ICAEW also offers practical learning resources such as the Analytics in Power BI Certificate, which helps finance professionals build skills in analysing trends, modelling data and producing clear reports. This means the skills gap is not just being left to individual firms to solve. ICAEW is helping students, members and firms build a more consistent foundation in data, analytics and technology.
What are the main challenges?
Data
Despite significant progress, data quality remains a major obstacle. Firms, especially on the smaller end, continue to face challenges with data extraction, completeness, consistency, and access. While a wide range of tools are becoming available to address this, better tools alone cannot solve the issue. During planning, auditors should assess the maturity of the client’s data management processes and infrastructure and establish early what data can or cannot be obtained.
Methodology and regulation
Methodology is important because firms need to show how analytics might fit into their audit approach and how the results provide appropriate audit evidence. If tools are not aligned with methodology, auditors may lack confidence in using them or may duplicate work through traditional testing.
Regulators and standard-setters are generally supportive of wider analytics adoption. Their concerns are more about how analytics is applied, how results are interpreted, and whether firms have documented the process and evidence properly.
Some firms are cautious because they worry about regulatory scrutiny, unclear guidance, and uncertainty over how analytics-based procedures interact with the requirements of auditing standards. This can lead firms to use analytics alongside conventional testing rather than replacing or reducing the extent of other procedures.
Looking ahead at AI’s impact on data analytics
AI is expected to accelerate the development of audit analytics. Many firms are exploring AI-enabled testing, automated data preparation and intelligent risk assessment, but AI should not be seen as a replacement for analytics. Instead, it is likely to enhance analytics by expanding what auditors can do, enabling them to analyse larger datasets and identify patterns, particularly in unstructured data.
What should stakeholders do to continue to improve usage of audit data analytics?
Calls to action
- Firms: Benefit from greater investment in upskilling, in tools, and by making bolder decisions about when to use analytics.
- Methodology providers: Prioritise developing work plans that better support and encourage the use of analytics, without introducing unnecessary burden.
- Regulators and standard setters: Provide better support by articulating and clarifying where and how current auditing standards support the use of analytics, and by sharing more specific, detailed examples of best practice.
- Software providers: Focus on meaningful engagement with all stakeholders to ensure that their software and products comply with the requirements of auditing standards and integrate well with other platforms.
- Professional bodies: Use central role among stakeholders, to represent and advocate for the needs of firms and members by facilitating debate about the three key barriers, and explore solutions to challenges around data quality.
Author
Yodahey Belete, intern at ICAEW with a background of study in Accounting and Finance.