A Guide to Data Analytics in Internal Audit

Internal auditors rely on data and analysis as the cornerstones of their workflow. And combining these two supercharges the internal audit process with more efficiency, accuracy, and results that inform good decision-making. Learn more about internal audit data analytics, how to implement data analytics in internal audit, and the benefits that data analytics bring to your personal career and professional responsibilities. 

Summary 

Internal audit data analytics leverages software, AI, and systematic data processing to replace manual testing with automated procedures, providing 100% population coverage and enhanced risk assessment while requiring strategic investments in individual upskilling and organizational technology. 

What Are Internal Audit Data Analytics? 

Data analytics in internal audit, also referred to as audit analytics, are audit tests performed using data and software instead of traditional manual testing. Rather than reviewing evidence in physical form, internal auditors process their data digitally, ideally, through automated procedures. This means that manual investigation is only required for outliers or potential exceptions. 

These analytics involve the systematic collection, processing, and analysis of large amounts of company data to identify trends, patterns, and anomalies that support audit decision-making, risk assessment, and control evaluation. The process relies on data drawn from diverse sources, including: 

Structured data: Organized information like financial transactions, inventory logs, and employee records. 

Unstructured data: Textual or multimedia content lacking a predefined format, such as contracts, emails, and reports. 

Semi-structured data: Formats like XML or JSON files. 

Internal auditors rely on accounting software to help automate and streamline their analyses, removing oversights and added time so they can focus on tracking down more complex patterns and getting to the bottom of anomalies. Advanced functions also leverage artificial intelligence (AI) and machine learning to analyze vast datasets simultaneously, identifying patterns and risks that are otherwise invisible without big data analyses. 

Why Are Internal Audit Data Analytics Important? 

All professions are increasingly reliant on technology to improve their efficiency and accuracy. Internal audit is no exception, where traditional audit practices fall short of assessing large business data that itself often depends heavily on advanced software. 

As an internal auditor, your ability to effectively leverage data analytics is vital to your individual audit performance and general career growth. Expert use of data analytics in internal audit leads to advantages like: 

• Efficiency: Analytics can be far more efficient than manual sampling. Once initial analytics are created, they can be automated to run continually with minimal manual effort. Rather than spending your time on these repetitive tasks, you can focus on the higher-level application of findings.
• Effectiveness and Total Coverage: Unlike traditional sampling, which has inherent sampling errors, data analytics can provide 100% coverage of a population. This provides a much higher level of assurance and a more comprehensive understanding of operations.
• Enhanced Risk Assessment and Fraud Detection: By analyzing historical patterns, auditors can prioritize efforts based on high-risk areas or control weaknesses. AI-driven tools can specifically flag suspicious behaviors and irregularities in real-time, leading to more timely fraud mitigation.
• Expanded Audit Coverage: Because analytics increase efficiency, they free up your time of meticulous and repetitive manual tasks. This allows you to broaden your scope and address more business processes and auditable entities.
• Meeting Regulator Expectations: Regulators increasingly expect audit functions to use data analytics, as it demonstrates proactivity and competence in managing compliance and risk. 
   

How to Implement Data Analytics in Internal Audit 

Consider implementing data analytics as a process of maturing—in the context of both your professional capabilities and as a team or company. 

For you as an internal auditor, it will require investment in training and education, even certification. Consider earning your Certified Internal Auditor (CIA) credential, through which you’ll learn the basics of data analytics (and gain many other career-boosting advantages). Likewise, you can leverage your continuing professional education (CPE) in data analytics courses that help you specialize and hone your expertise in the area. 

For company and firm-wide aptitude in data analytics, implementation requires a strategic investment in people, processes, and technology.

1. People and Culture 

• Train at the Top: Success requires the support of audit executives who can drive methodology changes and provide necessary authority.
• Establish a Dedicated Team: This may include members like an internal audit data analytics leader, a specialized team of auditors with IT/data aptitudes, and cross-functional colleagues that can promote excitement and adoption across other teams.
• Secure Investment: Create a strong business case, including examples of implementation and its benefits, to articulate the value to leadership and secure access to data sources across the organization.
   

2. Processes and Strategy 

• Start Small: Instead of a massive overhaul, start with small pilot projects in specific audit areas to test, learn, and build confidence.
• Embed Analytics into the Audit Life Cycle: Analytics should be integrated into every stage, from annual planning (identifying trends for risk ratings) to fieldwork (executing automated procedures) and reporting.
• Data Governance: Establish achievable goals for data governance. Do not wait for "perfect" data; treat governance as an ongoing journey. 
   

3. Technology and Data Acquisition 

• Obtain the Data: Use data already available from past audits, collaborate with business partners via APIs, or even gather new data through questionnaires if none exists.
• Select the Right Tools: Use software that can extract, manipulate, and visualize data. While audit-specific tools like ACL or IDEA are common, modular applications like Python, R, or SAS are also recommended. Visualization tools like Tableau or PowerBI help make findings more impactful and understandable. And for accounting and audit professionals of all levels, Excel expertise is a must-have.
• Leverage External Expertise: When internal resources are limited, partnering with third-party advisors can help bridge gaps in technical skills and infrastructure. 
   

4. Continuous Development 

• Upskilling: Encourage continuous learning through data analytics courses that can even count toward CPE credits.
• Performance Management: Integrate data analytics goals and metrics into the performance evaluations of audit staff to drive accountability. 
   

Advance with Certification 

Gain knowledge in internal audit data analytics and many other areas essential to growth in the profession by earning your CIA certification. With the global gold standard for internal audit certifications, you can advance your career and lead your organization in data analytics, risk management, decision-making, and more.

 Learn more about being a CIA and how to get there, in our FREE CIA Exam Guide!