A comprehensive blueprint to create the data-driven internal auditor.

Description:

This hands-on session equips internal auditors with the essential skills to align audit objectives with the right data sources, choose from various data access methods, and assess quality of retrieved datasets. We’ll identify key data risks, retrieve relevant data, and apply fundamental cleansing techniques to ensure accurate and reliable data for audit. Topics include: and, Assessing data quality, completeness, and reliability.

Learning Objectives:

• Highlight steps to document data processes for transparency and reproducibility.

• Procedures identify and address data integrity issues (e.g., Data profiling and cleansing)

• Overview of techniques to extract, transform, and load (ETL) data efficiently.

• Review approaches to document data completeness and accuracy.

Description:

With numerous possibilities for data analytics in audit, knowing where to start is critical - and not all data analytics opportunities are equal. This session explores structured approaches to brainstorming and prioritizing data analytics opportunities within audit engagements, ensuring auditors focus on the most impactful and feasible projects. Topics covered include: Brainstorming analytics opportunities in audits; Prioritization frameworks for selecting the best analytics use cases; Aligning analytics with risk-based auditing principles, and, Avoiding the mistake of analytics for analytics’ sake.

Learning Objectives:

• Familiarity with brainstorming techniques to identify high-value analytic opportunities.

• Knowledge of prioritization frameworks for selecting high-impact analytics.

• Awareness of personal assumptions and cognitive biases affecting data analytic planning.

Description:

This interactive practicum bridges the gap between audit objectives and data analytics execution. In this session, you’ll learn how to plan a clear and effective analytic approach, refine and prioritize analytics through stakeholder communication, and collaborate with technical experts to enhance your audits with more advanced techniques.

Learning Objectives:

• Practice formulating process-risk-control questions and analytical hypotheses.

• Describe the components of a structured analytic plan.

• Familiar with approaches to discuss and collaborate with data and IT specialists.

Description:

You don’t need specialized software to perform effective data analytics—most auditors already have access to capable tools. This session covers fundamental DA techniques and demonstrates how to apply them using commonly available audit tools, ensuring that auditors can integrate analytics with existing resources. Topics include: Overview of analytics tools: Excel, Power BI, SQL, Python, and audit-specific software; Common analytic techniques; and, Matching audit analytics needs with the right tool.

Learning Objectives:

• Awareness of common data technologies like Excel, Power Query, and Power BI.

• Knowledge of data transformation and aggregation techniques.

• Familiarity with functions and derived fields.

• Knowledge of basic text analytic techniques for working with unstructured text.

Description:

In this hands-on session, participants will conduct a mock audit using data analytics techniques covered in the prior session. You will perform tasks including data transformation, aggregation, risk scoring, trend analysis, outlier detection and basic text analysis on unstructured data. This session emphasizes matching audit analytics needs with the right tool, ensuring you build practical skills to support decision-making and enhance audit effectiveness.

Learning Objectives:

• Understanding of basic statistics and analytical concepts like trending, outliers, and risk scoring.

• Develop practical skills in data transformation and aggregation, including creating functions and derived fields.

• Apply basic text analytics techniques to interpret unstructured data.

• Translate analytical results into actionable insights relevant to audit objectives.

Description:

Data analytics isn’t just about running reports—it’s about following a structured process to ensure accuracy, reliability, and meaningful insights. This session walks auditors through the phases of the audit data analytics lifecycle and how to apply them effectively. Topics include: Key lifecycle steps: acquire, validate, prepare, analyze, report, document; Common data analytic pitfalls and how to avoid them; and, Ensuring audit integrity through structured data analytic workflows.

Learning Objectives:

• Understand the stages of the data analytics lifecycle for internal audit.

• Familiarity with techniques to extract, clean, merge, and transform data efficiently.

• Document audit analytics for regulatory and compliance purposes

Description:

In this practicum, you’ll transition from theory to action by working with a sample audit dataset. Building on the structured approach from the prior session, you will step through the entire data analytics lifecycle—from acquiring and preparing data to analyzing and reporting insights. This session features guided exercises and peer discussions to help you integrate analytics seamlessly into your audit process, ensuring both clarity and compliance with professional practices.

Learning Objectives:

• Demonstrate proficiency in executing the full data analytics lifecycle.

• Apply practical techniques to extract, clean, and transform audit data.

• Produce a concise analytical report that communicates key insights.

• Document your data-driven audit procedures for effective review and retention.

Description:

Audit reports must be more than numbers—they must tell a compelling story that drives action. This session explores how auditors can use visualization techniques to communicate audit findings effectively and ensure key stakeholders understand critical insights. Topics include: The Principles of data storytelling; Choosing the right visualizations for different audit insights; and, Communicating analytics findings to stakeholders.

Learning Objectives:

• Interpret analytical results to identify insights relevant to audit objectives.

• Apply reporting best practices and data visualization principles.

• Utilize a framework to select effective visualizations that communicate risk insights.

• Improve the clarity of analytics-driven audit presentations.

Description:

Building on the principles introduced in the prior session, this practicum puts theory into immediate practice. Participants will work with a provided audit dataset to extract key insights, select appropriate visualizations, and develop a brief narrative that communicates their findings effectively. Through group exercise, you will gain practical experience in assembling your data story, culminating in a short presentation for constructive feedback. This session is designed to reinforce core data storytelling concepts while honing your ability to present audit insights in a clear and impactful manner.

Learning Objectives:

• Analyze audit data to identify insights that drive audit objectives.

• Apply visualization techniques to enhance the clarity of your narrative.

• Develop a structured, compelling story that communicates risk insights.

• Present your findings in a succinct format, receiving actionable feedback for improvement.

Description:

Why audit once when you can monitor key risks and controls on a regular basis? This session introduces the concept of continuous auditing and how auditors can move from static, point-in-time reviews to ongoing, frequent risk and control monitoring and testing. Principles of continuous auditing and monitoring; Technologies and processes for continuous analytics; Overcoming challenges in implementing continuous auditing; and, Integrating analytics with GRC platforms.

Learning Objectives:

• Knowledge of continuous auditing concepts and techniques.

• Understanding of the benefits and limitations of continuous auditing.

• Identify suitable audit areas for continuous monitoring.

• Understand key technologies that support continuous auditing.

Description:

In this practicum, you will apply the continuous auditing concepts from the previous session in a simulated audit environment. Through guided exercises, you’ll identify key risk areas, brainstorm automated analytics ideas, and explore how to integrate GRC platforms to enhance proactive control monitoring. This hands-on session is designed to bridge theory and practice, offering immediate feedback and actionable insights to refine your continuous auditing approaches.

Learning Objectives:

• Analyze audit scenarios to determine where continuous monitoring adds value.

• Apply automation techniques to streamline proactive control testing.

• Integrate GRC functionality into your audit analytics framework.

• Develop and document a concise continuous auditing approach for real-world application.

Description:

AI and automation are changing the landscape of internal auditing. This session explores emerging technologies like generative AI and machine learning, discussing how auditors can leverage them while managing risks. Topics include: Generative AI and its impact on internal audit; Machine learning for risk detection and fraud analytics; and,  Ethical considerations and limitations of AI-driven audits.

Learning Objectives:

• Describe how AI and automation are being used in internal audit.

• Identify realistic AI applications for enhancing audit efficiency.

• Discuss ethical concerns and risks associated with AI-driven auditing.

• Assess whether AI solutions are appropriate for specific audit functions.

Description:

In this practicum, you’ll put theory into practice by exploring generative AI and automation tools in an audit context. The session includes an introduction to various AI platforms, a guided tour of how AI is embedded in your existing audit software, and interactive exercises focused on effective prompt engineering. Through collaborative, hands-on activities, you’ll develop and test AI-powered solutions to enhance risk detection and streamline audit workflows while considering ethical implications.

Learning Objectives:

• Gain practical experience with multiple AI tools that can be applied to audit scenarios.

• Identify and navigate the AI modules within your audit software—understanding where and how these features enhance analytics.

• Develop effective prompting strategies to elicit precise, actionable outputs from generative AI.

• Critically assess the potential risks, challenges, and ethical considerations associated with AI in internal audits.