Data Literacy Certificate: A Journey to Data Analytics

Who will benefit from this program?

This program is ideal for internal auditors seeking to strengthen their data literacy, apply analytics in audit engagements, and provide assurance over data governance and management. It also benefits audit leaders responsible for building or evaluating data-driven audit functions across their organizations.

Certificate Objectives

1. Apply knowledge of the Standards to evaluate internal audit’s role in providing assurance over data management and governance.
2. Understand the definition of data, its history, common data types, and how data is structured.
3. Understand how data is collected, managed, and analyzed to support decision-making.
4. Use a data literacy competency model to assess capabilities and design development plans in data governance, management, and analytics.
5. Understand and apply basic descriptive analytics to practical audit scenarios.
6. Understand basic data visualization techniques to clarify and enhance communication using data and visual elements.
7. Understand how internal audit evaluates and provides assurance over data governance policies and procedures.
8. Understand how internal audit provides assurance over data management controls to ensure data security, accessibility, and reliability.
9. Understand the tools and technology used to perform data analytics.
10. Understand how to elevate data literacy across the entire audit function.

Certificate Topics

1. Internal Audit Standards and the Role of Data

• Understanding IIA Standards related to data usage and audit assurance.
• Communicating insights using data.
• Managing audit-related data effectively.

2. Foundations of Data and Analytics

• Definitions, history, and future trends of data analytics.
• Language, structure, and classification of data.

3. Data Collection and Decision-Making

• How data is gathered, managed, and applied in audit contexts.
• Practical exercises using structured notation, qualitative analysis, and trend analysis.

4. Data Literacy Competency Framework

• Defining data literacy and maturity.
• Creating development plans for building competencies.

5. Applying Descriptive Analytics

• Understanding and applying key analytics types: qualitative, quantitative, ratio analysis.
• Performing core audit analytics like fraud detection, customer behavior, and payroll reviews.

6. Visualizing and Communicating Data

• Enhancing audit communication through visualization and storytelling.
• Choosing appropriate visuals for stakeholder audiences.

7. Auditing Data Governance

• Assessing data governance frameworks, roles, policies, and metadata.
• Providing assurance over governance processes.

8. Auditing Data Management

• Ensuring security, reliability, and accessibility of data.
• Evaluating architecture, modeling, storage, privacy, and operations.

9. Tools, Technology, and Automation

• Understanding platforms, automation, and AI in analytics.
• Evaluating technology’s role in performing audits efficiently.

10. Scaling Data Literacy Across the Audit Function

• Developing data analytics programs within the audit function.
• Strategies for implementation, partnership, and continuous improvement.