Applying Data Analysis in Internal Audit

The application of data analysis in internal audit has been a topic of increasing interest and discussion in my recent months. While numerous knowledge briefs and guides are available from respected organizations like the IIA¹ and ISACA²³⁴, these resources often present a more high-level and generic approach. As a professional with a background in data science, I've found that many existing resources in this area lack some depth and specificity to better bridge the gap between data analysis theory and its practical application in internal audit.

This observation has motivated me to create this comprehensive repository containing resources and examples for applying data analysis techniques in internal auditing. It aims to bridge the gap between data analysis theory and practical application in the field of internal audit including code examples, data sets and visualizations.

Methodology

1. Phase 1 - Scope & Plan
2. Phase 2 - Data Collection & Curation
3. Phase 3 - Analyze
4. Phase 4 - Interpretation & Communication

Workflow

flowchart TB
    A[Start]
    F[End]

    subgraph workflow [" "]
        direction TB
        subgraph Phase1["Phase 1: Scope & Plan"]
            direction TB
            B1[1.0 Initial Objective Setting]
            B2[2.0 Define Data Analysis Scope and Questions]
            B3[3.0 Identify Data Requirements]
            B4[4.0 Stakeholder Engagement]
            B5[5.0 Data Request and Acquisition]
            B1 --> B2 --> B3 --> B4 --> B5
        end

        subgraph Phase2["Phase 2: Data Collection & Curation"]
            direction TB
            C1[6.0 Data Validation and Cleansing]
            C2[7.0 Data Management]
            C1 --> C2
        end

        subgraph Phase3["Phase 3: Analyze"]
            direction TB
            D1[8.0 Conduct Initial EDA]
            D2[9.0 Develop and Execute Test Scripts and Queries]
            D3[10.0 Perform Targeted/Focused Analysis]
            D4[11.0 Interpret & Analyze Results]
            D5[12.0 Documentation and Iteration]
            D1 --> D2 --> D3 --> D4 --> D5
        end

        subgraph Phase4["Phase 4: Interpretation & Communication"]
            direction TB
            E1[13.0 Synthesize and Evaluate Findings]
            E2[14.0 Prepare and Communicate Results]
            E3[15.0 Document Technical Details]
            E1 --> E2 --> E3
        end
    end

    A --> B1
    B5 --> C1
    C2 --> D1
    D5 --> E1
    E3 --> F

    %% Additional paths
    D1 -.-> |Refine questions| B2
    D1 -.-> |Revisit data cleansing| C1

    classDef error stroke:#f00,stroke-width:2px;

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Available Guides

• Methodology

  • An overview of the approach I've implemented for audit preparation and data analysis activities based on the following concepts: CRISP-DM⁵ framework and Seven Steps to Empowerment With Data Analytics³

• 2.0 Formulate initial questions and hypotheses

  • This guide explores the application of data analysis techniques in internal auditing, adapting key concepts from academic research methodologies⁶ to the practical world of internal audit focusing on two distinct concepts:
    • Confirmatory vs. Exploratory Questions
    • Causal vs. Non-Causal Questions

Additional Resources

• NIST Exploratory Data Analysis: Link
• Altair Visualization Curriculum: Link
• Data Storytelling & Communication Cheat Sheet: Link
• The seaborn.objects interface: Link

Contact

For questions or feedback, please open an issue in this repository.

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tags: #data_analysis #audit

Footnotes

1. IIA Knowledge Briefs: Data Analytics, Parts 1-3. https://www.theiia.org/en/content/articles/global-perspectives-and-insights/2023/GlobalPerspectivesInsightsDataAnalyticsParts1-3/ ↩

2. Internal Audit Data Analytics for Beginners. https://www.isaca.org/resources/news-and-trends/industry-news/2023/internal-audit-data-analytics-for-beginners ↩

3. Seven Steps to Empowerment With Data Analytics. https://www.isaca.org/resources/news-and-trends/newsletters/atisaca/2023/volume-34/seven-steps-to-empowerment-with-data-analytics ↩ ↩²

4. Advanced Data Analytics for IT Auditors. https://www.isaca.org/resources/isaca-journal/issues/2016/volume-6/advanced-data-analytics-for-it-auditors ↩

5. CRISP-DM 1.0: Step-by-step data mining guide. https://www.semanticscholar.org/paper/CRISP-DM-1.0:-Step-by-step-data-mining-guide-Chapman/54bad20bbc7938991bf34f86dde0babfbd2d5a72 ↩

6. Card, D., Min, Y., & Serghiou, S. (2021, December 14). Open, rigorous and reproducible research: A practitioner's handbook. Stanford Data Science. https://stanforddatascience.github.io ↩