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Graduate School USA

Assessing the Reliability of Computer-Processed Data Course

via Graduate School USA

Overview

A defensible, Yellow Book–aligned process is necessary for assessing the reliability of computer-processed data. Using a structured, risk-based approach, you'll learn to plan, test, conclude, and report so your audits rest on complete, accurate, and valid evidence.

Syllabus

Module 1: Government Audit Standards, Policies, and Guidelines

  • Review GAO Government Auditing Standards (Yellow Book) requirements for sufficient, appropriate evidence and overall assessment of evidence.
  • Understand when and how to evaluate information systems (general, application, and user) controls that affect data reliability.
  • Apply guidance on using the work of others and specialists, and documenting qualifications, scope, and quality.
  • Identify required report content (objectives, scope, methodology) and how to disclose limitations and uncertainties.

Module 2: Data Reliability Considerations

  • Define data reliability (completeness, accuracy, validity, and consistency) and why it matters for audit findings.
  • Recognize common forms of computer-processed data (extracts, enterprise systems, spreadsheets, surveys) and typical reliability issues.
  • Use computer-assisted audit techniques (e.g., logical tests, duplicate checks, range and date tests) to evaluate sufficiency and appropriateness.
  • Understand common problems (incomplete, untimely, incorrect, or incompatible data) and their causes.

Module 3: Overall Framework for Assessments

  • Decide whether a data reliability assessment is needed based on planned use and risk.
  • Scope the extent of assessment using expected importance, corroborating evidence, and risk of using the data.
  • Focus effort on portions of data relevant to audit objectives; consider leveraging information/system control reviews when efficient.
  • Follow the framework stages: determine need and plan, conduct work, make the determination, and include appropriate report language.

Module 4: Planning and Performing the Assessment

  • Initiate reliability work early; determine timing, level of detail (record- vs. summary-level), and documentation needs.
  • Collect existing information: interview knowledgeable officials; obtain data dictionaries, system docs, and prior reviews.
  • Test data (counts, missing values, duplicates, ranges, identifiers, dates, relationships) and, when needed, trace to/from source documents.
  • Document plans, procedures, results, and conclusions clearly, using provided planning and summary worksheets.

Module 5: Documenting and Reporting Assessment Results

  • Synthesize testing and control information into an overall reliability determination tied to audit objectives.
  • Disclose limitations/uncertainties, describe data sources and methods, and explain population, period, and sampling as applicable.
  • Tailor report wording so users can reasonably interpret findings without being misled; describe any constraints on scope or access.

Module 6: Structured Approach for Assessing Reliability of Data

  • Apply a flexible, risk-based approach aligned with GAO’s Assessing Data Reliability guidance.
  • Leverage existing information, involve stakeholders, and perform only the work necessary to conclude “use or not.”
  • Use standardized tools (planning templates, documentation requests, and example data tests) to streamline assessments.

Module 7: Case Study

  • Work through a realistic scenario to plan, test, and conclude on data reliability.
  • Practice documenting decisions, communicating limitations, and drafting appropriate report language.

Taught by

Mark Gebicke, Penny Popps, and Lyndon S. Remias

Reviews

4.8 rating at Graduate School USA based on 4 ratings

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