Certification in Business Data Analytics (IIBA®-CBDA) — Lesson 2: Source Data, Ownership, Quality, Privacy, Preparation, and Integration

Learning Objectives

• Identify the dependencies connecting data sources, data ownership, and quality assessment across the workflow.
• Validate a source assessment and prepared dataset record against explicit acceptance evidence.
• Explain which data can be trusted and what limitations must accompany the analysis after testing the stated exception.

Detailed Topics

• Clarify data sources by identifying purpose, ownership, boundaries, dependencies, and decision criteria.
• Apply data ownership to the scenario and preserve the evidence supporting the selected approach.
• Compare alternatives for quality assessment and explain practical value, risk, and operating consequences.
• Challenge privacy constraints with a realistic exception that exposes assumptions or weak accountability.
• Document data preparation with acceptance conditions, review responsibility, and a clear next action.

Instructor Demonstration

The instructor demonstrates how to assess candidate data sources and prepare an integrated dataset with documented controls. The walkthrough explains which data can be trusted and what limitations must accompany the analysis, builds the source assessment and prepared dataset record, and tests the exception where a large dataset is considered suitable despite missing definitions, biased coverage, and unresolved privacy restrictions.

Hands-On Exercise

Complete a controlled exercise to assess candidate data sources and prepare an integrated dataset with documented controls and preserve the result in the source assessment and prepared dataset record.

1. Confirm the facts, stakeholders, constraints, evidence sources, and success conditions relevant to data sources.
2. Perform the central analysis involving data ownership, quality assessment, and privacy constraints.
3. Introduce the exception where a large dataset is considered suitable despite missing definitions, biased coverage, and unresolved privacy restrictions and revise the proposed response.
4. Complete the source assessment and prepared dataset record with ownership, acceptance evidence, unresolved concerns, and follow-up actions.

Scenario Analysis

• Identify the assumption with the greatest influence on which data can be trusted and what limitations must accompany the analysis.
• Compare two defensible responses involving privacy constraints and explain their trade-offs.
• Specify the evidence required before the source assessment and prepared dataset record can be accepted.

Troubleshooting Guidance

Trace the first difference between the intended outcome and the available evidence. Recheck scope, roles, source quality, dependencies, decision criteria, and retained records independently. The analysis remains incomplete while a large dataset is considered suitable despite missing definitions, biased coverage, and unresolved privacy restrictions.

Validation Checklist

• Scope and ownership for data sources are explicit.
• The selected treatment for data ownership is supported by reviewable evidence.
• The exception involving privacy constraints was tested and its impact recorded.
• The source assessment and prepared dataset record contains acceptance criteria, accountable owners, and next actions.

Lesson Outcome

Learners can apply Source Data, Ownership, Quality, Privacy, Preparation, and Integration, explain which data can be trusted and what limitations must accompany the analysis, and support the conclusion with a source assessment and prepared dataset record.