13.9 Glossary of Terms

1. Acceptance Criteria: The conditions that a deliverable must meet to be considered complete and approved by the stakeholder.

2. Business Requirements Specification (BRS): A document that defines the business problem, key questions, target audience, deliverables, success criteria, and constraints for a BI project.

3. Data Dictionary: A document describing every variable in a dataset — its name, data type, units, allowed values, and plain-language definition. Also called a codebook. In the spec-driven workflow, a data dictionary is created before the analysis begins (see also Chapter 5).

4. Data Specification: A document defining the data sources, variables, transformations, and quality standards for a BI project.

5. Deliverable: A concrete output of a BI project — a report, dashboard, model, dataset, or presentation — specified in the requirements.

6. Key Performance Indicator (KPI): A measurable value that demonstrates how effectively an organization is achieving a key business objective. In a BRS, KPIs define what success looks like in quantifiable terms (see also Chapter 15 for KPI design in dashboards).

7. MoSCoW Method: A prioritization framework that categorizes requirements as Must have, Should have, Could have, and Won’t have — used to manage scope with multiple stakeholders.

8. Problem Statement: A clear, concise description of the business problem or opportunity that a BI project aims to address.

9. Quality Criteria: Minimum standards that data must meet before analysis proceeds, including completeness, accuracy, timeliness, and consistency (Wang and Strong 1996).

10. Reproducibility: The ability for others (or your future self) to re-run an analysis and obtain the same results. Achieved through organized project structure, documented code, data specifications, and separation of raw and processed data (see also Chapter 5).

11. Requirements Engineering: The systematic process of defining, documenting, and maintaining requirements for a system or project (Sommerville 2016).

12. Scope: The defined boundaries of a project — what is included and what is excluded. Managed through the requirements specification.

13. Scope Creep: The gradual expansion of project requirements beyond the original specification, often resulting in delays and misalignment.

14. Spec-Driven Development: The practice of defining requirements, data specifications, and validation criteria in writing before building an analysis.

15. Stakeholder: Any person who will use, fund, approve, or be affected by the results of a BI project.

16. Success Criteria: The specific, measurable conditions that determine whether a BI project has achieved its objectives.

17. Test-Driven Development (TDD): A software engineering practice where tests are written before the code that must pass them. Adapted for BI as validation-first analytics (Beck 2003).

18. Validation Check: A specific, testable condition that a dataset, model, or deliverable must satisfy — used to verify correctness at each stage of the analysis.

19. Validation-First Analytics: The practice of defining expected outputs and success criteria before building an analysis, ensuring that correctness is verified systematically.