Chapter 16 Glossary

The glossary provides a quick reference to concepts used throughout the book. Detailed definitions and applications appear in the chapter where each term is introduced.

  • Accountability: The obligation to complete agreed work, communicate progress and problems, and accept review.

  • Analytical plan: A documented explanation of the question, data, methods, validation, outputs, assumptions, and decision criteria.

  • Analytical question: A question that identifies what will be described, compared, explained, predicted, or projected using data.

  • Audience: The people for whom a communication is designed and who will interpret or use it.

  • Baseline: A simple reference method or condition used for comparison.

  • Benchmark: A reference value or standard used to evaluate performance or position.

  • Call to action: A clear statement of the response or next step an audience is asked to consider.

  • Churn rate: The proportion of eligible customers lost during a defined period.

  • Citation: A formal acknowledgement identifying the source of data, ideas, methods, or other material.

  • Client: The person or organization that commissions, sponsors, receives, or uses project work.

  • Clustering: An unsupervised method that groups observations according to a defined measure of similarity.

  • Codebook: A structured description of variables and coded values.

  • Comparability: The degree to which values from different sources can be meaningfully compared.

  • Confidentiality: The obligation to prevent unauthorized disclosure of information.

  • Customer acquisition cost: The attributable cost of acquiring a new customer under a stated allocation rule.

  • Customer lifetime value: The estimated economic value produced by a customer over the relationship under a stated model.

  • Data dictionary: A structured reference documenting the meaning, format, values, source, quality, and use of data elements.

  • Data leakage: Use of information during model development that would not legitimately be available when a prediction is made.

  • Data lineage: The traceable path from a source through transformations to an output.

  • Data stewardship: Responsible management of data throughout its lifecycle.

  • Data-use agreement: A formal agreement defining permitted data, users, purposes, safeguards, disclosure, and retention.

  • Deliverable: A defined product that must satisfy stated content and quality expectations.

  • Dependency: A relationship in which one task or input must be completed before another can proceed.

  • Derived variable: A variable created from source fields using a documented rule.

  • Discount rate: The rate used to convert future amounts into present value.

  • Executive summary: A concise, self-contained account of the problem, evidence, conclusions, recommendations, and material limitations.

  • Exploratory data analysis: A structured investigation of data quality, distributions, relationships, limitations, and analytical possibilities.

  • Forecast: An estimate of a future value based primarily on patterns learned from observed data.

  • Handoff: The organized transfer of files, documentation, context, and usage guidance.

  • Implementation question: A question about how an action will be carried out, monitored, and adjusted.

  • Interim review: A structured examination of progress, evidence, risk, and remaining work before final delivery.

  • Iteration: A deliberate return to an earlier stage so that work can be revised using new evidence.

  • Metadata: Information describing a dataset’s content, structure, collection, quality, ownership, and use.

  • Milestone: A meaningful checkpoint indicating that a stage is complete or ready for review.

  • Missingness: The amount, pattern, and process through which expected values are absent.

  • Practical significance: The degree to which a result is consequential enough to matter for a decision.

  • Primary data: Data collected directly for the purpose currently being investigated.

  • Privacy: Rights and expectations associated with information about individuals.

  • Projection: A conditional estimate of a future quantity under stated assumptions.

  • Provenance: The documented origin, context, ownership, version, and transformation history of data.

  • Recommendation: A proposed action responding to an established problem or opportunity and supported by evidence.

  • Reproducibility: The ability to recreate results from documented data, code, settings, transformations, and decisions.

  • Retention rate: The proportion of eligible customers who remain active from one defined period to the next.

  • Risk register: A maintained record of possible events that could harm the project and the planned response.

  • Scenario: A coherent combination of assumptions used to examine one possible outcome.

  • Scope: The agreed boundary of the questions, data, methods, periods, and outputs included in a project.

  • Sensitivity analysis: An examination of how results change when important assumptions or inputs change.

  • SMART objective: An implementation objective that is specific, measurable, achievable, relevant, and time-bound.

  • Storyboard: A preliminary sequence of messages and evidence used to design a coherent communication.

  • Team charter: A documented operating agreement governing how a project team will work together.

  • Thematic cohesion: Alignment of questions, evidence, language, conclusions, and recommendations around a central purpose.

  • Unit of analysis: The entity represented by one observation in an analysis.

  • Utilization rate: The number of events or services used relative to a defined population and period.

  • Validation: Evaluation that estimates how a method performs beyond the data used to fit or select it.

  • Visual encoding: The use of visual properties such as position, length, colour, or shape to represent data.