Knowledge Graph
Datasive Glossary
Core concepts in data strategy, AI governance, and decision evidence.
Glossary terms
Decision Evidence
Core DatasiveBoard-ready artifacts that prove ROI, assign ownership, and control risk at the end of a data engagement. Unlike dashboards, decision evidence includes recommendations, quantified impact, and the next executive action.
Related: Decision Pack, Counterfactual ROI
Decision Pack
Core DatasiveA concise, board-ready package that frames one decision, its financial impact, and required data. It replaces slide-heavy reports with clear ownership, risks, and execution steps.
Related: Decision Evidence, Use Case Portfolio
Fractional CDO
Core DatasiveA part-time executive who brings data and AI leadership without a full-time hire. Typically structured in 90-day sprints with decision packs, governance controls, and ROI measurement.
Related: Data Strategy Roadmap, Data Operating Model
Shadow AI
AI GovernanceUnapproved AI usage by employees or teams outside formal governance. It increases risk of data leakage, IP loss, and AI Act exposure.
Related: AI Acceptable Use Policy, LLM Gateway
AI Risk Register
AI GovernanceA living inventory of AI use cases with risk level, data exposure, owner, and mitigation plan. It is the backbone for board reporting and audit readiness.
LLM Gateway
AI GovernanceA secure control layer that routes all LLM prompts and responses through logging, redaction, and DLP. It enables compliance, auditability, and model governance without blocking usage.
Related: AI Acceptable Use Policy, Shadow AI
AI Acceptable Use Policy
AI GovernanceA short, enforceable policy that defines approved AI tools, data classes, and usage rules. It prevents Shadow AI while keeping teams productive.
Related: LLM Gateway, AI Risk Register
EU AI Act
RegulationEuropean regulation defining obligations for AI systems based on risk. It requires governance, transparency, and human oversight for high-risk AI use cases.
Related: AI Act High-Risk Classification, NIST AI RMF
NIST AI RMF
RegulationA risk management framework for trustworthy AI with Govern, Map, Measure, and Manage functions. It is widely used as a practical governance baseline.
Related: AI Risk Register, EU AI Act
AI Act High-Risk Classification
RegulationA classification that triggers stricter obligations such as risk management, transparency, and human oversight. It applies to AI systems that impact safety, rights, or critical services.
Related: EU AI Act, AI Risk Register
Modern Data Stack (MDS)
InfrastructureCloud-native architecture combining warehouse, ingestion, transformation, and BI tooling. It accelerates analytics but can create tool sprawl without governance.
Related: Data Stack Rationalization, FinOps (Data)
Data Stack Rationalization
InfrastructureA structured reduction of overlapping tools and unused capacity in the data stack. It protects velocity while lowering spend and simplifying governance.
Related: Modern Data Stack (MDS), FinOps (Data)
FinOps (Data)
InfrastructureFinancial operations applied to data platforms: cost allocation, usage visibility, and guardrails. It targets 20–40% savings without slowing delivery.
Related: Baseline KPIs, Data Stack Rationalization
Data Quality SLA
Data QualityService-level targets for accuracy, completeness, timeliness, and consistency of key datasets. SLAs link data reliability to business risk and ownership.
Related: Data Ownership, Data Observability
Data Observability
Data QualityThe ability to detect, diagnose, and resolve data incidents across pipelines. It goes beyond monitoring by focusing on data health and impact.
Related: Data Quality SLA, Data Ownership
Data Operating Model
StrategyThe way decision rights, ownership, and execution are organized across data teams and business units. It defines who decides, who builds, and how value is captured.
Related: Data Ownership, Data Strategy Roadmap
Data Maturity Assessment
StrategyA structured diagnostic of governance, architecture, quality, analytics, and culture. It identifies gaps and prioritizes a 90-day roadmap.
Related: Data Strategy Roadmap, Decision Pack
Data Strategy Roadmap
StrategyA 12-month execution plan that sequences use cases, governance, and platform work. The first 90 days focus on decision packs and ROI proof.
Related: Decision Pack, Use Case Portfolio
Use Case Portfolio
StrategyA prioritized list of data and AI use cases ranked by impact, feasibility, and risk. It keeps delivery focused on measurable value.
Related: Decision Pack, Baseline KPIs
Baseline KPIs
MeasurementThe starting metrics used to measure impact from data initiatives. Without baselines, ROI claims become unverifiable.
Related: Counterfactual ROI, Decision Evidence
Counterfactual ROI
MeasurementThe difference between what happened and what would have happened without the initiative. It is essential for credible ROI reporting.
Related: Baseline KPIs, Decision Evidence
Board Reporting (Data)
GovernanceQuarterly reporting that summarizes data and AI risk, value, and execution status for the board. It focuses on decisions, not dashboards.
Related: AI Risk Register, Decision Evidence
Data Ownership
GovernanceClear accountability for data domains, KPIs, and decisions. Ownership makes quality SLAs and ROI capture enforceable.
Related: Data Quality SLA, Data Governance Framework
Data Governance Framework
GovernancePolicies, roles, and controls that define how data is managed and used across the organization. It reduces risk while enabling faster decision-making.
Related: Data Ownership, AI Acceptable Use Policy
Decision Intelligence
AnalyticsA discipline that links data, models, and human judgment to better business decisions. It focuses on outcomes rather than reporting volume.
Related: Decision Pack, Decision Evidence