Glossary
D
26 entries beginning with D, the same definitions the articles use.
Data (Use and Access) Act 2025
UK legislation that replaced Article 22 of the UK GDPR with Articles 22A-22D, permitting solely automated decision-making more widely in exchange for four safeguards: information, representations, human intervention, and the right to contest.
Related: The Reasoning Gap: The Capability the Law Now Demands of Boards
See also:Reasoning Gapprobabilistic systemMinimum Lovable Governance
Data centre diplomacy
Goldman Sachs’ concept that data centres act as embassies in the AI era, letting nations and companies use AI infrastructure siting as a tool for strategic alliances, economic advantage, and geopolitical influence.
Related: UK AI Energy Constraints: From Niche Concern to Investment Banking Focus
Data lake
A centralised repository, built here using AWS Lake Formation, for retaining data from a decommissioned environment, offering reduced storage costs, increased analytics capability and lower management overhead.
Related: Planning a cloud migration? Here's what you should consider
See also:Machine learningData monetisation
data localisation
Requirements to store and process data within a jurisdiction; they can impose higher operational costs while conflicting with the cost pressures driving businesses toward cheaper foreign AI infrastructure.
Related: Beyond Regulatory Uncertainty: Thoughts on the UK's AI Sovereignty Challenge
See also:Sovereign SpecialisationPrincipled StandardisationAdaptive Localisation
Data Loop Concept
A compounding cycle in which AI-integrated workflows generate higher-quality, better-structured operational data that feeds back into an organisation’s AI systems, improving their performance and, in turn, producing still better data over time.
First introduced in: The Great Remaking: Why Fast Following Does Not Work When the Gap Compounds
Related: The Great Remaking: Why Fast Following Does Not Work When the Gap Compounds
See also:Compound Loop
data moat
A durable competitive advantage built not from data volume but from data provenance: accumulated, contextualised, AI-refined operational data that exists only because an organisation has been redesigning how its work is done.
Related: The Great Remaking: Why Fast Following Does Not Work When the Gap Compounds
Data monetisation
Turning an organisation’s data assets into strategic and financial value, whether through direct revenue streams, operational improvement or competitive positioning that can raise exit multiples.
Related: The enterprise data advantage: Turning information assets into strategic valueDemystifying data monetisation: Insights for private equity portfolio companies
See also:Money for Old Rope
Data space
A federated ecosystem in which organisations share data under agreed rules without surrendering control of it; common standards for identity, contracts, and usage control replace the central platform, and EU law is legislating the model into trade.
Data stewardship
Applying to data the strategic disciplines used for physical assets: named ownership, regular condition assessment, preventive maintenance, impairment testing and strategic value review.
Related: The Invisible Asset: Why Boards Should Govern Data Like It's on the Balance Sheet
Data-as-a-Service
A direct monetisation model providing customers access to structured data sets through APIs, one of several modern approaches that go beyond simple content licensing.
Related: The enterprise data advantage: Turning information assets into strategic value
Decision Analytics Concept
AI applied to Board decision-making that models what could happen, evaluates responses, and quantifies outcomes across scenarios, integrating internal metrics with external signals rather than merely reporting historical performance.
First introduced in: Implementing Decision Analytics: A Practical Guide for Boards
Related: Transforming the Board: Using Decision Analytics for Strategic Advantage
See also:Predictive indicatorMaximum Fidelity
Decision Fluency Concept
A chief executive’s hands-on familiarity with AI tools sufficient to judge what a bet is worth, distinguished from coding skill and treated as a duty rather than a nicety.
First introduced in: AI and the CEO: Choosing the Bets That Matter
See also:Directorial AI Literacy
Decision velocity
The rate at which AI systems make decisions, rising from hundreds per day to millions per second, outpacing traditional quarterly reviews and annual audits designed for far slower oversight.
Related: Transforming the Board: Using Decision Analytics for Strategic Advantage
See also:Velocity MismatchDecision Analytics
Decommission cost
The often-overlooked cost of retiring the old environment after migration; business cases frequently fail to deliver promised savings because no workstream addresses decommissioning, leaving legacy contracts and data-centre leases running.
Related: Planning a cloud migration? Here's what you should consider
Deep Learning
A subset of machine learning using neural networks to handle complex data structures, powering applications from autonomous vehicles to real-time language translation and medical image diagnosis.
Related: Introducing the AI Stages of Adoption: A framework for understanding AI readiness in your business
See also:Machine learningNeural network
Demand response
Programmes where large consumers adjust load or sell capacity back to the grid during peak periods, letting data centres monetise excess generation while maintaining operational uptime.
Related: A New Grid Actor: AI Infrastructure Is Becoming Energy Infrastructure
Deterministic automation
Automation that follows the exact same steps every time to produce identical outputs for identical inputs, eliminating the inherent randomness of probabilistic models such as LLMs for critical business workflows.
Related: The Return of Traditional AI: Organisations Are Rethinking Their LLM-First Strategies
See also:Large language model
Diffusion model
A generative technique that creates images (or other media) by iteratively removing noise; the basis of most modern image generators.
See also:Generative AI
Digital exhaust
The operational data produced as a by-product of day-to-day operations, such as process metrics and performance data, which can yield insights for the organisation and its wider industry.
Related: The enterprise data advantage: Turning information assets into strategic value
Digital likeness
A digital representation of a person’s features that AI can use to create synthetic performers; union agreements increasingly require explicit consent before its use.
Related: Dawn of the three-hour work week: AI's impact on employment and compensation
Digital transformation
Broad organisational change enabled by technology that reshapes how a business operates, extending well beyond IT to touch every function and requiring top-down executive commitment.
Related: Why now is not the time to take your foot off the gas
See also:Cloud computing
Digital twin
A live virtual replica of a physical asset, process or system, used to simulate, monitor and optimise its real-world counterpart.
Related: World Models: The Next Horizon in AI for Predictive Enterprise Intelligence
Directorial AI Literacy Concept
Not technical fluency but four capacities: interrogating maturity claims, assessing governance adequacy, identifying material AI risk and exercising independent judgement on AI decisions a director cannot fully see.
First introduced in: AI and the Director: A Practical Playbook for Governing What You Can't Fully See
Dispatchable capacity
Generation that can be called on as needed. A data centre campus running below full utilisation holds spare capacity it can dispatch during off-peak periods, effectively operating as a power station.
Related: A New Grid Actor: AI Infrastructure Is Becoming Energy Infrastructure
Double bubble
The phenomenon, observed in cloud transformations, of running legacy systems alongside new implementations for an extended parallel period, materially affecting the financial case and equally applicable to AI.
Related: Rethinking Business Cases in the Age of AI: Building Your AI Business Case
Duty of care
A director’s obligation to act with diligence proportionate to the materiality of the issues they oversee, a standard that now extends to developing AI literacy.
Related: Return-to-Work Briefing: Five Forces Reshaping the Board AI Agenda in 2026
See also:Directorial AI Literacy
No D entries match. or try the site search.