Enterprise AI
Data governance
Also known as: data stewardship
Data governance is the set of policies, roles and controls that decide how an organisation collects, stores, describes and uses its data. It covers ownership, quality standards, access rights, retention and compliance with privacy law. In AI work, it also governs which data may be used for training, retrieval or prompting.
What it is
Data governance is the operating model for data as an asset: who owns each dataset, what it means, where it lives and who may touch it. It combines written policy with practical artefacts such as data catalogues, definitions, access rules and audit trails. The related term data stewardship usually describes the day to day human role of maintaining quality and meaning within that framework.
Why it matters
AI assistants and search systems increasingly read whatever an organisation publishes or exposes through feeds, APIs and knowledge bases, so inconsistent or unauthorised data travels further and faster than before. Governance keeps product details, pricing, availability and claims consistent across the sources machines read, which reduces contradictory answers. It also protects the business from using customer or licensed data in ways that breach contracts or privacy law.
How it works
Practitioners start by cataloguing key datasets, naming an owner and steward for each, and agreeing definitions for core entities such as customer, product and lead. They then set access tiers, retention periods and approval routes for new uses, including whether a dataset may be sent to third party AI tools. Quality rules and monitoring are attached to the most business critical fields so breaks are caught before they reach public channels.
When it applies
It applies whenever data is shared across teams, published externally, or fed into analytics and AI systems. It becomes urgent when an organisation adopts AI tools, merges systems after an acquisition, or operates under regimes such as UK GDPR.
Examples
- A retailer agrees a single owner for the product feed so price and stock fields match across the website, Google Merchant Center and its AI chat assistant.
- A B2B software firm publishes an internal policy listing which customer datasets may and may not be pasted into public AI chatbots.
- A marketing team adds a data steward to the CRM who maintains lead source definitions so attribution reporting stays comparable across quarters.
How it is measured
- Share of critical datasets with a named owner and steward
- Data quality pass rate against agreed validation rules, such as completeness of mandatory fields
- Number of access or policy exceptions raised and time taken to resolve them
- Time to fulfil data subject access or deletion requests
Insights on Data governance
- Google Ask Advisor Promises First Insight in Minutes
- The ChatGPT Data Agent Removes the Query, Not the Responsibility
- OpenAI Reaffirms Zero Data Retention. Who Watches Your Agents Now?
- GA Now Writes Your Data Story First. Who Edits It?
- ChatGPT Can Guess What You Never Told It. New Study Proves You Can't Stop It.
Related terms in Enterprise AI
- AI agentsAI agents are software systems that use a language model to plan and carry out multi-step tasks, rather than simply returning a block of text. They can call tools, query APIs, browse websites and write to other systems in pursuit of a goal, with varying degrees of human oversight. The term covers everything from a scripted assistant that books a meeting to a research agent that gathers sources and drafts a report.
- AI chipsAI chips are processors designed or optimised to run machine learning workloads, especially the large matrix operations behind training and inference. The category covers GPUs, tensor and neural processing units, and custom ASICs, usually paired with high-bandwidth memory and fast interconnects. They are also called AI accelerators or AI semiconductors.
- AI cost managementAI cost management is the practice of tracking, forecasting and controlling what an organisation spends on AI systems, including model API calls, inference compute, vector storage and the tooling around them. It applies familiar financial discipline to usage that is variable, token based and easy to scale without noticing. The goal is predictable spend at an acceptable level of quality and latency, rather than the lowest possible bill.
- AI drug discoveryAI drug discovery is the use of machine learning and computational models to support the early stages of finding and refining new medicines. Models are applied to tasks such as predicting protein structures, identifying candidate molecules, prioritising targets and estimating properties like toxicity or binding affinity. The aim is to narrow a very large search space before expensive laboratory and clinical work begins.
- AI infrastructureAI infrastructure is the stack of hardware, networking, storage and software needed to train, fine-tune and serve AI models at scale. It spans accelerators such as GPUs, the data centres and power that house them, and the orchestration and serving layers that turn raw compute into working model endpoints. For most marketing teams it is a cost and capacity constraint they consume through APIs rather than something they build.
- AI procurementAI procurement is the process of evaluating, buying and contracting artificial intelligence tools and services, from assistants and content platforms to models accessed through an application programming interface. It covers commercial terms alongside security, data protection, accuracy and integration questions that do not arise with ordinary software. Most organisations run it as a structured review with pilots, stakeholder sign off and defined exit conditions.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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